camera trap literature review

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• Published: 29 November 2021

Camera trap placement for evaluating species richness, abundance, and activity

• Kamakshi S. Tanwar 1 ,
• Ayan Sadhu 1 &
• Yadvendradev V. Jhala 1  

Scientific Reports volume  11 , Article number:  23050 ( 2021 ) Cite this article

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• Conservation biology

An Author Correction to this article was published on 13 January 2022

This article has been updated

Information from camera traps is used for inferences on species presence, richness, abundance, demography, and activity. Camera trap placement design is likely to influence these parameter estimates. Herein we simultaneously generate and compare estimates obtained from camera traps (a) placed to optimize large carnivore captures and (b) random placement, to infer accuracy and biases for parameter estimates. Both setups recorded 25 species when same number of trail and random cameras (n = 31) were compared. However, species accumulation rate was faster with trail cameras. Relative abundance indices (RAI) from random cameras surrogated abundance estimated from capture-mark-recapture and distance sampling, while RAI were biased higher for carnivores from trail cameras. Group size of wild-ungulates obtained from both camera setups were comparable. Random cameras detected nocturnal activities of wild ungulates in contrast to mostly diurnal activities observed from trail cameras. Our results show that trail and random camera setup give similar estimates of species richness and group size, but differ for estimates of relative abundance and activity patterns. Therefore, inferences made from each of these camera trap designs on the above parameters need to be viewed within this context.

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Introduction.

Reliable estimation of species richness, abundance, activity and subsequent monitoring play a pivotal role in achieving specific conservation goals through evidence-based management 1 . However, selection of suitable techniques requires a-priori assessment of their accuracy, precision, replicability, and cost-effectiveness to meet the desired objectives before the technique is recommended on a large scale. Camera traps have been widely used as a wildlife monitoring tool due to their objectivity, ease of use, and ability to generate information on a large spectrum of species 2 . Camera trapping surveys are primarily designed to document species richness 3 , occupancy 4 , abundance indices 5 , 6 , estimate abundance of individually identifiable species in capture-recapture framework 7 , 8 , 9 , 10 and determine their activity patterns 11 . However, with the technological advances, researchers started using camera traps to study population ecology 12 , camera trap-based distance sampling 13 , behavior 14 , forest ecology 15 and carrying out conservation assessments 16 .

A basic assumption of all inferences from camera trap studies is that the data generated are unbiased representation of underlying parameters (of species richness, abundance, temporal activity, either after correcting for effort 7 and/or detection 9 , 17 ). However, a typical capture-recapture study is designed to maximize detections of the target species and is essentially non-random and often not systematic 18 . Such camera trap designs also generate secondary data on several non-target species which are often used to infer their relative abundance indices 19 , 20 , 21 , activity patterns 22 , 23 , and occupancy estimates 24 . However, these inferences on the non-target species can be biased due to the sampling design and camera placement. Due to differential use of trails by different species 25 , biases can occur in estimating relative abundance, group size, and temporal activity 21 . In a review of 266 camera trap studies, Burton et al. 18 found 47.6% of studies using the same surveys to estimate variables of non-target species e.g. occupancy, relative abundance, and activity pattern. Attempts to evaluate if such designs result in a biased inference and of what magnitude have been few 26 , 27 , 28 , 29 , 30 . Di Bittetti et al. 26 and Blake and Mosquera 27 have summarized that a combination of trail and off-trail cameras will provide a comprehensive picture of species composition and their relative abundance. While citing the above-mentioned approach Cusack et al. 28 , Wearn et al. 31 and Kolowski et al. 29 described difficulties in selecting the proportion and spatial distribution of these trail and random locations in a systematic sampling design. Thus, they recommended the use of random camera setup and emphasized its importance in sampling microhabitats. However, they also showed that in order to eliminate biases in inferences made at the community level and overcome lower capture rates from random design, a large sampling effort would be required.

Herein, we deployed camera traps on trails to maximize photo-captures of tigers ( Panthera tigris ) and leopards ( Panthera pardus ) (trail cameras), and sampled simultaneously the same extent with randomly placed camera traps (random cameras). We computed the rate of species accumulation, species richness, relative abundance, detection probability, group size and daily activity pattern of large and medium sized terrestrial mammals using both camera placements and compared their outcomes. We also calculated density estimates of ungulates from distance sampling and of tiger and leopards from spatially explicit capture-mark-recapture and regressed them against RAI values obtained from trail and random cameras. This experimental setup permits us to test if camera trap placement is an important aspect to be considered for estimating species richness, abundance, and activity.

The study was carried out in Ranthambhore National Park, (76.23 E to 76.39 E and 25.84 N to 26.12 N) situated in the semi-arid part of western India. The terrain is rugged and hilly, interspersed with valleys and plateaus which makes for largely two types of habitat i.e. woodland and savannahs. The area is dominated with tropical dry deciduous forest (dominated with Anogeissus pendula ) and scrubland-thorn forests (dominated with Grewia flavescens, Capparis sepiaria ). Ranthambhore experiences sub-tropical dry climate with hot and dry summer (March–June), moderately wet monsoon (July–September) and dry winter (October–February). A small solitary stream along with man-made lakes and water holes manages to sustain the faunal assemblage of the park through the dry months. The flagship species of Ranthambhore National Park is the tiger and it serves as the source population of tiger in the semi-arid landscape of western India 32 . Other large carnivores include leopard, striped hyena ( Hyaena hyaena), and sloth bear ( Melursus ursinus ). Meso-carnivore guild comprised of jungle cat ( Felis chaus ), golden jackal ( Canis aureus ), caracal ( Caracal caracal ), desert cat ( Felis silvestris ), rusty spotted cat ( Prionailurus rubiginosus ), fox ( Vulpes bengalensis ), and honey badger ( Mellivora capensis ). Small carnivores include small Indian civet ( Viverricula indica ), Asian palm civet ( Paradoxurus hermaphroditus ), ruddy mongoose ( Herpestes smithii ), Indian grey mongoose ( Herpestes edwardsii ), and small Indian mongoose ( Herpestes auropunctatus ). Herbivores includes spotted deer ( Axis axis ), sambar ( Rusa unicolor), blue bull ( Boselaphus tragocamelus ), Indian gazelle ( Gazella bennettii ), wild pig ( Sus scrofa ), gray langur ( Presbytis entellus ), rhesus macaque ( Macaca mulatta ), black-naped hare ( Lepus nigricollis ), Indian crested porcupine ( Hystrix indica ), and peafowl ( Pavo cristatus ). For species richness we also included squirrels, monitor lizards and birds (grouped into two: ground dwelling and other birds) in our analysis. The National Park area of Ranthambhore is mostly inviolate, however, in the peripheral areas herders often breach the boundary wall and push their cattle inside the Park for grazing. We therefore also recorded all domestic and feral livestock (cattle, buffalo, goats, camels, donkeys, and dogs) that were photo-captured.

Field method

Camera trapping.

The study area was divided into grids of 2 km 2 for systematic deployment of camera traps for both the placements. Trail cameras were deployed targeting population estimation of tigers and leopards in a mark-recapture framework and were positioned at locations to maximize their photo-captures. Tigers and leopards mostly use forest roads, animal trails, dry river beds, and fire lines to patrol their territories and to commute 33 . After a reconnaissance survey for carnivore signs and usage, a pair of camera traps was deployed at the most suitable locations within each grid to photo-capture tigers and leopards (October to December 2018). Trail cameras (Cuddeback™, WI5411 USA) were deployed at 106 locations, and operated for 25 days constituting an effort of 3537 trap day (no. of cameras × no. of operational days). Cameras were tied to a pole/tree at the height of 30–45 cm from the ground, and placed 3–5 m away from the middle of the trail to ensure full-body capture of the target animals. The time delay between successive pictures was kept as ‘Fast as Possible’ mode (1–2 s delay), however, at night the delay increased to 8–10 s depending on the battery conditions (which is required to recharge the white light flash).

For the random design, 31 infrared flash cameras (Reconyx ® Hyperfire HC500, WI 54636USA) were placed at random locations with (centroids of the sampling grids) a fixed bearing to maintain a random field of view. The camera height was kept at 30–45 cm above ground. The ‘No delay’ setting of the camera allowed it to take consecutive pictures without any lag. Random cameras were operated for 40 days constituting an effort of 1035 trap days, each camera was visited after 5–7 days to check their set up, battery status and to download the data.

Analytical methods

Species richness and accumulation.

Photographs obtained from both the camera trap setup were archived and manually segregated to species. Since the number of trail cameras far exceeded the number of random cameras, we used only the estimates derived from paired cameras (one trail camera per site paired with a proximate random camera, distance range 90–900 m, Fig.  1 ) for meaningful and unbiased comparisons. Thus the richness and accumulation comparison was carried out using the data generated from 31 trail and 31 random cameras. Number of each species photo-captured by trail and random cameras were recorded to calculate species richness and accumulation. To compare species richness obtained from these two camera deployment designs, we generated sample-based species accumulation (richness) curve from incidence data 34 . Confidence intervals (95%) were computed based on unconditional variance following the method of Colwell et al. 35 , with 100 permutations. For both camera placement designs, species accumulation curves were computed based on the time taken to accumulate new species and reach an asymptote.

A. Locations of random and trail cameras placement within Ranthambhore National Park. The solid black circles represent trail cameras placed in the proximity of random cameras, i.e., paired trail cameras. Inset: B. Study area extent in Ranthambhore Tiger Reserve (RTR); C. Location of RTR in India. The maps were created using QGIS ( ver. 3.10, https://download.qgis.org ).

Relative abundance index

A careful scrutiny of each individual photo sequence was done to determine independent photo-capture events. Successive photo-captures (< 30 min apart) of the same species were considered as one event wherever the individual photo-captured animal(s) could not be identified with certainty (on the basis of gender, age class, and unique body markings). We used all random (n = 31) and trail (n = 106) cameras to calculate the Relative Abundance Index (RAI). In case of trail cameras, captures of the same individual at a location in both camera units were considered as a single capture (identified by the time of captures). The sampling effort was the sum of the number of days each camera was operational throughout the session; in case of trail setup, operational days of a camera station (camera station consists of two camera units facing each other on a trail) was considered for effort calculation. Species RAIs were calculated for trail and random cameras as the number of independent events of each species, divided by the total sampling effort of all the cameras multiplied by 100 5 , 36 i.e. independent photo-capture events in 100 trap-nights.

Furthermore, we plotted robust density estimates of tigers and leopards obtained using spatially explicit mark recapture and ungulates obtained from line transect based distance sampling from Ranthambhore Tiger Reserve reported in 37 against RAI values obtained from trail and random cameras. Since density estimates were cotemporaneous and from the same region, the scatter plot, scaling, and correlation between density estimates and RAI enabled us to evaluate the relationship between abundance and RAI and biases (if any) between different camera placements.

Detection probability

In order to estimate detection probability of species, we analyzed their presence /absence data within a multi-method occupancy framework 38 where the two camera designs were taken as the two methods. For occupancy analysis, we used all the random (n = 31) and trail (n = 106) cameras as occupancy framework accounts for heterogeneous sampling effort while estimating the detection probability. Our aim was not to estimate the occupancy of the species in the study area, but to compare the detectability of the species by two camera trap placements. The sampling grids of 2 km 2 were considered as the unit for occupancy analysis. The multi-method occupancy framework incorporates—(i) a local occupancy parameter (θ) (representing the probability of a region in the immediate vicinity of the camera is occupied), (ii) a site occupancy parameter (ψ) (describing the proportion of the sampling sites being occupied by the species during the study period), and (iii) detection probability (p s t , ‘s’ sampling method and ‘t’ occasion) 38 . Site-wise detection histories were made for each species using photo-captures obtained from the camera traps of both the sampling designs. Detection probabilities (occupancy estimation) were computed using the software PRESENCE 39 .

Activity pattern

Camera traps provide a non-invasive way to observe and quantify animal activity at the population level in a relatively cost-effective manner 11 . We used the time stamp metadata obtained from random (n = 31) and trail (n = 106) cameras to compute the activity pattern of wild ungulates and their major predators in the study area using the ‘overlap’ package in R 40 . ‘Overlap’ fits a kernel density function which corresponds to the photo-capture rate of the species in a time interval. The area under the curve (derived from kernel density function) represents the proportion of time the species was active. Frequency of camera trap images of a species in time reflect the activity of the species 41 . We estimated the degree of overlap (Δ—Delta 4 ) between the wild ungulate activity recorded from random and trail cameras. Due to very few captures of carnivores in random cameras, we computed their activity only from trail cameras.

We calculated the group size of wild ungulates from the camera trap photo-captures. We counted the number of individuals of a species in an image and all images from the consecutive camera trap photos within 10 min to record group size. Any animal getting photo-captured after an interval of 10 min from the last photo-capture of the same species was considered as a member of a different group. We differentiated different individuals of a group by their physical characteristics and body markings to the best of our ability. Finally, we compared the frequencies of different group sizes observed from random and trail camera setups.

A total number of 32 species were photo-captured in trail cameras (n = 106), and 25 species were photo-captured in random cameras (n = 31) (Table 1 ). However, both random and paired trail cameras (n = 31, trail cameras placed in the proximity of random cameras) detected 25 species (equal species richness), out of which 23 were common for both setups (Supplementary Table 1 ). The rate of species accumulation for trail camera setup was higher than that of random setup (Fig.  2 ).

Species accumulation curves from trail (yellow line) and random (blue line) camera setups describing the rate at which species were captured in two setups. The vertical bars represent 95% confidence intervals.

A total of 25,394 and 46,010 animal images were obtained from trail and random cameras, respectively. The relative abundance index (RAI) values of species obtained from random cameras were ordinated in the same order as absolute densities of these species while RAI of tigers and leopards from trail cameras were much higher than that from random cameras (Table 1 , Fig.  3 ).

Scaling RAI values from different camera trap designs with absolute density. Only RAI’s from random camera trap placement designs had significant correlations with absolute density.

The RAI values obtained from random camera traps were highly correlated (r = 0.93, p < 0.05) with the density estimates obtained from SECR and distance sampling, while the RAI values from trail cameras were not significantly correlated (r = 0.38, p > 0.05) (Fig.  3 ) since tiger RAI was much higher. The linear equation depicting relationship between density and RAI obtained from random camera was:

Here, a unit increase in density causes a 0.38-unit increase in RAI. The high correlation suggested that the relative abundance index obtained from random cameras can be used as a surrogate of abundance and also as an index to monitor trends in wildlife populations.

Carnivore detection probability, obtained from occupancy estimation, was an order of magnitude higher on trail cameras (Table 1 ). It is noteworthy that herbivores detection probabilities in trail and random cameras were similar, whereas hare, porcupine, peafowl, and grey langur had higher detection probabilities on trail cameras (Table 1 ).

According to the trail camera photo-captures, spotted deer, blue bull, wild pig, and Indian gazelle showed predominantly diurnal activity with very few captures at night, while sambar showed activity peaks in the early morning and late afternoon hours (Fig.  4 ). Contrastingly, data from random cameras showed spotted deer to have major activity peaks in the morning, with considerable photo-captures in the evening as well as at night. Sambar showed crepuscular activity peaks with night time activity in random cameras. The activity peaks for blue bull changed considerably in random cameras, where the species showed night time peak in activity (Fig.  3 ). Tiger and leopard showed nocturnal activity with crepuscular peaks from trail cameras; due to very less number of independent photo-captures in the random setup, we could not compute the temporal activity from random cameras for these carnivores. The trail cameras detected a higher percentage of activities for all the ungulates, except spotted deer, than random cameras (Table 2 ). Overlap values of Δ between random and trail cameras were least for blue bull (0.41 maximum difference) and highest for sambar (0.72 highest similarity) (Fig.  4 ) suggestive of substantial differences in estimates of percent time active between the two camera setups.

Activity pattern of wild ungulates ( L to R from top: spotted deer, sambar, blue bull, Indian gazelle, and wild pig ) and their major predators ( tiger and leopards ) in the study area. In each graph, the solid-black and dotted-blue line represents the species’ activity pattern obtained from random and trail cameras, respectively; the grey shaded polygons depicted the overlap between two curves. The vertical dotted gray line shows the timing of sunrise and sunset in the study area. Activity pattern of tigers and leopard was computed only from trail cameras.

The average group sizes of all the ungulates obtained from both the camera setups were comparable, however, larger congregations were observed from trail cameras (Table 2 ). The frequency of different group sizes observed in random and trail cameras were comparable for all wild ungulate species (Fig.  5 ). Groups consisting of larger number of individuals were common in spotted deer, however, for sambar, blue bull, Indian gazelle, and wild pig single individuals were captured most frequently (Fig.  5 ).

Herd size of wild ungulates (L to R from top: spotted deer, sambar, blue bull, wild pig, and Indian gazelle) recorded from the random and trail camera setups.

Our results have implications on inferences of past studies and insights for planning future studies that use camera trap data to infer community composition, abundance, behavior and demographic parameter. Species accumulation curves act as a baseline to improve the efficiency of future community surveys 42 , therefore it is important to inquire about the optimal sampling design for this purpose. Species assemblages recorded in random and paired camera setups were the same, however, the rate of species accumulation was faster in trail camera setup than the random camera trap setup (Fig.  2 ). The above findings suggest that trail cameras placed for targeting large carnivore abundance estimation (in mark-recapture framework) can be used to generate species inventories in a short amount of time 28 .

Large carnivores, which occur at low density, patrol their territory using certain routes, were poorly captured in randomly placed camera traps (Table 1 ). Thus, the abundance indices of large carnivores obtained from random cameras were lower than that of the trail cameras. Smaller carnivores, like their larger counterparts, were significantly less represented in the randomly placed cameras. Moreover, it seems reasonable that carnivores (with soft pads) prefer mud roads or animal trails over random walk in a landscape with sharp pebbles and thorny vegetation. These findings were further endorsed by higher detection probability (Table 1 ) (derived from multi-method occupancy analysis) of carnivores in trail cameras over the random cameras. Similar findings were published from the studies which found more carnivore captures on the trail compared to the off-trail and random cameras 26 , 28 . However, areas where man-managed road/trail densities are significantly low, studies did not find any differences in captures between trail and non-trail cameras 27 . In our opinion, if the objective is to assess relative abundance of various species within an ecosystem and compare these with density, then trail based RAI results are biased for large carnivores and random placement design results provide unbiased estimates of relative density (Fig.  3 ). However, if the objective of the study is to compare the relative abundance of the same species over time the use of trail-based camera placement would likely provide more precise estimates due to higher capture probability and therefore be more useful in detecting population trends. Caution should be exercised while comparing population trends using RAI values obtained from trail cameras, as detection rates can be influenced by camera placement, field expertise in choosing locations to maximize photo-captures and animal movement rates at these non-random selected locations 43 . Thus, bias may not remain consistent over different sampling intervals when using RAI obtained from trail cameras.

Ungulate species did not show significant differences in photo-capture rates from trail and random cameras (Table 1 ). Wild ungulates spend a large proportion of time foraging, and use trails mostly while moving from one foraging patch to another 44 . In consonance with our hypothesis, this explains the greater number of wild ungulate photo-captures in random cameras compared to trail cameras. While the average group size captured in both trail and random cameras were comparable, trail cameras recorded larger groups for ungulates (Table 2 ). This was likely as the species are known to move in bigger herds while they split into smaller sub-groups for foraging to avoid competition 45 , 46 . Although detection probabilities of wild ungulates were similar from the trail and random cameras, their activity patterns were substantially different. Trail cameras captured exclusively diurnal activity for all wild ungulate species, while the random cameras showed a more realistic activity pattern with records of night-time activity for spotted deer, sambar, and blue bull (Fig.  3 ). Trails are extensively used by predators during night, avoiding the use of trails at night was likely an anti-predatory behavior by wild ungulates 47 , 48 . Published activity patterns of these species have been obtained from trail cameras that focused on population estimation of large carnivores 22 , 23 and therefore are likely biased towards diurnal activity.

Our study shows that both trail and random placement of cameras provide similar inference on species richness and composition, but trail cameras had faster accumulation rates and were therefore more cost-effective. Additionally, information on illegal activities inside the PA obtained from trail cameras were more comprehensive than random cameras. The relative abundance index (RAI) from both camera designs was similar for wild ungulates but much lower for carnivores in random setup. Our results for RAI suggest that random camera placement design is unbiased for estimates of relative abundance of species within a community, but biased data from trail cameras could still be used for estimating trends in abundance over time for any species within the same geographical area of sampling. Contrary to our findings, a few studies have reported the superiority of random camera setup over the trail-cameras for detecting rare species 27 , 28 , however, this was not the case for the semi-arid system that we studied. Activity patterns of ungulates and proportion of time active significantly differed between random and trail cameras. We propose that random cameras provide a more realistic representation of wild ungulate activity while trail cameras are better suited for estimating activity of carnivores.

Finally, no single method can address all the aspects concerning multiple species ecology or behavior, therefore camera trap surveys need to be tailor-made to cater to specific objectives 49 . Trail-based camera trapping is an important conservation tool for monitoring abundance of large carnivores that is required for their effective conservation. We show that ancillary data generated from this effort can additionally provide information on species richness, species specific trends in abundance and activity patterns of carnivores while inferences on activity patterns of ungulates from trail cameras can be biased.

Change history

13 january 2022.

A Correction to this paper has been published: https://doi.org/10.1038/s41598-022-05223-w

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Acknowledgements

We thank the National Tiger Conservation Authority, Wildlife Institute of India, and Forest Department of Ranthambhore Tiger Reserve for necessary permission, support, and logistics. We would like to thank Sourabh Pundir, Akshay Jain, and Adarsh Kulkarni for assisting in data collection and GIS work. We are thankful to our field assistants for their hard work.

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K.S.T. and A.S. conceived the study, performed the computations, and drafted the article. A.S. and Y.V.J. assisted with the analysis and writing of the final manuscript. Y.V.J. provided resources and supervised the work. All authors reviewed and approved the final manuscript.

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Tanwar, K.S., Sadhu, A. & Jhala, Y.V. Camera trap placement for evaluating species richness, abundance, and activity. Sci Rep 11 , 23050 (2021). https://doi.org/10.1038/s41598-021-02459-w

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Systematic review article, spatially explicit capture-recapture through camera trapping: a review of benchmark analyses for wildlife density estimation.

• 1 School of Biological Sciences, University of Utah, Salt Lake City, UT, United States
• 2 Department of Wildland Resources, Utah State University – Uintah Basin, Vernal, UT, United States
• 3 College of Sciences, Koç University, Istanbul, Turkey

Camera traps have become an important research tool for both conservation biologists and wildlife managers. Recent advances in spatially explicit capture-recapture (SECR) methods have increasingly put camera traps at the forefront of population monitoring programs. These methods allow for benchmark analysis of species density without the need for invasive fieldwork techniques. We conducted a review of SECR studies using camera traps to summarize the current focus of these investigations, as well as provide recommendations for future studies and identify areas in need of future investigation. Our analysis shows a strong bias in species preference, with a large proportion of studies focusing on large felids, many of which provide the only baseline estimates of population density for these species. Furthermore, we found that a majority of studies produced density estimates that may not be precise enough for long-term population monitoring. We recommend simulation and power analysis be conducted before initiating any particular study design and provide examples using readily available software. Furthermore, we show that precision can be increased by including a larger study area that will subsequently increase the number of individuals photo-captured. As many current studies lack the resources or manpower to accomplish such an increase in effort, we recommend that researchers incorporate new technologies such as machine-learning, web-based data entry, and online deployment management into their study design. We also cautiously recommend the potential of citizen science to help address these study design concerns. In addition, modifications in SECR model development to include species that have only a subset of individuals available for individual identification (often called mark-resight models), can extend the process of explicit density estimation through camera trapping to species not individually identifiable.

Introduction

Camera traps and benchmarking biodiversity.

Human-induced changes to both terrestrial and marine ecosystems are intensifying, especially in areas of the world with historically high levels of biodiversity ( Venter et al., 2016 ). Human activities have a direct effect on biodiversity, altering ecosystems around the globe ( Cardinale et al., 2006 ; Estes et al., 2011 ; Hooper et al., 2012 ). During this period of rapid change, and in order to better understand the effects of human activity on biodiversity, it has become increasingly important to provide baseline measurements of species distributions and population sizes, especially for rare, elusive, and difficult-to-monitor species like carnivores, which play particularly important roles in regulating ecosystems ( Beschta and Ripple, 2009 ; Laundre et al., 2010 ; Ripple et al., 2014 ). Providing these benchmark analyses, and establishing the methodology and analysis framework to compare changes over time, is essential to understanding and quantifying the ways in which these species are both affected by rapid change and how they, in turn, affect human well-being.

Camera traps have been used in animal ecology studies for decades, and are particularly suitable for studying large carnivores, which can be difficult to study with other methods ( Griffiths and Van Schaik, 1993 ; Rowcliffe and Carbone, 2008 ; Trolliet et al., 2014 ; Burton et al., 2015 ). Cameras provide researchers with a non-invasive survey tool to sample wildlife communities and usually require less intensive labor commitment than standard trapping and marking techniques ( Meek et al., 2014 ). Consequently, camera traps have become powerful research tools for scientists and wildlife managers investigating a wide variety of ecological questions, management situations, and conservation strategies ( Karanth and Nichols, 1998 ; and Glen and Dickman, 2003 ; Hirakawa, 2008 ; O'Connell et al., 2011 ; Meek et al., 2014 ).

Measuring Biodiversity: Density Estimation

In order to measure how species respond to rapid change, and to establish proper avenues for comparative studies, researchers must first establish a reference or baseline population size. In biodiversity studies, density estimation is often considered the gold-standard of population assessment and for species conservation, wildlife management planning, and long-term population monitoring ( O'Connell et al., 2011 ; Tobler and Powell, 2013 ; Royle et al., 2014 ). Wildlife density has long been estimated through capture-recapture methods ( Otis et al., 1978 ). Karanth (1995) and Karanth and Nichols (1998) pioneered the use of camera traps in a photographic capture-recapture framework to estimate population size of tigers Panthera tigris in Nagarhole, India. The authors used camera trap images, which come with an accompanying GPS coordinate (and date and time stamp), as individual “captures.” They then used the photographs from these individual captures to build a dataset of multiple individual tigers. From there, they could create separate capture histories for each one. Since this work, multiple independent investigations have adopted camera traps and this analysis framework to estimate the densities of tigers in other areas of the world ( O'Brien et al., 2003 ; Linkie et al., 2006 ; Harihar et al., 2009 ; Gopal et al., 2011 ), as well other individually identifiable animals ( Kelly et al., 2008 ; Paviolo et al., 2008 ).

Many of these early investigations relied on closed model capture-recapture methods ( Otis et al., 1978 ; White et al., 1982 ). This method requires compiling individual-specific capture histories across a defined study area where the boundaries of an individual animal's movement may not be well-known. The detection histories contain information about individual capture probability, and can thus be used for estimating population abundance. However, these models provide little information on the movement patterns of each individual, as well as the spatial distribution of the traps themselves. Therefore, under this framework, density is estimated according to an arbitrarily set area, usually defined as the camera trap polygon plus a buffer with radius equal to either the maximum distance moved by an individual across the trap array or half the distance moved ( O'Connell et al., 2011 ). As density requires both an abundance and an area, arbitrary designation of area is an obvious hindrance to closed model capture-recapture methods. Consequently, this method is often considered to measure density implicitly ( Royle et al., 2014 ). That is, density is estimated without explicitly measuring all of its elements. The population size is functionally unrelated to an explicitly monitored area, which can make it impossible to compare across studies or even different models ( Royle et al., 2014 ). Furthermore, research has shown this method to consistently overestimate density by underestimating the distances moved by individual animals ( Obbard et al., 2010 ; Noss et al., 2012 ; Pesenti and Zimmermann, 2013 ).

Spatially Explicit Capture-Recapture

Spatially explicit capture-recapture (SECR) density estimation was developed independently by Borchers and Efford (2008) and Royle and Young (2008) (see also Efford, 2004 , 2011 ; Efford et al., 2009 ; Royle et al., 2009 ). What separates SECR density estimation from closed capture-recapture models is the incorporation of an explicit spatial component to each individual's detection history, as well as a defined state-space over which density is estimated ( Efford and Fewster, 2013 ; Royle et al., 2014 ). Therefore, SECR analysis represents an explicit way of measuring density (i.e., both components of density are estimated without ad hoc calculations). However, because of the additional parameters to estimate, SECR models can be more data hungry than their implicit counterparts ( Royle et al., 2014 ).

A detailed breakdown of SECR analysis is beyond the scope of this paper. Efford et al. (2009) offer a thorough introduction and explanation of SECR analysis through Maximum Likelihood-based methods, and Royle et al. (2014) provide a thorough introduction and explanation of SECR analysis through Bayesian techniques incorporating data augmentation. Here, instead, we provide a brief summary based on the work of Royle and Young (2008) and Borchers and Efford (2008) .

SECR models are hierarchical , where the full model is described by multiple component models ( Royle and Dorazio, 2008 ). The first of these components describes the distribution of activity centers s , or home range centers, of individual animals. In this characterization, s i represents the geographic point where individual i 's movement is centered (the movement around the point s i is then described according to a specific probability function), and s i ; i = 1, 2,…, N represents the activity centers of every individual within a defined state-space S , the region over which density is estimated ( Royle and Young, 2008 ). This model is a spatial point process, capable of measuring density as either constant across the state-space or with spatial variation ( Efford et al., 2009 ; Royle et al., 2014 ). S is typically described by specifying coordinates of a polygon that is substantially larger than the area sampled, allowing some individuals to have s i outside of the sampled area. As mentioned above, individuals are assumed to move around the state-space randomly as specified by some probability distribution. Finally, the sum of activity centers, N , over the state-space S , specified u , represents the estimated population density.

Another component, the observation model, describes y , or how the observed data occur based on the locations of N individuals ( Efford et al., 2009 ; Royle et al., 2014 ), as it is assumed that individuals are sampled imperfectly due to detection probability being <1. The observed data are binary observations during a specific sample that state whether an individual was captured or not ( Royle and Young, 2008 ). These observations are used to create encounter histories for each individual. In addition, each encounter comes with a pair of coordinates that specify where each encounter occurred. These encounters are defined by at least two parameters, p and σ , which describe the probability of capturing or detecting an individual at a given location by using the distance between each individual's activity center and a given encounter location. In this formulation, when individuals are marked, p ij is the probability of capturing individual i at trap location j , and σ is the spatial scale parameter that defines how capture probability declines with distance ( Efford et al., 2009 ; Royle et al., 2014 ).

The most basic SECR models come with the following major assumptions: (1) within the population of interest, and during the period of study, there exists both demographic and geographic closure; (2) individual activity centers are randomly distributed and do not change; (3) the probability of detection at a given location is a function of distance to an individual's activity center; and (4) there is independence in individual encounters among individuals and within the same individual.

The first assumption means that basic SECR models assume no exit or entry into the population through either recruitment or mortality or permanent emigration or immigration from the area of study. However, the model does allow for “temporary” variability to encounter around the state-space ( Royle et al., 2014 ). Violations of closure can result in detection probability estimates that are too low or the effective trap area being considered too small, resulting in positive bias in resulting density estimates ( Dillon and Kelly, 2008 ; Obbard et al., 2010 ). Typically, practitioners are encouraged to either (a) keep their survey period as short as possible or (b) use an open population model (e.g., Gardner et al., 2010a ; Ergon and Gardner, 2014 ; Schaub and Royle, 2014 ) to avoid violating this assumption. The second assumption deals with the distribution of individual activity centers across the state space. This is often referred to as the “uniformity assumption,” ( Royle et al., 2014 ) modeled as,

This creates what is known as a homogenous point process model; however, inclusion of site-specific covariates can make it possible to estimate density as a function of state-space heterogeneity ( Royle et al., 2018 ). Accompanying this assumption is that individual home range centers are spatially stationary for the duration of study. However, this assumption may be relaxed by modeling s i with some type of latent movement model. Thus, the activity centers of all or some of the individuals within a population are allowed to drift ( Royle et al., 2016 ).

The third assumption states that each animal has an activity center and the probability of capture decreases with distance to that activity center. Typically, a half-normal detection function is applied to describe how detection probability decreases with distance, but a variety of functions are available. In this formulation, the detection function is described by the detection probability and the scale parameter, which denote the probability of detection when the distance between an individual and their activity center is 0 and how that probability declines in response to distance, respectively. The most basic models assume that these parameters do not change across individuals, but this assumption can be relaxed to vary across time, individuals, and covariates ( Royle et al., 2018 ). Finally, the assumption of independence of encounters states that the encounter of one individual does not affect the encounter of another individual at the same trap, and encounter of an individual at one trap location is independent of encounter at any other trap location. It is natural to think that species may have a behavioral response to certain areas, making them more or less likely to visit specific trapping locations. Recent model developments allow for this behavioral response to be explicitly accounted for ( Gardner et al., 2010b ; Royle et al., 2011 ).

Camera Traps and SECR Analysis

Camera trapping lends itself well to measuring density through SECR analysis. SECR analysis requires marking a sample of individuals and monitoring their presence across multiple surveys and study sites ( Borchers and Efford, 2008 ; Efford et al., 2009 ; Royle et al., 2014 ). Traditionally, monitoring requires setting up live-trapping stations, using natural marks or marking individuals caught in each trap, and repeating the process over a given time-frame. This results in multiple visits (usually daily) to each trap station, individual processing of animals caught in the traps, and consistent maintenance of traps to ensure that each is capable of capturing animals, resulting in a time and effort-intensive process that hinders the number of traps that can be deployed during a particular investigation ( Jimenez et al., 2017 ; Loock et al., 2018 ; Whittington et al., 2018 ; Petersen et al., 2019 ). This is problematic for species with low detection or capture rates due to natural rarity or large individual home ranges. To compensate, researchers are required to increase the duration of time each trap is active during a season, which can lead to violations of the closure assumption.

However, camera traps are non-invasive, remote sensing devices that can monitor animal populations over a wide-geographic area ( Kelly et al., 2008 ; Linden et al., 2017 ; Luskin et al., 2017 ). They are relatively cost and time-effective monitoring tools, requiring no intensive and individually-invasive capturing techniques, and they can be paired with other methodological approaches that bolster the predictive power of population monitoring investigations ( O'Connell et al., 2006 ; Lyra-Jorge et al., 2008 ; De Bondi et al., 2010 ; Roberts, 2011 ; Welbourne et al., 2016 ). Camera surveys require little maintenance once initially setup, and they offer the unique ability for researchers to mark individual animals without having to maintain the traps they were caught in or process the individuals captured. Furthermore, since SECR analysis requires that density is estimated over an explicitly determined state-space, and that a state-space is typically defined as the polygon surrounding the outermost traps of a particular array (aka the minimum convex trap polygon), using camera traps instead of other trapping methods allows researchers to explicitly adjust the size of their study area. Finally, the ease of setup and relatively low maintenance requirements for camera traps allows researchers to establish a higher density of traps within their camera array compared to more traditional methods, with more than one trap within the average home range size of the species studied, another requirement of SECR analysis ( Borchers and Efford, 2008 ; Royle et al., 2009 , 2018 ).

In this review, we aim to explain the current extent of camera trap SECR analysis, identifying whether benchmark density estimates have been precise enough to monitor change over time, especially for species where no other estimates exist. Our goals were to (1) summarize the current efforts of SECR analysis through camera trap surveys and (2) analyze study design criteria to identify important predictors of density precision and suggest recommendations to improve density precision in future studies. Our review provides an accurate picture of the current direction of the science. We document the publication outlets, species studied, and geographic extent of these efforts. As a guide for future research, we highlight the analysis software used, the study designs adopted, and both the amount of effort and number of detections recorded. Finally, we report on the study design factors that lead to increases in density estimation precision and how incorporation of new analysis techniques, online technologies, and citizen science may offer ways to increase these factors for future investigators, as well as pave the way for new developments.

Materials and Methods

Literature review.

Our literature review took place between 24 April and 21 October 2019. We searched the Web of Science TM for papers using the following title and topic search terms: “spatial capture-recapture” AND “spatially explicit capture-recapture” AND “spatial mark-recapture” AND “spatially explicit mark-recapture” AND “spatial mark-resight” AND “spatially explicit mark-resight” AND “spatially explicit density estimation.” We reviewed the resulting dataset of 309 papers and included only those that used camera traps. The resultant dataset included 88 scientific articles. We then expanded this dataset by searching through all studies citing Royle et al. (2014) , which resulted in an additional 7 studies. The final dataset included 95 papers ( Supplementary Table 1 ).

Categorical variables were extracted from each study. We recorded the title, author(s), journal, year, pagination, class and species studied, and continent and country of focus for each study. If more than one species was included in a single study, a separate record was produced for each. This resulted in a dataset with 110 species-specific records. Each study's objective was classified as either single-species, two-species, or multi-species density estimation. Spatially-explicit capture-recapture (SECR) analysis is typically done using freely available data analysis software and can be implemented in either a maximum likelihood or Bayesian framework, so we recorded the method of analysis as either MLE (for maximum likelihood), Bayesian, or both, and the statistical program used to implement the analysis was also included in the database. We recorded whether or not each study used site-specific covariates within their analysis framework. For studies that paired non-covariate spatially explicit density estimation with diet, movement, or occupancy analyses that included site-specific covariates ( n = 8), the study was classified as using covariates and the discrepancy was noted on a separate column in the dataset. We recorded any methods (simulations, occupancy analysis, live trapping, etc.), besides spatially explicit density estimation through camera trapping, implemented during the course of each study. Furthermore, if a study made any comparisons between SECR and another density estimation framework ( n = 23), the specific models compared and the results of these comparisons were recorded. Finally, we recorded if each study included baited camera trap stations and whether or not community engagement or citizen science was implemented during any stage of the project.

We extracted a number of numerical variables from each study. The number of camera stations was recorded as the average number of stations implemented per year of study. We recorded the length of each study in years. We included, when recorded, the minimum convex polygon of the camera station array. If this camera polygon was not reported in the manuscript ( n = 5), the state-space of the study was used instead (see section Measuring Biodiversity: Density Estimation above). We recorded the average camera spacing in meters. When the average spacing was not explicitly reported, we recorded the average of the reported camera spacing range ( n = 13), the minimum distance between stations ( n = 1), or the maximum distance between stations ( n = 1). The number of trap days was recorded as the total accumulated effort for all camera stations across all years of survey. This total was then averaged across years for analysis. The total number of photo-captured target species was recorded, as was the total number of individuals tracked throughout the study. The scaling parameter, σ, was recorded for each study as the average across years per species using either the author-specified top model or the author-reported model average. If the best model was not specified ( n = 9), σ was extracted as the average across all models reported. When more than one area was surveyed during a particular study and no average was recorded ( n = 7), the scaling parameter was recorded as the weighted average of estimates based on the size of each area's assessed state-space. Furthermore, if the scaling parameter was reported to vary based on sex ( n = 8), the estimate was averaged using an assumed 1:1 sex ratio ( n = 7) or the specified sex ratio provided ( n = 1). Density was recorded as the number of individuals reported per 100 km 2 on a per species basis. Estimates were averaged across year using either the top model reported or the author-reported model average. As with the scaling parameter, when the best model was not specified ( n = 9), density was extracted as the average across all models reported. When more than one area was surveyed during a particular study and no average was recorded ( n = 7), density was recorded as the weighted average of estimates based on the size of each area's assessed state-space. One study did not report the specific state-space of each area surveyed, so the density estimate for this study was calculated without area-specific weights. Lastly, to assess the precision of density estimates, the coefficient of variation (CV) was calculated on a per species basis across studies. When the standard deviation of the maximum likelihood estimator or the posterior standard deviation of density were not explicitly reported, the standard error was used to calculate CV ( n = 12). One study provided only a 95% confidence interval, and the standard deviation for this study was calculated as the range of the confidence interval divided by 3.92 (assuming a normally distributed density estimate).

Data Analysis

In an effort to identify important study design parameters for increases in density precision, we modeled each study's coefficient of variation against study design parameters. However, all predictor variables were correlated with at least one other variable (Pearson's r > 0.5). Therefore, we conducted Principal Component Analysis (PCA) on study design factors and modeled density precision as a function of the first three principal components (PC1, PC2, and PC3), which collectively accounted for 72.6% of the variation in study design factors. Since each predictor was on a different scale, predictor variables were standardized to have a mean = 0 and a standard deviation = 1 before running the PCA. We then used PC1, PC2, and PC3 as covariates in modeling density precision to study design components using a Gaussian linear model. We determined significant associations between precision and principal components at α = 0.05. Predictors included in the PCA were: density, target captures, individuals monitored, camera stations, camera days, and study area.

Dataset Summary

SECR analysis through camera trapping has focused on multiple species across a wide geographic range. The results from our dataset were published in 37 different journals. Five journals accounted for 42.1% ( n = 40) of publications (PLoS One = 13, Oryx = 12, Biological Conservation = 7, Ecology and Evolution = 4, and Nature = 4). Publication rate has steadily increased since 2010 (the earliest publication year included in our dataset), with 67.3% ( n = 64) published between 2015 and 2019 ( Supplementary Table 1 ). All studies focused on mammals. Of the 110 species density estimates, 60.9% ( n = 67) were of 10 different species: leopard ( Panthera pardus ) = 17, tiger ( Panthera tigris ) = 14, jaguar ( Panthera once ) = 8, clouded leopard ( Neofelis nebulosi ) and ( Neofelis diardi ) = 7, cougar ( Puma concolor ) = 5, ocelot ( Leopardus pardalis ) = 4, domestic cat ( Felis catus ) = 3, leopard cat ( Prionailurus bengalensis ) = 3, red fox ( Vulpes vulpes ) = 3, serval ( Leptailurus serval ) = 3. All other species were included in fewer than three occasions ( Supplementary Table 1 , Figure 1 ). 90.9% ( n = 100) of estimates were of carnivores, and of those 82% ( n = 82) were of felids. 91.6% of studies focused on only one species ( n = 87), 5.3% on two species ( n = 5), and 3.1% on more than two species ( n = 3). SECR studies using camera traps were conducted on six continents, with Asia and South America representing 58.9% (Asia = 38, South America = 18) of all studies ( Supplementary Table 1 , Figure 1 ).

Figure 1 . Camera trap SECR study spatial distribution and species focus. (A) Global spatial distribution of camera trap SECR studies by country. (B) Number of studies focused on the top 10 focal species. The top 10 species listed accounted for 61% of all SECR camera trap studies. Most (75%) SECR camera trap studies focused on felids.

SECR models incorporated both maximum likelihood and Bayesian analysis methodologies. Researchers estimated density using exclusively maximum likelihood estimation 46.3% ( n = 44) of the time, with 72.7% ( n = 32) of these studies using the R package secr ( Efford, 2010 ) for analysis; Bayesian inference was used exclusively in 35.8% ( n = 34) of studies, where the program SPACECAP ( Gopalaswamy et al., 2012 ) was used for analysis in 40.6% ( n = 13) of these studies; and both methods were incorporated in the remaining 17.9% ( n = 17) of studies, with secr or SPACECAP used in 88.2% ( n = 15) of these studies.

Camera trapping methodology varied in both spatial scale and temporal extent, resulting in highly variable numbers of target captures and individuals monitored. Most studies lasted for 1 year or less (71.6%, n = 68, mean = 1.9), and a median of 57.5 camera stations were deployed per study per year (mean = 100.1, min = 12, max = 849). Surveys lasted for a median of 3,124 camera-days per year (mean = 7,762, min = 478, max = 114,854). The minimum convex camera trap polygons covered a median area of 306 km 2 , with large-scale, multi-year studies having a major effect on the mean (mean = 2,646, min = 4, max = 70,096). Camera stations were placed, on average, 1,962 m apart (median = 2,000, min = 100, max = 8,740), and bait was used in 24.2% ( n = 23) of studies. SECR studies recorded a median of 129.5 detections of their target species (mean = 340.1, min = 21, max = 3,163) and resulted in a median of 27 individual animals tracked (mean = 60.8, min = 4, max = 1,240). The median scaling parameter varied across species and dietary preferences ( Supplementary Table 2 ). Density was lowest for large carnivores and varied across species and geographic locations ( Supplementary Table 2 ).

Camera trapping studies deployed for SECR density estimation incorporated a number of supplemental methodologies and compared the effectiveness across these methods, as well as across data analysis approaches and modeling schemes. Twenty-two (23.2%) studies incorporated site-specific covariates into their analysis. As noted in section 2.1, 36.4% ( n = 8) of these studies used the information from site-specific covariates in analysis separate of density estimation through SECR analysis. Slightly under half of studies (46.3%, n = 44) incorporated methodologies in addition to camera trapping. Of these methodologies, GPS tracking, telemetry, and live trapping were used most frequently (27.3%, n = 12), followed by simulations (22.7%, n = 10). Nineteen studies (20.0%) compared the results of SECR analysis with closed-population capture-recapture analysis ( n = 16), Random Encounter Model analysis ( n = 1), distance sampling analysis ( n = 1), and Royle-Nichols occupancy analysis ( n = 1). Authors self-reported that SECR analysis either outperformed closed-population capture-recapture or they recommended SECR analysis 93.8% of the time ( n = 15). One study self-reported that closed-population capture-recapture analysis outperformed SECR analysis. Twenty-six (27.3%) studies surveyed across multiple years or seasons.

Density Precision Predictors

The precision of density estimates, as measured through the coefficient of variation (CV), was reported or extracted as explained in section 2.1 for 90 species-specific density estimates. The median CV was 30% (mean = 31.1%). 75.6% ( n = 68) of studies reported a CV of ≤40%, but only 21% ( n = 19) of studies reported a CV ≤20%.

The first three principle components of our PCA, which accounted for 72.6% of the variation in study design characteristics, described axes of increasing camera stations and camera days (PC1), increasing density and individuals monitored (PC2), and increasing density and decreasing individuals monitored (PC3; Supplementary Tables 3 , 4 ). Density precision did not differ significantly across PC1 and PC3 ( p = 0.131 and p = 0.919, respectively; Supplementary Table 5 ). However, density precision increased significantly with higher values of PC2 (increases in density and individuals captured; p = 0.038; Figure 2 ).

Figure 2 . Study design characteristics predicting increases in density precision. Density precision increased with increasing values of PC2 (describing axes of increasing density and increasing individuals monitored). Data points are species-specific values of the Coefficient of Variation. Larger values mean lower precision. Blue line and shaded area represent the slope and 95% Confidence Intervals from our linear model. Dashed gray line represents the mean Coefficient of Variation in our review.

In this review, we summarized the current publication extent, geographic coverage, and species focus; study design specifics; and available analysis pipelines of SECR camera trap studies. Our review highlights the flexibility of SECR analysis through camera trapping, which makes this methodology a tool for providing benchmark analysis of previously understudied species. Our review also sheds light on the current geographic and species bias toward areas with rare, elusive, and individually-identifiable species, particularly large felids. We also found that many studies produced relatively imprecise density estimates (see below for details), and that precision could be increased with increases in the number of individuals captured, which can be accomplished with a larger study area.

Benchmarking Rare and Elusive Species

Our review highlights the importance of camera trapping for studying rare, elusive, and human-intolerant large carnivores ( Ripple et al., 2014 ). These are species that are both exceptionally important to ecosystems throughout the world and difficult to study through other means. For many of the species in our review, the density estimates calculated were the first reported population estimates for them, highlighting the ability of camera traps to monitor previously understudied species, providing benchmark estimates that can be compared over space and time. As the world continues to change at an increasingly rapid rate, benchmarking and archiving density estimates for these species will be critical for tracking the effects of rapid global change.

SECR density estimation through camera trapping is currently focused on rare, elusive, large-ranging, and individually identifiable carnivores, specifically large felids, and this methodology represents one of the best ways to study these species. More than a third of all studies included in this review focused on one of three species: leopard, tiger, and jaguar (35.5%, n = 39). This explains the subsequent bias in geographic focus of camera trap SECR studies (Results section; Figure 1 ). Focusing on species that are capable of individual identification through photographic analysis alone is the obvious reason for this bias, as it represents the simplest avenue to robust density estimation without the need to employ more-intensive methodology (e.g., live trapping; scat, environmental, and/or hair sampling for DNA analysis, etc.). Large felids tend to be wide-ranging, naturally rare, and heavily affected by human influence ( Seidensticker and McDougal, 1993 ; Turner, 1997 ). Many of these species are currently threatened or endangered with extinction, so information about their population densities and trends through space and time, especially in relation to human influence and climatic change, is needed for their continued conservation ( Ripple et al., 2014 ). Since these species require large areas of undisturbed habitat, they tend to be excellent indicators of general ecosystem health and conservation of these umbrellas species is thought to affect the conservation of other species at lower trophic levels ( Dalerum et al., 2008a , b ; Estes et al., 2011 ).

Addressing Imprecise Density Estimates

Although the goal of many studies in this review was to assess the current population size of a particular species and/or lay the framework for a long-term monitoring project, multiple density estimates from studies included in this review may not be precise enough to monitor population trends through time. The mean reported or derived coefficient of variation (CV) was relatively high (31.1 %). In fact, less than a quarter of studies reported high precision in their density estimates (CV ≤20%). Conducting a power analysis before implementing a specific study protocol can reduce “… [wasting] time and effort on a program that is unlikely to yield useful information” ( Gerrodette, 1987 ). This power analysis can be conducted for multiple fieldwork scenarios using the readily available software TRENDS and the R package emon ( Gerrodette, 1993 ; Barry and Maxwell, 2017 ). For example, using emon and the average density and standard deviation of tigers in our dataset (CV = 0.31), assuming a normal distribution for random values and that density is measured twice per year, the likelihood of detecting a 50% linear decline in tiger density over 10 years is only 32.7%. This likelihood increases to 68.0% with a CV = 0.20 and to 89.2% with a CV = 0.15. This simple exercise shows that a majority of camera trap monitoring programs designed around species where precise density estimates are needed to assess population change through time may be inadequate. Furthermore, pairing simulation with SECR density estimation through camera trapping has great potential. Only 10.5 % ( n = 10) of studies performed any type of simulation before implementing their field protocol. Conducting simulations before implementing field protocol can help elucidate the effects particular study designs could have on density estimation, and recent developments in SECR simulation and design (see Efford, 2019a , b ; Efford and Boulanger, 2019 ) make it relatively straightforward to evaluate study designs using prior information. Given that the majority of studies were conducted on species where prior information on home range size and density were available (over 60% of studies were conducted on only 10 species), including this information into simulation models could help structure studies where a certain measure of precision is needed to monitor population trends. For example, Efford (2019a) designed the R package secrdesign and the accompanying web-based application “SECRDESIGNAPP” ( Efford, 2019b ) for researchers of all levels of statistical proficiency. Using the average study design characteristics for tiger SECR studies in this review ( Supplementary Table 2 ), as well as the accompanying average density in the above power analysis, assuming a grid-based design with a half-normal detection function, Poisson distribution for n , and three temporal replicates per site (a common camera trap study design used in SECR analysis), the program recommends that this design proceed with caution. SECRDESIGNAPP makes this recommendation based on the power to detect a trend in population density exceeding 80% only in cases of a net density decrease of ≥64.1% or a net increase of ≥94.9%. With all of the other study design criteria held equal, a similar study would need to deploy 240 camera stations (nearly 100 more than average) to achieve a design that meets the app's recommendations for statistical power. Moving forward, we strongly recommend future studies conduct these simulation exercises before following through with a potentially unsatisfactory field protocol.

Increasing Density Precision

Density precision increased with increasing values of baseline density and the number of individuals captured. As the former cannot be controlled by researchers beforehand, the best way to increase precision from a study design perspective would seem to be through increasing the number of individuals captured. This can be done naturally by increasing the survey area, thus exposing a greater number of individuals to sampling. However, increasing survey area is not always feasible in many typical research situations. Investigators are hindered by the amount of resources available to them, and any one study's scale can be limited by labor, money, time, political boundaries, and other factors. In order to increase the efficacy of SECR density estimation through camera trapping, especially in the context of long-term population monitoring, researchers must adopt new techniques and technologies [e.g., automatic detection through artificial intelligence ( Norouzzadeh et al., 2018 ), online data entry and verification platforms (eMammal:: https://emammal.si.edu/ )] to increase the scale of their investigations and improve the precision of density estimates.

Future Research Using Camera Traps and SECR Analysis

There are exciting avenues through which research using SECR analysis and camera traps could be expanded. The incorporation of community science (aka citizen science) into SECR camera trapping studies can increase the scale of their investigations. Community science has expanded recently due to changing views of science and because of its scientific and societal benefits ( Silverton, 2009 ; Adler et al., 2020 ). One of the hallmarks of community science is its ability to increase the spatial scale and temporal extent of investigations ( Devictor et al., 2010 ; Abolafya et al., 2013 ; Jarvis et al., 2015 ; Adler et al., 2020 ). Specifically, community science has been shown to be effective in gathering baseline population and habitat usage data tracked through both space and time ( Conrad and Hilchey, 2011 ; Sullivan et al., 2017 ; Horns et al., 2018 ; Neate-Clegg et al., 2020 ). Community science allows for the effective tracking of species distributions, as it allows projects to cover much greater areas than through more traditional methods ( Gallo and Waitt, 2011 ; Hawthorne et al., 2015 ; Chandler et al., 2017 ). With camera traps, volunteers can setup cameras, maintain them in the field, and even upload and tag images to an online database. Furthermore, employing volunteers to help setup camera traps may even be a way for researchers to access land not previously available (e.g., private land, farmland, etc.). Finally, online camera trap databases (e.g. eMammal: https://emammal.si.edu/ ; Smithsonian Wild: http://siwild.si.edu ; Wildlife Insights: https://www.wildlifeinsights.org/home ; and the Urban Wildlife Information Network: https://urbanwildlifeinfo.org ) make it possible for online data entry, data upload, project management, and expert review, each of which is critical to the operation and maintenance of a community science project, and these above-mentioned programs have already initiated multiple successful citizen science initiatives.

It is important, however, to note the potential drawbacks and limitations of citizen science camera trapping projects. A consistent and critical challenge to citizen science is maintaining data quality and consistency ( Hecker et al., 2018 ). For example, qualitative analysis of citizen science data quality showed that only 62% of citizen science data meets scientifically accepted precision parameter thresholds ( Aceves-Bueno et al., 2017 ; Adler et al., 2020 ). Citizen science data quality can be improved with close communication between project leads and volunteers and rigorous citizen science training, but this requires both extensive time and resources ( Dickinson et al., 2010 ; Vann-Sander et al., 2016 ; Alexandrino et al., 2019 ). Additionally, collaboration with citizen science projects and online programs such as eMammal ( https://emammal.si.edu/ ) make it possible for experts to review each citizen science classification. Another potential limitation of any citizen science camera trapping project is the ability to retain volunteers ( Sauermann and Franzoni, 2015 ; Seymour and Haklay, 2017 ; Alexandrino et al., 2019 ). In one study, Wald et al. (2016) found that only a few participants complete large portions of work. The authors suggest that providing project-based benefits to return participants, sharing data with participants, and consistent communication between scientists and participants could address these low levels of retention. Furthermore, scientists must understand and empathize with the motivations of both new and return participants, especially with how these motivations change as volunteers progress through the project ( Rotman et al., 2012 ).

Finally, modifications to spatially explicit density estimation are worth noting. Spatially explicit mark-resight models ( Kelly et al., 2008 ; McClintock et al., 2009 , 2012 ) incorporate information about both marked and unmarked individuals to estimate density. By using both marked and unmarked animals in density analysis, they have the ability to potentially expand the number of species that can be studied using camera traps by including species where not all individuals are identifiable. Gilbert et al. (2020) recently reviewed the methods for estimating the abundance of unmarked animals using camera traps, as well as their potential shortcomings, assumptions, and recommended uses. Although the authors show that mark-resight methods are not consistently used to estimate abundance or density of unmarked animals (appearing in < 5% of included studies) throughout the camera trap community and that relative abundance across study covariates remains the most common methodology, the method holds promise and is becoming increasingly more common.

Conclusions

Camera traps have been used for population monitoring for decades. Spatially-explicit mark recapture (SECR) methods make it possible to accurately estimate density over a given area, eliminating the need for ad hoc approaches like estimating individual movement through the maximum distance traveled across camera stations or applying an arbitrary buffer around the camera trap array. Currently, SECR analyses have focused on large-ranging, rare and elusive, and easily identifiable carnivores, specifically felids. These analyses have answered previously unknown questions about how these species are distributed across particular landscapes. However, a bias toward spotted, striped, or individually-identifiable animals has left much of the world's species out of the conversation when it comes to camera trap SECR benchmark studies. Furthermore, this review shows that some density estimates may not be precise enough to monitor population trends over space and time, and we offer some recommendations for increasing density precision in future studies. Conducting power analysis or simulations using readily available software should help future researchers and managers design SECR studies that meet their desired ability to monitor trends through space and time. We recommend that studies focus on increasing the total number of individuals monitored throughout a study area, which can be done by increasing the area of the camera trap array. As many studies lack the resources or labor to accomplish such an increase in effort, we recommend that researchers think about ways to incorporate new technology, such as machine-learning, web-based data entry and deployment management, and citizen science into their study design, while recognizing that the latter comes with associated drawbacks and limitations. Lastly, SECR model development to include species that have only a subset of individuals available for individual identification (often called mark-resight models), which incorporate information from both these individuals and individuals captured without individual markings, hold promise in extending the process of explicit density estimation through camera trapping to species not individually identifiable.

SECR density estimation through camera trapping is a powerful tool in the conservation biologist's or land manager's toolbox. If executed effectively, these models can be used to monitor populations of rare, elusive, large-ranging, and individually recognizable species, making it one of the best ways to benchmark the current standing of species with recognizable individual markings.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author Contributions

AG developed the idea for the paper, conducted the literature review, analyzed the data, and wrote the manuscript. MC helped develop the idea for the paper and reviewed the manuscript. ÇŞ helped develop the idea for the paper and wrote the manuscript. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

AG would like to thank the Global Change and Sustainability Center at the University of Utah for supporting this work. ÇŞ thanks Hamit Batubay Özkan and Barbara J. Watkins for their generous support. The authors would like to thank Roland Kays, Adam Duarte, a reviewer and the handling editor for their helpful comments. Their revisions greatly improved the quality of the manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fevo.2020.563477/full#supplementary-material

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Keywords: citizen science, conservation biology, biodiversity monitoring, mammals, Carnivora, wildlife ecology, density estimation

Citation: Green AM, Chynoweth MW and Şekercioğlu ÇH (2020) Spatially Explicit Capture-Recapture Through Camera Trapping: A Review of Benchmark Analyses for Wildlife Density Estimation. Front. Ecol. Evol. 8:563477. doi: 10.3389/fevo.2020.563477

Received: 18 May 2020; Accepted: 01 December 2020; Published: 18 December 2020.

Reviewed by:

Copyright © 2020 Green, Chynoweth and Şekercioğlu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Austin M. Green, austin.m.green@utah.edu

This article is part of the Research Topic

Benchmarking Biodiversity in an Era of Rapid Change

Best Practices for Managing and Publishing Camera Trap Data

Other formats, suggested citation, contributors, persistent uri, document control, 1.1. why this guide, 1.2. target audience, 1.3. what this guide is not about, 2.1. what are camera traps, 2.2. why are camera traps used, 2.3. camera trap project life cycle, 3.1. what are camera trap data, 3.2.1. participants and roles, 3.3.1. timestamps, 3.3.2. file naming, 3.3.3. storage, 3.4.1. column naming, 3.4.2. location, 3.4.3. camera model, settings and alignment, 3.4.4. deployment groups, 3.4.5. covariates, 3.5.1. classification, 3.5.2. citizen science, 3.5.3. artificial intelligence, 3.5.4. media- or event-based classification, 3.5.5. common or scientific names, 3.6.1. agouti, 3.6.2. camelot, 3.6.3. trapper, 3.6.4. wildlife insights, 3.6.5. wildtrax, 4.1. fair camera trap data, 4.2.1. stable unique identifiers, 4.2.2. sensitive information, 4.3. camtrap dp, 4.4.1. why not a sampling event dataset, 4.4.2. occurrence core, 4.4.3. audubon media description extension, acknowledgements.

This document is also available in PDF format .

Reyserhove L, Norton B & Desmet P (2023) Best Practices for Managing and Publishing Camera Trap Data. GBIF Secretariat: Copenhagen. https://doi.org/10.35035/doc-0qzp-2x37

Lien Reyserhove , Ben Norton & Peter Desmet

Tanja Milotic and Pieter Huybrechts contributed to the introduction and figures.

The document Best Practices for Managing and Publishing Camera Trap Data is licensed under Creative Commons Attribution-ShareAlike 4.0 Unported License .

https://doi.org/10.35035/doc-0qzp-2x37

v1.0, December 2023

Camera traps have emerged as important tools for monitoring the state of biodiversity and natural ecosystems. The proliferation of data from such sensors has made data management, rather than data collection, the limiting factor in camera trap-related research. This guide provides recommendations for camera trap data management and publication to GBIF. It is intended for anyone running a camera trap study, in particular data stewards, data publishers, and others working in biodiversity informatics.

1. Introduction

Camera traps have emerged as a powerful technology for the semi-automated monitoring of natural ecosystems. Their success has led to an exponential growth of camera trap data worldwide. Herein lies a major challenge: large volumes of data are waiting to be classified, interpreted and archived. Data management, rather than data collection, has become a limiting factor for camera trap research. The proliferation of camera trap projects has also led to a diversity of terminologies, classification methods and data management practices, which impedes transparency, interoperability, cross-project collaboration or meta-analyses. A large, interconnected network of remote cameras could act as an instrument for reliable, real-time biodiversity management and decision making, but if we are not able to combine data, camera trap research will lose its full potential. This is why camera trap data needs to be open and FAIR (findable, accessible, interoperable and reusable, see Wilkinson et al. (2016) ), so that both humans and machines can use this valuable data for present and future applications.

To optimize the (re)use of camera trap data, there is a need for best practice guidelines. Several guides exist that tackle one or more elements of the camera trap research life cycle: planning, technology and techniques, study design, data collection, analysis, data management methods and data publication ( O’Connell et al. 2011 ; Rovero et al. 2013 ; Meek et al. 2014 ; Cadman et al. 2014 ; Burton et al. 2015 ; Wearn and Glover-Kapfer 2017 ). Since then, the camera trapping community has made significant progress:

Specialized data management platforms are now primarily used to manage projects and data

Artificial intelligence (AI) and cloud computing are increasingly used to automate species recognition and will soon be applied to most recorded media

Data exchange is now facilitated by Camtrap DP, a data exchange format developed under Biodiversity Information Standards (TDWG). There is increased awareness of open science, FAIR data ( Wilkinson et al. 2016 ) and privacy regulations

No up-to-date guidelines are available that focus on camera trap data management and publication only. This guide aims to fill that gap.

This guide is intended to be useful for anyone managing a camera trap study. The specific focus on data management and publication makes this best practice guide extra useful for profiles such as data stewards, data publishers, database and information managers and students working with biodiversity informatics.

The authors of this guide have experience with traditional camera traps (designed for medium-to-large-sized terrestrial mammals), but many of the recommendations, as well as the Camtrap DP standard, should be applicable to data from other types of camera traps.

This guide primarily focuses on the management, quality control, enhancement and publication of camera trap data. The following topics are out of scope:

Planning a camera trap study: types of camera traps, study design, etc.

Camera trap deployment and collection: field work, baits and lures, data retrieval, etc.

Analysis: software for analysis, ecological modelling, bias correction, etc.

Data from moving cameras: underwater robots, vehicle-mounted cameras, drones, etc.

Many extensive guides on these topics are already available. See Table 1 and Table 2 for a brief overview.

2. The use of camera traps

Camera traps are recording devices that are deployed in the field to automatically capture images or videos of wildlife activity. They are also known as game cameras, trail cameras or scouting cameras. They can record media at a regular interval (time lapse) or when triggered by the activity of an animal. Traditionally, camera traps refer to those designed to record medium-to-large-sized terrestrial mammals with a passive infrared (PIR) sensor ( Hobbs and Brehme 2017 ), but other types exist for e.g. the marine environment or insects. Just like satellites, drones, GPS trackers or acoustic sensors, camera traps collect machine observations . Acoustic sensors in particular are quite similar to camera traps, using audio rather than image/video to monitor the surrounding ecosystem. All these technologies have the benefit that they can collect data at a scale and frequency that would be challenging to obtain through human observations, and typically suffer less from human bias, interpretation or interference.

In the past decades camera traps have been increasingly used to collect biodiversity data in a non-invasive manner with minimal disturbance of wildlife. As early as in the 1890’s, George Siras was the first to develop a method using tripwire and a flash system in which wild animals photographed themselves ( Kucera and Barrett 2011 ). The first scientific camera trap studies date back to the beginning of the 20th century ( Chapman 1927 ). Since those pioneering days, technological advances in digital photography and infrared sensors have led to cost-effective, non-invasive detections of elusive wildlife ( Burton et al. 2015 ). Camera traps have become popular research tools. They are easy to install, relatively cheap and do not require special permissions or training and are therefore being used by professional researchers and hobbyists at a very broad scale. Consequently, the number of annual publications concerning camera trap studies has grown more than 80-fold since the 1990’s. Camera trap technology is used to sample communities of medium-to-large-sized mammal and bird species inhabiting freshwater, terrestrial, fossorial, arboreal and marine habitats and have proven to be excellent tools to help biodiversity monitoring initiatives ( Delisle et al. 2021 ).

The most frequently studied animal taxa include ungulates, carnivores, primates and birds, although the most innovative sensors allow the detection of small mammals, amphibians, reptiles, fish and invertebrates as well ( Hobbs and Brehme 2017 ). Camera trap technology is suitable to gather occurrence data, as well as abundance, density, diversity and distribution of species ( Table 1 ) and to answer behavioural questions such as activity patterns and responses to human disturbance. Furthermore, camera traps are unselective in species observations and are therefore often used in species interaction studies. “Bycatch” data from target species studies could be useful for other studies as well. They are often used to monitor rare, threatened and endangered species in remote and inaccessible terrains.

The full life cycle of camera trap research includes the planning phase, the deployment of the camera traps in the field, the collection, management, analysis and sharing of data. This guide is not intended to cover all aspects of the camera trap project life cycle. The focus in this best practice guide lies on the management and publishing of the data. For readers interested in a general review of camera trap research, the planning phase, the deployment of camera traps in the field, we here provide an overview of the available resources (see Table 2 ).

For a camera trap dataset to be useful, you should clearly define the aim and objectives in the planning phase: what would you like to know from which species groups? Each aim brings along its own key characteristics to consider, such as camera height and direction, seasonality, bait usage, detection zone features, camera settings, trigger and flash type. These characteristics must be included in the published dataset in order to be useful in consequent analyses. We recommend consulting the key references in Table 1 for readers interested in this topic. Additionally, Wearn and Glover-Kapfer (2017) provides a comprehensive overview of more general, camera trap survey design aspects.

3. Managing camera trap data

Once a camera trap is operative in the field, it can generate hundreds, thousands or even millions of pictures or videos in the time frame of the project. Ultimately, these data need to be analysed. But first, data need to be retrieved, stored, organized and labelled. The content of the images needs interpretation or annotation, either manually or facilitated by technology. All these steps are considered in the data management process.

Data management is one of the real bottlenecks in camera trap research. The massive amount of information stored on memory cards requires a high investment in terms of human effort and time. If data storage and classification is lagging behind, large volumes of data will remain unused and will get lost eventually. Manual extraction, organization and labelling can introduce human errors and can lead to data loss. In addition, there is often more information in the image or video than the sole target species (group) or topic that is the focus of the project. Camera traps inherently detect multiple species and observations of non-target species provide valuable data for other research objectives. If all data, including non-target species, were annotated, more relevant outcomes to funds could be generated ( Young et al. 2018 ; Wearn and Glover-Kapfer 2017 ).

To conclude, every researcher should pay attention to data hygiene. The underlying idea is: rather have ten well-documented elements, rather than a hundred poorly documented ones. Only in this way can we turn the cacophony of raw image data into useful quantitative data.

In this section, we introduce a number of general conventions for sound data management data. We zoom deeper into what camera trap data exactly is. For each type of camera trap data ( metadata , media , deployments , observations ), we focus on how to best manage these, and what data management platforms you can use to facilitate you in doing so.

Intuitively, we associate camera trap data with the media files captured by the cameras . But to be able to use these for research they need to be documented with additional information. Media files should be classified to know what species were observed. Information on camera deployment , duration, location , alignment and sampling methodology is needed to know when, where and how likely those species were to be observed. Finally, we need information about the project/study as a whole to know the scope and who was involved.

Overall, we can distinguish four types of camera trap data:

Project metadata: information about the camera trap project/study as a whole

Media files: images, videos or sound files captured by the cameras , including their EXIF metadata

Deployments: information regarding the camera location and alignment , the sampling duration and covariates . Typically not automatically registered by the camera.

Observations: information regarding what can be seen or heard on the media files (i.e. objects of interest), such as animals, humans or vehicles. The aim is typically to record what animal species were observed, optionally including information on their group size, life stage, sex and behaviour.

These data can be organized in different ways, based on personal preference or what data management system is used. We can however identify a number of core concepts, hereafter referred to as classes, that constitute a “model” for camera trap data (see Figure 1 ). Those classes are project, organization, participant, deployment, location, device, media, sequence and observation.

3.2. Project Metadata

Project metadata document a camera trap project/study as a whole. Who was involved, what was the rationale, what sampling methods were used and what was the scope? Projects can vary a lot in size and are sometimes part of meta-studies or subdivided in subprojects to better manage their hundreds of thousands of deployments (e.g. Snapshot USA ). Describe projects at a level that makes sense. Is it possible to identify a person that can make decisions or answer questions about the project? Is it easy to describe the methodology? If the answer to those questions is no, then it might be better to consider those separate projects. Also note that we strongly recommend publishing data at project level, i.e. one dataset for one project (see Section 4 ).

When published, project metadata are a substantial part of the dataset metadata (see Figure 2 ), allowing others to discover your dataset when searching for certain keywords and to assess if it fits their research needs. When describing your project, think about what others would need to understand it. We recommend to cover the following aspects (but do not let perfection be the enemy of good):

Identifier and/or acronym

Description (including rationale)

Contributors and their roles (including organizations)

Link to website

Sampling design (simple random, systematic random, etc.)

Resulting geographic scope

Capture method (type of sensor, motion detection and/or time lapse)

Capture schedules (continuous, nightly, etc.)

Capture outages and unplanned events

Resulting temporal scope

Classification method (experts, crowdsourcing , AI )

Classification granularity and scope (i.e. which detectable organisms were classified: all animals, mammals only, known individuals only, etc.)

Resulting taxonomic scope

Data filtering before publication

Data management systems typically organize this information into the following classes: project , subproject , organization and participant (see Figure 1 ).

A participant is a person associated with a camera trap project. Information typically captured about a participant is their first name, last name, email, and ORCID. The role(s) of a participant is defined in relation to a project (e.g. principal investigator, contact person) and organization (e.g. researcher) (see Figure 1 ). Different names are used for similar roles (see Table 3 ). We recommend simplifying those to a limited set of controlled values (e.g. package.contributors.role ) when publishing data.

3.3. Media files

Media files are the raw data a camera trap collects. For most camera trap studies, these will be images (see Figure 3 for an example), but modern camera traps can record other types of media types as well, such as video or sound. Videos can capture animal behaviour in more detail than images and are often suitable for outreach, but require more battery power, larger file sizes and are harder to process.

An often used compromise is to take a series of images when a camera is triggered (e.g. 10 images, 1 second apart). When processing the media files, those related images can be combined in a sequence . A sequence not only combines images resulting from a single trigger , but also consecutive triggers that fall within a preset independence interval (e.g. 120s). That way, continued activity is captured in a single sequence / event (see Table 4 ).

A camera also records metadata when creating a media file. This can include date and time, camera settings (like shutter speed, exposure level, flash status) and other properties. For images, this information is stored as part of the file and is expressed in the Exchangeable Image File Format ( EXIF ) (see Table 5 ). Metadata for videos is less standardized, although some formats like AVI and MOV support EXIF.

Data management systems typically organize media files and the associated metadata into the following classes: media , media type and sequence (see Figure 1 ).

The date and time a media file was recorded is the most important aspect of its metadata. This information is used to assess when animals were observed and cannot be derived later (in contrast with e.g. location ). Since this information is derived from the camera’s internal clock, it is critical to verify it is set correctly. We recommend setting the clock to Coordinated Universal Time (UTC) or local winter time. Disable automatic switching to summer time and record the used time zone as part of the deployment .

Media files are best managed by a data management system . If you manage your media files yourself, then we recommend the following file and directory naming conventions:

Avoid renaming media file names. Rather, organize media files in one directory for each deployment . This also prevents raw file names from overlapping across cameras. Note that file paths may be used as identifiers in classification data.

Make sure that ordering files alphabetically also sorts them chronologically. This is likely already the case for sequentially assigned file names (e.g. IMG_4545.jpg ). Otherwise, start the name with the date ( YYYYMMDD ) or date-time ( YYYYMMDD_HHMMSS ). This can also be useful for directory names.

If you are naming files, use snake case ( image_1 ), hyphen case ( image-1 ) or camel case ( image1 or videoFile1 ) rather than white space ( image 1 ). Avoid special characters.

Do not store classification information as part of the media file name.

Be consistent.

Due to the large volume of generated data, it is not trivial to securely store, backup and manage media files. Cloud services or well managed institutional services are recommended, but these come at a substantial cost. We recommend the use of an online data management system to store your media files. Some offer this storage for free. It is also very useful if your data storage system can serve media files over http/https, using allows stable URLs and optionally authentication. This allows you to directly reference/hotlink media files in a published dataset (see accessURI ). Such a service is provided by e.g. Agouti ( Casaer et al. 2019 ) (through https://multimedia.agouti.eu/assets/ ), Flickr (through https://www.flickr.com/services/api/ ) and Zenodo (through the undocumented https://zenodo.org/record/{record_id}/files/{file} ).

3.4. Deployments

A deployment is the spatial and temporal placement of a camera . Deployments end by removing or replacing the camera, changing their position or swapping their memory card. The resulting media files are all associated with that deployment and are best organized as such. Deployment information includes camera location , duration, alignment and settings and other covariates such as bait use, feature type, habitat, canopy cover, etc. (see Table 6 ). This information is not captured by the camera and needs to be recorded manually. Note that even the duration can be longer than the timestamp of the first and last captured media file.

Data management systems typically organize deployments into the following classes: deployment , location , camera , deployment group and subproject (see Figure 1 ).

Deployment information is best recorded in a data management system . If you manage your deployment information elsewhere (e.g. a spreadsheet), then we recommend the following column naming conventions:

Use descriptive names, so users have an idea of what information to expect.

Separate words using snake case ( deployment_location_1 ), hyphen case ( deployment-location-1 ) or camel case ( deploymentLocation1 ) rather than white space ( deployment location 1 ). Snake case ensures the highest level of interoperability between systems, camel case is most often used in data standards.

Avoid abbreviations to mitigate the risk of confusion, except for well-known words like ID for identifier.

Avoid including units and data types. Describe these elsewhere (e.g. in a separate sheet, README document or Table Schema ), together with the column definition and controlled values (see Table 7 ).

A location is the physical place where a camera is located during a deployment. It can be described with a name, identifier and/or description, but we recommend always to record the geographical coordinates . Those are most commonly expressed as latitude and longitude in decimal degrees, using the WGS84 datum.

The coordinates are best determined using a GPS receiver at the location itself. If this is not possible, use (online) resources and georeferencing best practices ( Chapman and Wieczorek 2020 ) to obtain those. In addition to the coordinates and geodetic datum (e.g. WGS84) it is important to record the uncertainty of the coordinates , which is affected by several factors:

The extent of the location. Note that for camera traps this includes the detection distance , which is typically between 5 and 20 m.

The accuracy of the GPS receiver or georeferencing resource. Most GPS receivers obtain an accuracy of 5 metres in open areas when using four or more satellites ( Chapman and Wieczorek 2020 ). Forest canopy or limited satellite connection can reduce accuracy. Google Maps or Open Street Maps have an accuracy of 8m ( Chapman and Wieczorek 2020 ).

The coordinate precision . The less precise (and closer to the equator) the higher the uncertainty, e.g. WGS84 coordinates with a precision of 0.001 degree have an uncertainty of 157 m at the equator (see Table 3 in Chapman and Wieczorek (2020) ).

An unknown datum . This can range from centimetres to kilometres ( Chapman and Wieczorek 2020 ), so it is important to always record the datum used by the GPS receiver or georeferencing resource (WGS84 for Google Maps or Open Street Maps).

The combined maximum uncertainty is most conveniently expressed as a coordinate uncertainty in metres, allowing the location to be described with the point-radius-method.

The combined maximum uncertainty is most conveniently expressed as a coordinate uncertainty in metres, allowing the location to be described with the point-radius-method .

Most other properties associated with a location such as country and state, but even elevation, slope, land cover or leaf area index, can be derived from the coordinates using an online resource.

Since a deployment relates to the placement of a camera , it is important to capture information regarding its model, settings and alignment. The model consists of the manufacturer and model name (e.g. Reconyx-PC800 ). Except for the quiet period , most camera settings are typically automatically recorded as part of the EXIF metadata. The detection distance can vary a lot depending on terrain and vegetation and is best measured in the field by having someone move in front of the camera at different distances. The alignment is the physical placement of a camera in 3D space. It consists of camera height , camera depth , camera tilt and camera heading .

It can be useful to categorize deployments in deployment groups to facilitate their data management and analysis. A deployment group can be thematic (e.g. paired deployment), spatial (e.g. private land, open woodland) or temporal (e.g. summer 2005) in nature (see Figure 4 ). A single deployment can belong to zero or more deployment groups. Subprojects are a special kind of deployment group used to subdivide very large projects containing many thousands of deployments. This facilitates their management. A single deployment can belong to a single subproject.

Covariates are variables that may affect the behaviour and thus detection of animals. Recording those is important for further analysis of the data. Bait , feature type and habitat type are commonly recorded covariates. What and how to record covariates should be consistent within a project, but is typically not so across projects, unless they form part of a larger well-coordinated research study. To aid interoperability, we recommend making use of existing classification systems to record covariates:

Biomes/ecoregions ( Dinerstein et al. 2017 )

Ecological traits:

COMBINE ( Soria et al. 2021 )

PanTHERIA ( Jones et al. 2009 )

EltonTraits ( Wilman et al. 2014 )

AmphiBIO ( Oliveira et al. 2017 )

GlobTherm ( Bennett et al. 2018 )

AVONET ( Tobias et al. 2022 )

Open Traits Network

Habitat classification ( Jung et al. 2020 )

Land cover products ( Yang et al. 2017 ; Amatulli et al. 2018 ) ( http://www.earthenv.org )

Land cover type ( Buchhorn et al. 2020 )

Leaf Area Index ( Law et al. 2008 )

Primary productivity ( Zhao et al. 2005 )

Terrain ruggedness index (TRI) ( Riley et al. 1999 )

3.5. Observations

Observations are an interpretation of what can be seen or heard on media files . These are not limited to species observations, but can also indicate whether the media file contains a vehicle, human or unknown object, or that nothing of interest was observed ( blanks ). That is why they are sometimes also called classifications, annotations or identifications. The aim is typically to record what animal species were observed, optionally including information on their group size, life stage, sex and behaviour (see Table 8 ).

Observations are best recorded in a data management system , which will typically organize observations into the following classes: observation , observation type and sequence (see Figure 1 ). If you manage your observation information elsewhere (e.g. a spreadsheet), then we recommend to follow the same column naming conventions as for deployments.

Unfortunately, camera traps do not provide observations directly. Media need to be classified to obtain observations. This process can be performed in different steps and with different levels of precision and granularity:

Media does or does not contain object(s) of interest.

Object(s) of interest is a human or vehicle, or cannot be identified.

Object(s) of interest is an animal, identified at a high taxonomic level (e.g. a rodent).

Animal is identified at species or subspecies level (e.g. Sus scrofa ).

Animal is identified as a known individual (e.g. wolf Noëlla).

Other properties of the animal are recorded, such as group size, life stage, sex, and behaviour.

Classification can be done by humans and/or machines. These actors (experts, volunteers, AI) can reach different levels of precision (see Table 9 ), based on their expertise (can I reach such a precision?) and effort (do I want to reach such a precision?). Since classification can be very labour intensive for larger studies, it is best to use an approach that yields the necessary data efficiently. Citizen scientists, artificial intelligence and/or classifying events rather than individual media can help to speed up the process ( Green et al. 2020 ). Whatever the technique, we recommend to always record who made the classification and what type of technique (human vs machine) was used.

Citizen scientists are volunteers from the non-scientific community that help scientists in their work. They can contribute to camera trap studies in a number of ways, such as placing cameras and collecting/swapping memory cards. In a practice called crowdsourcing, researchers can also distribute the task of classifying media, by presenting these online to a community of citizen scientists. Each classification helps to confirm or improve the community’s opinion on the observed species ( Swanson et al. 2015 ; Hsing et al. 2018 ).

Most projects use established online platforms for crowdsourcing ( Fortson et al. 2012 ; Swanson et al. 2015 ), ( Chimp&See ), such as Zooniverse ( Simpson et al. 2014 ), MammalWeb ( Bradley 2017 ), Digivol ( Alony et al. 2020 ) or DoeDat ( Groom et al. 2018 ). These platforms give access to large, already existing volunteer bases, which is particularly important if classifications are needed within a short time frame. Note however that managing a citizen science project takes time and might be more beneficial for larger studies. In addition to uploading media to a platform, waiting for classifications, downloading consensus observations and dealing with non-consensus observations, you need to keep the community engaged and/or attract new members. It is also important to exclude sensitive media from the process, such as media containing humans (to protect their privacy) and rare species. This will require some type of preprocessing, which is where artificial intelligence (AI) comes in ( Weinstein 2018 ).

In the context of camera trap research, artificial intelligence (AI) typically refers to the use of computer vision for classification . These computer models are trained with already classified datasets and can process millions of media in a fraction of a time it would take a human ( Norouzzadeh et al. 2021 ). The field has seen significant advancements in recent years and models are now able to filter out blanks and media containing humans, recognize species, count or track individuals, as well as recognize individual animals ( Price Tack et al. 2016 ; Gomez Villa et al. 2017 ; Nguyen et al. 2017 ; Brides et al. 2018 ; Norouzzadeh et al. 2021 ; Yousif et al. 2018 ). New models are coming out every year, but especially their incorporation in data management systems will increase their use, especially by users that have no experience in machine learning. As such, computer vision will likely become the dominant technique to classify camera trap data in the near future.

Still, computer vision will not entirely replace human classification , since a large and diverse number of preprocessed data are needed to train the models. Unbalanced training datasets may produce low performance of the models, such as training datasets with a highly variable number of images of each species, or small and geographically limited datasets. Additionally, the accuracy of computer vision classification is currently still secondary to that of a human expert. A combination of AI-aided preprocessing and human verification is therefore recommended.

Classifications can be based on a single media file (typically an image ) or an event (typically a sequence of images). In the latter technique, all media files that belong to the event are assessed as a whole to determine the species and their number of individuals. This is less time consuming for human classifiers and can lead to better estimates of group size, since the number of individuals passing by a camera can be larger than those that can be seen in a single image. The disadvantage of event-based classification is that it is not possible to split the classification into events that are shorter than the one that is assessed (the same is true for videos classified as a whole). Nor can those classifications be used to train computer models, which require media-based training datasets.

As a result, data management systems may favour one technique over the other, or offer both. Resulting datasets can include media-based, event-based or both types of classifications.

Media can be classified using common (e.g. roe deer) or scientific names (e.g. Capreolus capreolus ) for taxa. Common (or vernacular) names are easier to remember and allow for better public engagement. The downside is that they are subject to translation, can vary regionally, sometimes refer to different species (e.g. “elk” in North America refers to Cervus canadensis , while in Europe it is used for Alces alces ) and might not exist for every species or language combination. Scientific names on the other hand follow strict nomenclatural rules, are globally consistent and are not subject to translation. We therefore recommend to always store the scientific name as part of the observation, even if only common names are presented to the user.

The list of scientific names that are available for classification in a project is best maintained in a single reference table. This facilitates the management of taxonomic classification and associated common names, and allows to restrict classification options to those species that are likely to occur. More taxa can be added if needed, but only after verification. This practice is used by most data management systems . To populate such a reference table, we recommend using an authorative source (see Table 10 ) and storing the taxon identifiers used by that source as reference.

3.6. Data management systems

Managing camera trap data can be daunting, especially for larger projects. Luckily, a number of software tools and platforms have been developed to help researchers with some or all of the aspects of camera trap data management ( Young et al. 2018 ). These initiatives were often started by research teams to facilitate their own needs, but some have grown to mature systems that can be used by anyone. We discuss and recommend five of those below (see Table 11 for an overview of their features). They support the entire life cycle of camera trap data management:

Create one or more projects

Invite collaborators with different levels of access

Upload media and creating deployments

Classify media to observations, optionally supported by AI and citizen science

Manage reference lists of species, locations, covariates, etc.

Engage the public by making some or all project metadata available on a website

Export data in a standardized format for further analysis and data publication

Archive data, including media files

Agouti ( Casaer et al. 2019 ) ( https://agouti.eu ) is an online system for managing camera trap data. It is maintained by Wageningen University & Research and the Research Institute for Nature and Forest (INBO), based respectively in the Netherlands and Belgium. Agouti is mainly used by European projects and is free to use.

Classification is event-based, but animal positions can be recorded at media level, allowing to record the necessary data for distance analyses ( Howe et al. 2017 ) and random encounter modelling ( Marcus Rowcliffe et al. 2011 ). AI classification is possible, using a dedicated species classification model that is updated regularly. Media containing humans are always hidden from the public. Data are stored on university infrastructure, which also offers long-term archival and hot-linking to media. Project metadata can be made available via a public portal. Data can be exported as Camtrap DP .

Agouti is a good choice for organizations who want a free full-feature European based service.

Camelot ( Hendry and Mann 2018 ) ( https://camelotproject.org/ ) is a local system for managing camera trap data. It is maintained as a volunteer initiative based in Australia. Camelot is free to use, open source, available for all major operating systems and requires installation. It is typically used as a local desktop application, but can be set up on a server allowing multiple users to connect via their browser. Classification is media-based with the option to classify multiple media at once. AI classification is not offered. Data can be exported in a custom format.

Camelot is a good choice for organizations and individuals who want a light-weigh solution they can manage themselves.

TRAPPER ( Bubnicki et al. 2016 ) ( https://os-conservation.org/projects/trapper ) is an online system for managing camera trap data. It is maintained by the Open Science Conservation Fund, based in Poland. TRAPPER is mainly used by European projects and is free to use. The software is open source and requires installation and hosting. Classification is media-based with the option to classify multiple media at once. AI classification is possible, using existing species classification models. Data can be exported as Camtrap DP .

TRAPPER is a good choice for organizations who want control over the software and where their data are stored.

Wildlife Insights ( Ahumada et al. 2020 ) ( https://www.wildlifeinsights.org ) is an online system for managing camera trap data. It is maintained by Conservation International, Google and other partners, based in the United States. Wildlife Insights is mainly used by projects in the Americas and uses a tiered subscription model (including free tiers). Uploaded media are automatically classified at media level by AI, using a dedicated species classification model developed by Google. Media containing humans are always hidden from the public. Further classification has the option to classify multiple media at once. Data are stored in the cloud, can be used by Wildlife Insights to train AI and must be made public after a maximum embargo period of maximum 48 months. Project metadata is always available via a public portal. Data can be exported in a custom format, based on CTMS ( Forrester et al. 2016 ).

Wildlife Insights is a good choice for organizations who want a full-feature service with powerful AI and open data requirements.

WildTrax ( Bayne et al. 2018 ) ( https://www.wildtrax.ca/ ) is an online system for managing camera trap data. It is maintained by the University of Alberta, based in Canada. WildTrax is mainly used by Canadian projects and is free to use (except for very large projects). Classification is media-based with the option to classify multiple media at once. AI classification is possible, but only at a broad level (blanks, animals, vehicles), species classification is not (yet) offered. Data are stored in the cloud. Project metadata can be made available via a public portal. Data can be exported in a custom format (with associated R package).

WildTrax is a good choice for organizations who want a free service based in Canada.

4. Publishing camera trap data

Data publication is the process of making biodiversity data open and FAIR ( Wilkinson et al. 2016 ). Adopting the FAIR principles guarantees that your camera trap data can be found, accessed, integrated and reused (see Section 4.1 ) for many applications, from biological use cases (species distribution modeling, population density estimation, etc.) to providing training data for machine learning model development. We recommend the use of the GBIF Integrated Publishing Toolkit (IPT) ( Robertson et al. 2014 ) to do so. It facilitates data publication and registration with the Global Biodiversity Information Facility (GBIF) , an international network and data infrastructure for biodiversity data.

Before you publish through GBIF, you must prepare (see Section 4.2 ) and standardize your data. Data standardization is the transformation of data to a specific data exchange format so it becomes interoperable with other data at GBIF. GBIF supports Camtrap DP and Darwin Core Archive as the data exchange formats for camera trap data. Recommendations for these formats are provided in Section 4.3 and Section 4.4 respectively.

We strongly recommend publishing camera trap data at project level, i.e. one dataset for one project. This makes it easier to describe the scope, methodology, contributors, funding sources, etc. in the metadata. For a general overview on how to publish data to GBIF, see GBIF Secretariat (2018) .

Imagine you need to aggregate all observations of muskrats recorded in Belgium in 2023. Doing so is hard if the data are scattered across sources and use different access protocols, field names and languages. Making these data sources FAIR means organizing them in such a way that everyone (humans and machines) can find, use, understand and combine them.

The easiest way to make a dataset findable is by providing meaningful metadata (e.g. title, description and keywords) and depositing it in a research repository (such as the IPT ( Robertson et al. 2014 ) in combination with GBIF). Repositories provide cross-dataset search functionalities and will assign each dataset a unique identifier so that it can be uniquely referenced and accessed . Adding an open license to the dataset will allow users to access and reuse the data (in addition to the metadata), while rich metadata (e.g. methodology) will enable users to determine if the dataset is fit for their purpose. The most challenging aspect is to make a dataset interoperable so that it can easily be integrated with other data. This can be achieved by using standards: research repositories will standardize metadata and Camtrap DP can be used to standardize the data. See also Bubnicki et al. (2023) (section "Is Camtrap DP FAIR?") for more information on how Camtrap DP enables FAIR data exchange.

4.2. Preparing data

Terms like deployments.deploymentID or dwc:occurrenceID expect an identifier that is:

Required to be unique , i.e. it uniquely refers to a record or concept.

Strongly recommended to be stable /persistent, i.e. it does not change over time and can safely be referenced.

Recommended to be globally unique , i.e. it uniquely refers to a record or concept in a global context.

If available, we recommend using the identifier assigned by the data management system , as is. These identifiers will be unique, most likely stable, and sometimes globally unique (e.g. a UUID ). They also allow users (with access) to look up the record in the data management system. We advice against appending elements to the identifier to make it globally unique, since this makes it more prone to change. Since datasets can be uniquely identified (e.g. with a DOI), it is sufficient if the identifier is unique within the dataset.

Camera trap data may contain sensitive information, such as personal information (e.g. names or images of living persons), the occurrence of sensitive (e.g. rare or endangered) species, the locations of actively managed cameras, or even notes and comments not intended for the public. We recommend following the best practices in Chapman (2020) , which favour generalization over restriction of the record as a whole.

Personal data

Personal data is any information that relates to an identified or identifiable living person. This information is subject to regulations such as GDPR . In camera trap data, personal data are the names of participants , their email addresses and the whereabouts of participants who setup the camera (identifiable by combining the name with the deployment date-time and location ). In Camtrap DP , person names can appear in package.contributors , package.bibliographicCitation , deployments.setupBy and observations.classifiedBy . In a Darwin Core Archive , person names can appear in the metadata and terms like dwc:identifiedBy .

We recommend contacting participants to ask if their personal information can be made public and to anonymize (e.g. anonymized:3eb30aa ) or exclude it when they prefer not to. Some data management system (such as Casaer et al. (2019) ) allow users to indicate their preferences and automatically anonymize their personal data in exports. Note that it may not be possible to permanently remove personal information from older versions of an already published dataset.

Sensitive media files

Media files containing identifiable persons is a form of personal data that should be identified and kept private in order to protect the privacy of the persons. The same may be necessary for media files containing vehicles or picturing camera setup . Media files containing sensitive species may need to be kept private if they allow to identify the location.

We recommend providing the URL to all media files (in media.filePath or ac:accessURI ), but regulating its access at the provider level (e.g. https://multimedia.agouti.eu/assets/813bafb2-befe-45fa-b0e3-080f1f019a70/file ). The expected access can be described in media.filePublic or ac:serviceExpectation . Note that in a Darwin Core Archive , observations (and media) of humans, vehicles, setup, etc. are typically excluded.

Sensitive location information

Camera trap data may contain location information of sensitive species. Locations of actively managed cameras can also be sensitive to vandalism and theft. We recommended following Chapman (2020) to determine sensitivity ( Chapter 2 ) and choose an appropriate generalization.

Whatever the selected level of generalization, document it in the dataset metadata and appropriate terms, so that users are aware. See Table 12 for an example.

Other sensitive information

Text fields such as comments and notes (e.g. deployments.deploymentComments or dwc:occurrenceRemarks ) may contain sensitive information such as person names , sensitive location information or information not intended for the public. We recommend verifying values and generalizing where necessary (see Chapter 3 in Chapman (2020) ).

We recommend the use of Camera Trap Data Package (Camtrap DP) to publish camera trap data. It is specifically designed for this type of data and can retain more information than a Darwin Core Archive ( Bubnicki et al. 2023 ). Some data management systems directly support it as an export format (see Table 11 ), reducing the need for data transformation when publishing through GBIF.

See the Camtrap DP website for term definitions, recommendations and examples.

Not all information in a published Camtrap DP is currently harvested by GBIF. The GBIF data model requires it to be transformed to Darwin Core before ingestion. This process is provided by the write_dwc() function in the R software package camtraptor ( Oldoni et al. 2023 ). This function implements the recommendations suggested in this document. GBIF will be able to process more information from a published Camtrap DP once it has implemented a new data model ( GBIF Secretariat 2022 ).

4.4. Darwin Core Archive

With their hierarchical events ( deployments , sequences ) and resulting observations , it seems logical to express camera trap data as Sampling-event data with an Event core (see Table 13 ) and an Occurrence extension (see Table 14 ). It allows us to provide detailed (though repeated) information about each type of event and offers the possibility to add a Measurement Or Facts extension with alignment and other information (mostly relevant for the deployments).

It unfortunately also impedes us from expressing information about the media as an extension , since the star schema design of a Darwin Core Archive does not allow to relate the Occurrence extension with an Audubon Media Description extension . It is technically possible to link the Audubon Media Description extension with the Event core , but the media would then not be linked to the occurrences and not appear on occurrence pages at GBIF.org. The only available option to express information about the media at an occurrence level would be to use dwc:associatedMedia , which would reduce it to a (list of) URL(s). License, media type, capture method, bounding boxes, etc. cannot be provided.

We therefore recommend expressing camera trap data as an Occurrence dataset with an Occurrence core and an Audubon Media Description extension (see Table 15 and Table 16 ). This treats media as primary data records , which is important given that they are the evidence on which the observations are based. Event hierarchy can largely be retained as well, since the Occurrence core allows to group occurrences into events ( dwc:eventID ) and parent events ( dwc:parentEventID ). By providing the event /sequence identifier in dwc:eventID and deployment identifier in dwc:parentEventID , observations can be grouped just like they would in an Event core and GBIF.org will automatically create event pages for those (see Figure 5 ). Event duration information however cannot be provided, but eventDate and samplingEffort can retain most of it. Information about the deployment location, habitat, sampling protocol, etc. is repeated for every observation in the deployment.

Term recommendations for the Occurrence core and Audubon Media Description extension are provided in Section 4.4.2 and Section 4.4.3 respectively.

As described above , we recommend to use of an Occurrence core for expressing camera trap data as a Darwin Core Archive . See Table 17 for term recommendations. These recommendations align with the GBIF quality requirements for Occurrence datasets ( GBIF Secretariat 2020 ) and use the same terminology (Required, Strongly recommended, Share if available).

Note that the Occurrence core should only contain animal observations , so classifications of blanks , vehicles and preferably humans should be filtered out. The number of records will depend on the size of the study, the classification effort (are all media classified?), the classification precision (see Table 9 ) and whether media- or event-based classification was used. Especially media-based classifications can substantially increase the number of occurrences, with little added benefit for ecological research. Camtrap DP is designed for both, but when publishing as a Darwin Core Archive, we recommend only providing event-based observations if available.

The nature of the resource. Use StillImage if the record is based on an image or sequence of images, MovingImage if based on a video. One can also use the broader term Image for all records.

dcterms:license

The licence under which the data record is shared. Very likely this will be the same licence as the one used for the dataset as a whole, but it is possible to deviate ( Waller 2020 ). To enable wide use, we recommend publishing data under a Creative Commons Zero waiver and to provide it as a URL: https://creativecommons.org/publicdomain/zero/1.0/legalcode . In Camtrap DP, this term corresponds with the path of the licence that has the scope data in package.licenses , although there it is specified for the dataset as whole, rather than per record.

rightsHolder

dcterms:rightsHolder

The person or organization (i.e. participant ) owning or managing rights over the resource. In all likeness the organization that decided under what license the data are published and/or the publisher of the data (i.e. the organization selected as publisher when registering a dataset with GBIF). Use an acronym if the organization has one. In Camtrap DP, this term corresponds with the title of the collaborator that has the role rightsHolder in package.contributors .

datasetID & datasetName

dwc:datasetID & dwc:datasetName

Respectively the identifier and name of the dataset. For dwc:datasetID we recommend using a stable URL or identifier that allows users to find information about the source dataset/study. In order of preference: dataset DOI ( https://doi.org/10.15468/5tb6ze ), study URL ( http://n2t.net/ark:/63614/w12001317 ), or study identifier used by the data management system . In Camtrap DP, this term corresponds with package.id , unless a better identifier is available (e.g. a DOI). dwc:datasetName should refer to the title of the dataset/study as referred to by dwc:datasetID . We recommend using the same value for the title in the metadata. In Camtrap DP, this term corresponds with package.title .

collectionCode

dwc:collectionCode

The name or acronym identifying the collection or dataset the record was derived from. Traditionally used to indicate a physical collection, we recommend to provide the name of the data management system (i.e. virtual collection) the record was derived from. This allows users to search for records from the same data management system across datasets. Recommended values: Agouti , Camelot , eMammal , Trapper , Wildlife Insights , etc. In Camtrap DP, this term corresponds with the title of the (applicable) source in package.sources .

basisOfRecord

dwc:basisOfRecord

The specific nature of the record. Set to MachineObservation for all records. While humans decide when and were to deploy a camera trap, and humans or machines (AI) can classify media, the capturing of the record is done by a machine responding to a sensor. This is critically different from human observations, where a human is actively in control of the decision whether to record an organism or not.

dataGeneralizations

dwc:dataGeneralizations

The actions taken to make the published data less specific or complete than in its original form. We recommend succinctly describing here what sensitive information of the record was generalized and how. Note that this information can be provided at record level and does not need to apply to the whole dataset. If important information was omitted altogether, use dwc:informationWithheld .

occurrenceID

dwc:occurrenceID

An identifier for the observation . Use a stable unique identifier . In Camtrap DP, this term corresponds with observations.observationID .

individualCount

dwc:individualCount

The number of observed individuals . Note that this number is dependent on the precision of the identifications. In Camtrap DP, this term corresponds with observations.count .

The sex of the observed individual (s). We recommend using the controlled values male and female , which are based on Camtrap DP and compatible with the GBIF Sex vocabulary . In Camtrap DP, this term corresponds with observations.sex .

dwc:lifeStage

The life stage of the observed individual (s). We recommend using the controlled values adult , subadult , and juvenile , which are based on Camtrap DP and compatible with the GBIF LifeStage vocabulary . In Camtrap DP, this term corresponds with observations.lifeStage .

dwc:behavior

The dominant behaviour of the observed individual (s). We recommend using existing or your own controlled values (e.g. grazing, browsing, rooting, vigilance, running, walking). In Camtrap DP, this term corresponds with observations.behavior .

occurrenceStatus

dwc:occurrenceStatus

A statement about the presence or absence of the taxon at a location. When reduced to species observations (filtering out blanks , etc.), camera trap data only contain presence records. Set to present for all records.

occurrenceRemarks

dwc:occurrenceRemarks

The comments or notes about the observation . These are typically notes (sometimes in the native language of the author) about the observation and/or observed individual (s) that were not or could not be recorded in another field. This information is potentially useful to publish, but may contain sensitive information . In Camtrap DP, this term corresponds with observations.observationComments .

dwc:organismID

An identifier for an observed and known individual that was recognized by colour ring, ear tag, skin pattern or other characteristics. Observations with dwc:organismID typically have dwc:individualCount of 1, unless the dwc:organismID refers to a known group. Unless a globally unique identifier is available and known for the individual, we recommend using the code/identifier assigned within the camera trap study to the individual, allowing users to find all observations of this individual within the dataset. In Camtrap DP, this term corresponds with observations.individualID .

dwc:eventID

An identifier for the event the observation belongs to. We recommend providing the identifier for the event (typically a sequence ) as used for event-based classification . Using an Occurrence core, events will not have their own records, but providing their identifier in dwc:eventID allows users to find all observations (and media) for a specific event. Use a stable unique identifier . Note that GBIF.org will automatically group observations with the same dwc:eventID as belonging together. In Camtrap DP, this term corresponds with observations.eventID .

parentEventID

dwc:parentEventID

An identifier for a broader event then those identified by eventID . We recommend providing the identifier of the deployment . Using an Occurrence core, deployments will not have their own records, but providing their identifier in dwc:parentEventID allows users to find all observations (and media) for a specific deployment. Use a stable unique identifier . Note that GBIF.org will automatically group observations with the same dwc:parentEventID as belonging together. In Camtrap DP, this term corresponds with observations.deploymentID .

dwc:eventDate

The date, date-time or date-time interval during which the event occurred. We recommend using a single timestamp for media-based classifications and an interval - consisting of the timestamps of the start and end of the event as identified by eventID for event-based classifications . Write timestamps in the ISO 8601 format ( YYYY-MM-DDTHH:MM:SS ), use / to indicate an interval and include the timezone ( +02:00 ) or convert and indicate as UTC ( Z ). In Camtrap DP, this term corresponds with observations.eventStart and observations.eventEnd , or observations.eventStart if both are equal.

dwc:habitat

A category or description of the habitat in which the event occurred. This is typically the habitat at the time of deployment, with values repeated for all records of this deployment. Values can be controlled, ideally using an existing classification system, or free-text descriptions. In Camtrap DP, this term corresponds with deployments.habitat .

samplingProtocol

dwc:samplingProtocol

The method(s) or protocol(s) used during the event . We recommend using the controlled value camera trap . This allows users to search for records with this protocol across datasets.

samplingEffort

dwc:samplingEffort

The amount of effort expanded during the event . We recommend providing the date-time interval the camera trap was deployed, using the same formatting conventions as eventDate . In Camtrap DP, this term corresponds with deployments.deploymentStart and deployments.deploymentEnd .

eventRemarks

dwc:eventRemarks

The comments or notes about the event . These are typically notes (sometimes in the native language of the author) about the deployment that were not or could not be recorded in another field. This information is potentially useful to publish, but may contain sensitive information . We also recommend this term for providing other (structured) information associated with the deployment, such as bait use, feature type or tags, as pipe ( | ) separated values. In Camtrap DP, this term corresponds with deployments.deploymentComments and relates to deployments.baitUse , deployments.featureType and deployments.deploymentTags .

dwc:locationID

An identifier for the location . This identifier allows users to find all observations (and media) for a specific location (across deployments). Use a stable unique identifier . In Camtrap DP, this term corresponds with deployments.locationID .

dwc:locality

The name of the location . This is typically a name or code assigned within the camera trap study. In Camtrap DP, this term corresponds with deployments.locationName .

minimumDepthInMeters & maximumDepthInMeters

dwc:minimumDepthInMeters & dwc:maximumDepthInMeters

The depth (in meters) below the local surface. For (marine) camera trap studies, this is the depth at which the camera is deployed. We recommend providing either a camera depth or camera height , not both. In Camtrap DP, this term corresponds with deployments.cameraDepth .

minimumDistanceAboveSurfaceInMeters & maximumDistanceAboveSurfaceInMeters

dwc:minimumDistanceAboveSurfaceInMeters & dwc:maximumDistanceAboveSurfaceInMeters

The height (in meters) above a reference surface. For camera trap studies, this is the height at which the camera is deployed. We recommend providing either a camera depth or camera height , not both. In Camtrap DP, this term corresponds with deployments.cameraHeight .

decimalLatitude & decimalLongitude

dwc:decimalLatitude & dwc:decimalLongitude

The geographic latitude and longitude of the location , in decimal degrees. Latitude values lie between -90 and 90, longitude values between -180 and 180. For camera trap studies, these are typically obtained by GPS and recorded in the data management system . We recommend providing the coordinates as stored in the data management system, unless they need to be rounded/generalization to protect sensitive information . In Camtrap DP, these terms correspond with deployments.latitude and deployments.longitude respectively.

geodeticDatum

dwc:geodeticDatum

The spatial reference system used for the geographic coordinates . For coordinates obtained by GPS this is typically EPSG:4326 (i.e. WGS84 ) ( Chapman and Wieczorek 2020 ). In Camtrap DP, WGS84 is implied for the terms deployments.latitude and deployments.longitude .

coordinateUncertaintyInMeters

dwc:coordinateUncertaintyInMeters

The horizontal distance (in metres) from the geographic coordinates describing the smallest circle containing the location . We recommend 30 meters as reasonable lower limit for coordinates obtained by GPS, but see Section 3.4.2 for details on what elements contribute to the uncertainty. Generalized/rounded coordinates in particular will increase the dwc:coordinateUncertaintyInMeters . In Camtrap DP, this term corresponds with deployments.coordinateUncertainty .

coordinatePrecision

dwc:coordinatePrecision

The decimal precision of the geographic coordinates >, if known. This information is known and we recommend providing it for generalized/rounded coordinates (e.g. 0.001 for coordinates that were rounded to 3 decimals). In Camtrap DP, this term corresponds with package.coordinatePrecision , although there it is specified for the dataset as whole, rather than per record.

identifiedBy

dwc:identifiedBy

The person or species classification model that identified the observed individual(s) and assigned the scientificName . We recommend providing a single name: that of the person or model that made the (most recent) classification. Although classifying can be broader than assigning a scientific name, it is likely to involve that aspect for animal observations . Note that this term contains personal data . In Camtrap DP, this term corresponds with observations.classifiedBy .

dateIdentified

dwc:dateIdentified

The date or date-time on which the identification was made. We recommend providing a single timestamp: that of the classification made by the person or model indicated in identifiedBy . This information is typically recorded by the data management system . Write timestamps in the ISO 8601 format ( YYYY-MM-DDTHH:MM:SS ) and include the timezone ( +02:00 ) or convert and indicate as UTC ( Z ). In Camtrap DP, this term corresponds with observations.classificationTimestamp .

identificationRemarks

dwc:identificationRemarks

The comments or notes about the identification. We recommend using this term to provide information on whether the classification was made by a human or species classification model as well as the degree of certainty if available (often recorded for AI classification). In Camtrap DP, this term relates to observations.classificationMethod and observations.classificationProbability .

dwc:taxonID

An identifier for scientificName . This identifier allows users to find all observations (and media) for a specific taxon. Use a stable unique identifier , preferably one assigned by an authorative source . In Camtrap DP, this term corresponds with the taxonID of the corresponding taxon in package.taxonomic .

scientificName

dwc:scientificName

The scientific name of the observed individual(s) . In Camtrap DP, this term corresponds with observations.scientificName .

dwc:kingdom

The kingdom in which the taxon with the scientificName is classified. It allows services like GBIF’s species name matching to disambiguate between homonyms. Most likely Animalia for all records, since camera trap data almost never contain classifications of plants, fungi or other kingdoms.

As described above , we recommend to use of an Audubon Media Description extension for expressing camera trap data as a Darwin Core Archive . See Table 18 for term recommendations.

Note that the Audubon Media Description extension can contain duplicates, an important difference with Camtrap DP’s media where each file is only listed once. Repeated occurrenceID are the result of a single event-based observation being related to multiple media files (e.g. observation 05230014 in Table 16 ). Repeated identifiers are the result of a media file being the source for multiple observations (e.g. multiple species observed in the same image, such as in media file e68deaed in Table 16 ). The extension should however contain unique occurrenceID + identifier combinations.

A foreign key to the occurrenceID in the Occurrence core , to indicate the relation between the observation and the media file(s) on which it is based. This term can contain duplicates, as this is a many-to-many relationship (see note in Section 4.4.3 ). In Camtrap DP, this term corresponds with observations.observationID , but the relationship between observations and media can be established in several ways: either directly via observations.mediaID or by selecting media that have the same media.deploymentID as the observation and a media.timestamp that falls between the observations.eventStart and observations.eventEnd of the observation.

dcterms:identifier

An identifier for the media file . Use a stable unique identifier . This term can contain duplicates, as this is a many-to-many relationship (see note in Section 4.4.3 ). In Camtrap DP, this term corresponds with media.mediaID .

The nature of the resource. Use StillImage for images , MovingImage for videos . Do not use dcterms :type , because that term expects a URL value.

ac:comments

The comments or notes about the media file. In contrast with eventRemarks and occurrenceRemarks , notes about the media files themselves are seldom recorded in data management systems . The term could be used to indicate if a media file was marked as favourite or noteworthy. In Camtrap DP, this term corresponds with media.mediaComments and relates to media.favorite .

dcterms:rights

The licence under which the media file is shared. Note that this applies to file referenced in accessURI , not the data in the Audubon Media Description extension (these fall under the dataset license). We recommend using the same license for all media files. To enable wide use, we recommend publishing media files under a Creative Commons Zero waiver or Creative Commons Attribution 4.0 International license and to provide it as a URL: https://creativecommons.org/publicdomain/zero/1.0/legalcode or https://creativecommons.org/licenses/by/4.0/legalcode respectively. Do not use dc :rights , because that term expects a literal value (the full-text copyright statement). In Camtrap DP, this term corresponds with the path of the licence that has the scope media in package.licenses , although there it is specified for the dataset as whole, rather than per record.

xmp:CreateDate

The date-time on which the media file was created. This information is typically extracted from the EXIF metadata by the data management system . Write timestamps in the ISO 8601 format ( YYYY-MM-DDTHH:MM:SS ) and include the timezone ( +02:00 ) or convert and indicate as UTC ( Z ). In Camtrap DP, this term corresponds with media.timestamp .

captureDevice

ac:captureDevice

The device(s) used to create the media file. We recommend providing the camera make and model (e.g. Reconyx-HF2X ). In Camtrap DP, this term corresponds with deployments.cameraModel .

resourceCreationTechnique

ac:resourceCreationTechnique

The method(s) used to create or alter the media file. We recommend using this term to provide the trigger method that was used to capture the media file, as controlled values: activity detection or time lapse . In Camtrap DP, this term corresponds with media.captureMethod .

ac:accessURI

The URI (Uniform Resource Identifier) that provides access to the media file. Although the term allows to point to relative file paths or offline storage, we strongly recommend to provide the http/https URL that serves the media file, if available (see Section 3.3.3 ). Use a http/https URL that serves the media file directly (not a HTML page embedding it), so it can be displayed on occurrence pages at GBIF.org. Camera trap images are typically small enough that it is not necessary to serve a reduced version of the file. In Camtrap DP, this term corresponds with media.filePath .

serviceExpectation

ac:serviceExpectation

The service expectations users may have of the accessURI . We recommend using the controlled values online for media files that are publicly accessible over http/https and authenticate for media files that are kept private over http/https (see Section 4.2.2.2 ). In Camtrap DP, these values related to TRUE and FALSE respectively in media.filePublic .

The file format of the media file. We recommend providing the media type (MIME type) using the controlled values image/jpeg , video/mp4 or video/mpeg of the Audiovisual Core Controlled Vocabulary for Dublin Core . Do not use dcterms :format , because that term expects a URL value. In Camtrap DP, this term corresponds with media.fileMediatype .

We thank the following people for making suggestions to this guide and/or providing technical support: Stephen Formel , Donald Hobern , Kyle Copas , Laura Russell , Matt Blissett and members of the Camtrap DP Development Team .

Movement in front of a camera . Movement can cause a trigger when certain conditions are met (e.g. within the detection zone and outside the quiet period ). Camera traps are typically deployed to capture wildlife activity, but may also record movements of humans, vehicles or vegetation.

Physical placement of a camera . Consists of camera height , camera depth , camera tilt and camera heading .

Ability to perceive, synthesize and infer information as demonstrated by machines, as opposed to humans/animals. In camera trap research, AI is mainly applied as machine learning to perform computer vision tasks. See also Section 3.5.3 .

Attractant used to encourage animals to investigate a location or specific point within the detection zone . Baits may be auditory, olfactory, visual, or some combination of these in nature. Whether bait was used for a deployment is useful to know for analyses, as it can alter the natural behaviour of animals. Can be expressed as a categorical description (e.g. acoustic , visual , scent ) or a boolean. Also referred to as lure ( Meek et al. 2014 ). deployments.baitUse in Camtrap DP.

Media without objects of interest. Blanks are typically the result of false triggers such as moving vegetation or fluctuating light. Marked as such when classifying to facilitate excluding them from queries.

Device designed to automatically capture media of (wildlife) activity , typically triggered by a combination of heat and motion. Of a certain make and model and uniquely identifiable by e.g. by serial number. Also referred to as camera trap , game camera , trail camera , scouting camera or device .

Depth of the (underwater) camera, a component of camera alignment . Typically expressed in meters below the local water surface. deployments.cameraDepth in Camtrap DP.

Height of the camera above the ground, a component of camera alignment . Can be expressed in meters above the ground or as a categorical description ( knee height ~ 0.5m, chest height ~ 1.5m, canopy ~ 3+m in Ahumada et al. (2020) ). deployments.cameraHeight in Camtrap DP.

Up or down orientation of the camera, a component of camera alignment . Can be expressed in degrees or as a categorical description ( parallel = 0°, pointed downward = -90° in Ahumada et al. (2020) ). deployments.cameraTilt in Camtrap DP.

Horizontal cardinal orientation of the camera, a component of camera alignment . Can be expressed in degrees from North or as cardinal directions (N, NW, etc.). deployments.cameraHeading in Camtrap DP.

Camera Trap Data Package. A community developed data exchange format for camera trap data, maintained under Biodiversity Information Standards ( TDWG ). See also Section 4.3 , Bubnicki et al. (2023) and the Camtrap DP website .

The act of classifying camera trap media , resulting in observations . Not to be confused with taxonomic classification. Can be performed in different steps and with different levels of precision, e.g. 1. media does/does not contain object(s) of interest (i.e. blank ), 2. object of interest is human/vehicle/animal or unknown, 3. animal is member of certain taxon (e.g. Sus scrofa, Rodentia), 4. animal is of certain sex/life stage, is known individual x or shows certain behaviour. Classification is typically labour intensive and therefore often aided by computer vision , volunteers and/or classifying sequences rather than individual media files . Also referred to as image classification , annotation or identification . See also Section 3.5.1 .

Scientific research conducted with participation from the public. Also referred to as community science , crowd science , crowdsourcing , or volunteer monitoring . In camera trap research, citizen scientists can participate in camera trap deployment and classification . See also Section 3.5.2 .

Performing computing tasks on a distributed IT infrastructure (“cloud service”). Typically at a cost (“pay as you go”) in return for better performance and less maintenance.

Processing, analysing and understanding of media by machines to aid classification , from object tracking to species identification. A form of artificial intelligence, typically trained with machine learning .

Ecological variables that may affect the behaviour and detection of animals (e.g. bait use , feature type , habitat , canopy cover). Recording covariates is important for further analysis of the data. See also Section 3.4.5 .

Format used to exchange data between systems (e.g. Camtrap DP and Darwin Core Archive ). Requires data transformation from the source system to the format and from the format to the target system. Well designed data exchange formats facilitate FAIR data exchange, use open formats and provide clear definitions. Also referred to as data standard (when approved through a ratification process).

Online or desktop application to manage camera trap data. Typically includes functionality to upload media , add deployment information, classify images, export data, invite participants and manage a project . See also Section 3.6 .

Online system for the long-term archival of (research) data (e.g. Zenodo , Dryad and the GBIF IPT ). Different from a data management system which is designed for the management (and not necessarily long-term archival) of data.

Standardized and widely supported data exchange format for biodiversity data, maintained by Biodiversity Information Standards ( TDWG ). See also Section 4.4 and the Darwin Core text guide .

Spatial and temporal placement of a camera to sample wildlife images. A camera placed at a location between 1 and 15 January 2020 is a different deployment than the same (or different) camera placed at the same location between 15 and 30 January 2020. Deployments end by removing or replacing the camera, changing their position or swapping their memory card. Also referred to as sampling point ( Wearn and Glover-Kapfer 2017 ), trap station session ( Hendry and Mann 2018 ) or visit ( Newkirk 2016 ). deployments in Camtrap DP. See also Section 3.4 .

Logical grouping of deployments , based on spatial, temporal or thematic criteria. A deployment can belong to multiple deployment groups. array ( O’Connor et al. 2017 ), camera trap array ( Meek et al. 2014 ), cluster ( Resources Information Standards Committee RISC 2019 ), paired deployment ( Kolowski and Forrester 2017 ), site and strata ( Sun et al. 2021 ) are spatial deployment groups. sampling campaign ( Lamelas-Lopez et al. 2020 ), sampling event ( Fegraus et al. 2011 ) and session are temporal deployment groups. subproject ( eMammal n.d. ), survey ( Resources Information Standards Committee RISC 2019 ; Tobler 2015 ) and tags are thematic deployment groups. study areas ( Newkirk 2016 ) can be considered a deployment group or project in its own right. deployments.deploymentGroups in Camtrap DP. See also Section 3.4.4 .

Furthest distance in the detection zone at which the camera detects activity . deployments.detectionDistance in Camtrap DP.

Area of a location in which a camera sensor is able to detect activity .

Action that occurs at a specific location for a specific duration. In camera trap research, events typically refer to animal activity recorded through one or more triggers and forming a sequence , but other definitions might be used when analysing data. Events can be indicated with observations.eventID , observations.eventStart and observations.eventEnd in Camtrap DP. In a Darwin Core Archive , deployments can also be considered events.

Exchangeable Image File Format. A format for storing metadata about a media file (e.g. creation date and time, format, resolution, shutter speed, exposure level, camera model), typically stored as part of the media file. media.exifData in Camtrap DP.

FAIR (meta)data are (meta)data that meet the principles of findability, accessibility, interoperability and reusability. The FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. See Wilkinson et al. (2016) .

Categorical description of a particular physical feature targeted during the deployment, such as burrow, nest site, or water source. deployments.featureType in Camtrap DP.

String describing the location of a file in a storage system (e.g. data/deployments.csv ). When served over http/https, the domain name and file path constitute the file URL (e.g. https://raw.githubusercontent.com/tdwg/camtrap-dp/main/example/deployments.csv ).

General Data Protection Regulation. A European Union regulation on information privacy, designed to enhance individuals' control and rights over their personal information. See Section 4.2.2.1 .

Categorical description of the environment and vegetation of a location . Classification systems exist to express habitat ( European Environment Agency EEA 2021 ; IUCN 2019 ) or vegetation type ( Vegetation Subcommittee 2016 ). deployments.habitat in Camtrap DP.

Static media file recorded by a camera . Has no significant duration or audio.

Minimum duration between consecutive triggers to be considered belonging to separate sequences . This duration (e.g. 120 seconds) can be defined in a data management system to automatically group media into sequences. This is different from the quiet period , which is a camera setting.

Distinct organism, typically an animal.

Physical place where a deployed camera is located. A location can be described with a name and/or identifier and coordinates in a certain reference system (e.g. decimal latitude and longitude in WGS84). Also referred to as camera location ( Newkirk 2016 ), station ( Van Berkel 2014 ; Zaragozí et al. 2015 ), project station ( WildCAM 2018 ) or trap station ( Hendry and Mann 2018 ). Deployment location with a deployments.locationName , deployments.locationID , deployments.latitude , deployments.longitude , and deployments.coordinateUncertainty in Camtrap DP. See also Section 3.4.2 .

Computational technique that makes use of (training) data and algorithms to imitate the way that humans learn, gradually improving accuracy.

Media files (plural) captured by a camera . Also referred to as photos ( Newkirk 2016 ). media in Camtrap DP.

A (audio)visual file captured by a camera . Can be an image or video . A media file typically has an identifier, file name, timestamp when it was created and associated metadata (e.g. EXIF ). To access a media file, one needs to know its file path and have the required access rights. Media with media.mediaID , media.timestamp , media.fileName , media.filePath in Camtrap DP. See also Section 3.3 .

Standardized expression of a file format (e.g. image/jpeg for an image ). Formerly known as MIME type. media.fileMediatype in Camtrap DP.

Result of a classification , i.e. a record of what can be seen or heard on media-files . Has an observation type to differentiate between animal and other observations. observations in Camtrap DP. See also Section 3.5 .

Categorical description of the type of observation . Recorded as part of the classification , allowing to differentiate between blanks , observations of humans or vehicles and animal observations. observations.observationType in Camtrap DP.

Entity comprising one or more people that share a particular purpose, such as a company, institution, association or partnership. Organizations can be directly associated with a project (e.g. as rights holder, publisher) or indirectly via the affiliation of the project participants . An organization is a package.contributors in Camtrap DP.

Person associated with a project , performing out one or more roles . Participant information typically includes name and contact information and is subject to GDPR . Organizations can also be considered participants. Also referred to as contributor , sometimes user . A participant is a package.contributors in Camtrap DP. See also Section 3.2.1 .

Function carried out by a participant in a project , such as project lead, data manager or volunteer classifying media. Participants can have multiple roles and roles are typically associated with different rights in a data management system (e.g. the right to invite new participants). Also referred to as participant type . package.contributors.role in Camtrap DP. See also Section 3.2.1 .

Scientific investigation by a number of participants , with a defined objective, methodology, and taxonomical, spatial and temporal scope. The objective of camera trap projects is typically to study and understand wildlife. Also referred to as study . package.project in Camtrap DP, where a dataset is associated with one and only one project. See also Section 3.2 .

Predefined duration after a trigger when activity detected by the camera sensor is ignored. deployments.cameraDelay in Camtrap DP.

Strategy for deploying cameras to facilitate a certain research purpose. Can be expressed as a categorical description (e.g. simple random , systematic random , opportunistic ). package.project.samplingDesign in Camtrap DP.

trigger sensitivity setting used on a camera sensor.

Device that detects changes in the environment, such as movement, heat, light, sound, or other stimuli. Modern camera traps typically use an integrated passive infrared (PIR) sensor that is designed to detect activity based on a combination of heat and motion.

Series of media files taken in rapid succession but separated by a time interval less than the set independence interval and forming an animated record of an event . Also referred to as series ( Bayne et al. 2018 ).

The act of deploying a camera in the field. Involves alignment , defining the camera settings and securing the camera to ensure optimal data captures. observations.cameraSetupType in Camtrap DP.

Geographic area containing multiple locations .

Automated identification and classification of different animal species based on visual or auditory data captured by camera traps.

Type of deployment group used to subdivide very large projects into more manageable units.

Sensor condition that prompts a camera to activate and capture media . Also used to indicate the series of consecutive media files resulting from that trigger. One or more triggers form a sequence . Also referred to as burst .

Universally Unique Identifier (UUID). A type of globally unique identifier that can be generated without a central registration authority. Example: 6d65f3e4-4770-407b-b2bf-878983bf9872 .

Moving media file recorded by a camera . Has a specific duration and can include audio.

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Recommended guiding principles for reporting on camera trapping research

• Review Paper
• Published: 06 May 2014
• Volume 23 , pages 2321–2343, ( 2014 )

Cite this article

• P. D. Meek 1 , 2 , 3 ,
• G. Ballard 1 , 2 ,
• A. Claridge 4 , 5 ,
• R. Kays 6 , 7 ,
• K. Moseby 8 ,
• T. O’Brien 9 ,
• A. O’Connell 10 ,
• J. Sanderson 11 ,
• D. E. Swann 12 ,
• M. Tobler 13 &
• S. Townsend 14  

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Camera traps are used by scientists and natural resource managers to acquire ecological data, and the rapidly increasing camera trapping literature highlights how popular this technique has become. Nevertheless, the methodological information reported in camera trap publications can vary widely, making replication of the study difficult. Here we propose a series of guiding principles for reporting methods and results obtained using camera traps. Attributes of camera trapping we cover include: (i) specifying the model(s) of camera traps(s) used, (ii) mode of deployment, (iii) camera settings, and (iv) study design. In addition to suggestions regarding best practice data coding and analysis, we present minimum principles for standardizing information that we believe should be reported in all peer-reviewed papers. Standardised reporting enables more robust comparisons among studies, facilitates national and global reviews, enables greater ease of study replication, and leads to improved wildlife research and management outcomes.

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Acknowledgments

We would like to recognise the role of the following organisations whose support helped augment the preparation of this manuscript; The Winston Churchill Memorial Trust, The Australasian Wildlife Management Society and the NSW Royal Zoological Society. Thank you to James D. Nichols and Andrew Bengsen who provided constructive comments on this manuscript. Marcella Kelly, Karen Hodges and an anonymous referee made several changes to this manuscript.

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List of standard camera trap terms (from Meek et al. 2012a ; Rovero et al. 2013 ) with additional definitions

Term used to describe the placement of a camera and the cardinal direction, it can be two dimensional thus a horizontal (standard placement—lens perpendicular to the ground) or vertical (lens facing downwards at the ground) alignment as well as a horizontal-cardinal direction

A camera trap setting that allows continuous images to be taken following a trigger event, see also rapidfire

A term used to describe a camera that captures images of wildlife using heat and motion sensing, time lapse, mechanical, seismic sensors or an active infra red sensor system

Connotation of a trap ‘set’ which describes the immediate area where camera/s are placed, can be more than one camera per set

The number of camera traps set in a certain pattern and defined location, referring to more than one camera trap at a study area

The acronym for Compact Flash cards, a mass storage device used by older camera traps, virtually all new models (at the time of publication) now use SD cards

Use of cameras set to catch illegal actions by people

A program function available on some models. This setting has many forms but typically allows the user to set a period of time where the camera trap is inactive or ‘hibernating’ before or between images

This refers to the aperture setting and its effect on the focus of objects in the front and rear of the image. Not often adjustable in camera traps

The area in which a camera trap is able to detect the heat signature and motion of a target

The period of time between independent triggers of distinct individuals, regardless of the number of images, to the last image in a sequence

Incorrectly detecting an animal or species when none is present

Failure to detect an animal or species when in fact it is present

Usually the centre of the image (if the image is composed correctly), the subject of interest, the lure, pathway or track centre or bait device

The area captured in a image, usually between 35° and 45°

A lens used by camera traps to direct infrared energy onto the passive infrared (PIR) sensor. These lenses are commonly seen in lighthouses and cause refraction of light

A white flash (xenon) used by some camera traps, now mostly superseded by white LED

An abbreviation for light-emitting diode, a form of light source used in modern white flash cameras

A generic term referring to an attractant used to encourage animals to investigate a specific point within the detection zone. Lures may be auditory, olfactory, visual, or some combination of these in nature

This setting is available in some camera traps and allows the device to be set to maximise clarity at night by reducing the illumination power and increasing the speed of the shutter, thus reducing blur

Passive detectors of infrared light

A camera trap setting that allows images to be taken continuously following a trigger event—see also burst mode

The acronym for Secure Digital cards. A removable digital storage medium that is currently the standard in camera traps

A setting, often adjustable, that reflects the camera’s response to heat in motion for PIR sensors. Higher sensitivity is associated with more images, and lower sensitivity with fewer images. Increased sensitivity, however, does not guarantee detection of a target

A series of still images or video taken in rapid succession but separated by a time interval less than the set independence interval and forming an animated record of a triggering event

A program function available on some camera traps. The time-lapse function, or similar function, typically allows a user to prescribe times of day and/or night when the camera is inactive, regardless of activity within the detection zone. Some time-lapse cameras do not have a PIR and, instead, capture images at prescribed times or intervals

Camera traps that do not have a PIR sensor and can be programmed to take images at predetermined times throughout the day regardless of any triggers

The speed of the camera from detection by the PIR sensor to the first image captured

The time difference between detecting heat in motion and capturing an image. Also known as response time. Slower trigger speed (i.e. more time elapsing between trigger and image capture) may decrease the likelihood of capturing a target

A program function available on some camera traps. Walk test, or similar, can be used to identify where a camera will respond to heat in motion. Consequently, it can be used to ‘focus’ the camera’s detection zone, as desired

A white flash consisting of white LED’s in an array similar to an infra red array that illuminates the subject at night in full colour and is faster than xenon flash technology

An incandescent or white flash that illuminates the subject at night in full colour

Remote camera, remotely activated monitoring camera, trail camera, spy camera, wildlife camera, camera trap, remote-sensing camera, sensor camera, remote sensing camera, remotely-triggered camera, game camera, photo-trapping, sensor camera, heat-and-motion sensing camera

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Meek, P.D., Ballard, G., Claridge, A. et al. Recommended guiding principles for reporting on camera trapping research. Biodivers Conserv 23 , 2321–2343 (2014). https://doi.org/10.1007/s10531-014-0712-8

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Received : 05 November 2013

Revised : 02 April 2014

Accepted : 21 April 2014

Published : 06 May 2014

Issue Date : August 2014

DOI : https://doi.org/10.1007/s10531-014-0712-8

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Motion-based video compression for resource-constrained camera traps

Field-captured video allows for detailed studies of spatiotemporal aspects of animal locomotion, decision-making, and environmental interactions. However, despite the affordability of data capture with mass-produced hardware, storage, processing, and transmission overheads pose a significant hurdle to acquiring high-resolution video from field-deployed camera traps. Therefore, efficient compression algorithms are crucial for monitoring with camera traps that have limited access to power, storage, and bandwidth. In this article, we introduce a new motion analysis-based video compression algorithm designed to run on camera trap devices. We implemented and tested this algorithm using a case study of insect-pollinator motion tracking. The algorithm identifies and stores only image regions depicting motion relevant to pollination monitoring, reducing the overall data size by an average of 84% across a diverse set of test datasets while retaining the information necessary for relevant behavioural analysis. The methods outlined in this paper facilitate the broader application of computer vision-enabled, low-powered camera trap devices for remote, in-situ video-based animal motion monitoring.

1 Introduction

30+ 30 + frames/sec). They too may collect data at preset intervals or when triggered, to allow analysis of dynamic interactions that aren’t fully captured in still images, such as bird courtship displays [ 12 ] , predator ambush strategies [ 20 ] , and animal pollination [ 14 , 25 , 16 ] .

However, a video camera trap’s rich data comes at a cost: video storage, processing, and transmission from remote devices with limited access to power, storage capacity, and transmission bandwidth, are challenging. Some systems, therefore, run only for a few hours or days at a stretch, avoid onboard-video processing, and require manual data transfer [ 6 ] . This inconvenience can reduce the value of a video camera trap. Ideally, it would be capable of remote autonomous operation with infrequent service visits, and all data would be streamed conveniently to wildlife management and research offices. It is this need for increased autonomy of video camera traps that drives our research. We address the challenge of limited storage and transmission bandwidth by reducing video camera data file size with a new compression algorithm tailored specifically for wildlife monitoring. We use insect pollinator monitoring as our case study to demonstrate algorithm performance.

Monitoring insect pollinators is valuable to support them in sustaining natural ecosystems [ 9 ] and global food production [ 10 ] . However, observing insects outdoors is difficult due to the complexities of their environment and the varied appearance and behaviour of the insects themselves [ 18 ] . Video capture is valuable in such studies to provide high spatio-temporal resolution movement data enabling fast-moving insect interaction monitoring.

Current video-based insect camera traps employ continuous recording [ 6 , 25 , 23 ] , time-lapse [ 17 ] , or motion-triggers [ 29 , 17 ] . Continuous video recordings require high storage capacity and transmission bandwidth. Time-lapse videos require less storage and bandwidth, but are prone to missing insect activity detail during non-recording periods. Motion-triggering aims to reduce storage and transmission requirements by recording video only when insects in view. However, widely-used hardware triggers like PIR sensors, have proven ineffective for detecting small insects [ 19 , 17 ] . Software-based motion triggers have also been implemented in insect monitoring, utilising techniques such as foreground-background segmentation [ 29 ] and deep learning [ 28 ] . Foreground-background segmentation-based approaches are susceptible to false positive detections caused by wind moving foliage or illumination changes. In comparison, deep learning triggers can minimise false positives by recording videos only when an insect is present in the camera frame. However, to be effective these models usually require substantial computational resources, specialised hardware, and detection models trained on a wide variety of species. This can limit their value for autonomous applications and makes them prone to mis-detections.

In this paper, we introduce a novel, effective approach that substantially reduces video size without compromising insect data. Our method processes camera data frame-wise and pixel-wise to identify image regions with motion, storing only the information relevant for animal monitoring and reconstruction of motion paths and animal-habitat (in our case, flower) interaction. The algorithm architecture is designed to accommodate limited power, storage, and bandwidth resources of camera traps installed in remote locations while improving processing throughput. We conducted a case study applying our algorithm to a set of four datasets representing diverse application environments. We demonstrate the practicality of our method for animal monitoring by extracting insect behavioural data from compressed videos.

In this section we describe the algorithm architecture (Figure 1 ) and its multi-threading approach designed to improve data throughput. The software is published as open-access on GitHub as EcoMotionZip 1 1 1 https://github.com/malikaratnayake/EcoMotionZip .

2.1 Reader component

The Reader captures video frames from a camera stream or pre-recorded video file and adds them to a queue for processing. Once all frames from the input stream have been passed to the Motion Analysis component for processing, the Reader terminates.

2.2 Motion Analysis component

The algorithm’s Motion Analysis component processes frames captured by the Reader thread to identify regions with motion. This process is designed to extract only information from video frames critical for animal behaviour analysis. It discards the remaining information to save storage space. Maintained data includes information for algorithms (and human viewers) to gauge (1) animal type / species, (2) movement paths / gaits, (3) observation time, and (4) an overview of the environment within the camera view.

Motion analysis begins by down-scaling each captured frame and converting it to greyscale to reduce computational load and improve processing efficiency [ 22 , 1 ] . Subsequently, inter-frame changes are detected by calculating the absolute intensity difference between pixels in adjacent frames. Pixel regions displaying a non-zero absolute intensity difference are preserved and enhanced to include a buffer region around them. This allows the algorithm to maintain detailed information about the animal and its immediate surroundings, facilitating subsequent behavioural analysis. The size of the buffer region can be customised by the user for the application. Subsequently, the frame containing motion regions is converted into a binary image, where pixels of non-zero intensity are retained while the rest are set to zero. Frames with no regions of detected motion are discarded completely. Frames with regions of detected motion undergo upscaling of the binary image to the original frame size and a bitwise product is generated between the upscaled binary image and the original frame. The resultant frame is then passed to the Writer component for storage. The Motion Analysis component terminates once all frames from the Reader have been processed. In addition, the algorithm records full frames at user-specified intervals (regardless of whether or not motion was detected in that frame) to capture a scene overview and changes in the environment that occur gradually during the recording period.

Alongside processed motion frames sent to the Writer, the Motion Analysis component transmits the frame numbers of the input and output videos, and whether or not a full image frame has been saved. This information is stored in a CSV file, for later reconstruction of animal motion and behaviour.

2.3 Writer component

The Writer component receives processed frames from the Motion Analysis component and re-assembles them into a video file. The Writer extracts the frame rate and resolution of the output video from the input video file. Additionally, the Writer stores a CSV file alongside the video file containing the supplementary data sent by the Motion Analysis component.

2.4 Test datasets

To assess our algorithm we used four public real-world datasets. These comprise multiple videos encompassing a range of typical insect monitoring scenarios, application contexts, scene complexities, and recording modes (Table 1 ).

3.1 Video compression

We evaluated video compression performance by comparing file sizes and frame counts (Table  1 ). To assess the effectiveness of our algorithm and ensure data needed for behavioural analysis is preserved, we maintained the original video resolution, frame rate and video codec. Test data was processed using a Raspberry Pi 5 (8 GB) single-board computer common in insect camera traps [ 13 , 6 , 29 ] .

width = colspec = Q[180]Q[145]Q[90]Q[69]Q[80]Q[69]Q[90]Q[100]Q[100], cells = c, cell11 = r=2, cell12 = r=2, cell13 = r=2, cell14 = c=20.176, cell16 = c=20.136, cell18 = r=2, cell19 = r=2, hline1,3,7 = -, hline2 = 4-7, Dataset & App. Env. (No. Videos ) Recording Method Raw Videos Processed Videos Frame Reduc. (%) File Size Reduc. (%) No. Frames File Size (MB) No. Frames File Size (MB) Naqvi et al. [ 17 ] Urban garden (3) MT/TL 5445 327 4147 70 23.84 78.51 Ratnayake et al. [ 24 ] Rural farm (10) Cont. 179912 10895 12093 260 93.28 97.61 van der Voort et al. [ 29 ] Controlled env. (1) MT 790 24 772 2 2.28 89.61 Droissart et al. [ 6 ] Multiple env. (3) Cont. 5471 73 3843 21 29.76 71.49

3.2 Information retention

We evaluated our algorithm’s ability to preserve relevant information for animal behaviour analysis for our case study by comparing the number of insect appearances detected in raw and compressed videos using both manual and automated techniques. We used the dataset Ratnayake et al. [ 24 ] for this experiment as it has the highest video compression and hence, plausibly, the highest likelihood of information loss. This dataset contains video of four insect types: honeybees, Syrphid flies, Lepidopterans, and Vespids. We followed the procedure in [ 25 ] to manually record insect events in this dataset. Results are shown in Table 2 and discussed in Section 4 .

To evaluate the suitability of the compressed videos for automated insect tracking, we used Polytrack [ 21 , 25 ] to extract insect trajectories and flower positions from the compressed videos. For this experiment, we utilised the pre-trained YOLOv8 object detection model with default software configurations [ 21 , 25 ] . The results are plotted as insect trajectories in Figure 2 . An example video showing insect trajectories extracted by the Polytrack software is included in the supplementary materials.

4 Discussion

Our compression algorithm analyses pixel-wise motion within video frames to remove frames and individual pixels devoid of animal motion information while retaining critical data. It achieved an average compression of 84% for insect monitoring videos in diverse environments (Table 1 ), while preserving key data for behaviour analysis (Table 2 , Figure 2 ). The significant file size reduction translates to reduced storage and bandwidth needs beneficial for resource-constrained, remote edge-camera traps.

The proposed algorithm consistently achieved percentage file size reductions exceeding frame reduction percentage across all datasets, especially in videos of environments with few background changes (e.g Naqvi et al. [ 17 ], van der Voort et al. [ 29 ], Droissart et al. [ 6 ] , Table 1 ). In these cases, our pixel-wise motion analysis selectively eliminated data from non-moving pixels but retained data from pixels with motion for analysis. This demonstrates the adaptability of our approach for different environmental conditions, abundances of animals in a frame, or recording trigger type.

In our experiments, the algorithm retained all data necessary for insect abundance estimations (Table 2 ). Also, manual observations of compressed videos revealed more fast-moving Vespids, relatively small Syrphids, and Lepidoptera, compared to raw video observations. This is probably due to the fact that our compressed videos simplify the task by focusing attention on key parts of the image to reduce user fatigue [ 27 , 30 , 7 ] . This added focus can potentially reduce the cost and improve the accuracy of ecological video analysis [ 2 ] .

We validated the compressed videos’ value for automated tracking and analysis with existing insect tracking software to extract insect trajectories (Figure 2 ). Notably, the Polytrack software [ 21 ] was not optimised for processing compressed videos, reducing its performance. Future work to tailor software for processing compressed video would certainly be valuable.

We used foreground changes as motion triggers without assessing events or objects causing the motion on the device. This records animal appearances with few false negatives (Table 2 ) and therefore preserves essential data. But compressed videos contained false positives caused by wind and illumination changes that add unnecessarily to the compressed video file-size in dynamic environments. Future methods to reduce false positives would improve the compression performance, possibly at the expense of onboard processing and resource costs. We note that whether or not this matters depends on the cost/benefit analysis of a particular researcher working with specific animals in specific environmental conditions. It is certainly worthy of future research if the file compression ratios we achieved with our method were found to be insufficient for a particular application.

5 Conclusions

This paper presented an algorithm to compress videos captured by resource-constrained camera traps that employs motion analysis to remove unwanted pixels and image frames. By analysing video frames pixel-by-pixel, our algorithm achieved an average compression of 84% on the test data while preserving all information crucial for animal behaviour analysis in our insect pollinator monitoring case study. This substantial file-size reduction significantly enhances the monitoring capabilities of remote camera traps by increasing monitoring duration, minimising storage requirements and bandwidth demands, and facilitating efficient data transfer from these resource-limited devices. Deploying this algorithm on camera traps has the potential to significantly advance ecological monitoring and conservation efforts by optimising the capabilities of existing systems.

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Interactions between livestock guarding dogs and wildlife in the Carpathian Mountains, Romania

Livestock guarding dogs (LGDs) are often suggested as a tool to help facilitate human-wildlife coexistence because they are considered effective at preventing livestock losses and reducing persecution of large carnivores. As LGDs have been observed chasing and killing wildlife, they could be perceived as predators or competitors in the environment, yet little is known about how the use of LGDs affects co-occurring wildlife. This research aimed to understand the ecological effects of using LGDs by 1) determining the wildlife species chased, killed, and/or consumed by LGDs, 2) quantifying LGD roaming behaviours by breed, sex, age, and reproductive status, and 3) quantifying spatial and temporal responses of wildlife to LGD presence. A detailed overview of the potential and currently reported ecological effects of using LGDs was gathered via a literature review. Then, in 2021, LGD-wildlife interactions were investigated in the Carpathian Mountains, Romania. Thirteen sites were visited where shepherds were interviewed, 129 scats collected, and a total of twelve sheep and 40 LGDs GPS-tracked for an average of three weeks. Camera traps were deployed across 315 km2 covering both pasture and forest. Wildlife remains in the scats were identified via traditional methods including microscopic hair analysis. Roaming behaviours were investigated from the GPS data by calculating pairwise distances between each sheep and LGD and the overlap in their daily home ranges, which were estimated using the Local Convex Hull (LoCoH) method. Habitat use by grey wolves, brown bears, red foxes, red deer, and wild boars was investigated from the camera trap data via detection rates, single- and two-species occupancy models, and activity patterns estimated by a nonparametric kernel density approach. There were 56 records in the literature widely reporting, mostly anecdotally, LGDs interacting with wildlife. Similarly, all thirteen shepherds reported that their LGDs chased wildlife and seven reported that their LGDs had injured or killed wildlife. However, there were low occurrences of wildlife in LGD scats with only 9% containing wild vertebrate remains (mostly wild boar in scats collected at one site on one day). Some roaming occurred with LGDs being found up to 4 km away from sheep, but LGDs predominantly remained in close proximity to livestock. On average, LGDs were within 200 m of the sheep during the day and within 100 m at night whilst sheep were enclosed in the sheepfold. Differences in distances between LGDs and sheep, and overlap in daily home ranges, were not predicted by LGD breed, sex, age, or reproductive status. Only red deer showed potential spatial and temporal avoidance of LGDs with lower detection rates, lower occupancy, and a reduction in daytime activity in areas of more frequent LGD use. Grey wolves were potentially attracted to areas used by LGDs. However, it was not possible to disentangle the effects of LGDs alone and the effects of LGDs, sheep, and shepherds combined. This is the first large-scale study assessing multiple elements of LGD behaviours and wildlife responses. Overall, there was little empirical evidence to suggest that LGDs have substantial detrimental effects on co-occurring wildlife in the Romanian Carpathian Mountains. These results help to establish that LGDs, both purebreds and mixed-breeds, are a suitable candidate tool for reducing the need for lethal control of wild predators and possibly helping to facilitate human-wildlife coexistence.

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• Published: 21 May 2024

A scoping review on bovine tuberculosis highlights the need for novel data streams and analytical approaches to curb zoonotic diseases

• Kimberly Conteddu   ORCID: orcid.org/0000-0002-3883-4137 1 ,
• Holly M. English 1 ,
• Andrew W. Byrne 2 ,
• Bawan Amin 1 ,
• Laura L. Griffin 1 ,
• Prabhleen Kaur 3 ,
• Virginia Morera-Pujol 1 ,
• Kilian J. Murphy 1 ,
• Michael Salter-Townshend 3 ,
• Adam F. Smith 4 , 5 , 6 &
• Simone Ciuti 1  

Veterinary Research volume  55 , Article number:  64 ( 2024 ) Cite this article

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Zoonotic diseases represent a significant societal challenge in terms of their health and economic impacts. One Health approaches to managing zoonotic diseases are becoming more prevalent, but require novel thinking, tools and cross-disciplinary collaboration. Bovine tuberculosis (bTB) is one example of a costly One Health challenge with a complex epidemiology involving humans, domestic animals, wildlife and environmental factors, which require sophisticated collaborative approaches. We undertook a scoping review of multi-host bTB epidemiology to identify trends in species publication focus, methodologies, and One Health approaches. We aimed to identify knowledge gaps where novel research could provide insights to inform control policy, for bTB and other zoonoses. The review included 532 articles. We found different levels of research attention across episystems, with a significant proportion of the literature focusing on the badger-cattle-TB episystem, with far less attention given to tropical multi-host episystems. We found a limited number of studies focusing on management solutions and their efficacy, with very few studies looking at modelling exit strategies. Only a small number of studies looked at the effect of human disturbances on the spread of bTB involving wildlife hosts. Most of the studies we reviewed focused on the effect of badger vaccination and culling on bTB dynamics with few looking at how roads, human perturbations and habitat change may affect wildlife movement and disease spread. Finally, we observed a lack of studies considering the effect of weather variables on bTB spread, which is particularly relevant when studying zoonoses under climate change scenarios. Significant technological and methodological advances have been applied to bTB episystems, providing explicit insights into its spread and maintenance across populations. We identified a prominent bias towards certain species and locations. Generating more high-quality empirical data on wildlife host distribution and abundance, high-resolution individual behaviours and greater use of mathematical models and simulations are key areas for future research. Integrating data sources across disciplines, and a “virtuous cycle” of well-designed empirical data collection linked with mathematical and simulation modelling could provide additional gains for policy-makers and managers, enabling optimised bTB management with broader insights for other zoonoses.

1 Introduction

Emerging infectious diseases represent a significant public health concern as they become more prevalent worldwide [ 1 , 2 , 3 ]. It is estimated that about 60% of emerging infectious diseases are zoonotic, 72% of which have been estimated to originate from wildlife [ 2 , 3 ]. In 2019, thirteen different zoonoses had confirmed cases in humans within the European Union [ 4 ]. This has likely been accelerated by exponential growth in global population size and mobility with associated increases in urbanisation and concurrent loss of natural habitats. It has also led to increasing occurrences of human-wildlife interactions (e.g., improper waste disposal, intentional feeding of wildlife, movement of wildlife to human-dominated areas) and, therefore, exposure to zoonotic diseases [ 5 , 6 , 7 ]. Contact between humans, livestock and other captive animals, and wildlife species is only expected to keep increasing, leading to concerns about increased incidences of zoonotic disease transfer [ 6 , 8 , 9 ]. The question, however, remains of how to best track and manage emerging diseases.

A critical example is Zoonotic Tuberculosis (zoonotic TB), which was estimated in 2016 to be linked to 147 000 human cases and 12 500 deaths worldwide [ 10 ]. Zoonotic TB is driven mainly by Mycobacterium bovis (i.e., the causative agent of Bovine Tuberculosis—also as bovine TB or bTB), which is transmitted by several wildlife hosts and livestock. Britain and Ireland, as well as many other countries worldwide [ 10 ], have been increasingly impacted by bTB, resulting in significant economic loss. In Ireland, for instance, 4.89% of cattle herds tested positive for bTB in 2023, leading to the humane killing of 28 868 cattle [ 11 ]. This is in addition to the economic costs associated with the national bTB eradication program with €92 million spent in 2018 alone [ 12 ]. Similar trends can be observed in the UK, with £70 million spent annually for bTB prevention and control [ 13 ]. This disease also raises welfare concerns for wildlife hosts, especially considering its high prevalence in the wild. Badgers ( Meles meles ), for example, have been shown to have a bTB prevalence exceeding 40% in hotspot areas in Ireland [ 14 ], and red deer in Spain have been estimated to have a prevalence of up to 50% [ 15 ].

Bovine TB eradication is prioritised by governments and researchers due to the significant health concerns and economic (trade) impacts. Despite decades of control efforts in several countries, the pathogen has successfully avoided eradication. There are complex reasons as to why this is the case [ 16 ], but a primary factor relates to its complex dynamics of transmission and maintenance across differing hosts and the environment. Therefore, new thinking may be required to further investigate if disease control can be driven toward eradication. Detecting gaps in the current bTB literature is an essential step required to identify target areas for future research and to further hone government eradication strategies.

One way in which this may be addressed, and which requires assessment as to its prevalence in the literature, is through multidisciplinary, coordinated collaborations between the public health sector, veterinarians, ecologists and wildlife managers. The importance of interdisciplinary approaches is highlighted by the interlinked nature of human, animal and ecosystem health, which led to the concept of “One World One Health™” [ 17 , 18 ]. Despite such multidisciplinary efforts, the effect of stressors (i.e., direct and/or indirect disturbances such as hunting, habitat loss, and more broadly habitat and climate change) on animal ecology within human-dominated landscapes and the potential emergence of zoonotic disease is still understudied [ 1 ]. For example, we are aware that human-driven changes in the environment can modify interactions between hosts, change host and vector densities, and alter host longevity and movement [ 19 , 20 ]. A study by Castillo-Neyra et al. showed that rabies transmission was spatially linked to water channels, which act as ecological corridors connecting multiple susceptible populations and facilitating pathogen spread and persistence [ 20 ]. However, with cities expanding and providing urban corridors to wildlife, pathogen persistence could become even more of an issue [ 20 ], confirming the importance of studying the effect of human perturbations on animal ecology and related implications in disease ecology.

Additionally, transmission of different zoonoses often involve multiple agents including humans and a diverse range of wild and domestic animals. In order to understand the processes behind their transmission, it is essential to clearly disentangle the role of each agent involved [ 19 ]. Due to the complexity of disease transmission and the maintenance of infection within multiple wildlife hosts, for example between bovine and badger populations in the case of bTB, the individual components of the transmission chain are often studied separately. This can limit our understanding of the subtle underlying effects explaining disease emergence and transmission. Therefore, a holistic approach is essential to develop a complete picture of the transmission dynamics of zoonotic diseases like bTB [ 19 ]; for example, recent research on rabies has shown how empirical data can be used to elucidate epidemiological dynamics [ 21 ].

However, even in cases where empirical data is used, there may be limited power, which can impact results and interpretation. In these cases, evidence from empirical data can now be boosted by mathematical simulations, which are powerful tools for predicting disease transmission trajectories [ 22 ]. Simulations of disease transmission through compartmental models (e.g., the Susceptible, Infectious, and/or Removed (SIR) model and its variations) have been used in a variety of disease systems, including the recent COVID-19 pandemic. COVID-19, however, is exceptional in the level of global concern garnered and resultant significant investment in funding. This meant that large empirical datasets were also made readily accessible, which made direct complex modelling possible [ 23 ]. Other zoonoses are typically more difficult to model this way due to the lack of empirical data on disease transmission and associated hosts [ 24 ]. Mathematical simulations, using for example SIR models, therefore create opportunities to also model these zoonoses. In addition, such simulations allow us to undertake experiments that are currently logistically unfeasible, too costly, too complex or on “unobservable” phenomena [ 22 , 25 ].

As mentioned, lack of information on associated hosts and transmission pathways is often a limiting factor in modelling zoonoses and may potentially also be an issue in bTB research. Studying interactions between and within host species, as well as the role played by each host in the transmission chain, can enable us to better understand zoonotic disease dynamics. While simulations can achieve much, it is important to note that interactions amongst wild animals are heterogeneous by nature and vary significantly between different populations as well as individuals. Therefore, it is important to account for this variability to understand the mechanisms behind transmission and subsequently be able to predict and control disease spread [ 8 ]. This can be achieved by using network modelling, where heterogeneous contacts between animals can be used to simulate disease transmission [ 8 , 24 ], for example using social network analysis (SNA) [ 8 ]. SNA can be beneficial for disease management since it enables us to identify “super-spreaders” (i.e., highly connected individuals) which can then be targeted for vaccination, allowing for a dramatic reduction in transmission [ 1 , 8 ]. In addition, new research is looking at integrating SNA with molecular epidemiology (phylodynamics) to better estimate transmission pathways and direction of transmission between individuals [ 26 ].

Finally, it is of key importance that models of disease risk and distribution consider variances across space and time [ 27 , 28 ], which enables us to identify disease clusters [ 5 ] and model host abundance [ 29 ]. As ecological processes occur at different scales (from single study sites to macroecological scales), the spatial scale used for disease distribution modelling is crucial in understanding how these processes exacerbate the spread of zoonotic diseases, such as bTB [ 30 , 31 ]. Large spatial scales (i.e., global, continental) can examine the broader picture and disentangle how host abundance and abiotic factors influence disease prevalence [ 19 ]. Smaller spatial scales (i.e., country, region) can be used to examine population dynamics and pathogen genetic diversity at the local level [ 19 ]. Temporal patterns are important to consider as many zoonotic diseases show seasonal variations (e.g., Zoonotic enteric diseases such as Salmonella spp, Escherichia coli , Giardia spp) as well as daily variations (i.e., due to the circadian rhythm of microbes and pathogens as well as chronobiology of wildlife hosts) in their infection patterns [ 32 , 33 , 34 ]. It is of key importance that any gaps in bTB research pertaining to factors discussed above be identified, in order to inform future research direction.

Here, we aimed to uncover empirical and methodological gaps in the peer-reviewed literature on bTB. Our intention is to use bTB as an example of a complex multihost zoonotic disease for which recent developments with sampling design, animal monitoring tools and technology, and mathematical modelling has helped to fill the gaps in knowledge and improve our understanding and ability to combat zoonotic diseases more generally.

To achieve our goal, we developed a scoping review of bTB multihost epidemiology focusing on 18 research questions (reported in Table  1 and conceptually summarised in Figure 1 ) regarding the type of study, whether, which and how wildlife species have been monitored, what kind of sampling designs and methodological approaches have been used, and whether epidemiological empirical data have been collected. We then gathered data from the peer-reviewed literature on the mechanisms driving inter- and intraspecies bTB transmission, looking in particular at novel and multi-disciplinary approaches. Our goal is that our work will spark renewed discussion on how to monitor and deal with zoonotic diseases, direct future research, and stimulate focused funding efforts (Figure 2 ).

Key host species and topics of interest we screened for in the bovine tuberculosis scientific literature published between 1981 and 2022 . bTB host species include cattle as well as a range of wild species: badger, wild boar, cervid species (with the following species identified in the literature screened: white-tailed deer, red deer, fallow deer, roe deer, wapiti elk, sika deer and muntjac deer), brush-tailed possum and wild buffalo. The circles on the outside illustrate the key information sought in peer-reviewed papers dealing with bTB, which has been expanded and clarified in Table  1 : type of data collected by researchers; whether spatial analyses were carried out (i.e., in cattle and or wildlife); what type of spatial and temporal scales were considered; whether environmental variables were taken into account (i.e., environment in the farm, environment around the farm and/or weather variables); whether the methodological approach captured the direction of disease transmission; whether the study used common epidemiological modelling techniques (i.e., compartmental models, transmission rates), or whether the study included intra/interspecies interactions in their methodology (i.e., what type of interactions did they look at - e.g., direct and/or indirect, what type of equipment was used to get interactions data and what methodology was used to analyse the data); finally, if human perturbations (i.e., forest felling, culling, vaccination) were taken into account when looking at variables affecting bTB spread, and management solutions to offset the spread of bTB, if any. Animal silhouettes were downloaded from PhyloPic [ 134 ]. Cattle, cervid, brushed-tailed possum and wild boar silhouettes are under: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. Buffalo silhouette is by Jan A. Venter, Herbert H. T. Prins, David A. Balfour & Rob Slotow (vectorized by T. Michael Keesey) under: Attribution 3.0 Unported (CC BY 3.0) [ 135 ]. Badger silhouette is by Anthony Caravaggi under: Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) [ 135 ]

Cascade diagram of the process used in the selection of relevant papers.

We conducted a scoping review (as per PRISMA guidelines) [ 35 ] by sourcing peer-reviewed papers using Web of Science (Clarivate, 2021 Online Version) focusing on bovine tuberculosis, and more specifically its most common cause, Mycobacterium bovis , in cattle and several key wildlife hosts. The search terms and list of articles have been summarised in Additional file 1 . We identified 3531 potentially relevant papers (i.e., the search included all years of publication) which were uploaded and screened for duplicates using EndNote (Clarivate, Version 20.1.0.15341)(Figure  2 ). Relevant articles were then selected using a PEO (Population, Exposure, Outcomes) eligibility criterium structure [ 36 ]. The aim of the PEO is to identify articles of interest by selecting the “Population” (i.e., the subject being affected by the disease/health condition) for a particular “Exposure” (i.e., a disease/health condition) and either a particular “Outcome” or “Themes’’ to examine [ 36 , 37 ]. The PEO eligibility criterium was chosen since it was in line with the recommendations given for scoping reviews that target literature on etiology and risk factors, such as a particular disease. We decided to use a modified version of the PEO framework structure which also includes themes of interest as potential “Outcomes” [ 37 ], as summarised in Table  2 to aid reproducibility. All papers that did not meet the eligibility criteria listed in Table  2 were removed (Figure 2 ). The papers were screened by one researcher who coded 18 variables (stored in an excel spreadsheet) to answer the questions of interest summarised in Table  1 . The results were then imported and plotted using ggplot2 in R version 4.1.1 [ 38 ].

Our results are based on 532 peer-reviewed papers published between 1981 and 2022. The study location of the papers was representative of 6 continents and 52 different countries (Figure 3 ). The continent with the highest number of studies on bTB is Europe ( n  = 303, 169 of which were from the UK), significantly higher than those carried out in much larger continents such as Africa, Asia, and both Americas (Figure 3 ). We screened all papers for 18 different variables (addressing our 18 questions, see Table  1 ) which we summarised in the following section under the heading: 3.1 general characteristics (Sub-headings: “Study species and wildlife species”; “Management and data type”), 3.2 data analysis (Sub-headings: “Spatial analysis, spatial scale and temporal scale”; “Farm environment and human perturbations”), and 3.3 epidemiological analysis (Sub-headings: “Intra- and interspecies interactions”, “Direction of transmission and compartmental models”). Note that most plots presented below have a sample size of n  = 532, corresponding to the number of papers screened, with a few exceptions where this sample size is higher (for example, in relation to temporal scale included in the study, if a paper reported multiple temporal scales, therefore contributing to multiple levels of a category) or lower (for example, in relation to epidemiology, where variables of interest were analysed only in the subset of papers describing studies that included epidemiological interactions).

World map showing number of papers screened per country . Number of papers per continent: Europe (303), Africa (68), Oceania (60), North America (53), South America (29), Asia (26).

3.1 General characteristics

3.1.1 study species and wildlife species.

We found that 41% of bTB papers focused on cattle only, whereas 30% of them included both cattle and wildlife species and 29% targeted only wildlife species (Figure 4 A). Among those papers reporting wildlife data, we found that the European badger attracted most research effort (50% of wildlife studies), followed by cervid species (28%: 13% red deer, 11% white-tailed deer, 5% fallow deer, 3% roe deer, 2% wapiti elk, from hereinafter referred to as simply elk, and < 1% of studies including sika and muntjac deer), wild boar (18%), brushed tailed possum (17%) and buffalo (4%) (Figure 4 B).

Species, data and study type . Number of papers screened and reporting data on A study species type (whether the study was on cattle and/or wildlife), B wildlife species, C management (whether a paper investigated potential management solutions and their efficacy), D and data type. Animal silhouettes were downloaded from PhyloPic [ 134 ]. Cattle, cervid, brushed-tailed possum and wild boar silhouettes are under: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. Buffalo silhouette is by Jan A. Venter, Herbert H. T. Prins, David A. Balfour & Rob Slotow (vectorized by T. Michael Keesey) under: Attribution 3.0 Unported (CC BY 3.0) [ 135 ]. Badger silhouette is by Anthony Caravaggi under: Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) [ 135 ].

3.1.2 Management and data type

Our results highlighted that only 25% of the studies dealt with management solutions (Figure 4 C). Management strategies mainly included culling (18%) or vaccination (6%), with 5% looking at other strategies (e.g., fencing, sterilisation). We also found that most papers gathered original empirical data (79%), and papers only using simulations were limited (4%), with a remaining 17% of papers combining empirical data and simulations (Figure 4 D).

3.2 Data analysis

3.2.1 spatial analysis, spatial scale and temporal scale.

We found that the majority of papers did not include any spatial analysis. Those that did focused on spatial patterns in wildlife (30%, Figure 5 B) slightly more than cattle (28%, Figure 5 A). Among the 149 papers that investigated spatial analysis in cattle, 58% looked at bTB risk and probability of infection; 16% looked at cattle interactions with wildlife, 13% analysed the spatial distribution of bTB positive biological samples, 11% investigated cattle movement outside the farm. Interactions between farm animals and cattle movement inside the farm were included in 5% and 1% of papers, respectively. Among the 161 studies which investigated spatial behaviour in wildlife (Figure 5 B), analysis was undertaken using a variety of methodologies; direct observations (36%), satellite GPS telemetry (19%), spatial patterns predicted by future scenarios modelling or mathematical simulations (19%), genetic samples (11%), camera traps (7%), proximity loggers (4%) and indirect observations (e.g., faecal samples for population density estimations; 1%).

Spatial and temporal analysis . Number of papers screened and reporting data on A spatial analysis of cattle (whether the study included any type of spatial analysis), B spatial analysis of wildlife, C spatial scale, and D temporal scale.

We also found that most papers included spatial scales at the regional level or smaller (72%), with less than 4% papers looking at national and/or international spatial scales (Figure 5 C). In regard to temporal scales, 36% of the studies considered interannual variability, whereas 17% tackled intra-annual variability. Thirty-six percent of the studies did not analyse any intra- or interannual temporal variability (Figure 5 D). Only 4% of the studies looked at fine-scale variability (e.g., days), whereas in a few instances the year of study was not reported at all (4%). Finally, 12% of papers included predictions for temporal patterns into future scenarios.

3.2.2 Farm environment and human perturbations

When looking at farm characteristics, 50% of the studies included some type of herd data (e.g., herd size, bTB history), with 44% not including any type of in-farm environmental variables (Figure 6 A) and 24% of papers incorporating other types of farm characteristics. These included environmental conditions on the farm (e.g., natural habitats, land-fragmentation; 13%), farm location in respect to other farms (11%) and farm location in respect to wildlife (6%).

Environmental variables . Number of papers screened and reporting data on A in-farm environment (whether the paper analysis included variables explaining environmental characteristics inside the farm), B outside farm environment, C human perturbations (whether the paper analysed the effect of human disturbances on bTB transmission dynamics), and D weather variables.

Environmental conditions outside the farm were included in 33% of the papers’ data analysis (Figure 6 B). These studies mainly looked at habitat characteristics around the farm (e.g., wildlife presence, natural habitats), with two papers also including variables focusing on habitat variation (e.g., forest clearfell, new artificial plantations). We also looked at weather variables (e.g., temperature, rainfall) and observed that 4% of papers included these as part of their analysis (Figure 6 D). Finally, 25% of the papers screened included human perturbation variables with the vast majority looking at the effect of vaccination and culling on transmission dynamics (Figure 6 C).

3.3 Epidemiological analysis

3.3.1 intra- and interspecies interactions.

We found that most papers (69%) did not include an analysis on interactions, with 25% of papers looking at intraspecies transmission and 14% at interspecies transmission (Figure 7 A). Among the interaction studies, 33% included direct interactions, 17% included indirect interactions and 51% included both (Figure  7 B). In addition, interaction data were mostly collected using simulations (39%), followed by technological tools (29%; e.g., GPS, proximity loggers, camera traps), and direct observations (23%), with genetic sampling used in 7% of papers (Figure 7 C). The methodology used to analyse interaction data also varied between papers with 28% of papers using differential equations (e.g., SIR models, discreate models), 19% social network analysis, 18% linear models (i.e., including generalized mixed models as well as simple linear regressions) and 38% using a variety of statistical techniques (e.g., t-test/ANOVA, stochastic models) (Figure 7 D).

Interaction analysis . Number of papers screened and reporting data on A interactions (inclusion of interaction analysis i.e., intra- and/or interspecies interactions), B interaction type, C the way interactions were monitored, and D interaction data analysis statistical approach.

3.3.2 Direction of transmission and compartmental models

We found that a limited proportion of the papers (8%) included direction of transmission in their analysis (Figure  8 A). We also found that epidemiological modelling techniques (e.g., compartmental models and transmission rates) were adopted in 15% of the studies (Figure 8 B).

Epidemiological analysis . Number of papers screened and reporting data on A direction of transmission (whether this was analysed in the paper, e.g., transmission across species), B epidemiological modelling (i.e., papers included compartmental models and/or transmission rates in the analysis).

4 Discussion

In this review we found that there has been significant research focusing on the badger-cattle bTB episystem. We acknowledge, however, that we also found a very limited number of studies on other episystems [ 39 , 40 , 41 ]. Our spatially-explicit overview of bTB research efforts (Figure 3 ) highlights how the badger-cattle episystem has been the focus of most research done to date, highlighting a huge amount of money and research effort on bTB transmission dynamics across Europe and particularly in Britain and Ireland. However, there has been far less attention given on other multi-host episystems of countries in southern Africa, Asia and both South and North America. We believe we have more to learn from these chronically understudied systems.

Our scoping review found a limited number of studies focusing on management solutions and their efficacy, with very few looking at modelling exit strategies [ 42 , 43 ]. This is due to the paucity of studies using mathematical simulations, not only to better understand and predict possible outputs of management solutions, but also to explore long-term bTB dynamics under different scenarios (e.g. [ 44 , 45 ]). Only a small number of studies have looked at the effect of human disturbances on the spread of bTB in wildlife host species, and this knowledge gap needs to be tackled as we are aware that human perturbations may exacerbate zoonotic outbreaks and spread [ 46 , 47 , 48 ]. Most of the studies we reviewed have focused on the effect of badger vaccination and culling on bTB dynamics with only three studies looking at how other human perturbations may affect these dynamics [ 49 , 50 , 51 ]. Additionally, only two focused on the effect of habitat change (e.g., clearfell forest operations) on bTB breakdowns [ 52 , 53 ]. Finally, we observed that there is only a few studies looking at the effect of weather variables (i.e., rainfall, soil humidity, temperature etc.) on bTB spread or risk [ 54 ]. This is especially important when considering wildlife-cattle transmission since it is now thought to occur also through environmental sources [ 55 ].

We have carefully evaluated the outcome of our scoping review, and in the following sections we have summarised data types and methodological approaches which, we believe, could contribute to gaining further insights into bTB epidemiology. Based on our review, we have identified a significant gap when it comes to prediction and simulation models, which would be a useful tool for managers to assess disease risk under different land use and climate change scenarios. Another major gap is the lack of integration between empirically-informed tactical (short-term decision support) and strategic (larger spatial scales and longer term) models being used concurrently in single studies (though we do note that there are exceptions, for instance Brooks-Pollock et al. [ 56 ]). Future research should include compartmental models fitted across space, linked via meta-populations and/or real-landscape multi-host episystems; or agent-based models (ABMs) with empirical data feedback loops. We describe such modelling approaches and their prerequisites in the following sections, beginning with data and monitoring programs, and we continue with recent advances in technology, mathematical tools and analytical solutions.

4.1 Empirical data and long-term monitoring programs: involving stakeholders and setting up fixed long-term monitoring stations across large spatial scales

As good quality data is required to generate informed strategies on wildlife interventions, we need reliable data sources to model spatial distribution and abundance of the host species involved in transmission. In reference to the badger-cattle bTB episystem in western Europe, both badgers (with several examples among the literature: [ 57 , 58 ]), and cattle [ 59 ] have been extensively monitored. However, in some populations, it is possible that deer and wild boar may also play a role in the local spread and maintenance of infection [ 60 ]. In Britain and Ireland, the significance of deer as a wildlife host impacting bTB epidemiology has been uncertain [ 61 ]. However, recent research is starting to uncover the role deer may play at local scales where conditions favour the transmission between badgers-deer-cattle [ 62 ]. There could be opportunities to gather data in collaboration with hunters (as has occurred in France [ 63 ] and Spain [ 64 ], for example) to have access to a high number of deer samples, within and across countries (large spatial scales) and across years (long-term temporal scales). Involving stakeholders like hunters may provide the unique opportunity to collect pictures of clearly infected animals (e.g., small to large white, tan, or yellow lesions on the lungs, rib cage, or in the chest cavity) - to be submitted via smartphone applications (see [ 65 ]). These stakeholders may also gather biological samples to be collected by government officials at ad hoc collecting centres. This type of information would boost opportunities for monitoring the dynamics of the disease across multiple spatio-temporal scales and in relation to bTB occurrence in the two other hosts in the system (badgers and cattle). The ability to involve stakeholders across large spatial scales (e.g., hunters, farmers, foresters) may help to establish systematic, relatively inexpensive, and long-term monitoring programmes. These can provide species presence-only and presence-absence data for Bayesian species and disease distribution models (described in Sect. " Modelling and mathematical simulations: social network analysis Bayesian species distribution models, and agent based models "), allowing managers to access up to date risk scenarios. This approach can also highlight hotspots of disease outbreak that could drive focused longitudinal studies using satellite telemetry on multiple species simultaneously. This would enable us to better disentangle species overlaps and contact rates [ 66 , 67 ]. The role of stakeholders/citizen scientists in this bTB example could be confirming infection, which is almost never inexpensive, although there is the hope that cheaper field tests will be released in the next decade. The veracity of the data collected and level of engagement from stakeholders/citizen could also be a problem which needs to be taken into consideration. For the time being, a well distributed number of samples could be collected from hunters to cover large areas systematically and limit the costs required for testing.

When it comes to establishing long-term monitoring programs, fixed long-term sampling stations across large-spatial scales can capture wildlife population spatio-temporal dynamics. This can, on one hand, provide data on occurrence, relative density, and spatio-temporal overlaps of the host species and, on the other hand, gather key empirical data required to parameterise mathematical simulations. Camera traps are a popular and effective tool for estimating state variables of wildlife populations [ 68 ]. For ungulates, they have successfully been used to understand temporal behaviour (e.g., diel activity patterns, [ 69 ]), spatial behaviour (e.g., occupancy, [ 70 ]), and abundance (e.g., density, [ 71 ]). Camera traps have been used for quantifying temporal and spatial overlap of wild ungulates with domestic animals in open systems [ 72 , 73 ] with varying results [ 74 ]. Kukielka et al. demonstrated their use in identifying hotspots of indirect wildlife–livestock overlap for the prevention of bTB crossover [ 72 ]. For wildlife, especially ungulates, camera traps offer powerful monitoring solutions not only to measure abundance and spatial overlap, but also to understand behavioural dynamics that may align closely with disease risk. An example is the use of camera traps to individually recognise animals, which has been shown to be possible in a recent study by Hinojo et al. [ 75 ]. They demonstrated how roe deer ( Capreolus capreolus ) antler shapes could be used to identify distinct individuals. This data could be used to obtain better estimates of abundance as well as to build wildlife social networks (which will be discussed in more detail in Sect. " Modelling and mathematical simulations: social network analysis, Bayesian species distribution models, and agent based models ") and therefore provide information on contact rates between and within species. The parameters from these analyses would be useful as an input for mathematical simulations to help better understand disease transmission dynamics in wildlife populations.

The use of camera traps as well as satellite telemetry can be quite challenging to use in developing countries since they can be extremely expensive (satellite telemetry, in particular) as well as difficult to use when collecting data in remote locations (camera traps, in particular). In addition, the invasive nature of satellite telemetry - which requires trapping animals - often makes it hard to collect data from enough individuals from an ethical, logistical and administrative points of view. Therefore, to improve our understanding of episystems in developing nations, advances in non-invasive diagnostic techniques and eDNA (i.e., a genetic sampling technique that uses environmental sources - such as water and soil - to extract genetic information used for biosecurity and biomonitoring purposes) are essential [ 76 , 77 , 78 , 79 ]. An example of a widely used non-invasive sampling technique is faecal sampling [ 76 , 77 , 78 , 79 , 80 , 81 ]. Faecal samples are a relatively inexpensive way of monitoring diseases and health status in wildlife species. It is also possible to collect a high number of samples in a short period of time, which is especially important for long-term monitoring programs of wildlife hosts. Collecting eDNA can be even faster and is especially useful for long term spatio-temporal dynamics of infectious pathogens at the wider scale, which can improve the monitoring of zoonoses at the country and continental level [ 77 ].

However, timing is key when monitoring diseases as infectious pathogens can mutate and be rapidly transmitted between wildlife, humans, and domestic animals, with potentially devastating impacts on human health and animal welfare. Therefore, novel and rapid genetic techniques, such as culture-free pathogen genetic sequencing [ 82 ], can greatly benefit disease surveillance by decreasing the time needed to sequence pathogens and, consequently, the time needed to make essential ecological management decisions and activate public health responses. In addition, these new sequencing technologies can be very useful during wildlife field studies in isolated areas since they can be rapidly deployed and need limited laboratory equipment for processing [ 82 ]. In addition, when monitoring zoonosis such as bTB and collecting related data (invasively or not) it is important to recall the characteristics of the bacterium itself, Mycobacterium bovis . For example, different lineages exist across the globe [ 83 ] with different strains potentially showing different evolutionary [clock] rates. This greatly affects the rate at which the bacterium needs to be monitored among countries, and we believe that faster sequencing technologies will be of great help in tracking the evolution and spread of different lineages, informing adaptive management of bTB (and zoonosis in general) at the local level.

4.2 Recent advances with technology can help to gather data for mathematical simulations: interindividual variability within animal populations and human socio-economic factors matter and should be taken into account

Animal-attached sensors, i.e., biologging [ 84 , 85 ], can allow us to disentangle animal behaviour and the movement patterns that promote disease transmission. GPS units are the most widely used of these sensors, providing data on animal space use. Proximity sensors can detect when two or more sensor-equipped animals interact and can be used to detect direct encounters which may result in disease transmission. Collars with both GPS units and proximity sensors have been used concurrently on badgers and cattle uncovering that, while badgers show a habitat preference for cattle pastures, there were rare to no direct contacts between the two species [ 86 , 87 ]. This indicates that environmental transmission may play an important role in the case of bTB [ 87 ]. As such, proximity sensors allow insights which are not obtainable through investigating shared space use alone. When the disease state of an individual is known, proximity sensors can also provide information on if and how the duration of exposure to said individual affects transmission rate to other members of the population [ 88 ]. Other biologging sensors, including accelerometers, magnetometers, and gyroscopes, are used to classify distinct behaviours from logged datasets [ 85 ]. Behaviour classification allows activity budgets to be built so that behaviours which increase the likelihood of acquiring or transmitting pathogens can be detected and mapped in the landscape. Accelerometers have also been used to compare micro-movements in diseased and healthy animals, with diseased animals exhibiting differences in posture, gait dynamism (e.g., the “bounce” in subsequent walking steps) and energy levels [ 89 ]. Monitoring such micro-movements in cattle could act as a warning sign to test herds for bTB when signs of illness are detected, e.g. by adapting existing systems in place to monitor lameness through accelerometry [ 90 ]. These effects of disease on the internal state of animals yield important insights into how disease status impacts animal movement patterns and therefore disease spread.

Biologging and satellite telemetry monitoring can, on one hand, provide answers aimed at understanding the transmission dynamics within multi-host disease systems [ 87 , 91 , 92 ] and, on the other hand, provide highly valuable empirical data that are strongly needed by parameter hungry mathematical simulations [ 88 ]. However, when tracking animals, special care should be taken to understand the behaviour of those animals that we are monitoring, and specifically whether we are following a bolder subset of the overall population that are easier to trap. This applies also to where we study animals which will provide empirical data for mathematical simulations, because behaviour and movement ecology may vary significantly depending on the level of human disturbance. We are aware that tracking multiple individuals of multiple species can be expensive and not accessible unless large amounts of funding is available. However, recent technological advances with satellite telemetry using LoRaWAN transmission technology [ 93 , 94 ] have been developed to monitor livestock at affordable prices (e.g. less than 100 euros for 1 GPS unit), opening up new opportunities for extensive monitoring programmes in wildlife, within and across species.

The concept of One Health has highlighted the role that human activities play in the spread of zoonotic diseases [ 95 ]. For example, urbanisation, improper waste disposal, and the intentional feeding of wildlife have been shown to result in wildlife movement into human-dominated areas [ 7 ], which may facilitate disease transfer to humans and other animal communities [ 96 ]. However, evidence has shown that only a select proportion of individuals within wildlife populations will engage in interactions with humans [ 97 ] or utilise these human-dominated areas [ 7 , 98 ]. Individual variation in movement patterns [ 99 ], sociability [ 100 ], and immunological defence [ 101 ], among others, impacts disease transmission and spread [ 102 ]. There is also evidence that certain behavioural types have higher infection rates than others (e.g. [ 103 , 104 ]), although the causal direction may be difficult to determine since infections also alter host behaviour [ 103 , 105 ]. Regardless, to gain a more complete understanding of disease spread, future studies should incorporate this individual variation. These studies often utilise direct behavioural observations, since these are an invaluable data source that can be used to determine which individuals in a known population are more likely to engage in close-contact interactions with humans [ 97 ] or access human areas (e.g., farmland) [ 106 ]. This can provide us with information on which individuals in a population may be at “higher risk” of transferring disease to humans or to other animal populations.

Nevertheless, considering human behaviour is also fundamental in infectious disease transmission. The One Health definition has changed in 2022 accordingly and now it includes the importance of society and its diversity in values and beliefs in effectively fighting zoonoses [ 107 , 108 ]. Collaboration between scientific disciplines is not enough to improve current and emerging infectious disease transmission. It is fundamental that community members and expertise at every level, from village to continent, be included if we wish to equitably improve human health and animal welfare [ 107 ]. In this way we may also improve the effectiveness of disease management solutions by tailoring them to communities instead of trying to use the same solutions in different areas without taking into account socio-economical differences.

4.3 Modelling and mathematical simulations: social network analysis, Bayesian species distribution models, and agent based models

Social network analysis (SNA) is a powerful tool in uncovering the causes and consequences of disease transmission within animal communities [ 109 , 110 ]. In the past decade SNA has mainly focused on understanding contact and transport networks of cattle and livestock movements, as well as wildlife movements [ 111 , 112 , 113 ]. Nonetheless, it could be expanded to better unravel the dynamics of disease transmission between wildlife populations and livestock [ 110 ]. Unlike in domestic cattle, the movements and interactions of wildlife can be challenging to track. As a result, a small proportion of individuals are typically monitored using biologging and satellite telemetry, as discussed earlier. Recent advances in statistical analysis of social networks have paved the way to obtain better inferences from limited data [ 114 , 115 , 116 ]. The first step is to identify the network metrics affecting disease transmission dynamics that best suits the disease system under study (e.g., transitivity, betweenness centrality) [ 114 , 115 ]. Using global metrics of a social network, for example, can help estimate potential changes in the overall structure of the wildlife population. A commonly used global metric when studying disease transmission dynamics is transitivity, which represents the tendency of a population to cluster together and is considered to be negatively correlated with disease transmission rates [ 113 ]. Local network metrics, on the other hand, can help in understanding social characteristics at the individual level. A type of local metric is betweenness centrality, which represents the tendency of an individual to serve as a bridge between one part of the community and another (i.e., a community in SNA is a group of nodes, for example individual animals, with denser connections between each other compared to other nodes in the network), helping the selection of individuals to be vaccinated/removed from the population.

Once we have selected the metrics to use, they can be tested via pre-network permutations of available observations to ensure that the available data sufficiently captures non-random interactions among the animals. However, when using small samples for SNA we also must be careful on what we infer from it. Recent research [ 115 ] has shown that estimates may be inaccurate, or “noisy”, at low sample sizes. Therefore, stable metrics with respect to low sample sizes should be identified before making inferences. Research on data collected for wild ungulates [ 115 ], for example, shows that the betweenness centrality values of smaller samples remain well correlated with those in larger samples, indicating that this metric can be used even when the social network is built using a small sample of the population. Similar correlation analysis can be done for other network metrics, mainly in cases of limited data availability for disease transmission. Whenever limited animals from a population are monitored, confidence intervals around the network metrics should also be obtained to make informed decisions using statistical evidence.

Using the methodologies discussed above (see Silk et al. and Kaur et al. for more details, [ 113 , 115 ]) we now have the possibility of analysing all telemetry data collected thus far on species involved in bTB transmission (e.g., badgers, wild boar; but also applicable to species from other disease systems) to test hypotheses on disease transmission dynamics. For example, we can now use these statistical techniques to better understand behavioural patterns of wildlife species, as well as comparing networks overtime and how wildlife behaviour can be affected by perturbations in the environment (e.g., climate change, land-use change or other type of anthropogenic factors) even when only limited data is available [ 115 ]. In addition, it will help in collecting future data since these methodologies can be used to estimate the minimum number of individuals needed in order to reliably build a social network, which can vary enormously depending on the scope of the project as well as the wildlife species of interest. This will, for example, help in answering specific questions regarding the role of deer species in bTB transmission by simultaneously collecting telemetry data on badgers and deer species in Ireland.

Knowing the distribution and abundance of wildlife vectors (i.e., a living agent that carries and transmits pathogens - e.g. HIV, Covid-19, bTB - to other living beings) is also essential when aiming to reduce zoonotic risk [ 117 , 118 ]. To that aim, Species Distribution Models (SDMs) can be used to produce models of the distribution and abundance of species based on occurrence data [ 119 ]. In recent years spatial modelling has undergone a conceptual and technical revolution. New modelling techniques within Bayesian [ 120 ] and Machine Learning frameworks [ 121 ] allow us to develop spatially explicit models of animal abundance and distributions with unprecedented accuracy, and the improvement of computational power allows computers to rise to the challenge and cope with the high computational demands of these models. The flexibility of the new techniques allows us to use different types of data (e.g., individual tracking data, survey data, and even citizen science data) and combine them in what are called Integrated Species Distribution Models (ISDMs), while still taking into account the different observational processes of each type of data, to produce accurate models even in data scarce systems [ 122 ]. In addition, these new techniques also allow for the calculation of uncertainty in a spatially explicit manner, which will help us evaluate the quality of the models and better interpret the results. Bayesian ISDMs using INLA (i.e., Integrated Nested Laplace Approximation) [ 123 ] were used to model the distribution of red, sika and fallow deer in Ireland, which are vectors of bTB [ 65 ]. The models produced, for the first time, relative abundance and distribution maps for each species, which will be an essential tool for deer population management and thus towards bTB eradication. They are already being used to determine high sika-density areas for a pilot study on the effect of deer on biodiversity, which will provide further management tools for the overabundant deer populations in Ireland. In addition, hierarchical Bayesian models are also the basis of a new project aimed at modelling European badger sett distribution, badger density, and their body condition. These three models will be linked to bTB infection in badgers and outbreaks in cattle, in an attempt for the first time to link badger spatial ecology to bTB management and eradication in Ireland (V. Morera-Pujol 2023, personal communication).

Agent-based simulations are another useful modelling approach, or complementary tool to traditional methods, when data is limited/not available; helping elucidate transient effects of wildlife disease transmission in human-dominated landscapes [ 25 ]. These models serve as a computational laboratory that allow researchers to plug-in available real-world data and parameterise both agents (for instance, a badger) and the environment (for instance, a mosaic of natural habitats and farms). This enables researchers to empirically test if animal behaviour in response to landscape change or management interventions modulates disease risk dynamics over time and space [ 124 ]. Recent technological advancements have bolstered agent-based simulations allowing for high-resolution spatio-temporal models that incorporate geographic information systems (GIS) data to create hyper realistic environments, and machine learning algorithms to introduce cognition and applied decision making for agents. Furthermore, process-driven agent-based models (e.g., disease transmission) can be integrated into larger mechanistic agent-based models (e.g., ecosystem scale epi-dynamics) for increasingly higher-resolution models that reduce uncertainty and overly-theoretical parameterisation of model entities [ 25 ]. The development of highly-realistic agent-based simulations, parameterised with high-resolution data, for the management of bovine tuberculosis in multi-host systems can contribute to answering important policy questions and how best to select management directions. In practice, this allows for the totality of data collected in complex multi-host systems to be incorporated into a single environment where they may be measured against one another in the simulation to deduce the possible effects of each predictor. Take for example the European badger as the primary wildlife host in Ireland as a case study. Badgers are prevalent in the agroecological mosaic of natural habitats and farms in Ireland. Agent-based simulations can utilise data from badger tracking studies [ 51 , 125 , 126 ], habitat suitability [ 127 ], culling and vaccination programmes [ 128 ] and disturbance regimes [ 52 , 129 ] to simulate badger movement and behaviour realistically. GIS data for farm location and characteristics [ 130 ], as well as ecological and environmental data streams, can then place the badger agent into a highly realistic environment to examine how these factors affect badger movement, behaviour, and other parameters, for instance, contact rates with domestic animals. Interactions between agents and the environment can be modulated by sub-models to further increase the strength of the model. For instance, weather sub-models (e.g., rainfall) may influence agricultural practice and thus contact rates, as well as the length of time Mycobacterium bovis persists in the environment. Alternatively, disease transmission could also be sub-modelled so that contact rates may/may not result in infection [ 25 ]. Finally, management decisions can be trialled within the simulation to see how likely decisions change the status of disease within the study system, allowing for “What if?” scenarios to play out without risk to animal or human welfare or livelihoods.

5 Conclusion

Our exploration of the recent literature on multi-host bTB episystems, as an example of zoonotic One Health challenges, has revealed a significant body of work utilising a diversity of methodologies at different spatio-temporal scales and subjects (individual vs. group) levels. There was a significant bias in the literature towards one particular episystem, the badger-cattle system that predominates in north-western Europe, reflecting large financial burdens (for both governments as well as the agricultural sector) and research funding investments. Alternatively, there were comparatively less publications from the global south, especially in complex muti-host episystems in southern Africa and India. In such episystems, the cost-effective and efficient collection, collation, and use of data are essential to drive greatest added value to inform on policy options.

Given the results from our scoping review, we reflect on several areas where progress could be made. This includes the need for high-quality data on wildlife hosts, even in episystems where significant research investments have already been made. Such careful collection and utilisation of empirical data could then help feed the development of social network analyses, Bayesian distribution models and eventually mathematical and simulation-based models. Mathematical simulations, such as ABMs trained on synthetic data and parameterised by real empirical data, can answer questions that would otherwise be too costly, unethical, or both. Such models can also be used to explore different scenarios in an increasingly human-dominated world, under different levels of land-use and climate change, or with the appearance of invasive species in already complex multi-host epidemiological systems. In addition, it can help build cross-disciplinary bridges with other areas, deriving significant insights into interspecific transmission like phylodynamic modelling.

We have used our Irish experience to inspire researchers from across the globe; Ireland invests considerably in surveying, culling, and vaccinating badgers [ 131 , 132 ]. However, the question remains - which applies to other countries and zoonotic episystems - should we be doing more or can we be smarter with the data we already have? We suggest the latter. Yes, there is a need to be smarter, arranging ad hoc data collections using the latest technological tools to estimate unknown or uncertain parameters. But we also have to focus our efforts on mathematical modelling (ABMs, INLA-Bayesian) to optimise our information gain from the large, high-quality datasets collected over the last few decades (and for sparser datasets, taking advantage of recently developed statistical tools for enhanced inferences, see [ 54 , 68 ]). We have (almost) all the data required to parameterise simulations with significant utility: this should be one main focus in future research. We believe that, ideally, the feedback of simulation and mathematical tools to inform data collection, and the “virtuous cycle” of feeding this new data to improve the next generation of model is a priority for decision making tools for policy makers and programme managers.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the “A scoping review on bovine tuberculosis highlights the need for novel data streams and analytical approaches to curb zoonotic diseases” repository [ 133 ].

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Acknowledgements

We would like to acknowledge Sylvia Power for helping in reviewing the final version of the manuscript.

This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 18/CRT/6049. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. In addition, HME is funded by an Irish Research Council Government of Ireland postgraduate scholarship. KJM and VMP is funded by the Department of Agriculture, Food and the Marine (DAFM) in Ireland through the research grant 2019-R-417.

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KC: conceived and designed the review, acquired, analysed and interpreted the data, wrote the first draft of the manuscript. HME, LLG: edited and revised the manuscript and contributed to the interpretation of the data. AWB: edited and revised the manuscript and contributed to the interpretation of the data, drafted the policy and research implications. BA, PK, VMP, KJM, AFS, MST: edited specific sections of the discussion and revised the whole manuscript. SC: conceived and designed the review, edited the manuscript and supervised the process. All Authors read and approved the final manuscript.

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Conteddu, K., English, H.M., Byrne, A.W. et al. A scoping review on bovine tuberculosis highlights the need for novel data streams and analytical approaches to curb zoonotic diseases. Vet Res 55 , 64 (2024). https://doi.org/10.1186/s13567-024-01314-w

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• bovine tuberculosis
• Mycobacterium bovis
• infectious disease management
• mathematical modelling
• multi-host disease
• wildlife host

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Analog-antianalog isospin mixing in K 47   β − decay

Brian kootte, h. gallop, c. luktuke, j. c. mcneil, a. gorelov, d. g. melconian, j. klimo, b. m. vargas-calderon, and j. a. behr, phys. rev. c 109 , l052501 – published 24 may 2024.

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We have measured the isospin mixing of the I π = 1 / 2 + ,   E x = 2.599 MeV state in nearly doubly magic Ca 47 with the isobaric analog 1 / 2 + state of K 47 . Using the TRIUMF atom trap for β decay, we have measured a nonzero asymmetry of the progeny Ca 47 with respect to the initial K 47 spin polarization, which together with the β asymmetry implies a nonzero ratio of Fermi to Gamow-Teller matrix elements y = 0.098 ± 0.037 for the 1 / 2 + → 1 / 2 + transition. Interpreting y as mixing between this state and the isobaric analog state implies a Coulomb matrix element magnitude 101 ± 37 keV. This relatively large matrix element supports a model from the literature of analog-antianalog isospin mixing, which predicts large matrix elements in cases involving excess neutrons over protons occupying more than one major shell. The result supports pursuing a search for time-reversal odd, parity-even, isovector interactions using a correlation in K 47 β decay.

• Received 4 March 2024
• Accepted 3 May 2024

DOI: https://doi.org/10.1103/PhysRevC.109.L052501

©2024 American Physical Society

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• 1 TRIUMF , 4004 Wesbrook Mall, Vancouver, B.C. V6T 2A3, Canada
• 2 University of British Columbia , Department of Physics and Astronomy, 6224 Agricultural Road, Vancouver, B.C. V6T 1Z1, Canada
• 3 University of Waterloo , Department of Physics and Astronomy, 200 University Ave W, Waterloo, Ontario N2L 3G1, Canada
• 4 Cyclotron Institute, Texas A&M University , 3366 TAMU, College Station, Texas 77843-3366, USA
• 5 Department of Physics and Astronomy, Texas A&M University , 4242 TAMU, College Station, Texas 77843-4242, USA
• * [email protected]

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Relevant allowed decay of K 47 , showing β − branches > 0.04 %, log ( f t ) ,   I π , energy [MeV], and isospin T of the isobaric parent P , analog A , and possible antianalog A ¯ . Thickness of γ transitions > 5 % indicate intensity.

TRINAT during the optical pumping time. Shown are β telescopes, mirrors for optical pumping light and its beams, magnetic-field coils, electric-field electrodes, and microchannel plates for electron and ion detection. A CMOS camera image of 1000 trapped atoms is superimposed. Distance between trap cloud and ion MCP is 9.7 cm.

Excited-state population during the optical pumping time for circularly and linearly polarized light. See text for deduction of nuclear polarization.

Time-of-Flight (TOF) of Ca 47   + 2 to + 7 ions started by shake-off e − , showing the modeled data decomposition. Blue histogram: TOF started by β in the Δ E − E telescopes, which have lower statistics but less background from untrapped atoms and accidentals.

(Top) Distribution along polarization axis Z of Ca + 2 , ... , 7 47 in coincidence with shake-off e − for the two polarizations. (Bottom) The asymmetry of these distributions A recoil , i.e., the difference divided by the sum of the top distributions. The nonzero asymmetry scales with y and directly implies a nonzero Fermi contribution.

Similar to Fig.  5 , but for β − Ca + 1 47 coincidences.

Effective Coulomb mixing matrix element H C as a function of log ( f t ) for isospin-suppressed β decay (Refs. [ 17, 20, 21, 22, 23, 24, 25, 26 ]). Solid squares are A − A ¯ mixing from Eq. ( 2 ) for K 47 decay's N = 20 shell crossing, Eq. ( 1 ) for P 26 assuming d 5 / 2 1 s 1 / 2 excess proton occupancy, and the approximate use of Eq. ( 1 ) [i.e., Eq. ( 2 ) divided by two] for all others [ 1 ].

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Intravenous Administration of Mesenchymal Stem Cell-Derived Exosome Alleviates Spinal Cord Injury by Regulating Neutrophil Extracellular Trap Formation through Exosomal miR-125a-3p

Yutaka morishima.

1 Department of Neurosurgery, Graduate School of Medicine, Hokkaido University, Sapporo 060-8638, Hokkaido, Japan; moc.liamg@9021akatuy (Y.M.); [email protected] (K.Y.); [email protected] (S.T.); pj.ca.iadukoh.dem@rumijuf (M.F.)

Masahito Kawabori

Kazuyoshi yamazaki, soichiro takamiya, sho yamaguchi.

2 Regenerative Medicine and Cell Therapy Laboratories, Kaneka Corporation, Kobe 650-0047, Hyogo, Japan

Yo Nakahara

Hajime senjo.

3 Department of Hematology, Faculty of Medicine, Graduate School of Medicine, Hokkaido University, Sapporo 060-8638, Hokkaido, Japan

Daigo Hashimoto

Sakiko masuda.

4 Department of Medical Laboratory Science, Faculty of Health Sciences, Hokkaido University, Sapporo 060-0812, Hokkaido, Japan; pj.ca.iadukoh.sh@adusamokikas

Yoichiro Fujioka

5 Department of Cell Physiology, Faculty of Medicine, Hokkaido University, Sapporo 060-8638, Hokkaido, Japan

Yusuke Ohba

Yuki mizuno.

6 Central Institute of Isotope Science, Hokkaido University, Sapporo 060-0815, Hokkaido, Japan; pj.ca.iadukoh.cir@onuzim (Y.M.);

Miki Fujimura

Associated data.

The data that support the findings of this study are available from the corresponding author [M.K.] upon reasonable request.

Spinal cord injury (SCI) leads to devastating sequelae, demanding effective treatments. Recent advancements have unveiled the role of neutrophil extracellular traps (NETs) produced by infiltrated neutrophils in exacerbating secondary inflammation after SCI, making it a potential target for treatment intervention. Previous research has established that intravenous administration of stem cell-derived exosomes can mitigate injuries. While stem cell-derived exosomes have demonstrated the ability to modulate microglial reactions and enhance blood–brain barrier integrity, their impact on neutrophil deactivation, especially in the context of NETs, remains poorly understood. This study aims to investigate the effects of intravenous administration of MSC-derived exosomes, with a specific focus on NET formation, and to elucidate the associated molecular mechanisms. Exosomes were isolated from the cell supernatants of amnion-derived mesenchymal stem cells using the ultracentrifugation method. Spinal cord injuries were induced in Sprague-Dawley rats (9 weeks old) using a clip injury model, and 100 μg of exosomes in 1 mL of PBS or PBS alone were intravenously administered 24 h post-injury. Motor function was assessed serially for up to 28 days following the injury. On Day 3 and Day 28, spinal cord specimens were analyzed to evaluate the extent of injury and the formation of NETs. Flow cytometry was employed to examine the formation of circulating neutrophil NETs. Exogenous miRNA was electroporated into neutrophil to evaluate the effect of inflammatory NET formation. Finally, the biodistribution of exosomes was assessed using 64 Cu-labeled exosomes in animal positron emission tomography (PET). Rats treated with exosomes exhibited a substantial improvement in motor function recovery and a reduction in injury size. Notably, there was a significant decrease in neutrophil infiltration and NET formation within the spinal cord, as well as a reduction in neutrophils forming NETs in the circulation. In vitro investigations indicated that exosomes accumulated in the vicinity of the nuclei of activated neutrophils, and neutrophils electroporated with the miR-125a-3p mimic exhibited a significantly diminished NET formation, while miR-125a-3p inhibitor reversed the effect. PET studies revealed that, although the majority of the transplanted exosomes were sequestered in the liver and spleen, a notably high quantity of exosomes was detected in the damaged spinal cord when compared to normal rats. MSC-derived exosomes play a pivotal role in alleviating spinal cord injury, in part through the deactivation of NET formation via miR-125a-3p.

1. Introduction

Spinal cord injury (SCI) occurs due to mechanical damage to the spinal cord, resulting in various degrees of motor, sensory, and autonomic dysfunction, with an annual incidence of 40–80 per million people [ 1 ]. This condition is complex and devastating, with limited treatment options currently available. Recent evidence suggests that neutrophil extracellular traps (NETs), consisting of extracellular DNA contents released from activated neutrophils, exacerbate the local inflammatory environment and hinder neurological recovery after spinal cord injury [ 2 , 3 , 4 ]. NETs have also been reported to exaggerate neurological condition in other neurological diseases, such as stroke and traumatic brain disease [ 5 , 6 , 7 ], and controlling NET formation is considered to be an important therapeutic intervention. Our group has focused on exploring the potential therapeutic use of mesenchymal stem cells (MSCs) for spinal cord injury [ 8 ]. We, along with other investigators, have observed that MSCs, when intravenously transplanted shortly after injury, effectively restore motor function in the animal SCI model. Nevertheless, the administered cells predominantly accumulate in the lungs and liver, with no detectable presence in the injured spinal cord [ 9 , 10 , 11 ]. This can be attributed to the bystander effect of stem cells, in which trophic factors, cytokines, and exosomes released from the cells play a role in reducing both local and systemic inflammation, consequently contributing to the rescue of the damaged spinal cord [ 12 ]. Among these approaches, stem cell-derived exosomes have been attracting considerable attention. Exosomes, nano-sized vesicles ranging from 40 to 200 nm, are characterized by a double lipid-layer membrane and encapsulate a myriad of molecules, including DNA, mRNA, microRNA (miRNA), and proteins [ 13 ]. These molecules can be transferred into target cells, offering therapeutic benefits in mitigating neurological diseases [ 14 , 15 , 16 ]. Their remarkable cryopreservation capacity and low immunogenicity have prompted considerations of MSC-derived exosomes as a promising alternative to MSC. Previous reports have demonstrated that MSC-derived exosomes exert their effects on the spinal cord by inducing a shift in the phenotype of macrophages and microglia from a proinflammatory (M1) state to an anti-inflammatory (M2) state. They also inhibit astrocytic gliosis and promote angiogenesis and neurogenesis [ 17 , 18 , 19 , 20 , 21 , 22 ]. However, the influence of exosomes on neutrophils, the most abundant and earliest cells recruited from the bloodstream to the spinal cord after an injury resulting in aggravation of inflammation, has not been extensively studied.

In light of these circumstances, our research aims to investigate the effects of intravenous administration of amnion-derived MSC (AMSC) exosome on SCI, with a specific focus on their interaction with neutrophils and the formation of NETs.

2.1. Exosome Characterization

Transmission electron microscopy (TEM) images revealed that the exosomes exhibited a consistent shape with an average diameter of approximately 80 nm ( Figure 1 A), which was further corroborated by the size distribution data from the nanoparticle analyzer ( Figure 1 B). Western blot analysis demonstrated elevated expression levels of CD9 and CD63, specific markers for exosomes, in comparison to the AMSC suspension. Conversely, the expression of calnexin, a marker with low expression in exosomes, was diminished in the exosome ( Figure 1 C). These results confirm the presence of exosomes in the AMSC-derived samples.

Characteristics of AMSC-derived exosome. ( A ) Electron microscopy shows vesicles with round shape morphology with diameter of approximately 100 nm. ( B ) Histogram of the size distribution by nanoparticle analyzer revealed that exosome size ranged from 70 to 300 nm with peak of 180 nm. ( C ) Western blot detection showed the exosome exhibited CD9 and CD63, while less detection was noted for Calnexin compared with the AMSC.

2.2. Intravenous Exosome Administration Ameliorates SCI Damage in Mice

The animals that received exosomes exhibited significantly better neurological recovery compared to those that received phosphate-buffered saline (PBS) ( Figure 2 A), starting one week after administration. Kluver–Barrera staining revealed that the damaged volume of the spinal cord tissue collected at 4 weeks post-SCI was significantly lower in the exosome-treated group ( p = 0.006) ( Figure 2 B,C). In the peri-injury area at 4 weeks post-SCI, there was a notable reduction in MPO-positive neutrophil infiltration in the exosome-treated group ( p = 0.037) ( Figure 3 A,B). Subsequently, early neutrophil infiltration and NET formation were examined in the day 3 section. Immunofluorescence imaging revealed that CitH3-positive cells, as well as MPO-positive cells, were significantly reduced in the exosome-treated group ( Figure 4 A,B) ( p = 0.023 p = 0.047, respectively). This phenomenon is further confirmed by the decrease in NETs, as demonstrated by peri-nuclear histone integration in the exosome-treated group ( p = 0.003, Figure 4 C,D).

Exosome administration ameliorated neurological function and reduced damaged lesions. ( A ) Animals that received exosomes demonstrated significantly enhanced neurological recovery, starting from 1 week after transplantation, in comparison to animals that received saline. ( B ) The Kluver–Barrera staining of the spinal cord 4 weeks after SCI showed the degree of spinal cord damage (the red dotted area) (scale bar = 200 μm). ( C ) The exosome-treated group exhibited a substantially smaller damaged lesion volume compared to the PBS group ( p = 0.0061). * p < 0.05, ** p < 0.01.

Neutrophil infiltration is inhibited by exosome administration. ( A ) Neutrophil infiltration in the spinal cord 4 weeks after SCI was assessed using the anti-MPO antibody. ( B ) The count of MPO-positive cells within the spinal cord was notably reduced in the exosome-treated group as compared to the PBS group (scale bar = 20 μm, p = 0.037). * p < 0.05.

Exosome ameliorated NET formation in spinal cord. ( A , B ) The quantity of NETs (CitH3) and activated neutrophils (MPO), assessed via immunofluorescence, were significantly reduced in the spinal cord following exosome treatment (scale bar = 50 μm, p = 0.0231, p = 0.0466, respectively) ( C , D ) Existence of NETs were further evaluated by super-resolution microscopy. Note that CitH3 is extruded around the nuclear in the PBS group. (scale bar = 20 μm, p = 0.0027). * p < 0.05, ** p < 0.01.

2.3. Ex Vivo Analysis of Exosomes for NET Formation

Peripheral blood was obtained to assess NET formation in neutrophils. Initially, we examined the uptake of exosomes by neutrophils. Labeled exosomes were co-incubated with peripheral neutrophils, and it was observed that the exosomes began to accumulate around the nucleus of neutrophils approximately 10 min after co-incubation ( Supplementary Video S1 ). Subsequently, neutrophils were activated by LPS and co-incubated with exosomes. The number of neutrophils positive for CitH3 and MPO was significantly reduced in the exosome-treated group compared to the PBS group ( Figure 5 A,B). This finding was further validated through flow cytometry (FACS) analysis, which demonstrated a significant decrease in CitH3-positive Ly6G+ neutrophils following exosome administration ( p = 0.002) ( Figure 5 C,D).

In vitro analysis of NET formation in mouse neutrophil. ( A , B ) CitH3 formation was significantly reduced in neutrophils treated with exosomes. (scale bar = 50 μm, p = 0.0101) ( C , D ) Flow cytometry analysis further demonstrated a decrease in cells expressing NETs in the exosome-treated group. ( p = 0.0019). * p < 0.05, ** p < 0.01.

To elucidate the functional mechanisms of exosomes in NET formation, neutrophils were electroporated with miR-125a-3p mimic and inhibitor to assess NET formation following lipopolysaccharide (LPS) activation. Exosome administration effectively inhibited NET formation, and miR-125a-3p electroporation modestly enhanced this treatment effect. Furthermore, the impact of exosome administration was entirely reversed by the addition of the miR-125a-3p inhibitor, underscoring the pivotal role of miR-125a-3p encapsulated within exosomes in NET formation ( Figure 6 ).

miR-125a-3p inhibits NET formation in mouse neutrophil. Exosome administration successfully attenuated NET formation following LPS administration, and miR-125a-3p mimic modestly enhanced treatment effect. The effect of exosome was entirely reversed by the addition of the miR-125a-3p inhibitor. (scale bar = 50 μm). ** p < 0.01.

2.4. Biodistribution of Exosomes

PET imaging of 64 Cu-labeled exosomes at one hour post injection revealed their substantial accumulation in the liver and spleen ( Figure 7 A). Biodistribution data obtained by tissue detection at 24 h post injection also revealed the same trend of liver and spleen accumulation ( Figure 7 B). Interestingly, the exosomes were found to be significantly higher in the spinal cord in the SCI model compared to the normal rat despite their limited presence ( Figure 7 C). This result indicates the potential for exosome accumulation at inflammatory sites.

( A ) 64 Cu-labeled exosomes primarily localized to the liver and spleen. ( B ) Organ radioactivity analysis indicated substantial exosome accumulation in the liver, spleen, and kidney, with a limited presence of exosomes in the spinal cord. ( C ) Exosome accumulation in the spinal cord was significantly greater in the SCI model when compared to normal animals. Note that the Y-axis values in this graph are considerably smaller in comparison to those in ( B ) ( p = 0.0234). ** p < 0.01.

3. Discussion

In the current study, we have demonstrated that intravenously transplanted AMSC-derived exosomes successfully mitigated neutrophil activation by inhibiting NET formation, resulting in the restoration of motor function. We were able to elucidate the underlying molecular mechanism responsible for this effect, which is induced by the exosomal miR-125a-3p. Biodistribution analysis using PET revealed that the majority of exosomes accumulated in the liver and spleen, with a notable quantity also observed in the injured spinal cord.

This study is built upon our previous research, in which intravenous administration of AMSCs for spinal cord injury resulted in significant functional recovery. However, it is noteworthy that AMSCs were not detected within the injured spinal cord itself [ 9 ]. Exosomes released from stem cells are considered to play a pivotal role in the therapeutic mechanisms of stem cell treatment, and numerous experiments are currently underway in the context of SCI [ 23 , 24 , 25 ]. The mechanisms can be categorized into two distinct patterns: alleviating damage and promoting neuro/angiogenesis. A majority of studies that have embraced early exosome administration focus on damage alleviation, encompassing anti-apoptosis, preservation of the blood–spinal cord barrier, and immunomodulation. While immunomodulation often involves the deactivation of macrophages, the therapeutic role of exosomes in neutrophil deactivation is rarely explored [ 26 ]. The underlying reason for this scarcity of research may be attributed to the challenges associated with ex vivo culturing of neutrophils compared to microglia/macrophages. Inflammation is known to manifest immediately following spinal cord injury, initially activating microglia within a few hours, followed by neutrophil infiltration into the injured spinal cord during the initial 1–3 days, and culminating with a peak infiltration of macrophages around one week after the initial injury. As the first non-resident cells to be recruited to the damaged spinal cord, we posit that controlling neutrophil deactivation is of paramount importance in ameliorating the cascade of inflammation in SCI [ 27 , 28 , 29 ]. In addition to the timing of administration, the number of administrations is also of significant importance. In this report, we administered exosomes for three consecutive days following SCI, which mimics the natural pattern of exosome secretion from MSCs [ 30 ]. This approach aligns with findings from Nakazaki et al., who observed that administering exosomes in fractions over three consecutive days resulted in superior functional improvement when compared to a single administration of the total exosome dose over the same three-day period [ 31 ]. Given the reported very short half-life of exosomes, multiple administrations to adequately cover the period of neutrophil activation are considered essential.

While data regarding the functional role of NET formation in SCI models remain limited, accumulating evidence substantiates the concept that NET formation exacerbates central nervous system diseases through various pathways, including direct neural damage, microglial activation, and the induction of reactive gliosis [ 3 , 4 ]. Feng et al. previously documented that NET formation worsens secondary injury after SCI by promoting inflammation and disrupting the blood–brain barrier (BBB), and the degradation of NETs through the use of DNase led to an enhancement in motor function following spinal cord injury [ 2 ]. Consistent with these findings, our current study observed an improvement in motor function, coinciding with the decrease in NET levels, underscoring a strong interconnection between NETs and the secondary inflammation resulting from spinal cord injury. Understanding the mechanisms by which exosomes alleviate NET formation is essential for enhancing their functionality, yet these mechanisms remain incompletely understood. Timelapse imaging has revealed that exosomes congregate around the neutrophil nucleus, suggesting a potential role in delivering active molecules to neutrophils. In our study, we selected miR-125a-3p as a therapeutic molecule for exosomes, given its well-documented involvement in inflammation and its abundant expression in AMSCs. As a result, the miR-125a-3p inhibitor successfully counteracts the NETs-suppressing effect of exosomes, confirming its role in anti-inflammatory processes. These findings are consistent with the work of Jin et al., who have exclusively reported that miR-125a-3p suppresses NETs by targeting the NF-κB signaling pathway mediated by Toll-like receptor 4 (TLR4) [ 32 ]. Kim et al. reported that mir-125a inhibits the NF-κB regulator tumor necrosis factor alpha-induced protein 3 to negatively affect NF-κB activity [ 33 ]. Additionally, mir125b, TLR4/JNK signaling has been reported to play a role in NET formation by neutrophils [ 34 ]. miR-125a-3p is also implicated in altering the phenotype of microglia by interfering with interferon regulatory factor 5 (IRF5). Knockdown of miR-125a-3p in MSC-derived exosomes resulted in increased IRF5 expression in spinal cord tissues [ 35 ]. Mir-125 is reported to show a significant correlation with inflammatory bowel disease in humans [ 36 ]. It is likely that miR-125a-3p operates through multiple mechanisms, and further evaluation is necessary to comprehensively elucidate its anti-inflammatory mechanisms. Furthermore, MSC-derived exosomes have been reported to attenuate NET formation through the inhibition of C5b-9 assembly [ 37 ]. It is conceivable that other pathways not involving miR-125a-3p may also contribute to this process.

Furthermore, our investigation revealed that exosomes are predominantly distributed in the liver and spleen, with a discernible quantity also observed in the injured spinal cord. While prior reports have documented the presence of exosomes in the spleen and damaged spinal cord, comprehensive knowledge of their distribution throughout the entire body remains incomplete [ 38 , 39 ], and the data presented in our study contribute significantly to this understanding. We observed that neutrophils obtained from the circulating rat blood exhibited low NET formation, which is consistent with the accumulation of exosomes in the spleen, where neutrophils are stored. The exact role of exosome accumulation in the liver remains uncertain. It could potentially be attributed to the uptake by Kupffer cells of eliminating unidentified substances. Previous reports indicate that exosomes that accumulate in the damaged spinal cord have a direct deactivating effect on microglia, ultimately leading to functional recovery [ 39 ]. The presence of exosomes in the damaged spinal cord during the acute phase, when local and systemic inflammation plays a pivotal role, warrants further investigation.

In this report, we utilized intravenous transplantation as one of the most commonly used methods. However, other transplantation routes have also been reported for exosome administration against spinal cord injury. Intraspinal injection offers the possibility of delivering a high concentration of exosomes, but the injection itself carries the risk of additional spinal cord damage [ 40 , 41 ]. Intranasal injection is currently gaining attention due to its minimally invasive method of delivering exosomes to the damaged spinal cord, although the absorption rate may vary depending on the patient’s condition [ 39 , 42 , 43 ]. The intrathecal injection can deliver a high amount of exosomes with a minimally invasive procedure [ 44 ].

Several limitations in our study warrant discussion. Firstly, we were unable to comprehensively analyze the content of exosomes to identify potential therapeutic candidates. This is because various elements, including proteins, mRNA, miRNA, and other unknown molecules, may contribute to the observed functional recovery, and it is quite difficult to investigate the full role of different molecules at once. Secondly, we were not able to detect the precise organ where the exosome acts against neutrophil. We assume that it would be in the spleen; however, circulating neutrophil can also be one option. Third, we did not investigate other inflammatory parameters of neutrophils, such as lesional cytokine expression. A fewer neutrophil accumulation in the exosome group at the late phase (28 days) and reduced SCI damage imply lower expression of inflammatory cytokines.

4. Materials and Methods

4.1. ethical approval.

Animal protocols were approved by the Animal Studies Ethics Committee of the Hokkaido University Graduate School of Medicine (approval number: 17-0065). All experimental procedures were conducted in accordance with the Institutional Guidelines for Animal Experimentation and the Guidelines for Proper Conduct of Animal Experiments by the Science Council of Japan.

4.2. Methods

4.2.1. culturing and isolation of amsc exosomes.

Human AMSC vials, provided by Kaneka Corporation (Osaka, Japan), were thawed and seeded at a concentration of 2.0 × 10 5 cells/mL in medium. The cells were incubated at 37 °C for 5 days. The medium was then replaced with serum-free cell culture medium (MEMα, 12561056, Thermo Fisher Scientific, Waltham, MA, USA) for 2 days to obtain exosome-containing medium supernatant. Differential centrifugation methods were employed for exosome isolation as previously described with minor modification [ 45 , 46 ]. Initially, the supernatant was subjected to a continuous centrifugation step at 2000× g for 10 min and 10,000× g for 30 min to remove dead cells and cellular debris. Subsequently, the supernatant was subjected to ultracentrifugation at 100,000× g for 70 min, resulting in the formation of a pellet consisting of small vesicles corresponding to exosomes. The size and morphology of the isolated exosomes were confirmed through TEM (H-7100, Hitachi, Tokyo, Japan) and nanoparticle tracking analysis (Videodrop, Myriade, Paris, France). Additionally, Western blotting was utilized to identify specific exosome surface markers, including CD 9 (1:500, 014-27763, Fujifilm Wako, Osaka, Japan), CD 63 (1:500, 012-27063, Fujifilm Wako, Osaka, Japan), and Calnexin (1:500, ab22595, Abcam, Cambridge, UK). Protein concentration of exosome diluted in PBS was evaluated by Pierce BCA Protein Assay kit (23227, Thermo Fisher Scientific, Waltham, MA, USA).

4.2.2. SCI Model and Exosome Administration

Female Sprague-Dawley rats, aged nine weeks and weighing between 200 and 233 g, were obtained from CLEA Japan, Inc., Japan. Female rats were selected because they possess shorter urinary tract than male rats, which lowers the chance of sepsis caused by anuresis after spinal cord injury. The rat spinal cord injury model was established following previously described methods [ 9 , 47 ]. In brief, the rats underwent dorsal laminectomy (T6–7) while under isoflurane gas anesthesia. Immediately after the laminectomy, a modified aneurysm clip (07-943-30, Mizuho, Japan) was used to create a spinal cord injury by extradural pinching of the spinal cord for 1 min [ 48 ]. Postoperative care included assisting the rats in voiding their bladders 2–3 times a day until they regained independent urination. Twenty-four hours post-spinal cord injury, the Basso–Beattie–Bresnahan (BBB) score was assessed [ 49 ], and rats with a non-zero BBB score were excluded from the study. Subsequently, 1 mL of AMSC-derived exosomes (100 μg) adjusted with PBS, or 1 mL of PBS alone, was administered via the tail vein. Exosomes were given for three consecutive days. BBB scores were then assessed at 1-week intervals for a total of 4 weeks following the surgery.

4.2.3. Histological Analysis

Immunohistochemistry was conducted to assess the volume of the injured spinal cord, neutrophil infiltration, and NET formation in the spinal cord on either day 3 or day 28, as previously reported [ 9 , 47 ]. On the day of sacrifice, the rats were deeply anesthetized and subjected to transcardial perfusion with PBS followed by 4% paraformaldehyde (PFA). The spinal cords were then extracted, fixed in 4% PFA for 24 h, embedded in paraffin, and sliced into serial longitudinal sections measuring 10 μm in thickness using a cryostat microtome (LEICA RM2125 RTS, Leica Biosystems, Nussloch, Germany). Kluver–Barrera staining, which involves Luxol fast blue (LFB) absence, was performed to assess the volume of the injured spinal cord 28 days after SCI. The following equation was employed to calculate the lesion volume: Lesion volume (mm 2 ) = πD 2 (H1 + H2)/6, where H1 represents the lesion length from the epicenter to the rostral end, H2 denotes the lesion length from the epicenter to the caudal end, and D is the diameter of the epicenter [ 9 , 47 ]. Neutrophil infiltration at day 28 was evaluated using an anti-myeloperoxidase antibody (1:1000, ab208670, Abcam, Waltham, MA, USA), followed by Histofine Simple Stain MAX-PO (Nichirei Biosciences Inc., Tokyo, Japan) for 30 min and reacted with 3,3′-diaminobenzidine (DAB) (Simple Stain DAB Solution, Nichirei Biosciences Inc., Tokyo, Japan) for 3 min. Images were obtained from the peri-damaged lesion (5 mm rostral and caudal from the epicenter of the damaged lesion). A total of five non-overlapping areas were selected, and the cells exhibiting positive signals were semi-quantitatively counted using automated cell counting software https://www.keyence.com/products/microscope/fluorescence-microscope/bz-x700/models/bz-h4c/ (Hybrid Cell Count, BZ-X Analyzer, Keyence, Osaka, Japan). Immunofluorescent staining was performed to assess NETs on day 3 specimens, as previously described [ 50 , 51 ]. In brief, spinal cords were harvested without PFA fixation and immediately mounted in the OCT compound, and Fresh-frozen sections were cryosectioned in the longitudinal plane. The spinal cord sections were incubated overnight at 4 °C with mouse anti-Histone H3 (citrulline R2 + R8 + R17:CitH3) (1:00, Ab5103, Abcam, Waltham, MA, USA) and Anti-mouse/rat MPO-FITC (1:200, LS-C140180, LSBio, Shirley, MA, USA), followed by Alexa Fluor 594 goat anti-mouse (1:500, Thermo Fisher Scientific, Waltham, MA, USA). A mounting agent with DAPI or Hoechst was used to visualize cell nuclei. The number of MPO-positive neutrophil and CitH3-positive NETs, as well as the ratio of integrated CitH3 over Hoechst, were evaluated as previously described.

4.2.4. Ex Vivo Assessment of Neutrophil and NET Formation

Neutrophils were isolated from the whole blood obtained by heart puncture of 10-week-old C57BL/6 mice according to the manufacturer’s protocol (Neutrophil Isolation KIT, Cayman Chemical, Ann Arbor, MI, USA). The quality of the isolated neutrophils was further confirmed by flow cytometry (FACS) by the expression of Ly6G (60-5931-U100, Tonbo Biosciences, Tokyo, Japan). In order to assess the NET formation in ex vivo neutrophils, isolated neutrophils were adjusted to a concentration of 5 × 10 5 cells and incubated with a total of 400 μL of the cell suspension in a 4-well chamber slide at 37 °C for 30 min. Subsequently, exosomes at a concentration of 100 μg/25 μL or 25 μL of PBS, along with 400 μL LPS at a concentration of 100 μg/mL, were added to the cells and incubated at 37 °C for 3.5 h as previously described [ 52 ]. Following the incubation, the cells were washed and then fixed with 300 μL of fixation buffer for 15 min. Then, 300 μL of 0.1% NP-40 was added and incubated for 5 min to permeabilize the cells. Mouse anti-Histone H3 was added, followed by Alexa Fluor 594 goat anti-mouse (1:500, Invitrogen, Carlsbad, CA, USA) and anti-mouse/rat MPO-FITC. Measurement of the ratio for CitH3 positive NETs over MPO positive neutrophil was evaluated using an automated cell/area counter (BZ-X Analyzer, Keyence, Osaka, Japan) at a magnification of 10×.

To elucidate the uptake of exosomes into neutrophils, exosomes were labeled using the ExoSparkler Exosome Membrane Labeling Kit (343-09661, Dojindo, Japan) following the manufacturer’s instructions [ 53 ]. Neutrophils, adjusted to a concentration of 5 × 10 4 cells/mL, were co-incubated with the labeled exosomes at a concentration of 100 μg/25 μL in a glass-bottom dishes (35-mm-diameter; Matsunami Glass Industry Glass, Osaka, Japan). Time-lapse fluorescence imaging and data analysis were performed essentially as described previously [ 54 ]. In brief, the cells were placed in a stage-top incubation chamber maintained at 37 °C on a Nikon Eclipse Ti2-E microscope (Nikon, Tokyo, Japan) equipped with a KINETIX22 scientific complementary metal oxide semiconductor (sCMOS) camera (Teledyne Photometrics, Tucson, AZ, USA), PlanApo 20×/0.8, or 60×/1.2 objective lenses, a TI2-CTRE microscope controller (Nikon), a TI2-S-SE-E motorized stage (Nikon). The cells were illuminated with an X-Cite turbo system (Excelitas Technologies, Waltham, MA, USA). The sets of excitation and emission filters and dichroic mirrors adopted for this observation included GFP HQ (Nikon) for ExoSparkler or DAPI-U HQ (Nikon) for Hoechst 33342. Confocal images and super-resolution images were acquired with an X-Light V3 spinning disk confocal unit (CrestOptics, Roma, Italy) and a DeepSIM (CrestOptics) for Eclipse Ti2 equipped with a Prime BSI sCMOS camera (Teledyne Photometrics), respectively. The cells were illuminated with CELESTA light engines (Lumencor, NW Greenbrier Parkway, Beaverton, OR, USA). The time-lapse imaging was configured at 5-minute intervals, covering a total duration of 1275 min.

Flow cytometry was further conducted to evaluate the NET formation in neutrophils as previously described with modification [ 55 ]. Fluorophore-conjugated or biotinylated monoclonal antibodies specific to mouse antigens were enumerated as follows: PE-Ly6G (clone 1A8, BD Pharmingen), PerCP cy5.5-CD11b (clone M1/70, eBioscience), MPO-FITC, and CitH3. The secondary reagent employed was Alexa Fluor 594 goat anti-mouse (Invitrogen, Carlsbad, CA, USA). Neutrophils were co-incubated with exosomes (100 μg/25 μL) or PBS (25 μL) for 3.5 h and subsequently activated for 24 h with LPS at a concentration of 1 μg/mL under standard conditions of 37 °C. Multiparametric analyses of the stained cell suspension were conducted utilizing a FACS Aria III cell sorter (BD) with FACS Diva software https://www.bdbiosciences.com/en-us/products/software/instrument-software/bd-facsdiva-software (BD). Neutrophils expressing NETs were delineated based on the CitH3+Ly6G+CD11b+ gating strategy and subsequently compared between the exosome-treated and PBS-treated groups.

To elucidate the therapeutic mechanism of exosomes on NET formation in neutrophils, we conducted an evaluation of exosomal miRNA. Based on a literature search and confidential data provided by Kaneka Corporation, in which miRNA-125a-3p (miR-125a-3p) were abundantly expressed in the AMSC, we selected miR-125a-3p as a candidate molecule. The electroporation of the miR-125a-3p mimic and inhibitor (has-miR-125a-3p, ThermoFisher Scientific, Waltham, MA, USA) was performed according to the manufacturer’s recommendations. Neutrophils isolated from C57BL/6 mice underwent two washes with PBS without Ca 2+ and Mg 2+ and were then adjusted to 2 × 10 6 cells. After removing the supernatant, 90 μL of R buffer (Thermo Fisher Scientific, Waltham, MA, USA) was added, and the electroporation procedure was conducted using a voltage of 1720 V, a pulse width of 10 ms, and a total of 2 pulses for 20 mM of miR-125a-3p mimic and 100 mM of miR-125a-3p inhibitor. Subsequently, the cells were seeded in a 1.5 mL microtube containing 0.5 mL of pre-warmed supplemented DMEM. After 12 h, exosomes were added to the exosome group, the group electroporated with the miR-125a-3p mimic (mimic group), and the group electroporated with the miR-125a-3p inhibitor (inhibitor group). The control group was established without exosome or miR supplementation. All of the groups were then stimulated with 100 μg/mL of LPS. Following a 24-h interval, immunofluorescence staining, as previously described, was used to quantify CitH3-positive cells within the population of DAPI-positive cells.

4.2.5. Biodistribution

Animal positron emission tomography/computed tomography (PET/CT) was utilized to evaluate the biodistribution of the intravenously administered exosome. The method for isotope pre-labeling of 64 Cu was performed as previously described with minor modifications [ 56 ]. First, the solution of exosomes was replaced with 0.1 M Na 2 CO 3 buffer (pH 10) by the PD-SpinTrap G-25 column (Cytiva, Tokyo, Japan). Separately, p-SCN-Bn-NOTA (Macrocyclics, Plano, TX, USA) was dissolved in 0.1 M acetate buffer (pH 6.0) and incubated with 64 CuCl 2 (138 MBq, PDRadiopharma Inc., Tokyo, Japan) at room temperature for 20 min to provide 64 Cu-SCN-NOTA. The concentration of p-SCN-Bn-NOTA in the reaction was 190 μM (106.4 mg/L), and the radiochemical yield of this step was 89%, as determined by thin-layer chromatography (TLC). 64 Cu-SCN-NOTA (12.5 μg) was then incubated with exosomes (526 μg) dissolved in 0.1 M Na 2 CO 3 buffer (pH 10) at 37 °C for 3 h. Finally, the reaction mixture was purified by PD-SpinTrap G-25 column three times to remove excess 64 Cu-SCN-NOTA and other low-molecular-weight impurities. At the time of this gel filtration, the reaction buffer was replaced with PBS. The radiochemical purity of 64 Cu-NOTA-exosome after the purification was 90.8%, as determined by TLC. The overall radiochemical yield without decay correction was 8.1%. All TLC analyses were performed using iTLC-SG plates (Agilent Technologies, Palo Alto, CA, USA), and the developing solvent was 50 mM EDTA (pH 5.5). The radioactivity on TLC was quantified by autoradiography (Fla-7000, Fujifilm, Japan). In this TLC system, the Rf values of 64 Cu-NOTA-exosome, 64 Cu-SCN-NOTA, and [ 64 Cu]CuCl 2 (forming complex with EDTA during the development) were 0.0, 0.5–0.8, and 0.9–1.0, respectively. The biodistribution of 64 Cu-NOTA-exosome was evaluated in normal and spinal cord injury model rats (9-week-old Sprague-Dawley). For spinal cord injury model rats, the experiment was performed the day after the surgery. 64 Cu-NOTA-exosome (100 kBq, 27 μg of exosome) was injected into rats via the tail vein. The rats were sacrificed at 24 h post-injection, and the organs of interest were removed and weighed. The radioactivity of these organs was determined by a gamma counter (2480 Wizard 2 gamma counter, PerkinElmer, Waltham, MA, USA). The uptake values of 64 Cu-NOTA-exosome are expressed as % injected dose per gram of organ (%ID/g). PET/CT imaging was performed on a spinal cord injury model rat ( n = 1) using an Inveon preclinical small-animal multimodality PET/CT system (Siemens Medical Solutions, Knoxville, TN, USA). PET images of 64 Cu-NOTA-exosome were acquired over 60–80 min after the administration of 64 Cu-NOTA-exosome (8.8 MBq, 149 μg of exosome). CT images were acquired following the PET scan. During the image acquisition, the rat was anesthetized using 2.0–2.5% isoflurane. The PET and CT data were reconstructed using the Feldkamp method and 3D-OSEM (2 iterations, 18 MAP iterations), respectively. PET and CT images were analyzed using an Inveon research workplace software v. 4.1 (Siemens Medical Solutions, Knoxville, TN, USA).

4.2.6. Statistical Analyses

All assessments were performed by blinded investigators. The data have been presented as mean ± standard error. Statistical analyses were performed using JMP Pro 14 software (SAS Institute Inc., Cary, NC, USA). Statistical comparisons between groups were made using the Welch’s t -test or Wilcoxon test. Probability values of p < 0.05 were considered statistically significant.

5. Conclusions

AMSC-derived exosomes play a crucial role in mitigating spinal cord injury, partially achieved by deactivating neutrophil NET formation via miR-125a-3p. Biodistribution analysis further indicates that the majority of exosomes are concentrated in the liver and spleen.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms25042406/s1 .

Funding Statement

This study was funded by the Japan Society for the Promotion of Science (Grant Number 23K08557 and 23K085353).

Author Contributions

Conceptualization, M.K., D.H., Y.O. and Y.K.; methodology, M.K., S.M., K.Y. and S.T.; formal analysis, M.K.; investigation, Y.M. (Yutaka Morishima), S.Y., Y.N., H.S., Y.M. (Yuki Mizuno), and Y.F.; writing—original draft preparation, Y.M. (Yutaka Morishima). and M.K.; writing—review and editing, M.F.; supervision, M.K.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The animal study protocol was approved by the Institutional Review Board (or Ethics Committee) of Hokkaido University Graduate School of Medicine (protocol code 17-0065).

Informed Consent Statement

Not applicable.

Data Availability Statement

Conflicts of interest.

The authors declare no conflicts of interest.

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