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Ecological Knowledge and Environmental Problem-Solving

Concepts and case studies.

This volume explores how the scientific tools of ecology can be used more effectively in dealing with a variety of complex environmental problems. Part I discusses the usefulness of such ecological knowledge as population dynamics and interactions, community ecology, life histories, and the impact of various materials and energy sources on the environment. Part II contains 13 original and instructive case studies pertaining to the biological side of environmental problems, which Nature described as "carefully chosen and extremely interesting."

• Environment and Environmental Studies — Ecology and Ecosystems

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National Research Council. 1986. Ecological Knowledge and Environmental Problem-Solving: Concepts and Case Studies . Washington, DC: The National Academies Press. https://doi.org/10.17226/645. Import this citation to: Bibtex EndNote Reference Manager

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• Open access
• Published: 10 April 2017

Constructing Ecological Networks Based on Habitat Quality Assessment: A Case Study of Changzhou, China

• Yu Gao 1 , 2 , 3 ,
• Lei Ma 1 , 2 , 3 ,
• Jiaxun Liu 1 , 2 , 3 ,
• Zhuzhou Zhuang 1 , 2 , 3 ,
• Qiuhao Huang 1 , 2 , 3 &
• Manchun Li 1 , 2 , 3  

Scientific Reports volume  7 , Article number:  46073 ( 2017 ) Cite this article

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• Ecological networks

Fragmentation and reduced continuity of habitat patches threaten the environment and biodiversity. Recently, ecological networks are increasingly attracting the attention of researchers as they provide fundamental frameworks for environmental protection. This study suggests a set of procedures to construct an ecological network. First, we proposed a method to construct a landscape resistance surface based on the assessment of habitat quality. Second, to analyze the effect of the resistance surface on corridor simulations, we used three methods to construct resistance surfaces: (1) the method proposed in this paper, (2) the entropy coefficient method, and (3) the expert scoring method. Then, we integrated habitat patches and resistance surfaces to identify potential corridors using graph theory. These procedures were tested in Changzhou, China. Comparing the outputs of using different resistance surfaces demonstrated that: (1) different landscape resistance surfaces contribute to how corridors are identified, but only slightly affect the assessment of the importance of habitat patches and potential corridors; (2) the resistance surface, which is constructed based on habitat quality, is more applicable to corridor simulations; and (3) the assessment of the importance of habitat patches is fundamental for ecological network optimization in the conservation of critical habitat patches and corridors.

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Introduction.

Ecosystems support life on Earth, and thus play a vital role in human well-being, either directly or indirectly 1 . In recent years, anthropogenic activity has facilitated the invasion of ecosystems by nonnative species and natural hazards, leading to the worsening of various environmental problems, including the degeneration of ecosystem services and a sharp decline in biodiversity 1 , 2 . Low continuity between habitat patches caused by the fragmentation of ecological landscapes (i.e., natural or semi-natural habitats) represents the greatest threat to biodiversity conservation 3 , 4 , 5 . However, growing environmental awareness and an improved understanding of how human communities interact with their environment have led to growing concerns about enhancing habitat patch continuity within ecosystems 6 , 7 . Nevertheless, recent studies show that economic growth has actually made humans more dependent on ecosystem services and biodiversity 8 . Therefore, it is particularly important to maximize ecosystem service values by constructing networks that enhance the functionality of urban ecosystem services 9 , 10 .

Research on ecological network construction has been widely carried out on a global scale 11 , 12 . The ecological network is a representation of the biotic interactions in an ecosystem, in which ecological corridors link protected habitat patches 13 . A habitat patch is a set of landscape patches, while a landscape patch is the basic unit—a relatively homogenous mosaic of familiar land-use types that differ from the surrounding background—that formulates landscape patterns. Previous studies have suggested that habitat patches are areas where organisms aggregate, representing stepping stones for migration 14 , 15 , while ecological corridors are narrow bands of vegetation that promote biological migration between the two habitat patches, allowing wild animals to survive 16 . These two landscape types form the core of ecological networks. However, one of the main limitations of studies on ecological newtorks is that the movement data of target speices are frequently unavailable. Therefore, there have been an increasing number of simulation-based studies on migration and biodiversity conservation in an attempt to address such defects 17 , 18 , 19 , 20 , 21 , 22 . In particular, methods based on graph theory emphasize the functional connection between habitat patches, which is the effective relationship between components of an ecological object or process with corresponding characteristic scales 16 . Graph theory has been gradually introduced to conduct research on the ecological networks of various land-use types, including cities, farmlands, and forests 20 , 23 , 24 , 25 .

The most important step when using graph theory is the construction of resistance surfaces. The resistance surface is a raster map (mosaic with a value, a larger value corresponds to higher resistance) indicating the level of disturbance or degree of difficulty that target species are expected to encounter when moving between patches, which significantly affects the outputs of ecological network research 26 . Therefore, many studies construct resistance surfaces based on various methods, such as biological behavior resistance estimates, expert scoring, entropy weighing, and landscape development intensity indexing 10 , 27 , 28 , 29 . However, most studies have constructed landscape resistance surfaces based on expertise and overall ratings for certain land-use types, leading to the resulting landscape resistance surfaces being heavily dependent on grading factors 5 , 26 . In fact, there are differences between the same land-use types owing to their different locations and surroundings. As a result, previous studies have weakened the differences in resistance of the same land-use type.

In this study, we propose a habitat quality-based method that involves simulating the sensitivity of different land-use types to impact factors. Within this framework, we evaluated the habitat quality and functionality of ecosystem services for each assessment unit 1 . Pixel-scale habitat quality was used to characterize the optimal survival, reproduction, and energy flow conditions that an assessment unit provided to organisms. Besides facilitating studies on how human activities affect habitat quality and animal migration, this information is expected to help estimate the landscape resistance of different assessment units. Good habitat quality promotes the dispersal of animals, and thus corresponds to low resistance. Although several studies on urban ecological network construction do exist 27 , 30 , 31 , a limited number of studies have analyzed the effects of different resistance surfaces on corridor simulations. In addition, these investigations weaken the significance assessments of habitat patches and corridors, which may be not conducive to the implementation of actual protection measures.

The ecological protection planning of Changzhou, China—which contains habitat patches but no corridors—was implemented to improve ecosystem services and biodiversity. Thus, this study proposes a set of procedures that could be applied to ecological network research by identifying potential corridors in Changzhou. Based on landscape ecology, graph theory, and habitat quality assessments, this study aimed to (1) propose a method for constructing landscape resistance surfaces; (2) quantitatively characterize potential corridors in the study area by using a minimum cumulative resistance model; (3) evaluate the importance of habitat patches and corridors; and (4) compare the effects of different methods of constructing resistance surfaces on potential corridor simulations and conduct an importance assessment of habitat patches and corridors. This information is expected to provide quantitative scientific data to enhance environmental protection in Changzhou.

Study Area and Data Preparation

Overview of the study area.

Changzhou (119°08′–120°12′E, 31°09′–32°04′N) is located in the Taihu Lake Plain, Yangtze River Delta, in the southeastern part of Jiangsu Province, China. It has a subtropical monsoon climate, with an annual average temperature of 16.5 °C and an annual precipitation of 1063.71 mm. From 2006 to 2014, the built-up area in the city increased by 25.68%, demonstrating clear urban expansion. However, the percentage of farmland, forest, grassland, and water body areas offering ecological services has decreased. The shrinkage of ecological land area, the fragmentation of habitats, and the obstruction of corridors are major contributors to the deterioration of ecosystem services in Changzhou, and present an increasing burden on environmental protection.

Source of data

The following datasets were provided by the Land and Resources Bureau: land-use change vector data (shapes with an attribute of its land-use type) from 2006 to 2014, administrative divisions, vector data of roads, vector data of built-up land, and ecological protection planning containing a nature reserve vector map of Changzhou. The nature reserve vector map is a highly important part of ecological protection planning in Changzhou. The map depicts the core area in Changzhou that could be protected by administrative means. Data regarding people per square kilometer of land area and Gross Domestic Product (GDP) per square kilometer in China (2010) were obtained from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences ( http://www.resdc.cn ). Certain factors (such as land-use type, level of human disturbance, and landscape function) were used to classify the landscape types in Changzhou as farmland, forests, built-up land, riparian green space, and other types. Because the riparian green space is always a strip of land along a river or lake, we integrated riparian green space and water bodies as riparian green space. This information was used to generate the landscape classification map of Changzhou.

Methodology

We identified potential corridors based on graph theory. The three main steps were 1) the selection of habitat patches, 2) the construction of resistance surfaces, and 3) the identification of potential corridors. First, habitat patches were acquired from the nature reserve vector map. To analyze the effect of different resistance surfaces on corridors, we constructed resistance surfaces based on three different methods: (1) the habitat quality-based model, (2) the entropy coefficient method, and (3) the expert scoring method. Second, based on different resistance surfaces and the minimum cumulative resistance model, we identified all potential corridors in Changzhou. The results generated three groups of habitat patches and corridors reflecting the three methods we used. The resulting habitat patches, selected from the nature reserve vector map, were the same, while corridors differed as a result of differences in the resistance surfaces. Thus, we were able to compare the effects of the different methods of construction on the simulated potential corridors. Finally, the importance of both habitat patches and potential corridors were assessed. A flow chart of the techniques used is shown in Fig. 1 .

Technical flow chart.

Target species and the selection of habitat patches

Connectivity is a species-dependent property 32 . Therefore, selecting target species that may be used as the basis for the study of ecological networks is necessary 27 . In this study, we focused on the common species of Changzhou, which were mostly small mammals, such as weasels, hares, and badgers. These animals prefer to move in vegetated habitats, such as forests and riparian green spaces. Connectivity is a species-dependent property 32 . Thus, based on the nature reserve vector maps provided by the Changzhou Bureau, we selected habitat patches that contained forested areas and riparian green spaces, and excluded river habitat patches, which would not suitable for our target species. These habitat patches serve as “sources” of matter, energy flow, and animal migration, and are also the origins where ecological processes develop 10 , 14 .

Changzhou is one of the most developed cities in China. As a result, the habitat patches in this city are significantly affected by urbanization. Habitat patches are mostly distributed in the western and eastern parts of Changzhou, with fewer patches located in the north-northeastern part ( Fig. 2(b) ). The degree of ecological land fragmentation in Changzhou was evaluated using the patch density index 33 . The index grew from 1.42 in 2006 to 2.10 in 2012 ( Fig. 2(c) ), illustrating that habitat patches became increasingly fragmented. Thus, spatial fragmentation and uneven distribution lead to difficulties in constructing an effective ecological network. In this study, 20 ecological sources were selected, as shown in Table 1 and Fig. 2(a) .

( a ) Habitat patches in Changzhou, ( b ) differences in the spatial distribution of different habitat patches (where, based on the center of gravity of Changzhou, study area was divided into eight equal areas. Then, the area of habitat patches located in different sectors was statistically calculated), and ( c ) variations in ecological land area (the total area of farmland, forests, and water bodies in Changzhou) and the patch density index in Changzhou during 2006–2014 (where the left axis indicates the ecological land area and the right axis indicates the patch density index values). (Created by ArcMap, version 10.2, http://www.esri.com/ . Boundaries of Jiangsu province and Changzhou, land-use type data and habitat patches acquired from Changzhou Land Resources Bureau).

Construction of landscape resistance surfaces

As shown in previous studies, the resistance surface has a major impact on the simulation of corridors 26 . Thus, to compare the effects of different landscape resistance surfaces on ecological network construction, and to verify the feasibility of applying the method proposed by this study landscape resistance, various methods used by previous studies were employed to construct different landscape resistance surfaces. Such methods included the entropy coefficient method 34 and the expert scoring method 27 .

Habitat quality-based method

Research has shown that habitat quality is associated with the intensity of human activity and the land-use types of surrounding areas 35 , 36 . The InVEST (Integrated Valuation of Ecosystem Service and Tradeoffs) model was developed jointly by Stanford University, the University of Minnesota, The Nature Conservancy, and the World Wildlife Fund 2 . This model simulates the combined effects of land-use changes and human activities on ecosystems, and it provides a visual tool that quantitatively evaluates ecosystem service functions 2 , 37 , 38 . The habitat quality component of the biodiversity module of this model was adopted in this study to analyze how the intensity of human activity influences habitat quality in Changzhou. The principles for calculating this model are:

The habitat quality results produced by InVEST were presented as raster data in the range of 0 to 1. A larger value corresponds to better habitat quality, and vice versa. When InVEST is applied to the construction of landscape resistance surfaces, habitat quality is converted to landscape resistance. Units of better habitat quality have smaller landscape resistance, and vice versa. The equation governing conversion is:

Entropy coefficient method

(Created by Fragstats, version 4.2, http://www.umass.edu/landeco/research/fragstats/ ).

Through an integrated assessment of the volume of information carried by each factor, an integrated weight coefficient is calculated based on the abundance of information that a factor provides. The principles for the calculation are 39 :

Expert scoring method

The expert scoring method examines the sensitivity of each landscape to human disturbance, and provides a quantitative characterization of the magnitude of its resistance 27 , 40 . First, we based the different land-use types on those suggested by experts of the selected focal species in Changzhou. Then, the resistance values were primarily chosen according to vegetation type, in addition to the area of green space and the degree of anthropogenic disturbance 30 . The resistance values were set between 1 and 1000 to indicate the level of disturbance or degree of difficulty that target species would encounter when moving between patches. The reference landscape resistance values are shown in Table 4 .

Simulation of potential corridors

Corridors are vital components of ecological networks. Corridors provide paths for animal migration, as well as conserving biodiversity and facilitating sustainable development 41 . As a result of increases in the intensity of human activity, ecological connectivity has sharply declined in Changzhou. In research on the construction of ecological networks, two corridor simulation methods are primarily used: (1) directly tracking animal migration paths and simulating ecological processes 42 , which is highly dependent on data availability, is difficult to perform, and is less commonly used in urban environment studies 26 ; and (2) model simulation, which resolves the difficulties of data collection to some extent, resulting in this model being widely applied in ecological network research 27 . In particular, the minimum cumulative resistance ( MCR ) model is a raster-based optimization algorithm that was originally designed to find the least-expensive path for a link between two nodes, since we assumed that target species follow an optimum route to minimize exposure to intervening low-quality landscape patches. This model reveals obstructions and boosts underlying migration processes 19 . This model is often combined with landscape graph theory to reflect complex network structures and ecosystem-scale relationships between energy, matter, gene migration, and the underlying surface 43 . In this process, two layers are used. The first is the habitat patches layer (treated as the nodes). The second is the resistance surface layer. The resistance value for each potential corridor is determined by the number of raster units that are covered and the landscape resistance of each unit:

where MCR is the resistance of a potential corridor between two habitat patches, D ij is the number of raster units between two habitat patches, R j is the resistance of a raster type, i is the number of habitat patches, and j is the number of raster units on the resistance surfaces.

Network continuity evaluation

Landscape continuity is the relationship between landscape elements in their spatial structures. This parameter is used to determine the structural characteristics of a landscape. It is commonly measured by continuity indexes, including the structural continuity index and the functional continuity index 44 . In recent years, quantitative assessments on continuity and the complexity of ecological networks based on landscape graph theory have increased 45 , with new indexes being developed, such as the overall network continuity index, based on graph theory 46 , 47 . Compared with traditional descriptive indexes on network complexity and continuity, graph theory-based evaluation indexes integrate the ecological attributes of a habitat into the calculation, giving these metrics ecological significance. Landscape continuity could be used to assess the importance of habitat patches and corridors. The evaluation indexes used in this study are shown in Table 5 .

The results of habitat patch importance vary with the selection of different indexes. In this study, importance indexes dIIC and Dpc ( Table 5 ) corresponded to the integral index of continuity ( IIC ) and probability of continuity ( PC ), respectively, and were applied to evaluate the significance of each habitat patch in upholding landscape continuity. Using the hierarchical clustering module in SPSS 21.0, each index was classified into three classes. As a result, the importance of habitat patches and corridors in the ecological network in Changzhou was determined. Thus, we determined the importance of habitat patches and corridors in the ecological network in Changzhou. At least one of the indexes ranked as “very important”, with such habitat patches being classified as a “primary patch”. When both indexes were ranked as “ordinary”, the habitat patch was considered to be a “tertiary patch.” In all other cases, habitat patches were considered to be a “secondary patch”. The same method was used to classify potential corridors.

Results and Discussion

Landscape resistance threshold recognition.

Previous studies have made the assumption that ecological connection becomes effective when a potential corridor between habitat patches has a cumulative resistance that is smaller than the threshold 48 , 49 . Therefore, the threshold represents the highest cumulative resistance that limits the migration of species between two patches. The methods used to identify the threshold value can be divided into two parts. The first part tracks the maximum moving distance of the target species 49 . The second part uses landscape continuity indexes and statistical principles, catching the breaking point or stable value of the calculated index values at different thresholds 48 .

The second method was expected to be more suitable for the present study. All links between habitat patches were calculated using Linkage Mapper 50 and Conefor Sensinode 2.6. Supporting previous studies, we found that calculating the threshold distance based on the highest number of landscape components ( NC ) is credible 48 . Thus, IIC and NC were used in this study. Two indexes were used to indicate connectivity between habitat patches. During this process, the values of IIC and NC can be calculated from a given hypothetical threshold. With a step defined as a landscape resistance value of 10000, the hypothetical threshold ranges from 0 to 400000, leading to variation in the index value. To compare the two indexes, the results of the calculation were normalized based on equation (3). For NC , a low value indicates good connectivity, while a high value indicates good connectivity for IIC . Thus, the green curve in Fig. 4 indicates the sum of these two indexes as calculated by:

The left vertical axis represents the normalized results of IIC and NC, while the right vertical axis is the sum of normalized IIC and normalized NC.

where Sum is the sum value of these two indexes, NC normalized is the normalized result of NC , and IIC normalized is the normalized result of IIC . Based on equation (7), we conclude that a high value of Sum indicates good connectivity.

The variation of IIC normalized and NC normalized showed that, when landscape resistance reaches 340000, IIC no longer increases rapidly, but becomes steady. In comparison, NC no longer varies, with the sum of these two indexes producing a steady curve ( Fig. 4 ). Therefore, a landscape resistance threshold of 340000 was confirmed to be applicable to Changzhou, with any values exceeding this threshold signifying disconnection.

The probability of continuity ( PC ), which is used to assess the importance of habitat patches, indicates the probability of a species migrating between habitat patches. If the value calculated by the PC is relatively high (nearly 1), habitat patches might be connected by the potential corridors. To compare IIC with PC , a coherent setting for IIC and PC is needed, which is calculated by a decreasing negative exponential function of distance 51 . In this study, a PC exceeding the threshold must be small, and was therefore set as 0.05.

Ecological network construction

An ecological network is the framework for environmental protection in a city. The identification of important habitat patches 9 and the functional connection between them 5 , 10 is critical for constructing ecological networks. The resistance surface covering the entire study area, together with the least-cost paths between pairs of habitat patches, is shown in Fig. 5 . An ecological network consists of habitat patches and potential corridors. Spatially, habitat patches are composed of landscape patches along the path of minimum resistance 52 . The potential corridors are strips connecting habitat patches, of which the accumulated resistance is lowest between habitat patches of all potential connections. However, the width of corridors could vary in different environments 27 . In this study, we assumed that a corridor width of 30 m could accelerate migration, which represented the cell size of the resistance surface.

Landscape resistance estimates based on ( a ) the habitat quality-based model, ( b ) the expert scoring method, and ( c ) the entropy coefficient method. Results of the potential corridor simulations, importance assessment of habitat patches, and potential corridors based on different models of resistance surfaces are presented according to ( d ) the habitat quality-based model ( e ) the entropy coefficient method, and ( f ) the expert scoring method. The legend corresponding to panels a–f is located at the bottom, where a yellow to blue scale indicates low to high resistance, respectively; the three types of patches are indicated by three shades of green; and the three types of corridors are indicated by light orange, orange, and red lines. ( g ) Principle of the importance evaluation of habitat patches. (Created by ArcMap, version 10.2, http://www.esri.com/ ).

Effects of landscape resistance surfaces

To compare the landscape resistance surfaces that were constructed, the average habitat quality of different landscapes was normalized, as illustrated in Table 6 . The estimated resistance was smaller for forests, farmlands, and riparian green spaces, which are less disturbed, highly vegetated, and more favorable for animal migration. In contrast, clear obstructions to animal migration were identified for roads and built-up land. Nevertheless, as a result of rapid urban expansion and the degeneration of the habitat quality of farmlands in Changzhou, the habitat quality-based resistance estimate for farmlands was higher than the estimates obtained by the other two methods. The results showed that while different resistance surfaces led to significant spatial offsets in the simulation of potential corridors, these results demonstrated high consistency in the identification of important habitat patches and corridors ( Fig. 5 ). Concerning assessment units, we found that the model constructed in our study is more sophisticated than the other two models, and is more suitable for research on ecological networks in fragmented landscapes 1 , 2 .

In addition, we analyzed the composition of potential corridors, which also revealed the effect of landscape resistance surfaces. We found that the target species preferred to move in vegetative habitats and riparian green spaces. However, different landscape resistance surfaces generated different corridor compositions. For instance, the corridors that were simulated from resistance surfaces constructed by the habitat quality-based model ( Table 6 ) consisted of 3.45% built-up land. This percentage was lower than those obtained by the expert scoring method and the entropy coefficient method (6.27% and 16.04%, respectively). As the main potential corridors are riparian green spaces, followed by forest and farmland, the results indicate that the resistance surfaces constructed by the habitat quality-based model are more suitable for ecological network analyses in Changzhou. Hence, under conditions with insufficiently detailed ecological data, or when it is difficult to obtain such data through experiments, it is more feasible to carry out ecological network analyses using the habitat quality-based model and the minimum cumulative resistance ( MCR ) model, rather than other methods, such as mark and capture 53 and wireless wildlife tracking 5 .

Importance of habitat patches

Once the ecological corridors are defined, the assessment of habitat patch importance is fundamental for optimizing the ecological network to construct and conserve critical patches and corridors 27 . In Changzhou, the Ge Lake and Changdang Lake Wetland Conservation Areas ranked high when evaluating the importance of habitat patches ( Fig. 5 ), as these areas contain extensive habitat patches and are well connected to natural landscapes in the central and southern parts of Changzhou. Wawushan Provincial Park and the Xinlong Ecological Forest belong to linear habitat patches and form natural corridors ( Fig. 5 ). These two sites reduce landscape resistance against animal migration, and are essential for enhancing the continuity of the ecological network. Nature Reserves in the central and southern parts of Changzhou, such as the Qianzi Lake Wetland Conservation Area, Yancheng Park, the Songjian Lake Wetland Park, and the Shahe Reservoir Restoration Area, were smaller than the Ge Lake and Taihu Lake Conservation Areas. However, these areas contain many potential corridors that could serve as excellent stepping-stone patches, which are advantageous to biological dispersal between habitat patches in regions with intensive human activity 54 . The inclusion of stepping-stone patches reduced the threats to biodiversity owing to isolated habitat patches, and also reduced resistance against animal migration. The results suggested that fragmented patches rarely provide favorable habitats for organisms to survive and reproduce, but clearly facilitate animal migration 30 .

Importance of corridors

At present, the IIC of the ecological network in the study area is relatively low. The growth of IIC and resistance shows a logistic function ( Fig. 4 ). When the resistance threshold was set as 340000, IIC became stable, with a value of just 0.007, signifying little potential for any connection between habitat patches. Among the potential corridors studied, the most important ecological connections were located between Taihu Lake and Ge Lake and between Ge Lake and the Changdang Lake Wetland Conservation Areas. The connections between Changdang Lake and the Qianzi Lake Conservation Areas and between Changdang Lake and the Liyang West Park were also of high importance. These four potential corridors combined with habitat patches to form a fundamental T-shaped habitat structure in Changzhou. This result is consistent with previous studies, which showed that rivers and coastal areas could be used as a means for ecological processes to enhance the connectivity of ecological networks and improve urban habitats in regions where ecological land is limited 5 .

Conclusions

This study suggested the use of model simulations to construct ecological networks. An ecological network was constructed for the city by integrating habitat quality, graph theory, and the minimum cumulative resistance model. Our results verify the reliability of using this method in ecological network research. It was concluded that (1) the proposed landscape resistance surface method provides a way of effectively overcoming research limitations caused by insufficient experimental data; (2) the use of different methods in constructing resistance surfaces considerably affects the delineation of potential corridors, but only slightly influences the evaluation of the importance of habitat patches and potential corridors; (3) the suggested procedure is reliable because the potential corridors are mostly composed of green spaces rather than built-up land; and (4) habitat patches of high importance and good quality should be prioritized in regions with limited green space. However, this study did not consider how corridor width and complexity affect the migration of different species. To promote regional sustainable development, optimal corridor widths for species migration and multi-scale compound ecological networks should be determined by future studies.

Additional Information

How to cite this article : Gao, Y. et al . Constructing Ecological Networks Based on Habitat Quality Assessment: A Case Study of Changzhou, China. Sci. Rep. 7 , 46073; doi: 10.1038/srep46073 (2017).

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Acknowledgements

This work is supported by a Project funded by China Postdoctoral Science Foundation (No. 2016M600392), the Program B for Outstanding PhD Candidate of Nanjing University (No. 201502B008), the Special Research Fund of the Ministry of Land and Resources for NonProfit Sector (No. 201411014-03). Sincere thanks are given for the comments and contributions of anonymous reviewers and members of the editorial team.

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M.C.L., Q.H.H., and Y.G. conceived the idea; J.X.L. and Z.Z.Z. processed the data; J.X.L. and L.M. analyzed the results; and Y.G. conducted the analyses. All authors contributed to the writing and revisions.

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Gao, Y., Ma, L., Liu, J. et al. Constructing Ecological Networks Based on Habitat Quality Assessment: A Case Study of Changzhou, China. Sci Rep 7 , 46073 (2017). https://doi.org/10.1038/srep46073

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DOI : https://doi.org/10.1038/srep46073

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A review of landscape ecology experiments to understand ecological processes

• Yolanda F. Wiersma   ORCID: orcid.org/0000-0003-4604-9240 1  

Ecological Processes volume  11 , Article number:  57 ( 2022 ) Cite this article

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One way in which we make inferences about ecological processes is via experimentation. Many ecological processes happen at landscape extents and it is at this extent that experimentation is more challenging. This review explores the intersection between experimentation, ecological processes and landscape ecology. Specifically, this review seeks to discover how scientists design experiments to understand ecological processes at landscape scales.

I found 87 papers where these three concepts intersected, and reviewed them in more depth to assess characteristics of scale (treatment and study area extent), replication, research question and experiment type.

Conclusions

The findings suggest that experimental approaches for understanding ecological processes are well established, and beginning to more readily accommodate spatial dimensions. However, there is room to integrate more spatially explicit, landscape-scale experiments into studies of ecological processes.

Introduction

A key tool for understanding mechanisms that shape patterns is via experimentation. This is true across scientific disciplines. Ecological processes, the focus of this journal, shape and influence ecological systems at all scales. Although research in this journal has traditionally examined ecological process at many different extents, ecological processes at large extents merit special consideration since these shape the systems that humans directly interact with, actively manage, and critically depend on. These include the agricultural and marine ecosystems that feed us, the forest ecosystems that provide timber and non-timber resources, and the myriad of ecosystems that provide carbon sequestration. These large extent systems of fields, forests and oceans are also the purview of the discipline of landscape ecology (Turner 2005 ; Turner and Gardner 2015 ). How to carry out experiments in landscapes to realize reliable inferences about the links between ecological patterns and ecological processes, and vice versa, is a key challenge for researchers, and is the focus of this review.

In a perspectives essay on experimental landscape ecology, Jenerette and Shen ( 2012 ) discussed different experimental approaches to identify how landscapes affect variation in ecological processes, and how landscape structure influences these processes. They highlighted the challenge of carrying out experiments at landscape extents (generally 10–100 s of kilometres), citing difficulty with replication, and the complexity of setting up experiments in spatially heterogeneous systems (Jenerette and Shen 2012 ). Many landscape-scale studies rely on observational data, and rely on correlations to infer processes, which may not capture the actually mechanisms at play.

Jenerette and Shen ( 2012 ) suggested four types of experiments that landscape ecologists could apply to help identify process variation within a landscape. These include distributed in situ experiments; ex situ experiments using samples collected throughout a landscape and brought back to the lab for analysis, translocation experiments and transport manipulations. Their group of experiment types to identify how processes responded to landscape structure echo many of the “classic” large-scale experiments, such as the experimental patches at the Savannah River Ecosystem, the Bowling Green fragmentation experiment, or systems such as Ecotrons (see an excellent summary of these types of experiments in Haddad 2012 ). Such experiments manipulate patch shape, connectivity, and fragmentation. Other experiments that can be used to infer how landscape structure affects process include manipulation of internal patch characteristics (e.g., via adding artificial structures, or adding nutrients), manipulation of landscape scale (e.g., mesocosms, microcosms, microlandscapes) or the construction of entire landscapes (Jenerette and Shen 2012 ). Wiersma ( 2022a , b ) summarized these approaches (large-scale manipulations, mesocosms, microcosms) along with in silico experiments (i.e., computer models) in more detail to show how researchers could harness these experimental tools to do spatially explicit experimentation (See Box 1 ). In this review, I pay particular attention to the experimental types (according to the taxonomy in Jenerette and Shen 2012 ) and tools used to study ecological processes at landscape extents.

Before discussing the literature review in more detail, a review of key terms is necessary. There has been much debate about whether we should consider observational studies to be “proper experiments” or not (Diamond 1983 ). In the mid-twentieth century, the increased reliance on sophisticated technology in the bench sciences, particularly molecular genetics, suggested to some ecologists that their field observational studies were too close to amateur natural history studies to be considered experimental and that manipulative experiments were the more reliable means for testing hypotheses (Kohler 2002 ). To clarify, by a manipulative experiment here, I mean an experiment whereby the researcher actively manipulates a factor of interest. This could be at a large extent in the field (e.g., via a controlled burn), in the field at smaller extent (e.g., via exclosures) or in a laboratory setting (e.g., experimental tanks or aquaria under controlled environments). Observational experiments (also called “natural experiments”) are those where natural processes have created the experimental treatment conditions. This treatment could be in the form of a disturbance, such as a flood or forest fire, or could be due to an underlying natural gradient (e.g., topography, soil moisture, light levels). If sampling is carried out as carefully as possible, so that experimental standards of control, replication, and randomization are applied, many suggest that observational experiments should be considered an equally credible approach as a manipulative one (Diamond 1983 ). Indeed, Diamond ( 2001 ) points out that heading to the field with a too narrowly focused experiment in mind can risk missing the chance to carry out an unplanned natural experiment. Laboratory/manipulative experiments have advantages of being easier to control for confounding effects, but being less realistic. Field manipulative experiments are more realistic, but can be logistically challenging to implement and have limited replication, and be influenced by stochastic events at the particular point in space and time they are implemented. Thus, it can be more challenging to meet the standards of experimental design in a manipulative field experiment (Diamond 1983 ; Wiersma 2022a ). Observational experiments are the most realistic, but the experimenter loses control over every aspect of the study except where and when they sample. For the purposes of this review, I am considering both observational (“natural”) and manipulative approaches in my consideration of what is an experiment. Moreover, this review is limited to experiments in ecological science. While studies of ecological processes and research in landscape ecology can certainly benefit from integration of methodology from the social sciences, an assessment of methodological approaches in social science is outside the scope of this review.

The experimental aspects of a control (a set of observations identical to the experiment minus the treatment factor), and randomization (ensuring experimental treatments and/or sampling are carried out without bias to underlying conditions) should be familiar to scientists. Conceptually, they are straightforward, but when working at large landscape extents, it can be difficult to implement these (Jenerette and Shen 2012 ; Wiersma 2022a , b ). The issue of replication can cause more confusion. Replication can happen at the experimental unit and at the sampling unit, and sometimes researcher can be confused as to what their sample size actually is. An experimental unit is defined by Krebs ( 1989 : 269) as “the smallest division of the experimental material such that any two units may receive different treatments”. A sampling unit, on the other hand, is the thing that the scientist measures to test the effect of the treatment. These can be the same thing; such as when plants are exposed to different light treatments in a greenhouse and the dried weight of the whole plant is taken to assess how light levels affect biomass. If the dark and light halves of the greenhouse had 200 plants each, then there are a total of 400 experimental units (200 × 2 treatment levels) and 400 sampling units. However, if four leaves from each of the plants were measured instead to assess the response, then there would still be 400 experimental units (200 per treatment), but 1600 sampling units. Confounding experimental units and sampling units incorrectly in the statistical analysis can lead to accusations of pseudoreplication (see chapter 4 in Wiersma ( 2022a , b ) for a detailed discussion of this issue as it pertains to landscape experiments).

In this review, I examine experiments designed to understand ecological processes, where space is either an implicit or an explicit component of the study design. Most happen at the ‘typical’ landscape extents of 1–100 km, but I did not limit the review to such studies, since what a small organism perceives as a landscape may be a very small area of just a few square metres or centimetres. My focus is to examine as wide a range of experiments about ecological processes as possible to deduce trends and best practices. There is value to taking landscapes/space into consideration when studying ecological processes. Although many papers published in this journal have examined spatial dimensions of ecological processes (e.g., Webb et al. 2012 ; Ahmed et al. 2016 ; Paca et al. 2019 ; Sieger and Hovestadt 2021 ; Barik et al. 2022 ; Bedane et al. 2022 ; Datta et al. 2022 ; John et al. 2022 ), few of these have been explicitly experimental. Thus, this review examines landscape experiments on ecological processes and experiments on ecological processes carried out with a landscape ecology focus.

Box 1 Six approaches to landscape experiments (from Wiersma 2022a )

Large-scale manipulative experiments—these refer to landscape experiments at extents of ~ 15 ha or greater. These can be observational or manipulative. Examples of long-term manipulative experiments of this type include the Savannah River Experiment (Brinkerhoff et al. 2005 ), the Biological Dynamics of Forest Fragmentation Project (Bierregaard et al. 1992 ) and the Stability of Altered Forest Ecosystems (SAFE) Experiment (Ewers et al. 2011 ).

Experimental model landscapes—these refer to landscape experiment that manipulate a smaller area (usually on the order of 1–15 ha), usually in a more anthropogenically manipulated landscape, such as an agricultural field. Examples include the Bowling Green Fragmentation Experiment (With and Pavuk 2011 ) and the Kansas University Fragmentation Experiment (Holt et al. 1995 ).

Mesocosms—these refer to experiments in artificial containers (e.g., tanks, aquaria), which are either assembled by the researcher (pots with investigator controlled plants grown in them), or are subsets of natural systems (e.g., aquaria with water from an adjacent pond). The experimental design places the mesocosms in situ in the natural environment for experimentation (Srivistava et al. 2004 ).

Microcosms—like mesocosms, microcosms are container experiments; the difference here is that microcosms are naturally occurring containers, or habitats/ecosystems with delineated boundaries, for example pitcher plants or tank bromeliads (Srivistava et al. 2004 ).

In silico landscapes—this refers to experiments involving computer models. These could include (but are not limited to) statistical models, mathematical models, cellular automata and agent-based models.

Novel landscapes—Wiersma ( 2022a ) highlighted how experiments in non-terrestrial landscapes such as seascapes (Pittman 2018 ) and riverscapes (Wiens 2002 ) create opportunities for different kinds of experiments. Similarly, experiments that take a landscape ecology lens to other disciplines such as acoustic ecology (“soundscapes”; Farina 2014) or medical science (“tumor-scapes”; Lloyd et al. 2015 ) or construct artificial landscapes in a laboratory setting (“microlandscapes”; Larsen and Hargreaves 2020 ) can offer new opportunities for experiments to address questions in landscape ecology.

I searched the journal database Scopus (which indexes 18,000 titles from over 5000 international publishers) on 30-May-2022 for papers that addressed ecological process experiments at landscape extents. The search string TITLE-ABS-KEY (“ecological process”) AND TITLE-ABS-KEY (landscape) AND TITLE-ABS-KEY (experiment*) yielded 177 papers. After removing duplicates and government reports and those where I could not access the document (see Fig.  1 for summary), I reviewed the abstracts of all papers, and excluded review/essay/op-ed papers (31), methods papers (8), those with no explicit experiment (24), and those that did not examine an ecological process (14). This yielded 87 papers (see Additional file 1 for full list). For each paper, I noted whether the experiment was observational or manipulative and whether the experimental design was spatially implicit or explicit. I also noted the spatial extent of the study area (if this was not reported, I attempted to infer it either via estimation from included maps, or by searching the internet for details on the study area), the spatial extent of the treatment units (to calculate the scope; Frazier 2022 ; Wiersma and Schneider In press), the degree of replication (of both experimental units and study landscapes) and the type of ecological process under assessment.

Schematic diagram of article identification and screening and sample size at each stage. Template for figure from: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. https://doi.org/10.1136/bmj.n71

Finally, I classified the experiment based on both type of question (following the taxonomy of Jenerette and Shen 2012 ) and by experimental method. For the latter, I used the six classes discussed in my book (Wiersma 2022a , b ) and summarized briefly in Wiersma ( 2022b ) and here in Box 1 . For clarity, definitions of meso- and microcosm here follow that in the book (Wiersma 2022a ), where mesocosms are artificial containers placed in the environment (e.g., tanks, aquaria) and microcosms are naturally occurring containers (e.g., pitcher plants, tank bromeliads). Microlandscapes refer to artificially constructed landscapes, which the experimenter manipulates under laboratory conditions (e.g., a dendritic network of pipes and petri dishes to assess ciliate movement). Because of the focus on ecological processes, I also noted a few other experiment types (e.g., food addition, seed addition, exclosures (to exclude predators/pollinators)) in addition to the categories in Wiersma ( 2022a , b ).

For the classification by experimental question, I tried to classify observational studies, even though Jenerette and Shen ( 2012 ) excluded such ‘natural experiments’ from their review. For example, I included observational studies that examined a natural disturbance as Type IV.13, even if the researcher did not actively manipulate the disturbance under study. Similarly, I classified observational studies that tested for differences in species distribution under different conditions as perception experiments (Type I.1), even if they were not explicitly manipulative. I did not classify the in silico experiments against Jenerette and Shen’s ( 2012 ) taxonomy, since their review focused on manipulative experiments.

Experiment types

The 87 papers reviewed represented a wide range of journals and disciplines (Table 1 ). Of these 87 papers, 15 were experiments in silico (discussed in further detail below). Of the remaining 72 papers, 17 were observational experiments, 45 were manipulative and 10 included a combination of an observational and manipulative experiment. Only one (Hess and Tschinkel 2017 ) used a full BACI (Before-After-Control-Impact) design. Three papers (Gornall et al. 2007 ; Lu et al. 2018 ; Menzies Pluer et al. 2020 ) had a lab component in addition to a field study, and one (Heggenes et al. 2017 ) transferred microcosms (lichen mats) from the field to the lab for the experimental treatment. The experimental methods are summarized in Table 2 . After in silico experiments, large-scale manipulative and large-scale observational experiments were most common (15 and 10 papers, respectively). As well, there were 11 experiments with some kind of addition, including food (6), artificial nests (1), seeds (2) and nutrients (2).

Research questions

The types of ecological processes addressed did not cover all the categories of Jenerette and Shen ( 2012 ); most common were manipulations of internal patch characteristics (Type III.7) and manipulation of disturbances (Type IV.13; although this count included natural disturbances; hence the number of observational studies in Table 3 does not match what is reported above.

Scale characteristics and replication

The spatial extent of the studies ranged from 78.5 m 2 to 20,300 km 2 and the size of the treatments from 4 cm 2 to ~ 500 km 2 for terrestrial studies, and 20 mL to 1000 L for aquatic/marine studies. The scope (ratio of extent to resolution/grain; Frazier 2022 ) ranged from 1.60 to 3.125 × 10 10 , with a mean of 4.17 × 10 8 . Variation in scope was narrowest for observational experiments and highest when studies combined both observational and manipulative experiments (Fig.  2 ). Replication of treatment units had a mean of 14.1 and median of 5 (range 1–320). Landscape replication was generally low, with 60 papers documenting an experiment in a single landscape, and only 7 papers documenting experiments in more than 2 landscapes (Marini 1997 ; Beckmann and Berger 2003 ; Cardoso et al. 2007 ; Hovel and Wahle 2010 ; Caballero-López et al. 2012 ; Bergerot et al. 2013 ; Augustine and Derner 2014 ; Giometto et al. 2014 ; Smith et al. 2014 ; Fronhofer and Altermatt 2015 ; Gillespie et al. 2017 ; Aristizábal and Metzger 2019 ; DiFiore et al. 2019 ; Menzies Pluer et al. 2020 ; Boone et al. 2022 ; Nunes and Byrne 2022 ).

Scope (ratio of grain to extent) of 43 papers in this review. Note the log scale on the y -axis. Scope here is the minimum per paper; some papers had different grain sizes; only the smallest grain was used for the scope calculations. Note that data were not always available to calculate scope, and I only calculated scope for areal studies; studies incorporating treatments by volume or linear treatments (e.g., along streams, or soil depths) were excluded

Modelling tools

The in silico experiments used a range of modelling/computer tools, including cellular automata models (3), demographic models (1), agent-based models (1), process models (2), GIS/remote sensing (3), habitat models (2), scenario models (1) and mathematical models (2). Interestingly, the only paper in the collection obtained with the keyword search above to appear in this journal, was an in silico scenario model of the influence of ecological, economic and social drivers on future ecosystem goods and services (Huber et al. 2014 ). The majority of the in silico experiments modelled some kind of response to disturbance: either fire (Davies et al. 2021 ), grazing (King and Franz 2016 ; Verma et al. 2020 ), or climate change (Keane et al. 2017 ; Cui et al. 2021 ). Others modelled species movement (Samarasin et al. 2017 ; Baggio et al. 2019 ) or habitat use (Rowland et al. 2018 ; Muñoz et al. 2021 ) and still others modelled abiotic processes such as carbon (Güneralp et al. 2014 ; Xu et al. 2017 ), vegetation dynamics (Rango et al. 2002 ) or hydrology (Govind et al. 2011 ).

This review is an exploration of whether and how the themes of ecological processes, experiments and landscape ecology intersect. My findings suggests that experiments on ecological processes that have spatial dimensions occur in many kinds of ecological systems, including oceans (e.g., Cardoso et al. 2007 ), forests (e.g., Hylander 2005 ), urban areas (e.g., Visscher et al. 2018 ) and agricultural systems (e.g., Ouyang et al. 2020 ). Several of the experiments took place in long-term landscape-scale experimental sites, such as the Biological Dynamics of Forest Fragments Project (Laurance et al. 2002 ), the Savannah River Experiment (Levey et al. 2016 ), the Inner Mongolia Grassland Experiment (Yuan et al. 2015 ) and the Kansas Fragmentation Experiment (Alexander et al. 2012 ). Leveraging such long-term projects is a strategic approach to integrating landscape ecology perspectives into studies of ecological processes, since these sites have long-term data, as well as logistical resources and supports for researchers (Wiersma 2022a ).

The papers I reviewed examined a wide range of ecological processes, ranging from dispersal of organisms (e.g., Fronhofer and Altermatt 2015 ) or seeds (e.g., Miguel et al. 2018 ) to nutrient stocks and flows (e.g., Yuan et al. 2015 ). I also found papers carrying out experiments on species interactions such as pollination (e.g., Schmucki and De Blois 2009 ), predation (e.g., Gering and Blair 1999 ) and herbivory (e.g., DiFiore et al. 2019 ). A number of papers had an “applied” focus to restoration or management of ecological systems as evidenced by papers in the Journal of Applied Ecology (6), Restoration Ecology (2). Journal of Environmental Management (2), Forest Ecology and Management (2), Ecological Applications (1) and Ecological Management and Restoration (1) (Table 1 ).

Overall, it appears that many experiments concerned with ecological processes have taken a spatial/landscape approach, and at a range of extents and landscape types. This is not surprising, but what may be more surprising is the relatively low number (87 papers) of papers that are explicitly experimental. If we limit our characterization of an “experiment” to just manipulative experiments and exclude observational experiments, then this number drops to 55. Moreover, for a review focused on ecological processes, there was only a single paper from this journal (Huber et al. 2014 ); this documenting an in silico experiment. Although other papers in Ecological Processes are spatially explicit and borrow concepts and tools from landscape ecology, these did not appear in the keyword search, and were not presented by their authors as experiments. This is likely due to the challenges of doing robust experiments in landscape ecology (Jenerette and Shen 2012 ; Wiersma 2022a , b ).

Even where there are robust spatial experiments, as evidenced here, there can be challenges for researchers to meet the criteria of good experimental design. The majority of experiments occurred in a single landscape, thus making it difficult to assess if the inferences gained from one study would apply in a different landscape. This finding speaks, perhaps to the “case study” approach that characterized early work in landscape ecology (Opdam et al. 2002 ). Although case studies, whether qualitative or quantitative may not be fully replicable experiments, they certainly have a place in research; indeed in the medical and psychological fields, case studies are a major element of knowledge advancement (Stake 2008 ). Thus, researchers and reviewers should not dismiss case studies just because they may not be fully reproducible. Indeed, well-documented case studies can form the basis for valuable meta-analyses (Harrison 2011 ; Koricheva et al. 2013 ; Gerstner et al. 2017 ).

Where there was high replication (more than 2) of the experiment in different landscapes, this was often in anthropogenic systems, such as agricultural fields (Caballero-López et al. 2012 ; Augustine and Derner 2014 ), or when investigating dispersal of organisms that operate at smaller extents, such as butterflies (Bergerot et al. 2013 ) and ciliates (Giometto et al. 2014 ). A few were able to replicate landscapes across a broader extent, such as DiFiore et al. ( 2019 ), who examined two distinct coral reef system in the Caribbean. Experiments in this review generally had limited treatment replication; with 39 of the studies have 10 or fewer treatment replicates, and 25 having fewer than 5. All of the manipulative experiments had some kind of control; observational studies generally were comparisons in space and/or time and did not always have a strict control.

Overall, it appears that ecologists of all types and throughout the world are applying a great deal of creativity to experiments on ecological processes in landscapes. Most are meeting criteria of control and treatment replication; replication at landscape extents in more challenging, which is understandable. While many of the papers reviewed did not explicitly focus on landscape ecology, thinking about ecological process experiments in landscape context could yield useful insights. Experiments at smaller extents may be a strategic way to meet criteria of good experimental design, and with some effort, the inferences might be able to be scaled up to the extents at which human management happens. The experiment on soil organisms’ feeding activities by Joschko et al. ( 2008 ) is a good example of cross-scale work on ecological processes in a landscape. Since ecological processes are scaled in space and time, and landscape ecologists are familiar with scaling issues, considering how to extrapolate from small-scale process experiments to larger-extent landscapes is likely the next frontier to explore. A recent review by Wiersma and Schneider (In press) examined whether microlandscapes and sampling at small scales can usefully be extrapolated to make inferences at larger scales. Larsen and Hargreaves ( 2020 ) reviewed the broad array of microlandscape experiments, but did not examine scaling up in detail. Cross-scaling is facilitated when experiments are at different scales, but have the same scope; where scope is defined as the ratio of the extent to the grain (Frazier 2022 ). The scope of studies in this review varied several orders of magnitude (Fig.  2 ), making it difficult to compare across experiments. Ecologists considering experimental approaches as a means of understanding ecological processes in space would be wise to consider scale effects when designing the experiment. Whether experiments are manipulative or observational, researchers should make careful consideration of sampling design (including grain/extent, and hence scope), and degree of replication, randomization, experimental control and reproducibility in their studies. Although case studies have their place, experiments facilitate better understanding of the mechanisms influencing ecological processes, and thus should not be cast aside simply because they are difficult to do at landscape scales.

Availability of data and materials

The list of all papers reviewed is provided as an Excel spreadsheet in the additional material.

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ORF, an operational framework to measure resilience in social–ecological systems: the forest case study

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• Francisco Lloret   ORCID: orcid.org/0000-0002-9836-4069 1 , 2 ,
• Pilar Hurtado 2 , 3 , 4 ,
• Josep Maria Espelta 2 ,
• Luciana Jaime 2 , 5 ,
• Laura Nikinmaa 6 , 7 ,
• Marcus Lindner 6 &
• Jordi Martínez-Vilalta 1 , 2  

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Resilience is commonly addressed when dealing with the sustainable planning and management of social–ecological systems, but we lack a unified framework for its quantitative assessment and application. We present an operational resilience framework (ORF) based on recognizing and relating several elements: system variables (e.g., ecosystem services), disturbances and stressors acting at given spatiotemporal scales, a reference state, and metrics comparing the observed system variables to the reference state. These elements fit into a rationale aimed at identifying resilience predictors suitable to be managed and co-drivers which describe non-manageable context, reflecting the mechanisms involved in resilience. By a systematic search of the presence of the ORF concepts in 453 empirical studies assessing resilience, we corroborate that ORF can be applied to studies on forest social–ecological systems. This literature survey shows that ORF elements are commonly recognized, although the logical narrative relating them is not always explicit, particularly in socioeconomic-focused studies. We advocate that the proposed ORF allows to standardize the terminology and to frame and measure resilience, allowing sounder comparisons and better-supported recommendations for the improvement of resilience in social–ecological systems, particularly in forest systems.

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Introduction

Generally, resilience can be described as the capacity of a system to absorb disturbances or environmental transformations, and recover and reorganize in a timely and efficient manner, retaining essentially the same structure, identity, feedbacks, and functions (Folke et al. 2004 ; Walker et al. 2004 ). The resilience concept evolved as an emergent property of complex dynamic systems connected across scales (Holling 1973 ; Holling and Gunderson 2002 ). This has made resilience particularly suitable to frame the performance of human systems giving rise to the concept of social–ecological resilience (e.g., Folke 2006 ; Biggs 2015 ), which has developed its own distinctness by incorporating aspects such as adaptability and transformability (Folke et al. 2010 ). Following this system perspective, in ecology, the resilience concept has been used to assess the behavior and persistence of ecosystems around dynamic equilibrium states in the face of environmental variability (ecological resilience), or alternatively, when thresholds are surpassed, shifting to alternative states (Scheffer et al. 2015 ). Since disturbances constitutes a major driver of such changes, a great number of studies have focused on system’s ability to recover the properties altered by a disturbance (engineering resilience) (DeAngelis 1980 ; Holling 1996 ).

The generality of the concept of resilience and its further application in multiple contexts have spread its use in environmental management and decision-making (Benson and Garmenstani 2011 ), especially in a context of sustainability (Xu et al. 2015 ; Elmqvist et al. 2019 ; Assarkhaniki 2023 ), and to support adaptation to climate change. In recent years, many proposals have emerged to build resilience conceptual frameworks considering the disturbance regime (Johnstone et al. 2016 ), the mechanisms involved (Elmqvist et al. 2003 ; Falk et al. 2022 ), the way to measure it (Ingrish and Bahn 2018 ; Bryant et al 2019 ), the scope of application (Garmestani and Benson 2013 ; Haider et al 2021 ), or its relationship with vulnerability (Miller et al. 2010 ), stability (Donohue et al 2016 ; Hillebrand et al. 2018 ; De Bello et al 2021 ; Van Meerbeeck et al. 2021 ), or sustainability (Redman 2014 ; Elmqvist et al. 2019 ).

Despite the proposals to assess and apply the resilience concept (e.g., Standish et al. 2014 ; Baho et al. 2017 ; Tamberg et al. 2021 ), the concept remains insufficiently implemented. These previous efforts generally build on a solid conceptual base aiming to address systems complexity (Folke 2006 ) and provide sound analytical insights, but fail to put in practice the enhancement of resilience in a comprehensive, synthetic way, encompassing both ecological and socio-economic perspectives (but see Camp et al. 2020 ). Thus, the current situation is that we miss a common, consistent, and unambiguous terminology, and we do not have a procedure to estimate and compare resilience across the vast range of domains in which the concept is used. Thus, we still lack an operational framework that integrates theoretical developments, empirical knowledge from distinct cases, and the views of social and decision-making agents to guide the selection and implementation of measures aimed at increasing the resilience of social–ecological systems (Donohue et al. 2016 ; Nikinmaa et al. 2020 ; Nikinmaa et al. 2023 ). To support the operational implementation of the concept, there is a need to: (i) supply information on current and future resilience; (ii) compare resilience among different contexts; (iii) establish targets for action plans; (iv) monitor the effects of specific policies on resilience after their implementation; (v) fit it into models to predict resilience; and (vi) support the identification of key factors that challenge or promote resilience.

Here, we present an operational framework to assess resilience (operational resilience framework ORF), in forests, but also suitable to be applied in different domains, from natural to socioeconomic ones. The proposed ORF provides a powerful tool to assess resilience in specific situations (specific resilience sensu Folke et al. 2010 ) and consists of a unified terminology with a glossary of terms necessary to assess resilience, and a sequence of steps needed for assessing resilience. We illustrate the applicability of the ORF by analyzing the content of a large number of empirical studies on the resilience of forest social–ecological systems. Specifically, we (1) test if ORF elements are found in publications studying resilience in forest social–ecological systems and (2) assess the different use of ORF elements in studies focused on ecological vs. socioeconomic aspects of forest resilience.

The operational resilience framework (ORF)

Orf’s rationale.

The structure of the ORF refers to the resilience of variables of interest in a given social–ecological system (“resilience of what”, sensu Carpenter et al. 2001 ), and to the disturbances or stressors that threaten the system (“resilience to what”, sensu Carpenter et al. 2001 ). Resilience can be then estimated by comparing system variables modified by disturbances or stressors with reference values that would correspond to the absence of disturbance or stress, or, alternatively, to situations in which the variables remain within acceptable thresholds (i.e., the reference state) (Fig.  1 A). The closer the variables of the system affected by disturbances or stressors get to these reference values, the greater is the resilience. To the extent that resilience is an emergent phenomenon caused by underlying mechanisms (Weise et al. 2020 ), it should be possible to find explanatory factors that encompass these mechanisms. Thus, a change in the values of these explanatory variables indicates that the resilience of the system likely increases or decreases. Note that we can distinguish factors that can be managed (e.g., forest structure) and therefore may be useful in determining actions that can be taken to promote resilience (i.e., manageable resilience predictors), from other factors that describe context situations that can hardly be manipulated (e.g., climate) but also determine resilience (i.e., resilience co-drivers). By applying this procedure, the ORF provides a consistent and comprehensive rationale to formally assess and compare resilience in different contexts.

A Graphical abstract summarizing the main elements of the operational resilience framework (ORF). Note that system variables (top right icon) are a subset of selected variables describing the whole social–ecological system (left icon) and the reference system corresponds to a scenario that serves as a basis for comparison with the observed system variables (bottom right icon). B Recognition of the ORF elements in two hypothetical study cases of forest resilience focused on socioeconomic (Example 1) or ecological aspect (Example 2). For the two examples, the elements of the ORF are highlighted in the text. Both examples correspond to an engineering resilience approach

The application of the ORF (see Fig.  2 for a roadmap) involves eight steps and is case specific, thus exhibiting great flexibility. Even though the ORF application depends on the specific context, it provides a common framework for resilience assessments carried out in very different situations. To better understand the rationale connecting ORF elements, we provide hypothetical examples related to forest social–ecological systems in Fig.  1 B and Supplementary Material 1. Also, we discuss in Supplementary Material 2 how several widely used concepts related to resilience (resistance and recovery, stability, vulnerability) can be framed within the ORF.

Roadmap and glossary of terms for applying the operational resilience framework (ORF) to assess resilience in specific cases (see text for more details). The road map includes eight steps: (1) rcognition of the resilience approach used in the assessment, (2) selection and quantification of system variables, with a particular focus on those describing ecosystem services, (3) identification and description of potential disturbance regimes and stressors, (4) identification and quantification of the reference state, recognizing its spatial and temporal scale, (5) measurement of resilience by applying convenient metrics, (6) identification of resilience predictors and co-drivers, and assessment of their effects, (7) prioritization of key resilience predictors, or combination of sets of them, (8) integrative assessments of resilience considering trade-offs and synergies between system variables

Elements of the ORF

The operational resilience framework is based on the following elements (see Fig.  1 B, and Supplementary Material 1 for examples):

Resilience approach

Among the vast literature addressing resilience conceptualization, we identify three basic approaches to resilience as described above, in which our operational approach can be framed: engineering, ecological, and social–ecological resilience. The determination of the approach used when assessing resilience will help to identify ORF’s elements. These approaches put the focus on different aspects. Disturbances, which may stay within the historical range of environmental variability, or disrupt such variability, are key in engineering resilience, which has been commonly used to disentangle causal mechanisms determining the persistence of system properties under specific spatiotemporal scales. In turn, nonlinear dynamics, uncertainty, and regulatory controls and self-organization are definitory of ecological and social–ecological resilience. In ecological resilience, thresholds involving state shifts as a consequence of stressors—such as changing environmental or socioeconomic conditions—constitute a crucial target, while studies on social–ecological resilience commonly address the role of changes in management regimes and decision-making affecting the system’ s capacity to adapt or transform. Despite these differences, our ORF identifies essential common elements, which allow the operational assessment of resilience under these different perspectives: system variables which characterize any system, the nature of disturbances or stressors, the selection of a reference state, and the application of suitable metrics to compare the observed state of system variables to the reference state.

System variables: resilience OF what?

System variables are quantitative variables describing the characteristics and performance of the social–ecological system that respond to disturbances, stressors, and other co-drivers (Nikinmaa et al. 2023 ). They provide a formal characterization of the whole system of interest accounting for their boundaries (Baho et al. 2017 ). These variables correspond to stocks or flows of energy, matter, or information, and can describe the system at different scales, from the individual (e.g., tree annual growth) and the local site (e.g., species richness), to the landscape (e.g., vegetation cover) and the whole country or state (e.g., timber production). System variables describe the system properties of interest whose resilience will be analyzed and correspond to the “of what” specification of Carpenter et al. ( 2001 ) or to “responses” (sensu Albrich et al. 2020 ). Some system variables commonly act as predictors of the resilience of other system variables (e.g., biodiversity may act as a system variable in itself or act as a predictor of ecosystem function variables). Our operational approach starts by identifying those system variables that are relevant for the analysis and promotion of resilience in a given context, and then searches for reliable predictors of that resilience. As ecosystem functioning and social and economic benefits are important aspects of social–ecological systems, system variables commonly correspond to ecosystem services. The use of ecosystem services as system variables is particularly useful since it allows connecting the functioning of ecosystems with the sustainability of the associated socioeconomic subsystem (Lecina-Díaz et al. 2020 ).

Although the complexity of a system should be recognized, it is convenient to select a limited number of system variables that define its functionality. Many system variables are likely to co-vary, due to causal relationships between them or because they are generated from common processes. For example, numerous variables may describe productivity or economic growth, and their resilience may show common patterns. However, these system variables may also represent complementary aspects of the system behavior. They may correspond to different subsystems, such as the ecological or the social one, and may even represent conflicting interests and cause trade-offs that must be balanced (Nikinmaa et al. 2023 ). For this reason, it is advisable to jointly analyze the resilience of different system variables. For this purpose, we can use: (i) multivariate approaches that describe the trajectory of the system in the face of disturbances or stressors across the space defined by different variables (Seidl et al. 2014 ), (ii) multi-criteria analysis (de Bremond and Engle 2014 ), or (iii) optimization models that maximize the joint resilience of different system variables (Pohjanmies et al. 2021 ).

Disturbance/stressor: resilience TO what?

Intense environmental or socioeconomic changes commonly affect the social–ecological system. These changes can be episodic (i.e., disturbances), gradual, or chronic (i.e., stress) and they may disrupt or be embedded within the historical functioning of the system. In the absence of such disturbances or stress, resilience does not actually apply and it cannot be measured. However, during periods without disturbances or significant stress, the system may acquire features that will improve (or reduce) its resilience when disturbances will eventually occur. For instance, an extensive and long-term institutional and legislative adaptation to forest multifunctional management may favor the resilience of the whole forest social–ecological system to the future shock-induced bark-beetle outbreaks (Hlásny et al. 2021 ), but this adaptation must be developed in advance of the outbreak.

The type of disturbances or stressors helps to guide the selection of the resilience approach to be used in the assessment. However, the distinction between disturbances and stressors is not always clear. Several types of disturbances have been recognized, from discrete, episodic pulses to press and ramp disturbances, and even small stochastic disturbances (Van Meerbeek et al. 2021 ), which would converge with the idea of stress (Grime 1974 ). Statistical criteria have been proposed to recognize extreme events and trends in relation to the historical series of the variability of environmental parameters, such as climate (Katz et al. 2005 ). In turn, stress extreme events can constitute disturbances when they involve the loss of stocks, such as biomass, accompanied by rapid changes in flows, which may be transitory. The set of characteristics of intensity, distribution in space, and sequence in time of the set of disturbances experienced by a system constitute its disturbance regime (Pickett and White 1985 ). Importantly, since the disturbance regime operates over time in a given territory, the spatiotemporal scale of disturbances needs to be defined to assess resilience. In turn, stress does not usually have such a clear episodic character as pulse disturbances, although it can exhibit clear trends associated with substantial variability in space and time. Disturbances and stressors do not usually occur in isolation; instead, different disturbance events together with stressors may affect the same social–ecological system. The resilience assessment would then refer to the compound effects of several disturbance and stressor types (Johnstone et al. 2016 ). For example, climate change implies (i) a warming trend that can increase stress, particularly when combined with a decrease in precipitation, (ii) an increase in climate variability that leads to extreme episodes (e.g., heat waves, droughts, hurricanes), and (iii) a boost of other climate-related disturbances (e.g., wildfires, pest outbreaks) or stressors (e.g., socioeconomic changes). Therefore, the resilience of social–ecological systems to climate change must consider this entire set of factors (Seidl et al. 2017 ).

Reference state: resilience COMPARED to what?

The reference state corresponds to a scenario that serves as a basis for comparison with the system state after disturbance or under stress (Grimm and Wissel 1997 ). The reference state is a fundamental piece of the ORF, as it enables the operationalization of the idea of retaining the properties and functionalities of the system after the disturbance or stress, as established by the concept of resilience (Folke et al. 2004 ; Walker et al. 2004 ). Note that the return to a pre-disturbance situation does not necessarily imply the maintenance of sustainable functionality, and this scenario should be included in the resilience framework by considering the reference state as described by a range of values—consistent with the "safe operating space" framework (Dearing et al. 2014 )—of desired conditions to be promoted, or undesired conditions to be reduced (Standish et al. 2014 ; Elmqvist et al. 2019 ). Therefore, the reference state is not an absolute value of the system, because according to the characteristics of the undisturbed or the desired state, different possible reference states may exist. Also, it is not necessarily a historical analog either, especially considering a climate change scenario. Particularly, when analyzing resilience under different scenarios (e.g., climate change or management), a specific scenario that may correspond to a baseline situation needs to be established as the reference state (Grimm and Wissel 1997 ), according to the considered resilience approach.

If we adopt an engineering resilience perspective, then the reference state corresponds to the undisturbed system. In a situation that assumes no historical legacies or high recovery rates, the reference state may correspond to the pre-disturbance situation, as used in many studies (e.g., Gazol et al. 2017 ; Stuart-Haëntjens et al. 2017 ). However, in systems affected by climate change or social and economic transformations, the past context cannot always be maintained, and the comparison with the system before being disturbed loses meaning. In such cases, comparison with a contemporary undisturbed reference may be more appropriate, at the cost of incorporating other sources of variability into the analysis (Bryant et al. 2019 ; Ibañez et al. 2019 ). Some of these difficulties can be overcome by the use of counterfactual approaches, which estimate the system state in the absence of the disturbance, everything else being equal (e.g., Martínez-Vilalta et al. 2012 ; Ovenden et al. 2021 ).

If the ecological resilience approach is adopted, the reference state corresponds to the range of system variable values that define the basin of attraction that separates it from different alternative states (Scheffer et al. 2001 ). So, in principle, discontinuous performance of the system—i.e., belonging to the different states—defines the reference state in the ecological resilience approach, differently from the engineering resilience. However, it is possible to identify early warnings of resilience loss when the variables of the observed system move away from values defining the basin of attraction, becoming unstable or experiencing critical behaviors, such as slow recovery after small disturbances (critical slowing down) (Scheffer et al. 2015 ).

In studies addressing social–ecological resilience, a persisting state may not be a suitable reference state as it may correspond to an undesirable situation (Standish et al. 2014 ), or belong to a metastable regime (i.e., an adaptive cycle in the panarchy framework, Gotts 2007 ). In fact, the system may follow an irreversible time path constrained by stressors (social changes, climate change), punctuated by crises in which past undisturbed or stable states do not constitute appropriate reference states. In these cases, the degree of functionality of the system corresponds to collective decisions, including actions taken by political and economic agents aiming at specific goals, such as maintaining or increasing the well-being of people, the production of goods, the economic activity, or the conservation of natural processes and biodiversity (Camp et al. 2020 ). From a social–ecological perspective, a sustained level of ecosystem services provisioning is a good candidate for characterizing the reference state. As said above, here, the desired levels, which are the object of management, constitute the reference state. In this case, the establishment of the reference state may suffer from arbitrariness and therefore requires adequate justification. Importantly, the reference state does not necessarily correspond to a static situation and can be subjected to change as the socioeconomic context evolves.

Spatiotemporal scale: WHEN and WHERE is resilience operating?

Resilience is a temporal concept that refers to specific timescales and periods which should be explicitly defined (Standish et al. 2014 ). Disturbances, social shocks, environmental modifications, and their consequences for ecosystems (recovery, state shifts, reorganization, and adaptation) occur at specific temporal scales. Moreover, regulatory feedbacks of the system do not operate instantaneously and delays in the responses to disturbance and stress are the rule (Meadows and Wright 2008 ). Therefore, resilience must be explicitly referred to the time extent at which recovery from disturbances or response to stressors occurs. For example, a system may be erroneously considered to have low resilience because it has been given little time to recover after a disturbance, or to have high resilience because the stress experienced has been of short duration.

In addition, the temporal scale is tightly linked to the spatial one. Social–ecological systems are located in territories, which determine the extent of stocks and fluxes, as well as the regulatory and social contexts. Disturbance regimes are by definition framed within time and space, and stress impacts are also strongly dependent on their duration and location. Importantly, key mechanisms determining resilience often change across such spatiotemporal contexts (Jentsch and White 2019 ), as reflected, for instance, by cumulative effects (Johnstone et al 2016 ). In turn, the spatial scale is often strongly related to the level of organizations involved in decision-making. In fact, social–ecological systems are cross-scale hierarchically structured adaptive systems, as recognized in panarchy theory (Gunderson and Holling 2002 ; Berkes and Ross 2016 ). In these systems, resilience emerges as the result of the interactions between multiple related subsystems (i.e., economics, cultural, geopolitics) with organizational structures that operate at different paces across the geographical space (Garmestani and Benson 2013 ). Thus, for a comprehensive assessment of the system, it is worthwhile to measure different components of the system selected based on their linkages and role in the overall structure.

As a general rule, resilience should be analyzed considering larger spatiotemporal scales than just covering the appearance of single disturbances or stress (Johnstone et al. 2016 ). For instance, from an ecological resilience perspective, a system may appear very resilient because it remains in a given state over time, until the cumulative effects of stress and/or disturbances reach an ecological threshold that leads to a tipping point (Lenton 2011 ; Scheffer et al. 2015 ). Alternatively, mechanisms that promote resilience, such as the accumulation of stocks or the generation of trade networks or information flows, often operate before a shock occurs. In other words, a decoupling between the generation of the mechanisms that increase or reduce resilience and the moment of impact or recovery is common. Therefore, from the operational perspective of promoting resilience before the occurrence of disturbance or stressors, the effects of these mechanisms must be extrapolated to situations that have not yet occurred, including high-intensity disturbances. This forces us to consider broad spatiotemporal scales, as well as intensities and frequencies of disturbances and stressors that may not correspond to historical regimes.

Resilience metrics: HOW is resilience measured?

In the ORF, resilience metrics refer to formal procedures—quantitative or qualitative—to compare the observed system variable(s) after disturbance or under stress with those at a given reference state. There are many different quantitative methods to measure resilience according to the resilience approach, disturbances or stressors, number and attributes of the system variables, and features of the reference state. In addition, resilience metrics may vary according to the specific features of the system considered (e.g., its biogeographic or political context), the assessment goals, and the available information. Examples of resilience metrics focusing on disturbance analysis include indices based on comparison to undisturbed states (Lloret et al. 2011 ; Hillebrand et al. 2018 ), recovery parameters—such as recovery rate (Meng et al 2020 ), time to full recovery (Thum et al. 2016 ), or recovery to resistance biplot analysis (Ingrisch and Bahn 2018 )—, significant differences in statistical models (e.g., Waltz et al. 2014 ), and multivariate trajectories and distances in relation to a reference state (e.g., Sánchez-Pinillos et al. 2019 ). Examples of resilience metrics dealing with variability around a dynamic equilibrium include analysis of the system maintenance within a given state (Hirota et al 2011 ), variability estimations (Jourdan et al. 2020 ) and time series analysis (Gazol et al. 2016 ). Finally, early warning signals or critical slowing down (Forziery et al. 2022 ) have been proposed to assess the proximity to thresholds leading to alternative states.

The selected procedure to compare the observed state with the reference one is key for resilience analysis. It must allow the assessment of the effect of resilience predictors and co-drivers (see below) and, ideally, the intensity of their effect on different situations. In fact, the estimation of resilience may be strongly dependent on the specific metric used, since different methodologies focus on different temporal scales, or emphasize different components of resilience (e.g., resistance vs. recovery, see Zheng et al. 2021 ). Thus, for a comprehensive assessment of the system, measurements of different system’s parts, selected according to their linkages and role in the overall structure, should be the rule. This selection is a crucial step in the assessment of resilience to deal with the multi-scalar complexity of social–ecological systems and avoid overly simplistic approaches (Garmestani and Benson 2013 ).

Resilience predictors: are there parameters that can be MODIFIED/MANAGED to enhance resilience?

In the ORF, resilience predictors are factors that allow the estimation of the resilience of the system and can be modified through management. For instance, in forests, higher tree functional diversity may lead to higher resilience of primary production in the face of drought and other disturbances (Grossiord 2019 ). Resilience predictors are a key concept within the ORF: first, because they inform on specific targets to be acted upon to enhance the resilience of altered ecosystems (Standish et al. 2014 ), but, also because they can provide estimations of expected resilience—i.e., estimate the capacity of the system to absorb disturbances or stresses that have not yet occurred—that can be used in prospective decision-making and scenario planning. Note that although resilience cannot be measured properly in the absence of disturbances or stress, resilience predictors inform about the expected resilience of the system when these environmental changes may eventually occur and can become a target of management aiming to promote resilience.

There is a link between the resilience predictors and the system variables for which resilience is predicted. When statistical models are applied in the analysis of resilience, the predictors will correspond to significant explanatory factors of the resilience of specific system variables in terms of response to disturbances, or system behavior leading to alternative states. Importantly, operational resilience predictors need to be suitable to be managed to develop actions aimed at promoting resilience. In simulation models used to assess resilience, predictors can be selected for those factors that produce significant changes in the resilience of system variables. For example, forest management alternatives corresponding to different levels of tree diversity will act as resilience predictors, according to the forecasted resilience of, for instance, productivity. Importantly, resilience predictors are scale dependent. If they cease to be manageable at certain scales, they should be considered as co-drivers (see below). For example, the taxonomic identity of trees can be a good predictor of the resilience of forest primary production at the plot level, as it can be managed to favor certain species. However, at the regional level, this taxonomic identity appears to be constrained by biogeographical patterns and is difficult to manage, becoming a co-driver.

In addition, system variables are commonly subject to trade-offs which translate to trade-offs between resilience predictors (de Bremond and Engle 2014 ; Seidl et al. 2014 ; Pohjanmies et al. 2021 ). Thus, weight and threshold values should be integrated into algorithms or rationales (multivariate, structural equation models and causal analysis, network analyses, optimization modeling) developed for a comprehensive assessment of resilience predictors and system variables. Importantly, the contribution of actors, such as decision-makers and stakeholders, is essential to establish these trade-offs and synergies, as well as to evaluate the utility of resilience predictors to implement management actions in specific contexts (Nikinmaa et al. 2023 ).

Since we assume some level of causality in the relationship between resilience predictors and system variables, this relationship reflects the mechanisms underlying system functioning. For instance, diversity, connectivity, and adaptive capacity have been recognized as important mechanisms for social–ecological resilience in forest systems (Nikinmaa et al. 2023 ). This recognition of the mechanisms that promote resilience is essential for several reasons: first, to avoid spurious relationships which can lead to undesired collateral effects when implementing predictors in management or decision-making; second, it allows extrapolating the assessment of resilience and its implementation to situations other than those initially analyzed. For example, functional diversity in forests can promote the resilience of primary production through functional complementarity (i.e., resource partitioning among individuals or species), which is particularly important in fluctuating environments (del Rio et al. 2022 ). Alternatively, functional diversity may be more decisive for resilience in the face of some types of stress, such as drought, through the selection of species with certain traits (Grossiord 2019 ). In general, systems with multiple regulatory feedbacks and reservoirs tend to be more resilient than simple, impoverished ones (Meadows and Wright 2008 ; Standish et al. 2014 ; Jentsch and White 2019 ).

Co-drivers: are there factors that INFLUENCE resilience but are hardly manageable?

In the ORF, co-drivers are factors that mediate the response of the social–ecological system to disturbances or stressors, but conversely to resilience predictors, they cannot be managed to increase resilience. Co-drivers are in fact contributing to establish ceilings of the safe and just operating spaces in which the social–ecological system can be maintained (Dearing et al. 2014 ). Co-drivers often correspond to physical characteristics (e.g., soils and topography) and climatic conditions, which have a geographical basis, as well as socio-political contexts (e.g., national regulations) and legacies or path dependencies (Johnstone et al. 2016 ). The distinction between resilience predictors and co-drivers is important from an operational perspective (Albrich et al. 2020 ), although this distinction is not commonly recognized in the literature. Co-drivers are usually sensitive to the spatiotemporal scale, and at some level, they can become resilience predictors if they are manageable (see previous section). Notably, co-drivers often interact with resilience predictors, thus modulating the predictor´s role and providing a context in which the effect of resilience predictors is more or less significant.

Strictly, memory, land-use legacy, and path dependency cannot be managed and should be considered as co-drivers, although they can inform about management actions promoting future resilience. Climate change should also be considered as a co-driver, particularly in the short-term and at local scales, since the effects of climate change mitigation are far from being operative at these scales; however, mitigation actions could be considered as predictors when assessments of resilience at regional or global scales are based on projected scenarios. Disturbance regimes often act as co-drivers, particularly when assessing resilience in the face of an existing disturbance regime that cannot be modified (Halpin et al. 2016 ). When the disturbance regime can be manipulated as a management action to promote resilience, it would act as a resilience predictor. For example, the intensity of wildfires a priori determines the resilience of vegetation cover and therefore acts as a co-driver, but the number, frequency, and extent of prescribed fires used to reduce fuel are best seen as predictors of resilience. Similarly, the general regulations that influence the forest value chain, or the degree of connection of the markets of forest-derived products, often act as co-drivers since they cannot be easily modified locally; however, at a broader regional or national level, regulations can be changed to improve the resilience of certain variables describing the value chain, thus becoming predictors. Thus, co-drivers should be comprehensively addressed considering organizational levels or geographical contexts other than those in which managers are directly involved.

Exploring ORF’s applicability: presence of ORF elements in the research on forest resilience

To validate the elements of the ORF, we carried out a systematic bibliographic research using recent (2000–2022) scientific literature addressing resilience in forests (Scopus database, search string TITLE–ABSTRACT–KEYWORDS (“resilience” AND “forest”) ALL (“measur*” OR “manage*”) PUBYEAR > 1999, see Supplementary Material 3 for details). We distinguished those studies that mostly focused on ecological aspects of forest ecosystems (399 out of the 453 studies) from those addressing the socioeconomic aspect related to forests (56 out of 453 studies). In two cases, both focuses were considered in the same study. We screened each of the 453 studies, interpreting them in the light of ORF, thus, searching for the presence of the ORF’ s elements and recording the categories considered for each element. See Supplementary Material 3 for a detailed account of the categories of the ORF elements that are used in both ecological- and socioeconomic-focused studies.

Among ecological-focused studies, most of them followed an engineering resilience approach (74% of studies), while the ecological resilience approach addressing stability, thresholds, transitions, and trajectories to alternative states represented 26% of studies.

Most ORF elements are commonly found in the literature, although noticeable differences appeared between ecological- and socioeconomic-focused studies. System variables were well recognized in 90% of ecological-focused studies (Fig.  3 ), while this percentage dropped to 62.5% in socioeconomic-focused ones (Fig.  4 ).

ORF elements found in recent (2000–2020) literature studying the resilience of forest social–ecological systems, considering 399 papers focused on ecological aspects: A categories of system variables; B types of disturbance or stressor; C types of reference state; D temporal scale; E spatial scale; F types of metrics; G methodological approach; H types of resilience predictors; I types of co-drivers; “not specified” indicates that the ORF element could not be identified in the publication; “none” in H and I means that neither significant predictors nor co-drivers, respectively, were found in the publication. For each ORF element, the percentage of studies with a particular category or type is shown. See Supplementary Material 3 for a description of the categories or types

ORF elements in the recent literature studying the resilience of forest social–ecological systems, considering 56 papers focused on the socioeconomic aspects: A categories of system variables; B types of disturbance or stressor; C types of reference state; D temporal scale; E spatial scale; F methodological approach; G types of resilience predictors; “not specified” indicates that the ORF element could not be identified in the publication; “none” in G means that no significant predictors were found in the publication. Not enough data were recorded for metric types. For each ORF element, the percentage of studies with a particular category or type is shown. See Supplementary Material 3 for a description of the categories or types

Disturbances or stressors were described in a similar proportion (~ 80%) in ecological- and socioeconomic-focused studies. The reference state was identified in all ecological-focused studies, but this identification was only attained in less than half of the studies addressing forest socioeconomic aspects (39.3%). Although the methodology to assess resilience was commonly explained (~ 98% of studies, regardless of their approach), quantitative metrics to compare the affected system to the reference state were less commonly applied: 78% of ecological-focused studies and in a very low number (15%) of socioeconomic-focused ones. Finally, potential resilience predictors were identified in both types of studies (85% and 89% in ecological- and socioeconomic-focused ones, respectively), while co-drivers were also commonly identified in ecological-focused studies (92% of cases), but much less in socioeconomic-focused ones (19% of cases).

The most commonly used system variables in ecological-focused studies corresponded to forest structure, functioning, and composition (Fig.  3 ), while in socioeconomic-focused ones the system variables referred mostly to social and economic capital and activity (Fig.  4 ). The most usual stressor investigated in both types of studies was climate, particularly associated with extreme episodes, such as drought. Wildfires and intensive management were also commonly studied in ecological-focused studies, while socio-political and economic pressures were analyzed in socioeconomic-focused ones. The most frequent reference states were pre-disturbance or undisturbed situations in both ecological- and socioeconomic-focused studies. Temporal scales mostly ranged from yearly to decadal in both types of studies. While ecological-focused studies exhibited a wide range of spatial scales, socioeconomic-focused ones mostly addressed local resilience. Statistical comparison between the affected system and the reference state was the preferred approach for measuring resilience in ecological-focused studies. These statistical procedures often involved metrics that distinguish different components of resilience (i.e., resistance and recovery). In contrast, resilience estimations in socioeconomic-focused studies were mostly based on bibliography or questionnaires and interviews. Resilience predictors in ecological-focused studies were usually obtained from statistical analyses and they often corresponded to forest structure, function, and composition, which were commonly associated with management, including forestry planning and silvicultural practices. In socioeconomic-focused studies, the most important predictors were related to management practices and planning, and governance. Finally, co-drivers identified in ecological-focused studies corresponded mainly to climate, geographical and biological context, and soil characteristics, while the assessment of co-drivers in socioeconomic-focused studies was generally poor. See Supplementary Material 3 for a detailed account of the prevalence of the different categories in ORF’ s elements in the analyzed empirical studies.

Representation of ORF elements in empirical studies of forest resilience

Our search in empirical studies on forest resilience provided evidence that ORF’s elements can be recognized in them, which supports the applicability of this framework. The vast variety of situations covered by these studies underlines the potential of the ORF to constitute a common framework to assess resilience in forests and likely in other social–ecological systems. Although our interpretations are subjected to some degree of subjectivity or uncertainty, we unfold that the logical connections between ORF elements were not always explicit in many of the studies, hindering comparisons and the establishment of general patterns and recommendations for promoting resilience.

We found important differences between ecological- and socioeconomic-focused studies. In ecological studies, the ORF elements are mostly recognized and resilience is quantified and statistically assessed, allowing a neat application of the “resilience of what to what” framework and a solid identification and evaluation of predictors and co-drivers. In contrast, in socioeconomic studies, the robustness of the relationships between resilience predictors and system variables is often based on the particularities of each case. Thus, virtually no specific parameters (either predictors or system variables) are consistently used across studies. In addition, some parameters that could be considered as predictors are very broad (e.g., human demography, resources of the community) and difficult to connect with the ecological system, and therefore the effect on resilience would be at most indirect. In such studies, the ORF could help to discern more systematically the relevance of decision-making powers and processes (Olsson et al. 2014 ), as well as to frame questions about resilience “to whom”—i.e., as systems variables (Cretney 2014 ). Finally, in many socioeconomic-focused studies, resilience predictors have not been explicitly quantified. In this type of studies, qualitative analysis is common, and categorical predictors (e.g., managed vs . unmanaged forests) are likely to be adopted. Using the ORF framework as guidance could encourage researchers to identify a reference state also in these socioeconomic-focused studies, which would offer methodological rigor and stimulate the use of more quantitative measures for enhancing resilience.

The search in the forest literature allowed identifying the main categories within each element of ORF that are employed when assessing resilience in forests (see Supporting Material 2). The literature search also recognized noticeable gaps in our knowledge of forest resilience. For instance , many studies failed to obtain a significant effect of the hypothesized predictors and co-drivers on resilience, discarding preconceptions about resilience. Studies explicitly addressing the resilience of ecosystem services are very rare (but see Cantarello et al. 2017 ), despite the link that they provide between ecosystem functioning and social demands. Whereas several commonly studied system variables are associated with ecosystem services (e.g., carbon flows for climate change mitigation and timber provisioning), the specific goal of studying the resilience of ecosystem services is rarely addressed explicitly. Moreover, forest resilience associated with important sources of tree mortality, such as pathogens and pest outbreaks, seems under-considered, probably because most studies analyzing their effects focus on direct, short-term impacts rather than on later forest recovery. When considering the methodologies used to analyze resilience, experiments are rarely applied, likely due to the difficulties in performing them at stand or wider levels. The use of model simulations for studying forest resilience also appears underappreciated despite their suitability to assess future resilience under scenarios of climate change and management regimes. As expected, the number of theoretical studies and meta-analyses is also low.

General discussion and conclusions

We argue that the ORF provides a narrative of resilience that can be universally applied in operational assessments of resilience, which is currently lacking. This addresses important shortcomings identified in past research and applications of resilience concepts. The ORF is particularly valuable because it provides a comprehensive terminology and rationale, leading efforts towards the identification of predictors of resilience which could be the object of decision-making and management in operational situations. This encompasses both ecological and socioeconomic perspectives and is soundly based on the essentials of the resilience concept.

Thus, the ORF contributes to clarify the disparity of conceptual interpretations and the associated terminology by providing a set of elements applicable to different perspectives around resilience. In fact, the ORF is consistent with different concepts related to resilience (see Supplementary Material 2 for details) and sustainability. ORF’ s reference state explicitly recognizes the key role of the undisturbed state in the stability-related engineering resilience approach (Holling 1996 ) and, in fact, recovery and resistance (Grimm and Wiesel 1997 ; Lloret et al. 2011 , Ingrisch and Bahn 2018 ) can be considered as distinct components of resilience which are estimated by different metrics, closely dependent on the time scale and the period considered (Standish et al. 2014 ). The ORF also supports assessments following the ecological resilience approach, in which stability plays a key role (Grimm and Wissel 1997 ; Van Meerbeek et al. 2021 ; Donohue et al. 2016 ), by (i) highlighting the importance of selecting integrative and relevant variables describing the behavior of the system, (ii) promoting, through comparison with a reference state, the recognition of the boundaries defining stability and thresholds leading to tipping points; (iii) promoting the assessment of the underlying mechanisms, through the selection of significant predictors and co-drivers, (iv) incorporating variability measures and early warning signals as resilience metrics. Finally, the complementary relationship between resilience and vulnerability (Miller 2010 ) can be appraised by the correspondence of ORF’s elements with the risk/vulnerability framework (Lecina-Díaz et al.  2024 ). This correspondence highlights the role of stressors and disturbance regimes in both frameworks, the common mechanisms, reflected in predictors and co-drivers, that determine sensitivity or susceptibility—within the vulnerability framework—and resilience, and the role that adaptation capacity—within the vulnerability framework—can play in the recovery after disturbances and stressors. Therefore, the proposed ORF encompasses these concepts and provides a common narrative which recognizes shared goals and elements, while distinguishing their differences.

The explicit distinction between predictors and co-drivers in the ORF is a key contribution to clarify existing applications of the resilience concept, since it distinguishes actions predicted to enhance resilience from contexts (i.e., determined by co-drivers) in which these actions are likely to be more or less successful. Notably, these operational tools—resilience predictors and co-drivers—are firmly based on the mechanisms promoting resilience. In our literature search, we found evidence that the ORF elements allow implementing a consistent resilience assessment in a wide range of contexts of the analyzed literature on forest socio-ecological systems. Although not addressed explicitly here, this result also suggests that the ORF framework could be applicable to other, non-forested socio-ecological systems.

A common shortcoming for the operational assessment of resilience is the frequent vagueness of resilience enhancing measures (Baho et al. 2017 ; Moser et al. 2019 ), and this is particularly clear when resilience to climate change is considered. In this case, the ORF application allows (i) the identification of measurable characteristics of the system (e.g., ecosystem services) that are at risk under climate change; (ii) the clarification of specific drivers associated with climate change which are significantly impacting the systems (i.e., increase in temperature, extreme weather events, associated disturbance regime); and (iii) a formal assessment of the factors whose management is likely to improve resilience to climate challenges together with contextual co-drivers that modulate them and determine the boundaries of resilience.

ORF is also very flexible in the use of metrics, scales, disturbances or stressors, and particularly in the establishment of the reference state, which can be adapted to specific cases that not necessarily correspond to past situations and should dismiss undesired scenarios. Also, the ORF can also embrace qualitative analyses based on categorical parameters, which are more common in assessments based on stakeholder elicitation and on comprehensive social–ecological perspectives. The ORF’s procedure is mainly aimed at identifying a minimum set of elements needed to assess specific resilience (sensu Folke et al. 2010 ). In contrast, the analysis of general resilience (sensu Folke et al. 2010 ) in social–ecological systems usually follows an agent-based approach (Miller et al. 2010 ), to the detriment of the operability of the analysis and its further application. The narrative proposed by ORF can complement more holistic approaches, contribute to identifying descriptors of the whole system, and help to find predictors of the system behavior in terms of resilience.

In conclusion, we endorse the creation of a common protocol in resilience studies and applications, explicitly identifying the elements of the ORF and adopting its rationale and corresponding roadmap. This protocol will facilitate comparisons and the establishment and evaluation of decision and management goals, providing a unified approach to produce more robust and operational resilience assessments.

Data availability

The article is bibliographic, based on published literature, and the study has not generated data eligible to be stored in a public archive.

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Acknowledgements

We acknowledge the financial support from the European Union H2020-RUR-2020-2 project RESONATE (Grant no. 101000574) and the Spanish Ministerio de Ciencia e Innovación PID2020-115264RB-I00 grant. Marco Pattaca, Alice Ludvig, Stefanie Linser, Jette Bredhal Jacobsen, and Thomas Hlasny provided valuable suggestions in the development of the framework. Gabriela Rueda made the icons in Figure 1 .

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Review of studies applying Bronfenbrenner's bioecological theory in international and intercultural education research

1 School of International Education, Wuhan University, Wuhan, China

Irene Shidong An

2 Discipline of Chinese Studies, School of Languages and Cultures, The University of Sydney, Sydney, NSW, Australia

Rebeca Lima, University of Fortaleza, Brazil

The Russian-born American psychologist Bronfenbrenner's bioecological perspective on human development is an ideal framework for understanding how individuals negotiate the dynamic environment and their own identities in international and intercultural education settings. However, a review of the current literature shows that most studies either adopted the earlier version of the theory (i.e., the ecological systems theory) or inadequately presented the most recent developments of the bioecological model (i.e., the process-person-context-time model). The construct of proximal processes—the primary mechanisms producing human development according to Bronfenbrenner—has seldom been explored in depth, which means the true value of bioecological theory is largely underrepresented in international and intercultural education research. This article first presents a review of studies that adopt Bronfenbrenner's theory and then offers future directions for the scope and design of international and intercultural education research.

1 Introduction

Bronfenbrenner's ecological theory on human development 1 is one of the most influential and widely cited theories in the fields of human development and educational psychology (Weisner, 2008 ). Dissatisfied with the lack of child development research directly addressing how development is impacted by wider environments, Bronfenbrenner proposed an ecological model that can provide a framework and common language for conceptualizing the environment and identifying how the interactions and relationships among the components of the ecosystem may affect children's development (Shelton, 2019 ). A popular visual representation of Bronfenbrenner's ecological systems model is a diagram of the ecological system within which a toddler sits at the center, surrounded by a series of concentric circles demonstrating micro-, meso-, exo-, and macrosystems (Darling, 2007 ). An arrow representing the chronosystem (the influence of time) (Bronfenbrenner, 1994 ) 2 is also added in some diagrams (e.g., Porter and Porter, 2020 ). Although Bronfenbrenner initially formulated the framework to delineate these ecological systems, he later refined it into the Process-Person-Context-Time (PPCT) model (Bronfenbrenner, 1994 , 1999 ; Bronfenbrenner and Morris, 1998 , 2006 ) to comprehensively consider interactions among developmental processes, contextual and individual biological characteristics, and temporal aspects.

This theory, although originated in the field of developmental psychology, is also useful for educational studies since it informs practical applications for the construction of better educational environments. In one of his earlier works, Bronfenbrenner ( 1977 ) introduced an ecological approach to education, emphasizing the dynamic relationships between learners and their environments. He challenged the traditional view of relying solely on laboratory experiments in educational research and advocated for a more holistic and ecologically valid approach to studying educational systems and processes. His focus was on the significance of real-life settings and the dynamic interactions between learners and their environments. Bronfenbrenner emphasized that understanding how individuals learn within educational settings is contingent upon the interplay between the characteristics of learners and the contexts they engage with, highlighting the intricate connections among these environments. His later article (1994), titled Ecological Models of Human Development , published in the International Encyclopedia of Education , demonstrates his considerable influence in educational research. While Bronfenbrenner's theory is most applied in child development and parental education research, it has also found use in various education-related studies, such as educational accountability (Johnson, 2008 ), educational transition (O'Toole et al., 2014 ), computer-assisted language learning (Blin, 2016 ), early childhood education (Tudge et al., 2017 ), and higher education (Mulisa, 2019 ). For instance, Mulisa ( 2019 ) drew inspiration from Bronfenbrenner's theory and advocated for a holistic approach that emphasizes the proximal and active interplay between students and their environments. This approach emphasizes that students' learning should not be disconnected from the social ecology of higher education. Furthermore, educational outcomes should not be attributed solely to students' competence and curriculum quality. Educators and practitioners should employ comprehensive strategies to effectively manage multilevel socioecological factors that impact students' learning.

Specifically for the field of international and intercultural education, the merits of an ecological perspective are elucidated by Elliot and Kobayashi ( 2019 , p. 913):

[A] beautifully complex co-existence of two ecological systems develops once international students move away from their original (home country) ecological system to pursue an education in a new (host country) ecological system. Reciprocally interacting elements from various systems that affect personal, social and learning practices in particular are arguably crucial for these educational sojourners as they can lead to valuable learning opportunities as well as potential conflicts arising from competing influences emanating from the original and the new ecological systems.

Therefore, Bronfenbrenner's theory offers a nuanced and holistic framework that aids educators and policymakers in understanding, respecting, and effectively responding to the environmental complexities inherent in international and intercultural education. It helps educators appreciate the significance of diverse cultural contexts, values, and norms that influence learners, identify the crucial interactions and relationships in the intercultural settings that contribute to a student's adaptation and learning, and encourages students to engage with diverse environments for the development of intercultural competence.

This study aims to review and evaluate the application of Bronfenbrenner's developmental theory, as represented in empirical work on international and intercultural education. As noted in some critical reviews (Darling, 2007 ; Tudge et al., 2009 , 2016 ; Tudge, 2016 ; Jaeger, 2017 ), the ecological theory was evolving as Bronfenbrenner continuously revised, tested and expanded his understanding of development throughout his long career (Shelton, 2019 ), whereas not all studies are aware of its mature version, that is, the bioecological model. Therefore, it is crucial for researchers to recognize the updated version of the theory, which reflects the most recent advance of such a powerful framework. Our objectives are threefold: First, to provide a brief overview of the evolution of the ecological theory and its historical evolution. Second, to evaluate whether the researchers in the fields of international and intercultural education adequately represented the theory in their empirical research. Third, to clarify the value of the updated version of the theory and direct future research.

We will first explain the evolution of Bronfenbrenner's ecological theory and then present some scholars' critics of its misuse in the literature. This is followed by a review and evaluation of the international/intercultural education research that has applied different versions of the theory. It will reveal that the theory is underrepresented in the current international/intercultural education literature. The paper concludes with a discussion of future directions for international and intercultural research.

2 The evolution and different versions of Bronfenbrenner's theory

Several scholars have provided extensive discussion on how Bronfenbrenner's ecological theory of development changed over time, from one that appears to focus primarily on contexts of development to one in which proximal processes are foregrounded (e.g., Rosa and Tudge, 2013 ).

In brief, Bronfenbrenner's early work in the 1970s initially spotlighted environmental contexts in human development due to the prevailing lack of attention to contextual influences within developmental psychology. Therefore, his original ecological perspective “offers a foundation for integrating context into the research model” (Bronfenbrenner, 1979 , p. 21) and provides a theoretical framework that allows for the observation of a wide range of contextual influences on development (Bronfenbrenner, 1979 ). However, Bronfenbrenner was dissatisfied with the fact that the studies applying the model had a pervasive focus solely on contextual elements, resulting in an imbalanced focus on “context without development” (Bronfenbrenner, 1986b , p. 288). This overemphasis on context prompted a pivotal shift in the 1980s toward the integration of person, process, and time variables within the framework (Jaeger, 2017 ). Bronfenbrenner reformulated his model into the bioecological model by the late 1990s. This revised model positioned “proximal processes,” defined as “progressively more complex reciprocal interaction between an active, evolving biopsychological human organism and the persons, objects, and symbols in its immediate external environment” (Bronfenbrenner and Morris, 1998 , p. 996), at its core. This evolution culminated in the process-person-context-time (PPCT) model, a refined iteration that accentuated the interplay of proximal processes, individual characteristics, environmental contexts, and temporal dimensions in human development (Bronfenbrenner and Morris, 1998 , 2006 ).

While the earlier ecological model predominantly focused on environmental contexts, its emphasis on context may have led to a narrow perspective, overlooking the dynamic interplay between individuals and their immediate environment. This approach often merely compared individuals in various social or geographical contexts without delving into the developing mechanisms behind observed outcomes, assuming that all individuals in a given environment undergo the same developmental trajectory. Such an approach may, as Bronfenbrenner ( 1988 ) notes, “yield results that are not only likely to be redundant but also highly susceptible to misleading interpretations” (p. 27–28). One of the significant theoretical advancements in the bioecological model is the introduction of a critical distinction between environment and process, absent in the original ecological framework (Bronfenbrenner, 1999 ). While the former (environment) encompasses phenomena like mother-infant interaction and the behavior of others toward the developing person, the latter (process) is defined by its functional relationship both to the environment and to the characteristics of the developing person. The bioecological model proposes that the effects of proximal processes are more influential than those of the environmental contexts in which they occur. The evolution toward the bioecological model integrated the multifaceted interrelationships between developmental processes, individuals, contexts, and time, thereby offering a more comprehensive framework to comprehend the complexities of human development.

Bronfenbrenner ( 1995 ) highlighted Drillien ( 1964 )'s research to exemplify the nature and scientific promise of the updated version of the bioecological model. This longitudinal study assessed factors affecting the development of children with low birth weight compared to those with normal birth weight, across different social classes over 2 years. It found that a proximal process, in this case, mother-infant interaction over time, emerges as a significant predictor of developmental outcomes, as positive maternal interaction significantly reduces behavioral issues observed in the child. The study reveals that the power of this process varies systematically based on environmental context (i.e., social class) and individual characteristics (i.e., birth weight). It highlights that the moderating effects of person and context on the proximal process of mother-infant interaction are not symmetrical. In disadvantaged environments, this interaction has the most significant effect, especially benefiting infants with normal birth weight. Conversely, in more privileged social class settings, it is low-birth-weight infants who derive the greatest advantage from maternal attention during this interaction. Therefore, one should not over- or underestimate the power of any of these factors without considering their interaction with each other. Bronfenbrenner ( 1999 ) suggests that one distinct advantage of the bioecological model, compared to other analytic designs used for analyzing environmental influences on development, lies in its recognition of the interdependency and contextual variations among influencing factors. Thus, it can address the limitations of linear multiple regression models commonly used in psychological research, which assume additive effects, and offer a more differentiated understanding of how these factors contribute to developmental outcomes by considering their synergistic effects.

The upcoming sections will outline the key elements in both the earlier and updated versions of Bronfenbrenner's theory. This will serve as a groundwork for our subsequent analysis of existing studies utilizing these distinct versions of the theory. Many studies adopting the early model of concentric circles of environments use the name ecological systems theory (EST) (e.g., Porter and Porter, 2020 ; Trevor-Roper, 2021 ; Tong et al., 2022 ), which is an outmoded version and a facile representation of Bronfenbrenner's theory (Tudge et al., 2009 , 2016 ). Navarro et al. ( 2022 ) suggest that unless there are justified reasons for utilizing the earlier version, researchers should employ the latest version of the theory—the bioecological theory of human development along with the PPCT research model—and any modifications should be explicitly outlined. A summary of the key constructs in EST and the PPCT model is provided below.

2.1 The EST model

According to Bronfenbrenner ( 1986a , 1989 , 1994 ), the ecological environment of development encompasses the four layered systems detailed in his 1979 monograph and the concept of the chronosystem introduced in his later works. Several studies (e.g., Porter and Porter, 2020 ; Trevor-Roper, 2021 ; Tong et al., 2022 ) examining the influence of these ecological systems on development have referred to Bronfenbrenner ( 1989 )'s theory as EST. Although in a subsequent chapter titled “Ecological Systems Theory”, Bronfenbrenner re-evaluated his ideas from the 1979 monograph, shifting focus from context to person and process, studies using a model named after EST predominantly rely on his earlier conceptualization of ecological systems as developmental contexts. To accurately represent Bronfenbrenner's theory in the articles reviewed in this study, we use EST to denote his earlier attempt to define distinct ecological systems, namely the earlier version of his ecological theory. However, we will cite his definitions from the 1994 entry, as this is where the chronosystem was introduced as the fifth system, providing a comprehensive understanding of all five contextual influences on development as envisioned by Bronfenbrenner.

In the EST model, the development of an individual is influenced by four environmental forces, represented by nested circles (micro-, meso-, exo-, and macrosystem) and the flow of time (chronosystem). The innermost circle is the Microsystem , which is “a pattern of activities, social roles, and interpersonal relations experienced by the developing person in a given face-to-face setting with particular physical, social, and symbolic features that invite, permit, or inhibit engagement in sustained, progressively more complex interaction with, and activity in, the immediate environment” (Bronfenbrenner, 1994 , p. 39). Settings such as family, school, peer group and workplace are all regarded as microsystems. The next layer of the circle is the Mesosystem , which “comprises the linkages and processes taking place between two or more settings containing the developing person” (Bronfenbrenner, 1994 , p. 40), representing a system of microsystems. For instance, the linkage between school and family may affect a child's development. Then, there is the Exosystem , consisting of the “linkages and processes taking place between two or more settings, at least one of which does not contain the developing person, but in which events occur that indirectly influence” (Bronfenbrenner, 1994 , p. 40) the person's development. One example is the relationship between a child's home and their parents' workplace. The outermost circle is the Macrosystem , or “the overarching pattern of micro-, meso-, and exosystems characteristic of a given culture or subculture” (Bronfenbrenner, 1994 , p. 40). Finally, the Chronosystem “encompasses change or consistency over time not only in the characteristics of the person but also of the environments in which the person lives” (Bronfenbrenner, 1994 , p. 40).

As Bronfenbrenner's thinking progressed, he called into question the overemphasis on the central role of the environment in human development and gradually made the “marked shift” to a focus on processes and a more prominent role of the developing person, reconceptualizing his theory as a bioecological model (Bronfenbrenner and Ceci, 1994 ). He later labeled his work a PPCT (Process-Person-Context-Time) model of development (Bronfenbrenner and Morris, 1998 , p. 996). Each element of this newly evolving framework is outlined below.

2.2 The PPCT model

The PPCT model comprises the four defining properties of the bioecological model, emphasizing a simultaneous investigation of all these elements (Bronfenbrenner and Morris, 2006 ).

Process in the model, specifically encompassing proximal processes , refers to the “progressively more complex reciprocal interaction between an active, evolving biopsychological human organism and the persons, objects, and symbols in its immediate external environment” (Bronfenbrenner and Morris, 1998 , p. 996) over time. Notably, the sense in which Bronfenbrenner used the term “process” (e.g., Bronfenbrenner, 1986a , b ) in his earlier writings was different from the later concept of proximal process (Merçon-Vargas et al., 2020 ). The later formulations of proximal process illustrate the uniqueness of the concept and its importance to the theory. What is emphasized here is the joint function, involving complex interactions rather than simply the additive effects, of both human traits and the environment. It comprises the “primary mechanisms producing human development” (Bronfenbrenner and Morris, 2006 , p. 795). It is crucial to clarify the distinctiveness of this concept to grasp its meaning fully and prevent confusion with related concepts such as interaction. In the context of international and intercultural education, proximal processes may involve student-teacher interactions, peer relationships, and engagement with culturally relevant learning materials. However, to qualify as proximal processes, these interactions must adhere to the criteria outlined in Bronfenbrenner and Morris ( 2006 , p. 798). In simple terms, their measurement should encompass: (a) increasing complexity leading to either competence or dysfunction, (b) duration and frequency, and (c) reciprocal interaction (Navarro et al., 2022 , p. 236).

The Person in PPCT model is in contrast to most developmental studies' treatment of the cognitive and socioemotional characteristics of the person as measures of developmental outcomes. It is featured both as an initial factor influencing proximal processes and as a result shaped by the interplay between person, context, and proximal processes across time. It attempts to identify process-relevant person characteristics, which was labeled person forces/disposition (differences of temperament, motivation, persistence, etc.), resources (relate to mental and emotional resources such as past experiences, skills and intelligence and to social and material resources) and demands (personal stimulus such as age, gender, skin color and physical appearance) (Bronfenbrenner and Morris, 2006 ). These have the “capacity to influence the emergence and operation of proximal processes” (Bronfenbrenner and Morris, 2006 , p. 810). While Context includes the micro-, meso-, exo-, and macrosystems in the earlier EST model, the macrosystem was addressed more implicitly in writings about bioecological theory and the PPCT model (Navarro et al., 2022 ). The emphasis is on introducing a more significant domain within the microsystem structure, highlighting the unique impact of proximal processes involving interaction with objects and symbols, rather than solely with individuals (Bronfenbrenner and Morris, 2006 ). Finally, Time extends the original chronosystem (macro-time) to include another two levels: micro-time (what is occurring during some specific activity or interaction) and meso-time (the extent to which activities and interactions occur with some consistency in the developing person's environment) (Bronfenbrenner and Morris, 2006 ; Tudge et al., 2009 ).

All these elements in the PPCT model work interdependently and synergistically. Synergy is a key concept in the PPCT model, which refers to the cooperative action of these four elements, such that the total effect is greater than the sum of their individual effects (Navarro et al., 2022 ). To operationalize synergy in research, Bronfenbrenner and Morris ( 2006 ) suggest studying interactions between person and context, using multigroup models to analyze differences in developmental trajectories and outcomes across time. Navarro et al. ( 2022 ) demonstrate that the PPCT model has a minimum of four comparison groups by choosing two levels of a person characteristic and two levels of a contextual influence. These groups allow for an analysis to identify significant differences in developmental paths and outcomes among different person/context combinations over time.

Bronfenbrenner's bioecological model is no doubt a complex theory (see a summary of its constructs in Table 1 ). Bronfenbrenner ( 1986a ; 1988 ; 1999 ) acknowledged the complexity and ambition of such a comprehensive paradigm, recognizing that very few researchers can address all its components simultaneously in one comprehensive analysis. It is more feasible for researchers to break down these components into smaller combinations that work together cohesively (Bronfenbrenner, 1999 ). He also emphasizes that the purpose of presenting this ambitious design is not to set rigid criteria for all researchers but to offer promising paradigms that generate different research questions. The goal is to alert researchers to the complexities and potential interpretative ambiguities arising from the omission of crucial elements in their selected research designs. Many scholars agree that it is not necessary to include all the factors of the PPCT model in a single study (e.g., Tudge et al., 2016 ; Jaeger, 2017 ). However, Tudge et al. ( 2009 ) asserted that to employ bioecological theory to guide a study, all four elements of the model should be present, or it should be clearly acknowledged why one or more of the elements are not adequately assessed in a research design, so as to preserve the integrity of the theory.

Four constructs and their components in Bronfenbrenner's PPCT model [based on Bronfenbrenner and Morris ( 1998 ) and Tudge et al. ( 2009 )].

2.3 Critics of the misuse of Bronfenbrenner's theory

Some review articles found that the bioecological model had been misused in many studies. These studies either cited the outmoded version or inadequately explored its components while claiming to employ the PPCT model, disregarding the resulting ambiguity due to the omission of certain constructs. For instance, Tudge et al. ( 2009 ) reviewed 25 papers published between 2001 and 2009 and showed that all but four adopted the outmoded version of the theory, which resulted in conceptual confusion and inadequate testing of the theory. After 5 years, Tudge et al. ( 2016 ) conducted a reevaluation of 20 more recent publications. The study found that although 18 of them cited the mature version (after the mid-1990s) of Bronfenbrenner's theory, only two appropriately described, tested, and evaluated the four constructs of the PPCT model. In another commentary, Tudge ( 2016 ) indicates that there are explicit and implicit ways of using Bronfenbrenner's bioecological theory: the former explicitly links research variables and methods to bioecological theory, while the latter only examines person–context interactions over time without explicitly connecting these observations to the theory's constructs. This emphasizes the necessity for the appropriate application of Bronfenbrenner's updated theory, requiring explicit recognition of its constructs as influential variables for development, as detailed in Table 1 .

These reviews collectively underscore the persistent issue of inadequate adoption and exploration of the updated bioecological model, especially the nuanced constructs within the PPCT framework. The gaps identified in the literature necessitate a more thorough examination and explicit utilization of the updated theory to advance a comprehensive understanding of human development within international and intercultural education settings.

Their reviews included research up to 2016, when the model was not yet often extended to fields other than developmental science. In fact, the publications included in their reviews are mostly in the realms of family studies and child development. Therefore, this paper will review the current literature on international and intercultural education and evaluate how Bronfenbrenner's theory has been adopted in this research field.

3 Status of employing Bronfenbrenner in international and intercultural education: a review of current studies

The papers to be reviewed in this section are empirical studies in the fields of international and intercultural education that claim to adopt Bronfenbrenner's ecological theory. We followed the PRISMA guidelines (Page et al., 2021 ) to identify and screen the papers in the databases. The PRISMA flow chart is presented in Figure 1 .

PRISMA flow diagram for searching, identifying, screening, and evaluating studies [adapted from Page et al. ( 2021 )].

The terms used for searching studies using Bronfenbrenner's theory followed Tudge et al. ( 2009 ) and Jaeger ( 2017 ): Bronfenbrenner/bioecological/ecological systems theory/process–person–context–time/PPCT. We also used the keywords international/intercultural/study abroad/exchange/mobility/overseas to constrain the research field to international or intercultural education. We searched the Web of Science (WoS) databases (SSCI/SCI-Expanded/ESCI/A&HCI) (up until 12 September 2023) to ensure that the articles obtained were of good quality. We also conducted searches using a specialized database, EBSCO-ERIC (Educational Resource Information Center), up until September 12, 2023, to identify any additional studies specifically relevant to education. The following inclusion criteria were applied to the initial searches in both databases: (a) studies published in peer-reviewed academic journals, (b) studies published in English, and (c) empirically designed studies, excluding other types such as editorials and review articles. Additionally, we limited the WoS Categories to psychology, education, and related fields like linguistics and social sciences. We also included multidisciplinary categories to retrieve potential studies. Detailed search strategies, including filters and limits used for both databases, are specified in the Appendix . These searches yielded 182 results in the WoS databases and 130 in the ERIC database, totaling 283 after discarding duplicates.

The two researchers screened these records, encompassing titles, abstracts, and keywords, to determine their eligibility for further evaluation. Initially, they conducted independent screenings, resolving disagreements through collaboration. Subsequently, studies were manually eliminated if they: (a) were non-empirical, (b) did not pertain to intercultural or international education (for example, studies merely containing the keyword “international” but not related to international education), and (c) did not apply Bronfenbrenner's theory (for instance, studies related to ecological and environmental education containing the keyword “ecological” but not employing an ecological perspective to investigate educational issues). Studies with uncertainties regarding their article type, research scope, or theoretical perspective were reserved for further examination. Following this screening, 37 reports were initially considered for retrieval, although the authoritative versions of one article could not be retrieved. The researchers then thoroughly examined the full papers of the remaining 36 studies, discarding ten articles based on the aforementioned criteria. Consequently, 26 studies remained for inclusion in this review, as summarized in Table 2 .

Studies on international and intercultural education employing Bronfenbrenner's ecological theory reviewed in this study.

* Most recent Bronfenbrenner work cited by the author(s). ** Although there is a citation of Bronfenbrenner's work in 2009, the original publication dates back to 1979, so the most recent work cited is Bronfenbrenner's publication in 1993.

Some initial observations can be made from Table 2 . First, although we did not set the starting year for our search period, most eligible studies were published in the recent decade, suggesting that Bronfenbrenner's theory has been applied only to the field of international and intercultural education quite recently. Second, 18 studies cited Bronfenbrenner's work before the mid-1990s or named the theory EST or ecological model/theory; thus, they did not use the mature version. Another seven studies cited his work after 2000 and used the term “bioecological” (Bronfenbrenner and Morris, 1998 ; Bronfenbrenner, 2005 ), demonstrating the researchers' awareness of the recent update on the framework. The remaining study (Bhowmik et al., 2018 ), although cited Bronfenbrenner and Morris's ( 2006 ) work, did not use the term “bioecological” (instead, they named the theory “a socioecological model”) 3 . Third, most studies relied solely on qualitative methods to collect and analyze data.

The studies can be grouped into several categories according to how Bronfenbrenner's theory is used: loosely connected to Bronfenbrenner's theory, EST-based, and based on the updated version of the bioecological model (see detailed categorization in Table 3 ). Recognizing that the application of Bronfenbrenner's theory is still in its infancy in international and intercultural education research, our objective is not to critique individual articles but to understand the extent to which the empirical studies we reviewed reflect the recent development of the theory.

Categorization of the studies reviewed.

3.1 Studies loosely connected to Bronfenbrenner's theory

Four studies (Suárez-Orozco et al., 2010 ; Bhowmik et al., 2018 ; Elliot and Kobayashi, 2019 ; Trevor-Roper, 2021 ) are only loosely connected to Bronfenbrenner's theory, although they cite his work, either the early or the mature version. These studies only mention Bronfenbrenner in their papers but have not systematically applied his theory. Bhowmik et al. ( 2018 ) cite Bronfenbrenner's work without using the constructs of his theory for data analysis. Elliot and Kobayashi ( 2019 ) only mention the coexistence of two ecosystems of international students but have not specified the components in each layer of the ecosystem. Suárez-Orozco et al. ( 2010 ) reference Bronfenbrenner's early work (Bronfenbrenner, 1977 ) to highlight the significance of contexts and characteristics affecting students' performance. However, while their research explores the influences of school, family, and individual characteristics on immigrant children's academic trajectories, it lacks a systematic foundation based on Bronfenbrenner's theory. Moreover, the study findings are not explicitly interpreted in connection with Bronfenbrenner's framework. Similarly, Trevor-Roper ( 2021 ) only briefly discusses that the EST model is helpful in appreciating the complexity of higher education environments in international education but does not follow the model's constructs to frame the data analysis.

In other words, Bronfenbrenner's theory only serves as an overarching philosophical perspective rather than an operational model that guides detailed data analysis procedures in these studies. Such an approach partially overlaps with Tudge ( 2016 ) description of the “implicit way” of using Bronfenbrenner's theory, which only examines person–environment interactions and the complexity of the environment. This can be problematic since it oversimplifies the richness of Bronfenbrenner's theory and does not sufficiently demonstrate its value for international and intercultural education.

3.2 Studies based on EST

Twenty studies (McBrien, 2011 ; Jessup-Anger and Aragones, 2013 ; Elliot et al., 2016a , b ; Li and Que, 2016 ; Taylor and Ali, 2017 ; Vardanyan et al., 2018 ; Zhang, 2018 ; Emery et al., 2020 ; Merchant et al., 2020 ; Porter and Porter, 2020 ; Conceição et al., 2021 ; Winer et al., 2021 ; Chkaif et al., 2022 ; Ngo et al., 2022a , b ; Tong et al., 2022 ; Xu and Tran, 2022 ; Marangell, 2023 ; Rokita-Jaśkow et al., 2023 ) are based on the early version, that is, the EST model, although some of them cite Bronfenbrenner's later work and use the term “bioecological.” Three sub-categories can be identified: partial adoption, full adoption, and extended adoption of EST.

3.2.1 Partial adoption of EST

Ten of the 20 studies, including Jessup-Anger and Aragones ( 2013 ), Elliot et al. ( 2016b ), Li and Que ( 2016 ), Taylor and Ali ( 2017 ), Vardanyan et al. ( 2018 ), Emery et al. ( 2020 ), Merchant et al. ( 2020 ), Porter and Porter ( 2020 ), Winer et al. ( 2021 ), and Rokita-Jaśkow et al. ( 2023 ) are all classified as partial adoption.

Elliot et al. ( 2016b )'s study on international students' academic acculturation focuses exclusively on the chronosystem in the EST model. They identify different forms of personal transition, societal transition, and academic transition of international students. Conversely, Emery et al. ( 2020 )'s study explores the experiences of internationally adopted youths across various systems (micro-, meso-, exo-, and macrosystems), with a specific focus on the mesosystem, where schools are pivotal in providing support. Their study does not address the chronosystem.

Jessup-Anger and Aragones ( 2013 ) primarily delve into the influence of developmentally instigative characteristics (Bronfenbrenner, 1993 ) on interactions of study abroad students in host countries, discussing micro- and mesosystems. In Merchant et al. ( 2020 )'s work on refugee students, they highlight the mesosystem (interactions between families, peers, and schools) and exosystem (neighborhood and community organizations) as influential in shaping students' wellbeing. Li and Que ( 2016 )'s study focuses on integration challenges faced by newcomer youths in a Canadian city, emphasizing themes related to the exosystem (public transportation), microsystem (family support, social interaction), and individual factors like language barriers and job pressures. Porter and Porter ( 2020 ) analyze factors influencing Japanese college students' decisions to study abroad, considering various ecosystem layers (micro- and mesosystems as immediate environments, and exo-, macro-, and chronosystems as distant environments). They omit the mesosystem due to limited participant input. Conversely, Taylor and Ali ( 2017 ) incorporate the mesosystem while excluding the exosystem in their examination of international students' adjustment to studying in the UK. They do not distinctly explain the rationale for excluding the exosystem, potentially due to data limitations.

Rokita-Jaśkow et al. ( 2023 )'s study on the school socialization of bi/multilingual children examines the microsystem (teachers), mesosystem (classmates and parents), and exosystem (representatives of the education system). However, it omits the macro- and chronosystems within the EST framework without providing an explanation. Vardanyan et al. ( 2018 ) employ Bronfenbrenner's EST concepts in their data analysis, emphasizing the micro- and mesosystems, with limited focus on the chronosystem. In contrast, Winer et al. ( 2021 ) explore immigrants' children's sense of belonging within the microsystem (their rooms in their homes), mesosystem (a shared living building), and macrosystem (their neighborhood). However, they do not introduce or investigate the exo- and chronosystems.

These studies collectively illustrate that the EST is a multifaceted model, demanding multiple investigations to comprehensively explore the entire ecological system (Elliot et al., 2016b ). However, there is a need for more explicit justification when certain constructs within the model are excluded from analysis, as this exclusion affects the overall comprehensiveness of the theory.

3.2.2 Full adoption of EST

Four studies investigate all the components of EST. McBrien ( 2011 ) delves into the challenges encountered by refugee mothers as they adapt to settled lives and explores their children's schooling experiences in the context of all the components within the EST. Ngo et al. ( 2022b ) investigate the impact of contextual factors on the professional development experiences of Vietnamese English as a foreign language lecturers across different contextual levels within the EST model. Tong et al. ( 2022 ) use the EST model to offer a visual metaphorical illustration of the major themes at each level of an Australian–Chinese student's developmental ecosystem in Hong Kong and tease out the risk and protective elements in this ecosystem that influenced the student's developmental trajectory. Zhang ( 2018 ) examines how academic advising with international students was shaped by individual backgrounds and multiple layers of environmental influences.

These studies meticulously examine each construct of EST within the context of international and intercultural education and demonstrate the relevance of the model in fostering positive interactions in intercultural settings.

3.2.3 Extended adoption of EST

Six articles extend the model to some degree. Chkaif et al. ( 2022 ) combine EST with Yu et al. ( 2021 ) to generate a refined model for international education, with the macrosystem being revised to include the global dimension. Conceição et al. ( 2021 ) expand upon the investigation of the chronosystem within the EST by integrating transformative learning theory to illustrate the personal growth and development of study abroad students over time. Elliot et al. ( 2016a ) propose an academic acculturation model illustrating the transition between two ecosystems of a study-abroad sojourn. Marangell ( 2023 )'s study on students' experiences at an internationalized university applies a person-in-context (PiC) model (Volet, 2001 ), which adapts Bronfenbrenner's EST. The PiC model centers on the “experiential interface,' where individual and environmental dimensions interact, and explores how congruence between these dimensions fosters motivated and productive learning. Ngo et al. ( 2022a ) incorporate EST into an integrated framework for effective professional development, encompassing three dimensions: context, content, and process. Finally, Xu and Tran ( 2022 ) extend the investigation of the person at the center of EST by employing the needs–response agency theory.

These studies provide nuanced perspectives that enhance EST's applicability in international and intercultural education and underscore the importance of the continuous evolution of the theory to address the complexities of educational systems in an increasingly interconnected world. However, the expansion of the theory also introduces extra complexity and challenges in operationalizing and measuring the constructs, and care should be taken to disentangle various factors.

The EST-based studies reviewed above offer valuable insights into international and intercultural education within Bronfenbrenner's early EST model by discussing various aspects, such as the impact of cultural contexts, policy frameworks, academic transitions, peers, and advisors, all of which are crucial in understanding educational experiences in diverse cultural settings. Nevertheless, the absence of the PPCT model in these studies limits the exploration of the dynamic processes and interactions between individuals, their contexts, and the outcomes of international and intercultural education.

3.3 Studies based on the updated version of the bioecological model

The final two studies (Xu et al., 2021 ; Liu et al., 2022 ) was more pertinent to the bioecological model, although they do not mention the PPCT model. They differ from other studies reviewed above in that they not only recognize the existence of the mature version of Bronfenbrenner's theory but also employ it to guide their data analysis. For instance, Liu et al. ( 2022 ) state that while they acknowledge the influence of ecological systems in Bronfenbrenner's early model, they further embrace his later theoretical development of the bioecological system, which considers the individual as an active agent in proximal processes. Xu et al. ( 2021 ) also comment in their article that previous studies applying Bronfenbrenner's theory to address academic acculturation neglect a thorough identification of individual and contextual forces and fail to delineate the dynamic interactions between them. Therefore, both studies employ the updated version of the theory by highlighting how the “person” constructs (dispositions, demands, and resources) interact with environmental contexts to shape development. Liu et al. ( 2022 ) investigate the academic career development of Chinese returnees with overseas PhDs. (CROPs) and find that preferences for stability (dispositions), social networking establishment and maintenance (demands), and a lack of experience with local academic and publication cultures (resources) are important factors. Xu et al. ( 2021 ) examine Chinese doctoral students' international education experiences in Australia and suggest that personal characteristics, such as inward management practices (dispositions), social networking maintenance (demands), research outputs, and health status (resources), are the engine of development.

These two studies contribute to the field of international and intercultural education by recognizing and utilizing this updated version of Bronfenbrenner's theory. However, both studies only briefly mention the concept of “proximal processes”—the core of the mature version of the bioecological model—without identifying what they were and how they contributed to development. For instance, Xu et al. ( 2021 ) acknowledge that the core driving force in the bioecological system relies on PhD students' ability to initiate their autonomy as they negotiate, utilize, and create resources for their development in both their home and host environments. However, they state that “a fine-grained elaboration of these practices is neither the focus of this study nor possible to accomplish in a piece of this length” (p. 1354). Notably, these practices embody potential proximal processes of interest in the bioecological model. Furthermore, neither study adopts a PPCT design. Despite acknowledging the interplay between Person and Context factors in development, the absence of specifying the proximal processes mediating these effects limits the studies from achieving the synergistic design envisioned in Bronfenbrenner's bioecological theory.

3.4 Summary

This review indicates that the application of Bronfenbrenner's bioecological theory in international and intercultural education research is still in its infancy. Most studies have adopted the elements of EST to explain international and intercultural contexts for education, while the updated version of PPCT has been inadequately explored. In the study where PPCT is referenced (Emery et al., 2020 ), it is mentioned as background information rather than being utilized as a framework for interpreting empirical findings. Proximal processes, crucial in the PPCT model and critical to international and intercultural education, have seldom been explored in depth, which means that the true value of Bronfenbrenner's theory is largely underrepresented.

4 Conclusions and future directions

Bronfenbrenner's theory has undergone continuous refinements and reformulations over time and has evolved from an ecological to a bioecological theory, incorporating a four-element model (PPCT), in which the proximal process is given pride of place (Tudge et al., 2016 ). Previous reviews have criticized the misuse or partial representation of Bronfenbrenner's theory in the field of developmental science, especially in family studies and child development (Tudge et al., 2009 , 2016 ; Tudge, 2016 ). However, as Bronfenbrenner's theory has become influential in other fields in recent years, how the theory has been employed in these fields is a compelling question. This review addresses this issue and provides new insights for scholars in the field of international and intercultural education who are interested in applying Bronfenbrenner's bioecological theory.

In international and intercultural education, students experience a collide of at least two ecosystems consisting of complex elements and relationships. Therefore, Bronfenbrenner's bioecological perspective on human development is an ideal framework for understanding how an individual negotiates the dynamic environment and their own identity in these intercultural settings (Elliot and Kobayashi, 2019 ; Xu et al., 2021 ; Xu and Tran, 2022 ). The theoretical merit of the PPCT model is that it allows researchers to capture the dynamics and relationships between organisms and environments rather than presenting the developing person and influencing contextual factors discretely. Although the fields of education and development have benefited from a focus on contextual influences on human development situated within the early ecological model of Bronfenbrenner ( 1979 ), the PPCT model can inspire new ways of thinking about contextual influences (Bronfenbrenner, 1999 ). Firstly, this model refines the concept of microsystem by emphasizing proximal processes within these microsystems, identifying them as pivotal mechanisms through which development occurs. Secondly, the PPCT model posits that these proximal processes act as moderators, shaping the impact of contextual influences. Bronfenbrenner's work underlines that while contexts exert significant influence, the quality and nature of proximal processes within these contexts can moderate or amplify their effects on individual development. This model thereby deepens our understanding by emphasizing the interactive and dynamic nature of contextual influences.

However, a review of the existing literature indicates that when Bronfenbrenner's theory is applied to international and intercultural education research, either the earlier version of EST is used or the mature version is only partially applied, without paying the due attention to proximal processes and how they are jointly influenced by the personal characteristics, various levels of contexts and time variables. Based on this review, we propose the following future directions for international and intercultural education research regarding theoretical perspectives and methodological designs.

4.1 Future directions for theoretical perspectives

For scholars seeking to apply Bronfenbrenner's theory in their empirical studies, we propose two recommendations.

First, consistent with other scholars' previous reminders (e.g., Tudge et al., 2009 ; Rosa and Tudge, 2013 ; Navarro et al., 2022 ), we also emphasize on the importance for studies to clearly specify which version of the theory they are adopting and to provide a rationale for their choices. This clarity will help avoid the “two-fold disservice” pointed out by Tudge et al. ( 2009 , p. 198) and thus allow for an accurate understanding of the theoretical framework, enhance the comparability and consistency of research findings, contribute to the cumulative knowledge in the field, and facilitate the comparison and synthesis of findings across studies. Scholars may choose to adopt the early version of the theory, the EST model, if their study primarily focuses on environmental factors, or they may opt for the mature version of the theory, the PPCT model, if they aim to highlight the crucial impact of proximal processes, and their dynamic interplay with individuals, their characteristics, and their immediate and remote environmental contexts and historical time. However, it is misleading if a study claims to use Bronfenbrenner's “bioecological” theory and only refers to the EST model without acknowledging the updated PPCT model or if a study only partially adopts some constructs in either model without explaining the rationale. Researchers should carefully consider whether a theory is appropriately represented to ensure the credibility and robustness of the research.

Second, to advance the field of international and intercultural education research, we suggest employing the mature version of Bronfenbrenner's bioecological theory, which emphasizes the significance of proximal processes. This shift in emphasis can contribute to a more comprehensive understanding of how individuals actively engage with their educational environments. In child development research, a study involving the PPCT model may examine how regularly occurring parent–child interactions, such as joint storybook reading (e.g., Barnyak, 2011 ), are influenced by important characteristics of the child and some relevant aspects of the context (Tudge et al., 2009 ). Similarly, in international and intercultural education, researchers can gain deeper insights into the everyday activities that shape individuals' development in diverse cultural contexts by focusing on proximal processes. For example, interactions with local people and peers are two different types of proximal processes that an international student may encounter in the host country, which may have either positive or reverse effects on their development. Merçon-Vargas et al. ( 2020 ) further propose the notion of inverse proximal processes to expand the conceptual framework of proximal processes and to address the potential negative impact of certain interactions and activities on human development, particularly in disadvantaged environments. This concept suggests that in disadvantaged environments, detrimental or dysfunctional interactions occurring regularly over extended periods of time are linked to higher levels of dysfunction and lower competency. The notion of inverse proximal processes allows for a more inclusive and expansive use of Bronfenbrenner's bioecological theory.

One question concerns the identification and measurement of proximal processes for investigation, and there is no straightforward answer as Bronfenbrenner did not provide a definitive guideline. In Drillien ( 1964 ) study, Bronfenbrenner identified mother-infant interaction as a proximal process, measured by maternal responsiveness through family observations and interviews. He highlighted that a more comprehensive understanding of proximal processes should also encompass the infant's responsiveness to changes in the mother's behavior, reflecting the reciprocal nature of proximal processes. For Small and Luster ( 1990 )'s study, parental monitoring was identified as a proximal process, assessed through a questionnaire on adolescents' perceptions of parental efforts to stay informed and set limits on their activities outside the home. These examples indicate that diverse tools such as observations, interviews, and questionnaires can measure proximal processes, provided they align with the concept's definition. Therefore, we concur with Navarro et al.'s ( 2022 ) guideline that measures of proximal processes should consider: (a) increasing complexity over time (either inverse or positive); (b) reciprocity between the developing individual and the interacting person(s)/object(s); and (c) duration (i.e., microtime) and frequency (i.e., mesotime) to ensure regular occurrence over an extended period. We also regard it appropriate to design measurement methods tailored to specific research questions.

Previous literature in international and intercultural education has identified key processes contributing to student development, such as interacting with native speakers (Campbell, 2011 ), engaging in cultural activities (Isabelli-García, 2006 ), and attending international courses (Jiang et al., 2023 ). Therefore, studies aiming to investigate these as proximal processes need to examine the level of complexity, mutual engagement, and regularity of these activities and their changes over time. For instance, a study on interacting with native speakers might scrutinize conversation topics for complexity, native speakers' responses for reciprocity, and the duration and frequency of these conversations for regularity. Meanwhile, research on cultural activities might explore the complexity and regularity of different tasks within these activities and how they stimulate subjects' “attention, exploration, manipulation, elaboration, and imagination” (Bronfenbrenner and Morris, 2006 , p. 798).

By following these recommendations, scholars can enhance the applicability and relevance of Bronfenbrenner's theory in the context of international and intercultural education and contribute to the advancement of the empirical field.

4.2 Future directions for methodological designs

Future research in international and intercultural education can benefit from several key methodological considerations. First, studies framed by the bioecological perspective should aim to meet the requirements of a PPCT study design. Bronfenbrenner did not conduct his own research using the PPCT model; instead, he referenced other scholars' work to showcase his concepts. Therefore, interpreting and applying the PPCT model can pose challenges. Navarro et al. ( 2022 ) provide a detailed guideline of how a study should address all the PPCT components to ensure that its design enables a “Bronfenbrenerian synergistic analysis” (p. 240). For instance, the guidelines highlight that PPCT studies must be longitudinal, as the outcome must be measured at a developmentally relevant time point after the proximal process(es), which, as another requirement, should be examined regarding increasing complexity, reciprocity, and duration and frequency. They also note that when applying the bioecological theory and the PPCT model, it is crucial to carefully choose the pertinent elements of person, context, process, and time by thoroughly reviewing empirical studies and pertinent theoretical perspectives. Meanwhile, the synergy among these components should be elaborated, which means the “cooperative action of discrete agencies such that the total effect is greater than the sum of two or more effects—taken independently” (Bronfenbrenner and Morris, 2006 , p. 800), suggesting the use of multigroup models (Navarro et al., 2022 ). Navarro et al. ( 2022 ) demonstrate that the PPCT model allows for the comparison of at least four groups based on different person/context combinations. Quantitative research can use mediational models to assess developmental differences over time, while qualitative researchers will also need to ensure their individual participant selection meets these criteria.

By aligning the research design with the principles and guidelines outlined, researchers can ensure a comprehensive and systematic examination of the four components in the PPCT model. Let us consider how the studies based on the updated version of the bioecological model reviewed above can be redesigned to more closely approximate the PPCT design. For instance, consider Liu et al. ( 2022 )'s research where they identify several factors influencing CROPs' career development, including: (a) the lack of recent experience and familiarity with local academic and publication cultures hindered career development, (b) interactions in the microsystem with senior leaders, line managers, and colleagues had negative impacts, leading to academic pressure and mental health concerns, and (c) the macrosystem of Chinese higher education, driven by the ambition to establish world-class universities, shaped the microsystem's interactional hostility due to marketization and globalization influences in international higher education. Building on these findings, a research approach based on the PPCT model might explore the relationships between these factors using a longitudinal design. Subjects could be categorized along two dimensions: CROPs' familiarity with local academic publication cultures (a Person factor) and the type of university they are working in (a Context factor). This differentiation might involve a national funded university aiming for higher rankings in the Chinese higher education system, reflecting a specific macrosystem, and Sino-Foreign Joint Venture Universities, which mirror a distinct macrosystem akin to Western educational culture. Interactions with senior leaders might serve as a proximal process, varying in positivity or negativity. Developmental outcomes could encompass academic competence (e.g., publications, grants, self-efficacy) and dysfunction (e.g., stress, mental health issues). One potential outcome of such a design might reveal that positive interactions with senior leaders foster academic competence among those familiar with local academic cultures and alleviate academic stress among those less acquainted. However, these interaction effects may differ between national funded universities and Sino-Foreign Joint Venture Universities. This assumption draws from Liu et al. ( 2022 )'s evidence highlighting that in the Chinese culture, “Big Figures” (Da Lao in Chinese) impact resource allocation, potentially influencing the culture of national funded university more significantly. Such insights would deepen our understanding of the intricacies within intercultural settings in higher education.

The PPCT model also offers a means to address conflicting research findings in international and intercultural education research. In the sphere of study abroad research, for instance, there has been extensive exploration of outcomes and influencing factors such as living conditions (Allen et al., 2006 ), social networks (Magnan and Lafford, 2012 ), and duration of stay (Dwyer, 2004 ). However, this body of work often yields conflicting or overly generalized conclusions (Pinar, 2016 ). Take living condition as an example, while some studies emphasize the positive influence of living with host families on language and intercultural competence development (Allen et al., 2006 ), others, such as those by Isabelli-García ( 2006 ) and Jackson ( 2009 ), highlight potential negative effects if interaction with the family is troubled or almost non-existent. Critiques by Coleman ( 2013 ) emphasize the oversight of contextual uniqueness and individual variables in demonstrating study abroad benefits, echoed by Ushioda ( 2009 ) emphasis on the varied impact of social or individual factors. In considering the bioecological model, the study abroad setting does not predict learning outcomes in isolation; rather, it is the activities (proximal processes) in which the students engage that wield greater influence. Therefore, employing a PPCT design could effectively address these controversies by illuminating how varied proximal processes produce differed developmental outcomes as a joint effect of individual characteristics and contextual factors.

Let us envision a hypothetical study that utilizes the PPCT design to navigate the controversial outcomes regarding the influence of host family contexts on students' development of intercultural communication competence. Previous research has demonstrated that the experience of residing with host families may yield positive or negative outcomes contingent upon the established relationships, influencing the shared time and dynamics of interactions between family members and students (Lafford and Collentine, 2006 ). Extensive evidence has indicated that cooperative roles adopted by host families facilitate high-quality interactions, allowing students to practice language skills, receive corrective feedback, and acquire new insights, thereby positively impacting linguistic and cultural knowledge (Knight and Schmidt-Rinehart, 2002 ; Schmidt-Rinehart and Knight, 2004 ; McMeekin, 2006 ). Conversely, Magnan and Lafford ( 2012 ) highlight factors such as limited patience to communicate with students having lower language proficiency, time constraints due to schedule disparities, interpersonal incompatibility, or stressful coexistence, negatively affecting the learning process. In these studies, individual factors such as language proficiency and personality, along with contextual elements encompassing host family dynamics, are often considered as working independently on students' development of intercultural competence. However, the PPCT design seeks to surpass this simplistic additive effect by investigating the synergistic impact of diverse individual and contextual factors. Within this design, subjects can be concurrently stratified across both personal and contextual dimensions. For instance, different levels of student language proficiency could be matched with variations in host family dynamics, including experiences in intercultural encounters, cultural exchange opportunities, and family routines. Proximal process, in this context, delineates the interaction between students and host families, gauged not solely by the depth and degree of mutual engagement in conversations but also by their frequency and consistency, thereby embedding a Time factor. Furthermore, employing a pre-test and post-test design would allow for the observation of development over time.

The bioecological model can provide hypothetical outcomes of implementing such a design. According to the bioecological model, the potency of proximal processes is intricately tied to the characteristics of the developing individual, the environment, and the specific developmental outcome under scrutiny. Building on Small and Luster ( 1990 ) findings, Bronfenbrenner and Morris ( 2006 ) posited a hypothesis: for developmental outcomes of competence 4 , proximal processes exert the strongest influence within the most advantageous ecological niches. Therefore, a speculative outcome from the proposed research design suggests that student-host interaction might yield the greatest positive impact within favorable host family dynamics, particularly among students displaying the highest language proficiency. This proposition underscores the notion that same levels of host family dynamics may not yield identical effects across all students. Neglecting this distinction disregards the potential for students with superior language skills to benefit more from high-quality student-host interaction, perhaps due to their ability to utilize richer language resources during such interactions. Practically, these hypothetical findings imply diverse intervention strategies tailored to students with varying language proficiency levels to optimally allocate resources within study abroad programs. For instance, interventions targeting students with higher language proficiency might emphasize engaging in proximal processes that demand advanced language skills. Conversely, students with lower language proficiency could benefit from focused support to match them with host families known for patience and assistance in language development. This approach would ensure maximum benefit from proximal processes aligned with their proficiency levels.

These hypothetical implications derived from the PPCT model in the two examples above await empirical validation through future research endeavors. Nonetheless, this illustration posits that the PPCT framework presents distinct advantages in both international and intercultural research and practical application domains. Firstly, it helps enhance predictive precision. By comprehensively analyzing the intricate interactions between multiple factors, the PPCT model offers greater predictive accuracy regarding the effectiveness of interventions or strategies across diverse scenarios or individuals. Secondly, it offers a holistic understanding of development. Embracing personal attributes, contextual elements, and developmental processes across time, the implications derived from the PPCT model encourages holistic approaches to educational interventions. Thirdly, it can inform tailored interventions. The PPCT model facilitates the identification of synergistic relationships among variables, enabling researchers to craft interventions precisely tailored to specific contexts or individuals. Finally, it can also help optimize resource allocation for maximum positive outcomes.

Another methodological implication is that to enhance the depth and richness of insights, future studies can diversify their data collection and analysis methods by incorporating both quantitative and qualitative approaches. Table 2 shows that the research reviewed in this study relies largely on qualitative approaches. However, Navarro et al. ( 2022 ) provide some useful illustrative examples of how both quantitative and qualitative researchers can utilize bioecological theory and PPCT. Jaeger ( 2017 ) recommends that a hierarchical linear modeling analysis might best approximate Bronfenbrenner's preference for research since it considers a wide range of complex variables for development. In addition to traditional qualitative methods, researchers can also consider employing the qualitative comparative analysis (QCA) method, which offers a systematic approach to analyzing complex causal relationships by identifying configurations of conditions that lead to specific outcomes. It is particularly appropriate for capturing the complexity of Bronfenbrenner's theory and allows researchers to identify patterns and combinations of conditions that are necessary or sufficient for certain educational outcomes. For example, they can examine how different combinations of individual characteristics, environmental factors, and developmental processes interact to influence educational experiences in diverse cultural settings.

Furthermore, to foster innovation in methodological design, researchers can draw inspiration from other disciplines to expand the methodological toolbox in the field of international and intercultural education. For instance, employing biomarkers as a measure of physiological responses (Yrttiaho et al., 2021 ) within the bioecological framework offers interesting possibilities for future research (Navarro et al., 2022 ) and could provide valuable insights into the biological underpinnings of individuals' adaptation and development within diverse cultural contexts. Such innovative approaches can offer unique perspectives and contribute to a more holistic understanding of the complex interplay between individual, culture, and education. These methodological considerations can inform researchers to advance the field, deepen our understanding of educational experiences in diverse cultural settings, and contribute to evidence-based practices that promote positive educational outcomes for individuals in intercultural contexts.

In conclusion, this article has reviewed studies utilizing Bronfenbrenner's ecological theory in the context of international and intercultural education. These studies have demonstrated the value of employing this theoretical framework to understand the complex interactions between individuals and their environments in diverse cultural contexts. While some studies have focused on the early version of the theory, others have recognized the more recent bioecological model. Moving forward, it is crucial for researchers to specify the version of the theory they are adopting and to consider incorporating the mature version of the PPCT model. Additionally, future research should explore innovative methodological approaches to gain a comprehensive understanding of the intricate dynamics at play in international and intercultural education.

Author contributions

PT and IA designed the study and discussed the findings collaboratively. PT was the main contributor for drafting the manuscript. IA contributed extensively to the revised version. All authors agreed on the final version of the manuscript.

Search strategy for WoS and ERIC databases

Search strategy for wos databases.

Boolean: (TS=(Bronfenbrenner OR bioecological OR ecological systems theory OR process–person–context–time OR PPCT)) AND TS=(international OR intercultural OR study abroad OR exchange OR mobility OR overseas).

• Document Types: Article
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Boolean: (Bronfenbrenner OR bioecological OR ecological systems theory OR process–person–context–time OR PPCT) AND (international OR intercultural OR study abroad OR exchange OR mobility OR overseas).

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Funding Statement

This study is supported by World Languages and Cultures Research Fund granted by China Center for Language Planning and Policy Studies (Project No. WYZL2022HB0010), Wuhan University Office of International Affairs Research Fund (Project No. 600405502), and Wuhan University- Duke Kunshan University Joint Research Platform Seed Fund (Project No. XXWHUDKUZZJJ202301).

1 As will be elucidated in more detail in this paper, Bronfenbrenner's theory evolved from an early version of the ecological systems theory to a bioecological model. The general term “ecological theory” is used to encompass both the early version and its recent reformulation of the bioecological paradigm.

2 In Bronfenbrenner ( 1986a , 1989 )'s earlier theorization, the chronosystem represents a particular type of research design, which should not be confused with the various environmental systems differentiated in his 1979 monograph. However, in his 1994 work, chronosystems are treated as a fifth systems parameter that “extends the environment into a third dimension” (p. 40). Thus, a full representation of Bronfenbrenner's theorization of ecological systems as contexts of development encompasses the four-layered systems conceptualized in 1979, as well as the chronosystem in his subsequent works.

3 Bhowmik et al. ( 2018 ) did not explain why they chose “socioecological model” over more established terms. This lack of clarification leaves room for various interpretations. It might suggest a focus on social aspects within the ecological framework or a departure from the strict bioecological perspective. However, without the authors' explicit explanation, it is hard to determine their intent or whether it is a misapplication. Therefore, clear terminological justifications are crucial, especially when diverging from recognized theoretical labels. This absence of clarification might lead to misunderstandings or ambiguities in understanding Bronfenbrenner's theory and its adaptations.

4 Development, as per the bioecological model, serves as a neutral term encompassing both positive changes ( competence ) and negative changes ( dysfunction ) (Bronfenbrenner and Morris, 2006 ). The hypothesis regarding the developmental outcome of dysfunction differs from that concerning competence. For instance, in Drillien's study, proximal processes had their greatest buffering effect on development of problematic behaviors of infants in the most disadvantaged environment but on the healthiest infants (Bronfenbrenner and Morris, 2006 ).

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.

Publisher's note

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• Chapter 6. Ecological studies

Geographical comparisons

Time trends, occupation and social class.

• Chapter 1. What is epidemiology?
• Chapter 2. Quantifying disease in populations
• Chapter 3. Comparing disease rates
• Chapter 4. Measurement error and bias
• Chapter 5. Planning and conducting a survey
• Chapter 7. Longitudinal studies
• Chapter 8. Case-control and cross sectional studies
• Chapter 9. Experimental studies
• Chapter 10. Screening
• Chapter 11. Outbreaks of disease
• Chapter 12. Reading epidemiological reports
• Chapter 13. Further reading

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