primary production research articles

ORIGINAL RESEARCH article

Long-term spatiotemporal variation of net primary productivity and its correlation with the urbanization: a case study in hubei province, china.

• 1 Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, China
• 2 Bureau of Geology and Mineral Exploration of Anhui Provincial, Hefei, China
• 3 National Satellite Ocean Application Service, Ministry of Natural Resources, Beijing, China
• 4 Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing, China

Net primary productivity (NPP) is a critical component in terrestrial ecosystem carbon cycles. Thus, quantitatively estimating and monitoring the dynamics of NPP have become key aspects for exploring the carbon cycle of terrestrial ecosystems. Anthropogenic activity, such as urbanization, has significant effects on NPP and increases pressure on the natural resources of a specific region. However, to date, although many studies have focused on the relationship between NPP variation and urbanization, they usually ignored any differences at a long-term spatiotemporal variation of urbanization factors, which led to the insufficient understanding of the urbanization-induced impacts on NPP. As a result, this study effectively explored the spatiotemporal variation of NPP from 2001 to 2012 and its corresponding relationship with urbanization, taking the Hubei Province in China as a case study area. To clarify the degree of urbanization, the spatial distribution and temporal variation of population and gross domestic product (GDP) were simulated based on the elevation-adjusted human settlement index and nighttime lights data. The major results showed that high NPP areas were located in those highlands with widespread woodland, in which the NPP value continued to grow during the period. The low NPP areas were mainly distributed in urban areas, and the NPP value had a continued and visible loss. The population and GDP both had a strong correlation with NPP. The significant negative correlation was concentrated in the center of Hubei, with a dense population and developed economy. In order to further realize their complex relationship, the correlation coefficients between the annual NPP and the two factors from 2001 to 2012 were calculated, and the changing trends were investigated. Overall, the findings of this study may provide a reference for studies on the interaction between ecological environment and socioeconomic processes under the background of global rapid urbanization.

Introduction

As one of the most important components of terrestrial ecosystems, vegetation can mitigate the trend of increasing atmospheric greenhouse gases, maintain the global climate, and adjust the global carbon balance ( Piao and Fang, 2003 ; Peng et al., 2015 ). Net primary productivity (NPP), which refers to the net accumulation of organic matter by plants over a period of time, is an important evaluation indicator of vegetation growth ( Buyantuyev and Wu, 2009 ; Shang et al., 2018 ; Yan et al., 2018 ). Moreover, as a critical component of the terrestrial ecosystem carbon cycle, NPP is a sensitive indicator of ecosystem’s health at both the local and global scale ( Fang et al., 2000 ; Gao et al., 2003 ; Xu et al., 2011 ). Therefore, quantitative estimates of vegetation NPP are critical for monitoring regional carbon exchanges, thus understanding ecosystem functions and further developing regional carbon management plans ( Yu et al., 2009 ; Zhang et al., 2016 ).

A series of models have been established to estimate the net primary productivity, which can be mainly grouped into three aspects: statistical models, process-based models, and light-use efficiency (LUE) models ( Potter et al., 1993 ; Wang et al., 2009 ; Chu et al., 2021 ). The Carnegie–Ames–Stanford approach (CASA) model, which is a typical LUE model, has been widely used to perform carbon cycling parameter estimations because of its applicability at both local and continental scales ( Wang et al., 2017 ; He et al., 2018 ). Based on the CASA model, lots of researchers have investigated the long-term variation of NPP in different regions, and many different conclusions have been reached. For example, Gao et al. estimated the grassland net primary productivity in northern Tibet using remote sensing and meteorological data for the period from 1981–2004 ( Gao et al., 2009 ). Potter et al. estimated the carbon flux of the ecosystem of Yellowstone National Park using MODIS data based on the CASA model ( Potter et al., 2011 ). Tan et al. calculated NPP of Xuzhou, China, and the results showed that the average NPP showed a decreasing trend from 2001 to 2010 ( Tan et al., 2015 ). Zhu et al. estimated NPP based on the CASA model in the Greater Khingan Mountain region and analyzed the temporal-spatial variability characteristics of net primary productivity during the period from 1982 to 2013 ( Xie et al., 2021 ).

As we knew, the distribution patterns of NPP and their changes are both driven by natural and anthropogenic factors ( Luo et al., 2018 ). Natural factors, such as temperature and precipitation, have significant influences on vegetative photosynthesis. In addition, the high amount of anthropogenic activities has increased the demand for resources and energy, which can strongly affect the carbon cycle of terrestrial ecosystems. Urban expansion converts vegetation into impervious surfaces, which leads to a significant decrease in vegetation productivity and carbon sequestration capacity ( Solecki et al., 2013 ). It means that the formation of NPP is a typical natural ecosystem function and can effectively indicate the ecological response of urbanization. In general, the urbanization level is very closely related to economic and demographic factors. There is a significant positive correlation between urbanization level and economic growth, and the higher the level of economic development, the higher the level of urbanization. The level of economic development can be quantified through the gross domestic product (GDP), which can be regarded as the typical characteristics of urbanization that can explain the emergence and development of cities ( Jiang and Zeng, 2019 ; Li, 2019 ). Meanwhile, the increasing intensity of the population makes the impact of anthropogenic activities on the terrestrial ecosystem more and more complex ( Shen et al., 2021 ). Urbanization is a process in which the population of a country or region shifts from rural to urban areas, the rural areas gradually evolve into urban areas, and the urban population keeps growing. Cities that are less populated tend to have a more compact land-scape structure and more vegetation in the city center. Therefore, the study of NPP changes during urbanization and their ecological impacts have become an important topic for clarifying the interactions between GDP and population. The previous studies have assessed the effect of anthropogenic activities on vegetation NPP dynamics by quantifying the population and GDP. For example, Li and Cheng found that there was a stable long-term equilibrium relationship between China’s urbanization development and economic growth from 1978 to 2004 ( Li and Cheng, 2006 ). Lu et al. concluded that population and GDP had a significant negative correlation with NPP in Southeast China in a specific year ( Lu et al., 2010 ). Zhao et al. chose NPP and GDP as proxy evaluators to explore the interaction between economic development and environmental change in China ( Zhao et al., 2011 ). Li et al. analyzed the impact of urbanization on vegetation degradation in the Yangtze River Delta of China, and the results showed that the rise of population growth rate and GDP growth has significantly deepened vegetation degradation ( Li, 2019 ). However, these studies only focused on either the spatial or temporal growth of population and GDP. They often ignore any differences at a long-term spatiotemporal variation of urbanization and lack a comprehensive measure associated with NPP relating to population urbanization and economic urbanization.

To overcome this issue, in this article, a long-term spatiotemporal variation of NPP and associated influence of urbanization in Hubei Province from 2001–2012 were monitored and analyzed. As a significant component of the Yangtze River economic zone development strategy, Hubei Province represents a typical region for studying the long-term impact of population and GDP on NPP and should receive considerable attention ( Chai et al., 2019 ). According to the Hubei Statistical Yearbook, the proportion of the urban population increased from 40.8 to 53.5% during the period from 2001 to 2012, and the GDP value grew from 388,000 million CNY to 1,963,200 million CNY. With the rapid growth of the population and the acceleration of urbanization, the ecosystem of Hubei changed significantly, and it is facing severe challenges caused by the pursuit of development. Therefore, understanding the impact of urbanization on NPP in this region has important practical significance. The main objectives of this article are: 1) to analyze the spatiotemporal variations and change trend of NPP in Hubei Province; 2) to estimate the distribution of the population and GDP density and explain their variation trends; and 3) to discuss the correlation relationship between the population and GDP with NPP from both time and space. The results of this study are of great theoretical and practical significance for the effective coordination of nature, economy, and society as well as for sustainable development in Hubei Province.

The remainder of this article is organized as follows: In the Data Description section, the materials and methods are described, including the study area, dataset, data processing, and methods. In the Results and Discussion section, the results and analysis are presented. The discussion is provided, and it includes information on the spatiotemporal variations of NPP, effects of urbanization on NPP dynamics, limitations, and future research recommendations. In the Conclusion section, the conclusions are drawn.

Data Description

Hubei Province is located in Central China (between 29°01′ and 33°06′ north latitude and between 108°21′ and 116°07′ east longitude), and it has an area of approximately 185,900 km 2 ( Figure 1 ) ( Wang et al., 2014 ; Chen et al., 2018 ). The terrain of Hubei is higher in the west and lower in the middle and includes various and complicated geomorphic types, such as mountains, hills, and plains, among which mountains account for approximately 55%, hills account for 24.5%, and plains account only for 20% of the total area. The province has a humid subtropical climate, with average temperatures of 15–17°C and rainfall of 1100–1300 mm ( Lin et al., 2016 ). The dominant land cover types in Hubei are forests, cropland, wetland, grassland, water bodies, and urban building. There are various vegetation types, including subtropical evergreen broad-leaved forest and subtropical mixed evergreen broad-leaved/deciduous broad-leaved forest ( Tao et al., 2017 ). In 2001, Hubei Province’s population was 59.56 million, and its GDP was RMB 0.39 trillion, which rose to 61.65 million and RMB 2.25 trillion in 2012. With the rapid growth of population and urbanization, environmental protection in Hubei Province is facing severe challenges caused by the pursuit of development.

FIGURE 1 . Study area location.

Data Preprocessing

Modis13q1 data.

The 16-day composition MODIS NDVI product (MOD13Q1) with a spatial resolution of 250 m between January 2001 and December 2012 was acquired from the National Aeronautics and Space Administration (NASA, http://edcimswww.cr.usgs.gov/pub/imswelcome/ ). The MOD13Q1 dataset is a MODIS Level-3 data product, which has been preprocessed with radiance calibration and atmospheric correction. The MODIS NDVI dataset was transformed to the Universal Transverse Mercator (UTM) with a World Geodetic System (WGS-84) datum using the MODIS Reprojection Tool (MRT), and monthly NDVI datasets were generated using the maximum value composite (MVC) method.

Nighttime Light Data

Global inter-calibrated nighttime lights (NTLs) (1992–2012), which were provided by Zhang et al. (2016) , were downloaded from the website of Yale University ( https://urban.yale.edu/data ). NTLs were generated from the stable nighttime light annual composite product (version 4) acquired from the National Oceanic and Atmospheric Administration’s National Geophysical Data Center (NGDC) using a novel “ridgeline sampling and regression” method. The DMSP/OLS stable nighttime light annual composite product cannot be used directly due to the lack of continuity and comparability. The “ridgeline sampling and regression” method can create a consistent NTL time series that can be applied globally. The NTL data need to be re-scaled by multiplying pixel values with a scaling factor of 0.01 and reprojected to a value equivalent to NDVI data. The bilinear algorithm was used to resample the NTL data to a pixel size of 250 m × 250 m to match the spatial resolution of the MODIS NDVI dataset.

Meteorological Data

The meteorological data included the monthly mean temperature, monthly cumulated precipitation, and the monthly total solar radiation from 2001–2012, and these data were acquired from the monthly datasets of the terrestrial climate data and the monthly datasets of the radiation data published on the China Meteorological Data Sharing Service System ( http://data.cma.cn/ ). There were 32 stations of temperature and precipitation and 11 stations of solar radiation. Meteorological data were interpolated to the same spatial resolution as NDVI data using the kriging spatial interpolation method.

Other Ancillary Data

Ancillary data used in this study include digital elevation model (DEM) data, land use/land cover data, the MODIS NPP products (MOD17A3), and the GDP and population data at the county level. DEM data with a spatial resolution of 90 m were obtained from the geospatial data cloud ( http://www.gscloud.cn/ ), and land use/land cover data were acquired from the MODIS Level-3 land cover type product (MCD12Q1). Land cover types were divided into evergreen needleleaf forest (ENF), evergreen broadleaf forest (EBF), deciduous broadleaf forest (DBF), mixed forest (MF), grassland, wetland, cropland, urban, and water bodies. The MODIS NPP products (MOD17A3) covering the period from 2001 to 2012 were selected to validate the simulated NPP results. Datasets with a spatial resolution of 1 km and a temporal resolution of 1 year were acquired from the Land Processes Distributed Active Archive Center ( https://lpdaac.usgs.gov ). All the above data were resampled to match the 250 m spatial resolution of MODIS NDVI data using the Resample tool of ArcGIS. The GDP and population data from 2001–2012 at the county level for Hubei Province (81 counties in total) were obtained from the Hubei Provincial Bureau of Statistics (Hubei Statistical Yearbook and Hubei Rural Statistical Yearbook).

NPP Estimation

The CASA model is based on light-use efficiency (LUE) theory, and it simulates net primary productivity by estimating optimal metabolic rates of carbon fixation under the limiting effect of temperature and water stress scales ( Potter et al., 2003 ). In the original CASA model, the maximum light energy utilization rate of all vegetation in the world was set to 0.389 g C MJ −1 . However, different vegetation types should correspond to different maximum light energy utilization rates. The researchers simulated the maximum light energy utilization rate of all vegetation types according to the actual vegetation distribution in China ( Guan et al., 2021 ). Therefore, the improved NPP estimation model developed was applied in this study to simulate NPP over Hubei Province from 2001 to 2012. NPP can be calculated based on the following equation:

where x represents the spatial location, t represents time, APAR represents absorbed photosynthetically active radiation, and ε represents light-use efficiency.

where SQL represents the total solar radiation per unit time, FPAR represents the fraction of photosynthetically active radiation, T1 and T2 represent the effect of temperature on light-use efficiency, and W represents the effect of soil moisture on light-use efficiency.

Population Density Mapping

It has been proved that the night light data are an effective tool for the spatialization of population and GDP density at national and province levels ( Yue et al., 2014 ; Song et al., 2015 ; Wang et al., 2018 ). The global inter-calibrated nighttime light (NTL) data were introduced in our study to spatialize the population and GDP density. Moreover, the human settlement index (HSI) is an index for mapping spatial population distribution by incorporating vegetation information into the nighttime light data. This index was proposed by Lu et al. (2008) and based on the rationale that impervious surfaces are closely and inversely correlated with vegetation abundance. Many studies have shown that elevation has a profound impact on the human population distribution because most human settlements occur at lower elevations in China ( Yue et al., 2003 ; Yang et al., 2013 ). If the influence of altitude is not taken into account, then large errors will be introduced into the population simulation results for high-altitude areas. In our study, the average population density and average altitude of 81 counties in Hubei Province were analyzed by exponential function regression. The coefficient of determination (R 2 ) was greater than 0.7 (the coefficient of the exponential equation was -0.002), which indicated that there was a strong negative correlation between altitude and population density. Therefore, an elevation-adjusted human settlement index (EAHSI) was used to estimate the population density in Hubei Province. The data include DEM, standard MODIS NDVI products (23 images per year), and nighttime light data from 2001 to 2012. The EAHSI is defined as follows:

where NDVI max is the maximum image of 23 MODIS NDVI composite images, NTL nor is the normalized value (0–1) of the nighttime light image, and NTL max and NTL min are the maximum and minimum values of the image, respectively.

A total of 41 cities were randomly selected from Hubei Province as experimental areas and the remaining 40 counties as verification areas. A population density model was then built by inputting the cumulative DN of EAHSI at the county level and the corresponding population of the 41 experimental counties into the regression. The model creates a spatial population density map, and the performance of the modeled population density results is calculated by the relative error (RE) and mean relative error (MRE) ( Yang et al., 2013 ; Yue et al., 2014 ; Sun et al., 2017 ). The RE and MRE are calculated as follows:

where POP m and POP a represent the simulated population and the actual statistical population, respectively, and n represents the number of counties.

GDP Density Mapping

Night light data have the strongest correlation with the sum of the GDP of secondary and tertiary industries; however, these data are not well suited for estimating GDP in rural areas. Therefore, the spatialization process of non-agricultural GDP (the GDP of secondary and tertiary industries) is similar to that of population, while that for agricultural GDP (the GDP of the primary industry) uses land cover data. In this study, MCD12Q1 land cover data were used to model the agricultural GDP, and the EAHSI images were used to model the non-agricultural GDP.

To conduct the spatial simulation of agricultural GDP, the land cover types related to agricultural activities in MCD12Q1 were combined into farmland, forestland, grassland, and water, which corresponded to the gross production values of agriculture, forest, animal husbandry, and fishery, respectively. The EAHSI image was used to model the non-agricultural GDP. When the non-agricultural GDP is spatialized, the minimum value of the NTL image (NTL min ) was determined. The agricultural and non-agricultural regions were divided by a threshold value. First, the mean NDVI value of artificial surfaces in land cover data was calculated for Hubei Province. Second, the regions with non-artificial surfaces larger than the average NDVI value and NTL DN values >0 were divided. The DN threshold value of the image was determined by the mean NTL DN value in this region.

Trend Analysis

Ordinary least square estimations were performed for each pixel to quantify the linear trends of NPP in Hubei Province from 2001 to 2012. The equation was calculated as follows:

where i represents the ordinal year, 1, 2, … , 12; n = 12 (the time series is from 2001 to 2012); θ i is the annual NPP, in the year i; and Slope is the slope of the linear fitting equation. Slope >0 indicates an increasing trend, and the converse denotes a decreasing trend. The F-test is generally used to determine the significance of the change trends. The equation for this test is as follows:

where U is the error sum of squares, and Q is the regression sum of squares, n = 12. Based on the results of the F-test and the trend analysis, the trends were classified according to four ranks: significant decrease (Slope <0 and p ≤0.05), insignificant decrease (Slope <0 and p >0.05), significant increase (Slope ≥0 and p ≤0.05), and insignificant increase (Slope ≥0 and p >0.05).

Analysis of the Impacts of Urbanization on NPP

In this study, a correlation analysis, which is a common method of analyzing the relations between the net primary productivity and associated influencing factors, was performed to quantify the impact of population and GDP on NPP at the pixel scale, and Pearson’s correlation coefficient, which can show the strength of the relationship between urbanization indicators and NPP, was calculated. The T-test was used to determine the significance of the correlation analysis. A value of p <0.05 was considered significant. The equation of the correlation coefficient is expressed as follows:

where x i is the NPP of the ith year, y i is the corresponding population or GDP density of the ith year, ‾x and ‾y are the means of x and y, respectively, and r is the correlation coefficient of the two variables x and y.

Study Process

The study process includes the following steps.

1) The NPP values in Hubei Province from 2001 to 2012 were calculated based on the modified CASA model. After that, the spatial patterns, temporal variations, and the spatiotemporal variation trends of NPP were acquired.

2) The MODIS NDVI data, SRTM DEM data, land cover map, NTL data, and other ancillary data were all effectively coupled. Then, the population and GDP density were spatialized under the specified scale.

3) The correlation relationship between the population and GDP density with NPP was calculated. Finally, the variation trend of NPP with population and GDP density was analyzed. The flow chart of this study is shown in Figure 2 .

FIGURE 2 . Methodological framework of this study.

Results and Discussion

Validation of the estimated results, validation of the npp calculations.

The MOD17A3 NPP products were used to assess the accuracy. First, the MOD17A3 NPP products were resampled to a 250-m spatial resolution. After that, the average NPP values estimated by the CASA model for different land cover types were calculated and compared with the MOD17A3 NPP. The linear regression analysis results between the NPP estimates based on the CASA model and MOD17A3 NPP products are shown in Figure 3 . A strong correlation was observed between them; the average relative error was 19.52%, and the correlation coefficient was 0.85. The results calculated in this article are similar to those of the MOD17A3 NPP products. Additionally, the estimated results of specific cities in Hubei Province can also be compared with those observed in previous studies. For example, the average inter-annual NPP of the evergreen needleleaf forest in Wuhan in our study was 578.06 g C/m 2 , which is similar to the value of 582.4 g C/m 2 based on historical data as estimated in Zhang et al, (2011) . Therefore, the CASA model in our study is practical and applicable for calculating NPP in Hubei Province.

FIGURE 3 . Correlation between the estimated NPP values and MOD17A3 NPP values.

Error Analysis of the Population Density Map

The NTL, NDVI, DEM, and census population data at the county level were used to generate the spatial population density maps of Hubei Province with a resolution of 250 m from 2001 to 2012 based on the population spatial model mentioned in the Population Density Mapping section. The cumulative DN values of the EAHSI at the county level were input into the regression with the corresponding population of 41 counties selected randomly in Hubei Province. Then, the overall accuracies of the regression model were evaluated by calculating the MRE in the remaining 40 counties. Table 1 presents the regression model among the cumulative EAHSI, population statistical data, and the results of accuracy assessment from 2001 to 2012. In the table, the R 2 represents the fitting degree of the regression equation of the population spatial model, and the values were all relatively large over the 12 study years. Their values were all above 0.79, and the highest value occurred in 2012. The excellent fitting effect of the model indicates that the night light data can well reflect the spatial distribution of the population. For the accuracy of the model, all the MRE values were less than 30%, indicating that the overall simulation error is small during the period from 2001–2012. In addition, the error in 2006 was the smallest whereas the error in 2005 was the largest, with MRE values of 25.82 and 28.86%, respectively.

TABLE 1 . Population spatial model from 2001 to 2012 (ρ and β are the population density and Ln values of EAHSI, respectively).

Error Analysis of the GDP Density Map

The spatialization process and error analysis of non-agricultural GDP density were similar to those of the population density. Table 2 presents the regression model between the cumulative EAHSI and GDP statistical data and the results of the accuracy assessment from 2001 to 2012. The table shows that the determination coefficients were all relatively large with values above 0.80 during the period from 2001 to 2012. The excellent fitting effect of the model indicates that the night light data can well reflect the spatial distribution of non-agricultural GDP. For the accuracy of the model, all the values of MRE were less than 30%, indicating that the overall simulation error was small from 2001 to 2012.

TABLE 2 . Non-agricultural GDP spatial model from 2001 to 2012 (ρ and β are the GDP density and Ln values of EAHSI, respectively).

Spatiotemporal Variation of NPP

In this section, the long-term characteristics of NPP in Hubei Province were evaluated from three aspects: spatial patterns, temporal variations, and the variation trend of NPP.

Spatial Patterns of Mean Annual NPP

In order to conveniently describe the distribution of NPP, the degree of NPP can be divided into four different grades with red, yellow, blue, and green, which mean very lower (NPP <400 g C/m 2 ), lower (400 < NPP < 600 g C/m 2 ), middle (600 < NPP < 800 g C/m 2 ), and higher (NPP >800 g C/m 2 ), respectively. According to the land cover type product MCD12Q1 in Hubei Province, the main land cover types are ENF, EBF, DBF, MF, grassland, wetland, cropland, urban, and water, respectively. Except for water, all of the land cover types had the influence on NPP. The spatial pattern of the mean annual NPP from 2001–2012 in Hubei Province is shown in Figure 4A , and the land cover types and mean NPP are shown in Figure 4B . As shown, the western part of Hubei has a high altitude, and the main land cover types are woodland (ENF, EBF, DBF, and MF) and grassland, which can be seen as the higher NPP grade areas. Woodland and grassland accounted for 63.78% of the study area, and the mean annual NPP value was more than 500 g C/m 2 . Among them, the mean annual NPP values of EBF and DBF were both more than 800 g C/m 2 , which were mainly distributed in the northwest of Hubei. East Hubei was the middle NPP area. There are three main land cover types, including cropland, little woodland, and grassland. Due to the hilly region, the mean annual NPP value was general lower than that of west Hubei. Central Hubei, especially in the south central region, is the Middle-Lower Yangtze Plain. It presents a flat terrain and concentrated population. The main land cover types are cropland, wetland, and urban. The mean annual NPP values of wetland and urban land were both lower than 500 g C/m 2 . Therefore, the very lower and lower grade NPP values were both distributed in this region. In addition, the different land cover types had a different combination of four NPP grades. Figure 4C explains the percentage of four NPP grades according to each land cover type. In the higher grade, EBF and DBF showed higher NPP percentages with 83.9 and 63.8%, respectively. In the middle grade, MF and grassland were the mainly distributed land cover types with 83.9 and 81.7%. In the lower grade, the proportions of wetland, cropland, ENF, and urban were higher than those of others, which were both more than 50%. In the very lower grade, urban and wetland were accounted for 37.7 and 25.9%, respectively.

FIGURE 4 . (A) Spatial distribution of the mean annual NPP; (B) land cover types of Hubei Province and mean NPP; and (C) percentage of four NPP grades for eight land cover types.

Temporal Variations of NPP

The temporal variations of the annual average NPP of Hubei Province from 2001 to 2012 are shown in Figure 5 . Generally, the annual NPP varied from 573.51 g C/m 2 to 701.85 g C/m 2 over the past 12 years, with the highest in 2004 and the lowest in 2011. The change in NPP can be divided into two sub-periods throughout the period. NPP showed an increasing trend from 2001 to 2004 (32.34 g C/m 2  yr), followed by a wavelike decrease from 2004 to 2012 (11.30 g C/m 2  yr). Table 3 lists the different changes for the four grades. In the very lower NPP areas (NPP <400 g C/m 2 ), there was a negative change with a value of −18.58 g C/m 2 from 2001 to 2012. The main reason for the decline was that more and more cropland and grassland are converted into urban land with the development of the economy and the acceleration of urbanization. The percentage of urban area was increased by 5.25% during the period, and approximately 90.36% of the total urban area has negative change rates on NPP, which led to a significant NPP loss in time and space. In contrast, the different positive changes occurred when the NPP value exceeded 400 g C/m 2 . In the lower NPP areas (400 g C/m 2 < NPP < 600 g C/m 2 ), there was a positive change with 10.60 g C/m 2 . The main land cover type is the cropland, and the percentage area increased from 55.67 to 58.42% from 2001 to 2012. In the middle NPP areas (600 g C/m 2 < NPP < 800 g C/m 2 ), the NPP value has increased by 5.44 g C/m 2 . The grassland is the main land cover type. Although the area of grassland has been reduced little from 74.67 to 68.69%, the percentage of cropland area increased from 31.33 to 34.93%, which still kept the NPP in an increased state. In the higher NPP areas (NPP > 800 g C/m 2 ), the NPP values have increased by 13.86 g C/m 2 . The main land cover type is DBF decreased from 91.72 to 82.70% from 2001 to 2012. But, the percentage of grassland area increased from 8.09 to 17.00% from 2001 to 2012. The high increase rate was maintained due to the significant NPP correlation of the DBF and grassland.

FIGURE 5 . Temporal variations of the annual average NPP.

TABLE 3 . Change of NPP for the four grades.

The Variation Trend of NPP

The slope of the equation obtained by linear least-square fitting of long-term NPP series can show the development trend of NPP over 12 years. As shown in Figure 6 , there are four different colors (red, yellow, green, and blue) representing the variation trends, including the significant decreasing trend (SDT, p >0.05), decreasing trend (DT, p <0.05), significant increasing trend (SIT, p >0.05), and increasing trend (IT, p <0.05). The area with DT of annual NPP accounted for 59.82% of Hubei Province and was distributed roughly in the central area of the study region. The area with an IT of annual NPP was mainly distributed in west Hubei and accounted for 25.37%. Only 7.88 and 6.92% of the study area showed SDT and SIT, respectively. Therefore, the number of pixels of the two variation trends is too small to find in the figure.

FIGURE 6 . Change trends in annual NPP within Hubei Province between 2001 and 2012.

Different land cover types had different responses to NPP, which was the major reason for the variation trend. The rate of NPP change for each land cover type is listed in Table 4 . It is found that there were only two land cover types with positive change rates, EBF (37.38 g C/m 2  yr) and DBF (4.03 g C/m 2  yr). The rest of land cover types all had negative change rates. The order of the decreasing rate from high to low was as follows: urban (-10.63 g C/m 2  yr), wetland (−9.95 g C/m 2  yr), ENF (−9.51 g C/m 2  yr), cropland (−3.52 g C/m 2  yr), MF (−2.64 g C/m 2  yr), and grassland (−1.73 g C/m 2  yr). Among all land cover types, grassland showed the most significant SIT with 7.91% and the most significant SDT with 8.55%. Moreover, the largest percentage of DT and IT occurred in urban areas with 87.28% and EBF with 90.19%. The main land cover types are cropland, grassland, and MF in the area with DT of annual NPP. They all had the negative change rates, and the percentages of DT were both more than 50% in these areas, which caused the decreasing trend. In the area with IT of annual NPP, the main land cover types are EBF, DBF, and grassland. There were higher positive change rates in EBF and DBF, which caused the increasing trend on the whole.

TABLE 4 . Rate of NPP change for each land cover type.

Correlation Between Urbanization and NPP

The urbanization process has an important impact on NPP variation. Population growth and economic development, a representation of the agglomeration effects, are highly correlated with urbanization. In this section, the relationships between NPP and the two urbanization indicators were described.

Population Density Map

The spatial distributions of the population density for Hubei Province from 2001 to 2012 are presented in Figure 7 . The unit of population density is defined as PD = 10,000/0.0625 km 2 because the resolution of the population density map is 250 m (0.25 km). The sparsely populated areas (population density less than 0.1 PD) were mainly distributed in the western areas of Hubei Province, such as Shiyan, Enshi, and Shennongjia Forestry District, while densely populated areas (population density more than 0.4 PD) were mainly in the highly urbanized regions, such as Wuhan City, Xiangyang City, Yichang City, and Jingzhou City. The population of sparsely populated areas increased from 22.87 to 47.87% from 2001 to 2004 and continued to decline after 2004, accounting for only 28.37% in 2012. The population in the densely populated areas decreased from 2001 to 2004 and increased steadily after 2004, accounting for 10.97% in 2012. In general, from 2001 to 2012, the proportions of the population in sparsely populated areas were large but showed a decreasing trend, whereas the population in densely populated areas increased steadily.

FIGURE 7 . Population density maps with a 250 m × 250 m grid (A) – (L) the different maps from 2001 to 2012.

Correlation Between Population and NPP

With the change in population density, the NPP values would change. Figure 8A shows the change in NPP in different population density ranges from 2001 to 2012. Even though the population densities of 12 years were quite different, the trends in NPP were the same. With an increase in population density, the average NPP value decreased. Figure 8B shows the annual average NPP changes during the period. It is found that the average NPP value was the minimum when the population density was more than 0.4 PD.

FIGURE 8 . (A) NPP change in different population density ranges from 2001 to 2012 and (B) annual average NPP change in different population density ranges.

To further investigate the correlation between NPP and population density, the correlation coefficient is calculated and analyzed from two aspects. First, the spatial pattern of the correlation coefficient is shown in Figure 9A . The negative correlation (NC) is labeled as yellow, significant negative correlation (SNC; p <0.05) is labeled as red, positive correlation is labeled as green, and significant positive correlation (SPC; p <0.05) is labeled as blue. The area with NC between NPP and population accounted for almost 85.49% of Hubei Province, of which approximately 15.08% showed SNC. The significant negative relationship was concentrated in the center of Hubei, which was mainly covered by cropland and urban areas. With the increase of population density, the negative correlation coefficients become more significant. The tendency and process of the mass population gathering toward big cities that leads to land-use/cover change. It is found that more and more cropland and woodland (including EBF, DBF, and MF) are converted into urban land during the period. This is probably the main reason for the decrease in NPP caused by population growth. Moreover, areas with PC accounted for almost 14.51% of the study region, and approximately 0.48% showed SPC. These areas were mainly distributed in the western Hubei mountains and eastern Hubei Province. Second, the main considerations and affecting factors of the correlation coefficient were provided. Figure 9B describes that the correlation coefficient is different when the population density ranges. The coefficients were all negative that shows NC and SNC were dominant in the five population densities. The negative correlations become larger and larger with increased population density. When the population density was more than 0.4 PD, the negative correlation coefficient with the population was the maximum. Besides, correlation coefficients were different for different land cover types, which could be seen as indirect impacts. As shown in Figure 9C , the negative correlation coefficient between NPP and population was the largest in urban, with the mean value of −0.46. The negative correlation coefficients of other land-use types were smaller. The order from high to low was mainly as follows: wetland (−0.39), EBF (−0.31), DBF (−0.30), cropland (−0.29), grassland (−0.28), and MF (−0.19). Although there was a positive correlation in ENF with a mean value of 0.01, it had little influence on NPP. Therefore, urban had more importance in influencing NPP than other land cover types.

FIGURE 9 . (A) Spatial distribution of the correlations between population and annual NPP; (B) correlation coefficients between population and annual NPP for the different population density ranges; and (C) correlation coefficients between NPP and population for different land cover types.

GDP Density Map

The spatial distributions of the GDP density for Hubei Province from 2001 to 2012 are presented in Figure 10 . The unit of GDP density is defined as GD = 100 million CNY/0.0625 km 2 . As shown in the figure, most of the GDP contributions were from regions with a concentrated population and a high level of economic development, especially from Wuhan City, which had a high level of urbanization. Areas with a GDP density greater than 0.1 GD mainly occurred in Wuhan City. In addition, the GDP density in most areas of Hubei Province was in the range of 0–0.01 D. GDP density ranging from 0 to 0.01 GD accounted for 95.31% of the study region in 2001, while in 2012 it only accounted for 76.94%. Since 2004, the GDP density had exceeded 0.1 GD. The proportions of GDP density greater than 0.1 GD increased from 0.06% in 2001 to 2.70% in 2012.

FIGURE 10 . GDP density maps with a 250 m × 250 m grid. (A) – (L) the different maps from 2001 to 2012.

Correlation Between Population and GDP With NPP

Similarly, the correlation between NPP and GDP is described as follows: Figure 11A shows the variation trend of the annual average NPP with GDP density. With the increase of GDP density, the average NPP value decreased. Therefore, the higher the GDP density, the greater the reduction rate of NPP. Figure 11B shows the annual average NPP changes during the period. The average NPP value was the minimum when the population density was more than 0.1 GD.

FIGURE 11 . (A) NPP and GDP density from 2001 to 2012 and (B) annual average NPP and its regression slope with GDP density.

The correlation coefficient was calculated and analyzed from two aspects. First, the spatial pattern of the correlation coefficient between GDP and NPP is shown in Figure 12A . The areas with negative correlation (NC) between NPP and GDP accounted for almost 73.97% of Hubei Province, of which approximately 12.6% showed a significant negative correlation (SNC; p <0.05). The areas with a positive correlation (PC) between NPP and GDP accounted for almost 36.03% of the study region, and approximately 6.48% showed a significant positive correlation (SPC; p <0.05). Areas with a negative correlation between NPP and GDP were mainly located in central Hubei Province, while the areas with positive correlations were mainly distributed in northwest, southwest, and southeast Hubei Province. Second, the affecting factors of the correlation coefficient are analyzed, including the GDP density and land cover types. The correlation coefficients for the different GDP density ranges are described in Figure 12B . The trend of the negative correlation was not the same as the one between NPP and population. In the beginning, it increased with the continuous increase of GDP density. When the GDP density was 0.2–0.3 GD, the negative correlation coefficient reached the maximum. After that, the negative correlation between NPP and GDP was not significant, which means that the growth of GDP might lead to the increase of NPP in some regions under a certain threshold. For example, the economic growth can further improve management capacity with good policies. Artificial management, such as irrigation and planting, resulted in improved vegetation coverage and increased NPP. Similar to NPP and population, correlations between NPP and GDP were also different for different land cover types. Urban had more importance in influencing the relationship between NPP and GDP than other land cover types. In Figure 12C , the cropland, wetland, ENF, and MF were all greater than −0.3. There was a positive correlation in EBF with a mean value of 0.26. But, its area was so small that it was almost negligible.

FIGURE 12 . (A) Spatial distribution of the correlations between GDP and annual NPP; (B) correlation coefficients between GDP and annual NPP for the different GDP density range; and (C) correlation coefficients between NPP and GDP for different land cover types.

Limitations and Future Research

Due to urbanization being a complex process, the interaction between terrestrial ecosystems and socioeconomic processes is also relatively complicated. The process of population urbanization is different in different stages of urbanization, so is economic urbanization. The relationship of NPP and urbanization needs to be studied over a longer period of time. Some researchers have indicated that the negative impacts of urbanization on vegetation gradually weaken with the continuous improvement of urbanization levels, and the positive impacts of urbanization on vegetation will be increasingly obvious. It is because that some anthropogenic activities have led to an increase of vegetation cover in urban areas, such as irrigation and tree transplantation. This means that urbanization unavoidably leads to the degradation of vegetation and the decrease of vegetation productivity. However, cities may further strengthen ecological management capacity as the economy grows. These measures can make a great contribution to the vegetation in urban areas. In addition, the population is gathering toward the city with the expansion of the city size. Population migration can improve the vegetation conditions in rural areas with the increase of urbanization levels. Although 12 years were analyzed to determine the impacts of urbanization on vegetation, the different stages of urbanization cannot be fully described. Therefore, in future studies, vegetation NPP should be estimated and monitored over a longer study period to explore the effects of population and GDP on NPP in different stages of urbanization.

Moreover, climate change is also a major controlling factor in the response of NPP. Research shows that the temperature had a significant decreasing trend during the period from 2001–2012, which was similar to the trends of the annual NPP ( Figure 13 ). Precipitation also tended to decrease but was not significant. The decreasing trend in temperature and precipitation changes the environmental conditions of vegetation growth, which further affects the vegetation distribution and vegetation net primary productivity. The two factors need to be further investigated.

FIGURE 13 . Inter-annual variations of temperature and precipitation of Hubei Province from 2001 to 2012.

In this study, the NPP in Hubei Province during the period from 2001 to 2012 was calculated, and the spatiotemporal dynamics of NPP and its relationships with the urbanization indicators (population and GDP) were investigated. The main conclusions can be summarized as follows: 1) The distribution pattern of NPP is as follows: northwest Hubei has a high altitude, and the main land cover type is woodland with high vegetation coverage and little human activities. Therefore, there were higher NPP values in northwest Hubei. The urban areas, which have an extensive distribution of urban and built areas, showed the lowest NPP values. In addition, the land cover type had significant influences on the spatial distribution of NPP. 2) During the study period, the average NPP value increased in high NPP areas. Because the inter-annual variation of NPP in DBF, the main land cover type in this region, was positive. On the contrary, the average NPP value decreased in low NPP areas. The urban areas continued to expand, but the inter-annual variation of NPP was negative in urban areas. From 2001 to 2012, NPP decreased by 18.58 g C/m 2 in low NPP areas. The losses of NPP in urban areas are continuing and evident. 3) Population and GDP density are the typical indicators of anthropogenic activities, which can play an important role in the distribution and dynamics of NPP. During the study period, there was a negative correlation between NPP, population, and GDP. On the whole, the highest values of negative correlation coefficients were found in urban areas and the lowest in woodlands. The expansion of the built-up land in the middle and east can decrease the size of green areas and reduce the productivity of vegetation. Meanwhile, the remaining woodlands are mostly distributed in the west. The estimated NPP values were likely to remain unchanged with the urbanization developments. In addition, the impact of human activity on NPP was different depending on the intensity of human activity. With the increase of population density, the negative correlation coefficients between population and annual NPP become larger. But, the correlations between NPP and GDP were not the same as the correlation between NPP and population. The negative correlation coefficients between GDP and NPP increased first and then decreased with the continuous increase of GDP density. This indicates that the impact of urbanization on NPP is not completely negative when GDP reaches a certain threshold.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material; further inquiries can be directed to the corresponding author.

Author Contributions

KW and CZ contributed to conceptualization; KW and CZ contributed to methodology; CZ contributed to writing—original draft preparation; and KW, CZ, YZ, and YX contributed to writing—review and editing. All authors have read and agreed to the published version of the manuscript.

The research was supported by the Global Change and Air-Sea Interaction II under Grant GASI-01-DLYG-WIND01 and in part by the National Defense Pre-Research Foundation of China during the 13th Five-Year Plan Period: the High Spectral Resolution Infrared Space-Based Camera and the Applied Technology under Grant D040104. this work was also supported in part by the National Natural Science Foundation of China under Grant 62071438.

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

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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APAR Absorbed photosynthetically active radiation

CASA Carnegie–Ames–Stanford Approach

DEM Digital elevation model

DBF Deciduous broadleaf forest

DT Decreasing trend

EAHSI Elevation-adjusted human settlement index

ENF Evergreen needleleaf forest

EBF Evergreen broadleaf forest

FPAR Fraction of photosynthetically active radiation

GLO-PEM Global production efficiency model

GDP Gross domestic product

HIS Human settlement index

IT Increased trend

LUE Light-use efficiency

MRT MODIS Reprojection Tool

MVC Maximum value composite

MF Mixed forest

MRE Mean relative error

NGDC National Geophysical Data Center

NDVI Normalized difference vegetation index

NTLs Nighttime lights

NPP Net primary productivity

NC Negative correlation

PC Positive correlation

RE Relative error

R 2 Determination coefficient

SDT Significant decreasing trend

SIT Significant increasing trend

SNC Significant negative correlation

SPC Significant positive correlation

UTM Universal Transverse Mercator

Keywords: NPP, CASA, spatiotemporal dynamics, urbanization, Hubei province

Citation: Wu K, Zhou C, Zhang Y and Xu Y (2022) Long-Term Spatiotemporal Variation of Net Primary Productivity and Its Correlation With the Urbanization: A Case Study in Hubei Province, China. Front. Environ. Sci. 9:808401. doi: 10.3389/fenvs.2021.808401

Received: 03 November 2021; Accepted: 02 December 2021; Published: 10 January 2022.

Reviewed by:

Copyright © 2022 Wu, Zhou, Zhang and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ke Wu, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Exploring spatial non-stationarity and scale effects of natural and anthropogenic factors on net primary productivity of vegetation in the yellow river basin.

1. Introduction

2. materials and methods, 2.1. study area, 2.2. data sources, 2.3. methods, 2.3.1. theil–sen median analysis and mann–kendall test, 2.3.2. hurst index, 2.3.3. multi-scale geographically weighted regression, 3.1. spatiotemporal variation in vegetation npp, 3.2. trend of change in npp, 3.3. regression coefficients of influencing factors, 3.4. spatial interactions between npp and influencing factors, 3.5. scale differences in influencing factors, 4. discussion, 4.1. increased trend in vegetation npp and its persistence, 4.2. spatial non-stationarity of natural and anthropogenic effects on npp, 4.2.1. the effect of natural factors on npp, 4.2.2. the effect of anthropogenic factors on npp, 4.3. scale effect analysis, 4.4. policy suggestion, 4.5. limitations and uncertainties, 5. conclusions, author contributions, data availability statement, conflicts of interest.

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Wang, X.; He, W.; Huang, Y.; Wu, X.; Zhang, X.; Zhang, B. Exploring Spatial Non-Stationarity and Scale Effects of Natural and Anthropogenic Factors on Net Primary Productivity of Vegetation in the Yellow River Basin. Remote Sens. 2024 , 16 , 3156. https://doi.org/10.3390/rs16173156

Wang X, He W, Huang Y, Wu X, Zhang X, Zhang B. Exploring Spatial Non-Stationarity and Scale Effects of Natural and Anthropogenic Factors on Net Primary Productivity of Vegetation in the Yellow River Basin. Remote Sensing . 2024; 16(17):3156. https://doi.org/10.3390/rs16173156

Wang, Xiaolei, Wenxiang He, Yilong Huang, Xing Wu, Xiang Zhang, and Baowei Zhang. 2024. "Exploring Spatial Non-Stationarity and Scale Effects of Natural and Anthropogenic Factors on Net Primary Productivity of Vegetation in the Yellow River Basin" Remote Sensing 16, no. 17: 3156. https://doi.org/10.3390/rs16173156

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Maximizing Legacy and Impact of Primary Research: A Call for Better Reporting of Results

Neal r. haddaway.

Centre for Evidence-Based Conservation, School of Environment, Natural Resources and Geography, Bangor University, Bangor, LL57 2UW UK

Much of the scientific literature in existence today is based on model systems and case studies, which help to split research into manageable blocks. The impact of this research can be greatly increased in meta-analyses that combine individual studies published over time to identify patterns across studies; patterns that may go undetected by smaller studies and that may not be the main subject of investigation. However, many potentially useful studies fail to provide sufficient data (typically means, true sample sizes, and measures of variability) to permit meta-analysis. Authors of primary research studies should provide these summary statistics as a minimum, and editors should require them to do so. By putting policies in place that require these summary statistics to be included, or even those that require raw data, editors and authors can maximize the legacy and impact of the research they publish beyond that of their initial target audience.

Introduction

Some 8323 scientific journals were listed in Journal Citation Reports in 2013, with tens of thousands more journals unlisted. The vast majority of these journals have been given impact factors in the lower end of the spectrum, giving a classic Poisson distribution with a median of approximately 0.5 and 1.0 (Thomson Reuters 2013 ). Thus, the majority of journals are typically more applied (i.e., focused on more practical subjects) than their counterparts at the far end of the spectrum, publishing research that targets specific audiences. Much of the research in these publications uses model species and habitats or case studies to simplify more complex systems (e.g., Rantalainen et al. 2008 ). While these studies are often quite specific, they can inform wider analyses if, for example, used in a meta-analysis and systematic reviews (SRs) (Pullin and Knight 2001 ).

Meta-analyses are statistical methods that combine like studies to create a single study of far greater effective sample size than any of its constituent parts (Glass 1976 ). These analyses are used where individual studies disagree, or where individual studies are thought to be of insufficient power to identify significant effects. Meta-analyses are powerful tools to increase the value and impact of research. Meta-analysis has been widely used in recent decades in medicine to identify significant patterns in the evidence that may go undetected in individual studies (O’Rourke 2007 ). Analyzed together, the evidence provided by individual studies is more powerful than the sum of its individual analyses. Furthermore, meta-analyses allow us to examine the effect of modifying factors that may not have been considered in the original research. For example, while individual studies on the effect of drainage on greenhouse gas emissions from peatlands may each have been undertaken in sites with a specific mean annual rainfall and temperature, when studies are combined in a meta-analysis the effect of meteorology on the relationship between land management and emissions can be examined (also referred to as sources of heterogeneity and effect modifiers ) (Haddaway et al. 2014 ).

Meta-analyses in the health sciences have identified significant positive effects of potentially life-saving therapies where individual studies have failed to find an effect. One example of the potential influence of meta-analyses on policy is demonstrated by the review of the use of streptokinase in the treatment of myocardial infarction (commonly known as a heart attack). A meta-analysis that arranged and analyzed studies cumulatively through time over a 30-year period identified a statistically significant reduction in mortality resulting from the therapeutic use of streptokinase following myocardial infarction. This significant effect was clear in the cumulative meta-analysis after only 14 years of research, but streptokinase was not widely recommended until more than a decade later when two large-scale trials (mega trials) identified a significant effect (Lau et al. 1992 ). This striking example demonstrates the potentially preventable loss of life that results from missing patterns in the evidence identified through pooling studies.

Meta-analyses in medicine, and more recently in environmental management and conservation (Gurevitch et al. 1992 ), have been developed even further by the establishment of systematic review methodology (Pullin and Stewart 2006 ; Higgins and Green 2011 ). Systematic reviews aim to identify all available evidence for a specific question using a detailed, pre-defined methodology. This methodology aims to minimize various biases, such as publication bias and selection bias that may affect traditional reviews.

The power and utility of meta-analyses, however, is reduced significantly when primary research does not report sufficient data to allow full quantitative analyses. These studies with missing data must be excluded from the analysis despite being relevant and providing some informative results. Broadly speaking, primary research articles should report three key measures to facilitate their inclusion in a meta-analysis: mean effect size , sample size , and measure of variability (typically standard deviation, standard error, or confidence intervals). Effect sizes are summary statistics that estimate the magnitude of effect of a specific intervention (e.g., application of a pesticide) or exposure (e.g., soil water content). One form of effect sizes where studies report their results in the same units would be the raw mean difference, the control sample mean subtracted from the intervention sample mean, which represents the direct additional effect of the intervention in meaningful units. Other examples of effect sizes include correlation coefficients, risk ratios, and specific effect sizes designed for meta-analysis such as Hedges g . Different effect size types are suitable for different outcome measures and data types (Borenstein et al. 2011 ). Measures of variability indicate the uncertainty of effect size estimates and are used in meta-analyses to weight studies according to the variability in the data around the sample means, in order to give more weight to more precise studies. A range of possible variability measures can be used in meta-analyses as these are interchangeable. Sample sizes relate to the true sample size of the study and should not include pseudoreplicates. True replicates are those samples that are measured at the same level as that at which the intervention is experienced: if treatments are delivered at the field level, then replicates are fields and NOT plots within fields.

Where quantitative data for the key measures described above are not presented in the text or tables of relevant studies, this information can often be extracted from figures of summary metrics or raw data (e.g., Tummers 2006 ). In some cases other data can be included in a meta-analysis. For example, meta-analysis can be performed on p values (Fisher 1932 ), but such analyses do not consider the magnitude or the direction of effect, and cannot investigate sources of heterogeneity, so should be restricted to use when other options for meta-analysis are exhausted (Jones 1995 ).

Where data on key measures are missing from some studies, for example variability measures, it may be appropriate to impute these values (see Harris et al. 2009 ). Imputing involves replacing a missing value with an appropriate substitute. It enables the inclusion of studies that would otherwise be excluded due to the lack of reported data, and thus mitigating the potential impact this would have on the power and bias of the pooled effect (Wiebe et al. 2006 ; Burgess et al. 2013 ). This may be generated, for example for variability measures, in one of a number of ways: it may be based on an understanding of the population being studied; from a mean variability identified from other studies included in the meta-analysis; or from the largest variance reported in other included studies in order to be more conservative. One final option is to perform multiple imputation using several methods and substituting some form of average where the data are missing. Imputing is often appropriate in medicine, where meta-analyses involve large numbers of studies and imputing of a small number of studies’ variability is less influential on the overall analysis. Meta-analysis in environmental sciences, however, rarely involves large samples sizes, and large proportions of the evidence base may be missing data. Three recent systematic reviews highlight this problem. A recent systematic review of the impact of terrestrial protected areas on human well-being identified 281 outcome measures across 49 studies, but 82 percent of these studies reported measures with no variability (Pullin et al. 2013 ). Another review of the impact of land management on lowland peatland carbon greenhouse gas flux identified 33 of 111 studies that lacked measures of variability, precluding their inclusion in meta-analysis (Haddaway et al. 2014 ). In a systematic review of the impact of reindeer grazing on arctic and alpine vegetation, currently underway, 30 percent of the included articles were unable to be included in meta-analysis due to a lack of either variability (10 of 53 studies) or true sample size (6 of 53 studies) (Bernes et al. 2013 ). Despite the availability and use of imputing methods in the health care discipline, these are not always feasible in the environment setting, and therefore there are even more imperative primary studies to report the variance data. Studied human populations are typically far less variable and more predictable than the range of studied populations included in meta-analyses in the environmental sciences (Haddaway et al. 2014 ). As a result, imputing in environmental sciences meta-analyses is rarely likely to be appropriate.

One other solution to the problem of missing values is to contact the authors of relevant studies with a request for supplemental data. Such requests are more successful with recently published manuscripts (Vines et al. 2014 ), where email addresses are supplied and are still functional. Email requests for data in meta-analyses typically have low success rates (e.g., Gibson et al. 2006 ), with only a small minority of contacted authors responding with usable data. The process should be encouraged where resources allow, since the increase in usable data is often valuable. For older research, however, such contact is often not feasible. This latter point raises concerns about a possible bias resulting from the presence of more usable data in meta-analyses from more recent research. Such bias should not be ignored, but little can be done to account for it.

In systematic reviews, study results can often still be synthesized narratively in the form of textual descriptions, tabulation, and the production of figures despite being lost from meta-analyses. However, such narrative syntheses are not as powerful as meta-analyses, which should be the main aim of a quantitative (aggregative) systematic review. Furthermore, if some studies are missing effect sizes and statistical results, little use can be made of their results.

Those with experience in meta-analysis and systematic review understand the value of well-reported summary data in primary research articles, and failing this, the provision of raw data. To ensure the legacy of primary research and maximize its value, however, it should be the priority of journal editors and manuscript authors to ensure that all primary researches report quantitative data either in summary or raw form. Summary data should be provided with measures of variability to ensure that it can be included in meta-analyses. Maximizing the use of existing evidence in meta-analyses may also potentially conserve resources that would otherwise be used for additional primary research, where answers already exist in the literature. This policy follows the recommendations made in the CONSORT Statement (BMJ 2010a , b ) in medicine that call for better reporting of clinical trials.

Some journals have recently begun to demand the publication of raw data alongside manuscripts. The Public Library of Sciences (PLoS), for example, amended their data policy in December 2013 to state that “PLOS journals require authors to make all data underlying the findings described in their manuscript fully available without restriction.” Such a policy is a bold move in a competitive publishing market; the majority of other journals, particularly those that are not fully turning to Open Access, may find such a move difficult to implement. Summary data for treatment and control groups in the form of means, sample sizes, and variability measures are a far simpler, yet just as effective, requisite that will maximize the legacy and usability of primary research.

Systematic reviews and meta-analyses include research from a range of time periods, not solely more recent publications. As the publishing world advances and reporting of raw and summary data improve, the historic research that lacks sufficient data to permit meta-analysis could be made useful with the establishment of a universal database for the deposition of raw and summary data. Such a database could mirror the advances in independent post-publication peer review such as www.PubPeer.com . This project would require a significant effort to establish, maintain, and advertise.

Acknowledgments

I thank Claes Bernes, Ruth Lewis, and two anonymous reviewers for their comments on a draft version of the manuscript.

is a Postdoctoral Research Officer at the Centre for Evidence-Based Conservation, School of Environment, Natural Resources and Geography, Bangor University, Bangor, LL57 2UW, UK.

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• Published: 22 August 2024

The demise of the antibiotic pipeline: the Bayer case

• Belma Skender   ORCID: orcid.org/0000-0002-3979-7163 1  

Humanities and Social Sciences Communications volume  11 , Article number:  1069 ( 2024 ) Cite this article

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Antibiotics, celebrated as symbols of scientific triumph and societal advancement, have played a critical role in combating infectious diseases. However, their overuse has inadvertently fueled the emergence of antibiotic resistance. The primary strategy to tackle the resistance has been to develop new antibiotics. However, since the 1980s, there has been a noticeable downturn in the development of new antibacterial drugs. This slump, often referred to as the ‘dry antibiotic pipeline’, was particularly prevalent during the 1990s and aligned with that; many major pharmaceutical companies exited the antibiotic field in the early 2000s. That is also the main timeframe this paper focuses on. This work analyzes the dry antibiotic pipeline as a historical phenomenon and sets the stage by outlining the narrative context and key actors involved. It then discusses critical elements such as the understanding of innovation, scientific advances, regulatory requirements for clinical trials, and commercial models. These elements were often considered factors explaining the difficulty of developing new antibiotics. Using the case of the pioneering German company Bayer, these elements are brought together to illustrate the complexity of the crisis in the research and development of antibiotics. The Bayer story provides new insights into the internal realities of the company and reveals a range of entangled and multilayered challenges, which ultimately resulted in Bayer abandoning its storied production of anti-infectives, particularly antibiotics, calling into question the common understanding of the dry antibiotic pipeline.

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Introduction.

Before the 20th century, options for combating infectious diseases were limited. However, the 1935 introduction of Prontosil, the first antibacterial product on the market, indicated the beginning of a new era in science and medicine (Greenwood, 2008 ; Lesch, 2007 ). The antibiotics facilitated a period of intensive drug innovation and opened the door to establishing the modern pharmaceutical industry. The scientific process was transformed into a more extensive, complex drug production system (Bud, 2008 ).

Along with the use of antibiotics in clinical treatments, antibacterial resistance was observed. Antibacterial resistance occurs when bacteria have a genetic composition that helps them survive exposure to drugs that are expected to kill them (Levy and Marshall, 2004 ). Consequently, unlike other drugs, antibiotics tend to lose their effectiveness over time (Rossolini et al., 2014 ). From the biological perspective, it is a natural phenomenon, expedited due to the increased use of antibiotics. Nonetheless, the common practice to counter antibiotic resistance was identifying an alternate antibiotic to circumvent the previous one. Resistance was typically not considered a pressing concern during most of the 20 th century. It was also believed that instances of mutation would be few and far between. Also, the general attitude leaned towards assurance that there was always the potential to discover a new drug or modify existing ones. Therefore, resistance also served as a catalyst for the invention of drugs, which, however, follow the same mechanism of activity (Landecker, 2016 ). The trust in the antibiotic potency and endless supply of them contributed to the idea that infectious diseases would be conquered once and for all. As a result, the United Nations of the late 1970s forecasted that even the most impoverished countries would experience an epidemiological transition by 2000, meaning that mortality and disease patterns caused by pandemics of infection would gradually be displaced by degenerative and man-made diseases. Such projections seem imprudent now. Paradoxically, it was during this exact period that infectious diseases made an unexpected resurgence. New infectious diseases started to appear, and old ones returned in more potent, resistant versions (Cooper, 2006 ). This has resulted in a pressing need for strategic investment and the development of new therapeutic options to address the need for new antibiotics (Miethke et al., 2021 ).

This paper discusses the looming crisis of antibiotics’ potency and development, and begins by taking into account the actors, companies, governments, and narratives related to the problem and how to deal with it. The paper progresses to elucidate the concept of innovation in drug development, how it was used by different stakeholders, promoting particular details in the process of bringing a new medicine to the market, and how that impacted the development of antibiotics. Subsequent sections examine factors like scientific challenges, the economics of drug development, and regulatory requirements for clinical trials, which were often used as arguments posing obstacles for pharmaceutical companies. In the second part, these ideas are discussed further through the example of the German company Bayer AG. The company, known for its dyestuff business, established its pharmaceutical department in 1888. Since then, the company has had a strong presence in the industry. Bayer is considered a traditional producer of anti-infectives, particularly antibiotics, being the first company to introduce an antibiotic to the market, namely Prontosil.

The paper highlights how Bayer AG could not go forward by re-using the same approach to kill bacteria, e.g., improving the old molecular scaffolds. Thus, the company ran out of new antibiotic molecules to sustain the pipeline by the end of the 1990s, meaning that nothing was considered worth going further in development. In addition, commercial models built around drug resistance and expectation of constant growth in sales did not pay off. The regulatory demands on clinical trials got stricter in the early 2000s, which had an important effect on the development of antibiotics. In the conclusion part, the paper underlines the complex entanglements that impacted the antibiotic field and how traditional ideas and company workflow, which worked well previously, had not been succeeded by a ‘better next generation’ of new antibiotics in the turn of new millennia, resulting in the ceased activities in producing new antibiotics. The paper elucidates that Bayer’s decision to exit the antibiotics field was influenced by both external factors and substantial internal dynamics. This perspective offers a more nuanced understanding of antibiotic research and development, challenging the notion of the dry antibiotic pipeline and its attributions to economic conditions or policy regulations at the time.

For this research, unstudied archival materials (internal company documents) from the Bayer AG company between the late 1980s and mid-1990s were collected and analyzed. Some materials span until the early 2000s (externally produced material collected by the company’s staff). These materials, stored in the diverse files in Bayer’s archive in Leverkusen, Germany, enabled me to conduct a comprehensive review of documents about the research and development of antibiotics. Furthermore, I searched and reviewed titles and abstracts of all papers published by Bayer’s employees for the same time period ( N  = 471), focusing on anti-infective research, medicines that work to prevent or treat infections, particularly antibiotics used against bacteria, looking for the people involved in this research and studied molecules. For this work, I used a free search engine, PubMed, for life science and biomedical topics. I applied the keyword ‘Bayer company’ screening for it in titles, authors’ affiliations, and abstracts for the period 1990–2010 ( N  = 116 results when the search was conducted in January 2022), and the keyword ‘Bayer and antibiotics’ in titles, authors’ affiliations, and abstracts for the same time period ( N  = 365 results for January 2022). There were 109 papers sorted for further reading, containing topics about antibiotics published by researchers affiliated with Bayer company. I analyzed these papers, keeping track of the relevant researchers ( N  = 28), and studied unique substances ( N  = 16) and groups of compounds ( N  = 20). From the initial screening of relevant research work of Bayer’s employees and further snowballing approach, I conducted semi-structured, in-depth interviews with five Bayer’s employees. These informants were involved in the company’s research and development of antibiotics between the late 1980s and early 2000s. Through the interviews, I tested my interpretations of the archival materials, inquired about time periods for documents that could not be accessed due to the rule of ‘the moving wall’ of the 30-year permission to look into documents, and learned about informal practices that could be missing from written documentation. Interviews were conducted once or repeatedly, lasted one to four hours, and were recorded and transcribed. Finally, to put the Bayer story into the larger context at the time, I screened contemporary news and research articles about antibiotics and the pharmaceutical industry in general.

Narrative, actors, and actions

Despite the increased awareness of antibiotic resistance over time, especially among experts, the topic was marginalized and not considered a medical challenge until the 1980s (Lie, 2014 ). At the same time, the pharmaceutical industry was considered capable of keeping up with antibiotic resistance (Podolsky, 2018 ). However, since the 1980s, the scientific community communicated the issue of antibacterial resistance with a more pressing agenda and quite disturbing narrative around population risk and responsibility (Podolsky, 2015 ). The need to address this topic more widely resulted in an explicit effort to frame antibiotic resistance as a shared global problem (Levy, 2002 ; Podolsky, 2018 ). By the mid-1990s, discussions about antibacterial resistance as a medical problem were growing, but little had been done on a political level. Some countries have started to discuss the issue as a potential concern. For instance, in the United Kingdom, the Science and Technology – Seventh Report, ordered by the House of Lords in 1998, pointed out that big pharmaceutical companies were still active in anti-infective research, with some projects specifically focused on antibiotics (House of Lords, 1998 ). From this report, it seems that the investment in the field during the 1990s was still prominent. The industry had great hope in targeted genetics to look for new drug targets and modes of action. Yet again, the idea of a potent pharma industry to solve the problem of antibiotic resistance was put forward by members of the meeting, the selected Committee, and the experts in the field as witnesses, who “accept the evidence that a system of self-regulation is in place through the Association of the British Pharmaceutical Industry” ( ibid) . So, the question of developing new antibiotics was left, with hopes and trust, to be addressed solely by industry. Nonetheless, the development of new antibiotics approved for the market had decreasing trends between the 1980s and early 2000s (Spellberg et al., 2004 ). Many large pharmaceutical companies have reprioritized their research and development. They either de-emphasized or no longer included antibacterials in their portfolios. By the mid-2000s, many big pharmaceutical companies, including Bayer, abandoned their efforts in antibiotics. Several factors were discussed, mostly a lack of commercial ‘value’ of antibacterial compared to the treatment of chronic diseases or tightening regulations of bringing new drugs to the market (Overbye and Barrett, 2005 ). Additionally, there was a call for significant paradigm shifts in the development and commercialization of antibacterials, challenging a commercial model for antibiotics.

Interestingly, a large European conference was held only a few years later after the UK report concerning the need for new antibiotics. The conference was organized by the Specialist Advisory Committee on Antimicrobial Resistance on behalf of the UK Department of Health and sponsored by the British Society for Antimicrobial Chemotherapy. It was held in Birmingham in December 2005. The conference brought together academia, industry, funding agencies, healthcare management, the European Medicines Agency, the European Centre for Disease Prevention and Control, European Directorates, and representatives of EU governments. A significant objective of the gathering was to examine the reasons behind the reduction in research and development activity by companies (Finch and Hunter, 2006 ). There was an apparent shift from relying solely on pharmaceutical companies to bringing different actors into the discussion to solve the need for new antibiotic development.

Pharmaceutical industry at the turn of millennia

To better understand the context at the time, we need to look into what happened with the industry at large. The pharmaceutical industry experienced significant transformations around the year 2000. Globalization brought potential profits but also heightened competition. Challenges like generic, ‘me-too’ drugs, reduced market exclusivity, and higher development expenses intensified the industry’s competitiveness. Robust research and development pipelines became crucial to maintain a competitive edge. Big companies adopted mergers, acquisitions, and licensing strategies to enhance their portfolios (Firestone, 2011 ; Kaitin, 2010 ; LaMattina, 2011 ; Lynch and Wong, 2001 ). The expansion of ongoing projects contributed to the rise of spending. According to Lynch and Wong, in 1980, Pharmaprojects, the database of commercial pharmaceutical research and development programs, documented information about 1658 projects under development. In 2000, the count had surged to almost 6000 active projects. Surprisingly, the quantity of new chemical entities in clinical development remained stagnant despite the increased number of available compounds (Lynch and Wong, 2001 ).

Furthermore, governments recognized the growing need for regulation of the pharmaceutical industry. Discussions focused on concerns regarding the inappropriate or excessive use of medicines and adverse drug reactions at the time. Also, there were instances of allegations that clinical trials were not adequately designed – that they could be designed to show the new drug in the best light – and sometimes failed to indicate the true effects of medicine on health outcomes relevant to the patient (The Health Committee, 2005 ). In the UK in 2005, The Department of Health admitted that the industry was left to its own devices for too long. According to the report, an effective regulatory regime to ensure that the industry works in the public interest was essential, but the present regulatory system failed to provide that ( ibid ).

Consequently, the need for an industry-led by the values of its scientists, not those of its marketing force, was highlighted ( ibid ). Another factor was the growing demand for evidence-based medicine. Drugs in development needed to prove greater efficacy over existing medicines, with safer profiles and, most importantly, cost-effectiveness. The idea from the mid-1990s resulted in the establishment of independent national institutes to secure quality assurance (Timmins et al., 2017 ). For instance, in Germany, the Institute for Quality and Efficiency in Health Care was established in 2004 to examine the benefits and harms of medical interventions for patients. Together with the Federal Joint Committee, both institutions started to assess the clinical benefits of drugs and interventions, drawing on principles of evidence-based medicine (G-BA, 2023 ; IQWIG, 2023 ; Schaefer and Schlander, 2019 ).

Despite the growing regulations, anti-infectives, the majority of which were antibiotics, were the fourth therapeutic category by sales at the turn of the millennium. By that time, Bayer had a strong presence in the market. Its leading product was ciprofloxacin (Cipro), approved in 1987, and Bayer strengthened its infectious disease franchise by launching Moxifloxacin in 1999. However, most of the company’s pipeline was in early development at the time (preclinical or Phase I) (Lynch and Wong, 2001 ).

Usual suspects of not developing new antibiotics: innovation, regulatory hurdles, and economy

In the corporate worldview, the development of a new drug is embodied in the metaphor of the ‘pipeline.’ The purpose of the drug pipeline is to transform novel compounds into medicine (Mather, 2005 ). The discovery of antibiotics has always been a dynamic process, where new and more potent drugs are needed to treat the pathogens selected by previous antibiotics. Continuous innovation of antibiotics is crucial due to the unpredictable nature of the development of resistance (McKellar and Fendrick, 2014 ). However, there is little clarity about what true pharmaceutical innovation means (Morgan et al., 2008 ). Indistinctness about the term presents a challenge as it is one of the most commonly used expressions in initiatives addressing the antibiotic resistance problem. More generally, innovation is commonly associated with a novel antibiotic class, a novel target (binding site), or a novel mode of action (Theuretzbacher, 2017 ). Nonetheless, these descriptions are still very broad and open to various interpretations.

Some companies use the U.S. Food and Drug Administration’s (FDA’s) “new molecular entities” (NMEs) categorization to measure innovation. However, there is a broad outline from the FDA on what drugs are novel and which offer significant therapeutic advances (Abraham, 2010 ; Fisher, Cottingham, and Kalbaugh, 2015 ). There is an important distinction between invention/discovery and product innovation in policy literature on innovation. Product innovation involves discovery plus the development of that discovery to bring it to a market (Freeman, 1995 ; Jevons, 1992 ). To follow the idea, innovation reflects a technological and commercial perspective. The first means a patentable technical novelty in terms of the molecule. The latter points to the phase when the regulators review the manufacturer’s drug-testing data to approve it for marketing. The legislation requires that manufacturers only demonstrate the quality, safety, and efficacy of their products. It does not require that the product represents a therapeutic advance in existing therapies unless that is an extra requirement for marketing approval imposed by regulatory agencies, which rarely happens (Abraham and Davis, 2007 ; Kepplinger, 2015 ; Morgan et al., 2008 ). In that way, the innovation within drug development is very much connected to the regulations posed by the government.

As regulators recognized the necessity of thoroughly testing modern prescription drugs for the benefit of public health and clinical application, they developed regulations on the pharmaceutical industry. However, starting in the late 1980s in the United States, the FDA faced criticism from some activists and pharmaceutical companies for taking too long to evaluate new applications (Van Norman, 2016 ). The industry began promoting the idea that regulatory agencies were inefficient and slow bureaucracies, leading to a notable reduction in the time taken to review and approve both priority and standard NMEs. On the other hand, the West German regulatory authority attributed the slow pace of drug reviews to the poor quality of industrial applications (Abraham and Davis, 2007 ; Daemmrich, 2011 ). Later, the German Ministry of Health replaced the previous regulatory agency with the Federal Institute for Medicinal Products and Devices (BfArM) in 1994 (Abraham, 2004 ; Abraham and Lewis, 2002 ). Eventually, BfArM had some of the quickest approval times in Europe despite the increasing number of applications (Lumley and Walker, 1996 ). Interestingly, the deregulatory culture, which placed less stringent regulatory requirements on the industry to encourage the development of therapeutically effective drugs, has led to a decline in innovation (Abraham and Reed, 2002 ).

The most obvious instance of this can be seen with antibiotics (Abraham, 2010 ). Bacterial resistance to existing antibiotics was a growing public health concern, a fact that the World Health Organization acknowledged as the area of greatest “pharmaceutical gap” (Kaplan and Laing, 2004 ; WHO, 2005 ). It was often explained by pharmaceutical companies reorientating their research and development efforts to more profitable medicines for chronic conditions (Bradley et al., 2007 ). However, according to Powers JH, antibiotics were still profitable classes of drugs in the 1990s, surpassed only by the central nervous system and cardiovascular medicines (Powers, 2004 ). In the 2003 Nature News piece about ‘Concerns raised over declining anti-infectives R&D’ by Jeffrey L. Fox, Steven J. Projan of Wyeth Research said that antibacterial agents represent about a $26 billion worldwide annual market (Fox, 2003 ). Yet, it was still far from blockbuster drugs used to treat or prevent chronic diseases. For example, Zithromax, the best-selling antibiotic, had revenue of $2.01 billion in 2003 compared to Lipitor, a cholesterol drug, which earned $9.23 billion in sales in the same year (Hensley, 2004 ). The financial returns on investment for antibiotic development were often considered insufficient compared to those of drugs for chronic diseases (Plackett, 2020 ; Projan, 2003 ). Interestingly, SJ Projan, a very well-known figure in the field of antibiotics who had expressed concern about the profitability of antibiotics for the pharmaceutical industry in the early 2000s, later took a contrary position (Projan, 2003 ; Projan and Shlaes, 2004 ). During an interview in 2011, he stated, “One of the great ironies about the reasons cited by many companies for leaving the field is the claim that there’s no profit to be made, even though the worldwide market for anti-infectives stands at $90B, compared to the global oncology market at around $70B.” He further added, “Therefore, the choice by some companies to forsake antibacterial R&D for what are assumed to be more profitable areas such as oncology is perplexing to me.” (Mintz, 2011 ). The interplay between profit and innovation in pharmaceuticals is complex. While profit motives can drive innovation, prioritizing commercial gains sometimes skews research agendas. This alignment shapes which diseases receive attention, potentially sidelining non-lucrative but pressing health issues, thus revealing the complex interplay between research and capital. Importantly, regulatory agencies play an essential role in this intricate dance by outlining the rules. Finally, various actors, such as governments, companies, international organizations, non-profit interest groups, and experts in the field involved in the process of bringing new antibiotics to the market, have their own way of framing the problem, how to tackle it, and what to prioritize, leaving little clarity about the possible and sustainable solution.

Bayer’s innovation and market success

This paper deals with the particular example of the German company Bayer AG (referred to as Bayer). The company was a pioneering force among firms from the late 19th century. Bayer significantly influenced the framework and operational norms of the entire pharmaceutical field (Bartmann, 2001 ; Lesch, 2007 ). According to Achilladelis and Antonakis, Bayer was one of the most innovative companies between 1880 and 1992 in terms of radical innovations and market success, especially in antibacterials (Achilladelis and Antonakis, 2001 ). Bayer also benefited from its close interaction with academics and public medical institutes, which in turn fostered the emergence of effective driving forces for innovation: market demand and competition (Verg, 1988 ). However, the company lost the competition in innovation due to growing competition (Achilladelis and Antonakis, 2001 ). It was particularly apparent in the example of no new antibiotic since sulfonamides. On the other hand, the company had strong in-house chemistry and biochemistry expertise, helping it improve its antibiotics production processes. Together with microbiologists, Bayer succeeded in increasing the yield of antibiotics (Dalhoff, 2008a ). Still, it was not until the late 1980s that the company introduced a new antibiotic to the market, namely Cipro. With a growing demand for new antibiotics in the 1990s due to the increase of resistance in clinical settings, the company saw an emerging opportunity for its antibacterial efforts (Dalhoff, 2008b ; Gradmann, 2016 ).

In that sense, the company was looking for ways to improve its research and development, bringing new ideas to the light. Innovation and its interpretation were very important for Bayer. The company related its understanding to the Austrian economist Josef Alois Schumpeter, according to which ‘an entrepreneur is by definition an innovator’ (Verg, 1988 ). In 1997, Bayer organized the press symposium “Innovation Perspective ‘97”, attended by 180 journalists from 25 countries in Leverkusen. The prominent company’s representatives described the meaning of innovation for Bayer. In the words of the Chairman of the Board Committee for Research and Development, Pol Bamelis, who used the quote: “Innovation – is the conversion of knowledge into money.” (BAL 716, 1997 ). Thus, the idea of innovation in Bayer was very much connected to the market revenue and a vision of future prosperity. Antibiotic Cipro was a great example of Bayer’s success in that sense.

The last blockbuster antibiotic

Since the mid-1980s, fluoroquinolones have become a major group of synthetic antibiotics with a broad range of activity. They were characterized by good penetration into tissue and body fluids and therapeutic ratios, which indicated the drug’s efficiency over toxicity. Bayer’s Cipro revolutionized the management of many conditions previously amenable only to intravenous therapy. In 1991, Cipro was one of the most widely prescribed antimicrobials and the world leader in its active substance class (BAL_001_011_Annual_report_1991, 1991 ). At the time, resistance to antibiotics, especially against penicillin, was a topic among microbiologists and physicians. Quinolones had the advantage of being synthetic, completely made in a laboratory. It was believed that it would be very difficult for bacteria to build up resistance against these compounds. Many companies were working on their own quinolones to be introduced to the market.

Bayer continued its effort to pursue the ‘next better Cipro.’ The continued interest was reflected in numerous meetings organized during a year on the topic ‘AKF Antibakterielle Therapie’ but also in the effort of screening a variety of compounds like oxazolidinones, quinolones, aminothiazoles, proton pump inhibitors, vancomycin analog test systems, to name few (BAL 800-320-451, n.d. ). In 1995, the meeting about the state of antibacterial therapy at Bayer evaluated different programs’ market options, strategic goals, and status. At the time, the antibacterial market was estimated to be about $ 20 billion in sales with three major classes of cephalosporins, quinolones, and macrolides. The accumulated costs for Bayer were estimated to be 32 million Deutsche marks (roughly corresponding to $ 20 million). Cipro sales for the same year reached DM 1.8 billion, emphasizing the commercial gain of antibiotics and motivating the company to intensify its efforts (BAL_001_011_Annual_report_1995, 1995 ). One of the strategic goals was to secure a position in the antibacterial market by introducing a new and superior class of compounds. In 1995, Bayer created the idea of the company’s ‘Vision for 2010’. The molecules were set on ‘an overall plot of commercial values versus profitability of technical success,’ indicating critical and high-potential projects for the company, where Cipro was positioned highest. The company also set a rational focus on medical needs, patient potential, and market potential, evaluating projects with sufficient commercial value for further development (BAL 800-320-548, n.d. ).

Bayer also considered and researched the options of quinolones for new indications such as malaria, peptic ulcer, cancer, and tuberculosis. Under intensive evaluation, Bayer had four quinolones (BAL 800-320-539, n.d. ). At that time, it was known that bacterial resistance to antibiotics prescribed for respiratory infections was increasing, documented by a surveillance program known as the Alexander Project. In addition, the estimation was that more than $ 4 billion was spent every year treating resistant bacterial infections (BAL 800-320-451, n.d. ). This presented an interesting commercial niche to bring new antibiotics to the market. To increase its chances, Bayer employed extramural research collaborations with major academic contacts in Canada, the US, and Germany. The anti-infective team from Bayer was also considering cooperation with China. The aim was to identify structures from the pool of structurally diverse components from Chinese medicinal plants. In that sense, the idea was to collaborate with the Kunming Institute of Botany, Chinese Academy of Science. Bayer was also working on licensing opportunities with companies such as Cubist Pharmaceuticals, Micrologix, Biofor, Ribogene, Xoma, Avid, and Lynx Therapeutics. By the end of 1996, Bayer had over 400 targets, as was noted by a former employee, Dr. Burkhard Fugmann (BAL 800-320-451, n.d. ).

The next better thing

Many years after the Cipro introduction in the late 1980s, there was still no successor in the market despite several ongoing programs and many promising molecules. Finally, by the end of the 1990s, moxifloxacin, which turned out to be potent, clinically effective, and appeared free from significant or unexpected toxicity, filled a therapeutic vacuum. It was a novel, improved analog with an expanded spectrum and high efficacy. Its activity suggested that, where many considered Cipro inappropriate for respiratory infections, moxifloxacin became an agent of choice (Ball, 2000 ). Bayer launched moxifloxacin in Europe and the US in 1999 with an estimated annual sales potential of 1.7 billion Deutsche marks ($931.6 million). Moxifloxacin was regarded as a younger sibling of Cipro but was less active in terms of the width of the spectrum. It had been a molecule among three siblings. Ultimately, for development, the one with the best toxicity profile was chosen and not the most active one. Before, Bayer had BAY y 3118, which was more phototoxic than Sparfloxacin. Bayer sunk a lot of money into research and development on BAY y 3118. “When you choose it just by looking at the bacteria, forgetting about the host, you would pick 3118.” noted Bayer’s employee, a medical lead for moxifloxacin development (M. Springsklee, personal communication, April 25, 2023).

Moxifloxacin was developed as a new alternative for community-acquired pneumonia and respiratory infections. In 1997, Bayer began its phase two clinical trials with moxifloxacin. The company was facing competition because others were also developing quinolones at that time. The project had the highest priority development in the whole world of Bayer, as described by the global clinical lead of the project. The project was high-intensity and high-profile. Therefore, it was decided that all parts of the research and development would be done internally. “We also had counted every day, So the decision to start phase three to end submitting the regular document was 1066 days […] I think still is a record development time for a large phase three program for antibiotics. “(M. Springsklee, personal communication, April 25, 2023). Moxifloxacin binds to two essential bacterial enzymes responsible for the replication, translation, repair, and recombination of deoxyribonucleic acid. In addition, it is more potent against Gram-positive pathogens and less prone to the mechanism of quinolone resistance than Cipro (Dalhoff, 2008b ; Pong et al., 1999 ; Spence and Towner, 2003 ). In that sense, Bayer experts argued that it was possible to shorten treatment with a higher dose, which would, in turn, minimize the development of resistance. “The idea was you need to do something bactericidal. Ideally, you want something that kills bacteria very quickly. So, you can reduce treatment time. That was basically what we did with moxifloxacin. As short as possible treatment, which was back then a paradigm shift.” The discussion was about doing the most active compound at the maximum tolerated dose (M. Springsklee, personal communication, April 25, 2023). However, moxifloxacin was a very expensive compound to make as it has two chiral centers. The marketed form is chiral and consists of a single isomer only. That has to be done using certain parameters within the production process, which are very difficult to control and are not revealed in the patent, consequently, that made moxifloxacin very expensive. “Moxifloxacin, when launched, was per kilogram six or eight times the price of a one-kilogram Cipro.” (M. Springsklee, personal communication, April 25, 2023). The project was almost discontinued because it was so expensive to produce. Over the years, Bayer succeeded in bringing down the cost of its production. Nonetheless, its margin was always lower than the margin of supply. Nonetheless, given its high production price, Bayer needed strong arguments on how to promote this antibiotic. Moxifloxacin is directed toward community-acquired pneumonia, so its use is limited compared to Cipro. However, moxifloxacin had a certain advantage of once-daily use, which brought down an administration-related cost. Also, whether you use it, i.v. or oral, you get exactly the same exposure. Thus, the two forms have an absolute bioequivalence, which is not true for most other chemicals. These were complex arguments intended to be understood by specialists, not general practitioners. “We always tried to profile it more as a high-end antibiotic.” (M. Springsklee, personal communication, April 25, 2023). That approach also allowed for a higher price, which works well with limited use.

However, at the time, levofloxacin was on the market, so competition was high for moxifloxacin. Levofloxacin was much more successful in the US market, especially coming from an American company, Johnson & Johnson. On the other hand, moxifloxacin did well in the Asian market, especially in China. Moxifloxacin was the first modern quinolone that was approved in China (M. Springsklee, personal communication, April 25, 2023). It was the biggest-selling antibiotic despite its high price due to the growing Chinese economy in the early 2000s. Moxifloxacin was launched in the Chinese market in 2002 as tablets and later in 2004 as i.v. form (Li et al., 2013 ). Key markets were major hospitals in large and medium-sized cities in China. Moxifloxacin grew at a rapid pace after entering the Chinese market, with its annual sales value rising ten times in 10 years (from CNY 60 million in 2005 to CNY 649 million in 2014, roughly corresponding to $ 7 million to 89 million) (Wood, 2015 ). To put it into perspective, moxifloxacin’s overall sales, as noted in annual reports from 2005 and 2014, were 364 and 381 million euros, respectively (roughly corresponding to $ 437 and 520 million), making China a prominent market for this antibiotic (Bayer, 2005 , 2014 ).

“ I kind of realized while we did the work for Moxifloxacin that it will be very, very, very tough to do any further evolution on the molecular front, so to speak, of quinolones.” (M. Springsklee, personal communication, April 25, 2023). By the end of the 1990s, it became challenging to find a better quinolone. It would have to be a compromise between activity, toxicology, tolerability, and spectrum of activity, and not to talk about already existing competition.

The crisis of the next better thing and the end of in-house research

Bayer’s next antibiotic endeavor was from the class of penems, a type of β-lactam not occurring naturally, namely Faropenem, which the company in-licensed from Suntory, Japan. The company acquired a worldwide patent license in 1999 and planned to clinically develop and market the drug from 2004 (The Pharma Letter, 1999 ). As described by the Bayer former microbiologist: “[…] and the reason for this, very simply, was that because Bayer didn’t have any development agent in the anti-infectives area anymore.” (A. Dalhoff, personal communication, January 31, 2023). In 2001, in Bayer’s Stockholders Newsletters, Bayer’s CEO announced how the company is in the development of “[…] an antibiotic called faropenem that we believe will achieve sales of €500 million a year” (Bayer Stockholder Newsletter, 2001 ). No additional explanation was provided regarding this specific target. However, Bayer’s, 2000 annual report reveals that ‘[…] the success story of Bayer’s Health Care segment is a story of innovative products, many of which are blockbusters with annual sales of at least €500 million (Bayer AG, 2000 ). Moreover, a 2001 estimate by the Tufts University Center for the Study of Drug Development indicated that the average cost of advancing a pharmaceutical compound through screening, chemistry, pre-clinical development, and clinical testing was at least USD 500 million (DiMasi et al., 2003 ). However, there was also limited information about the calculations that reached this figure in this case.

A product development unit was formed for the Faropenem project, which was Bayer’s philosophy on organizing high-priority projects back then. Such units looked like small biotechs with overall lead on top of it, and every quarter, the unit had to defend its progress and the expenses. It meant that the group consisted of a variety of specialist with the leader on top of it to deal with the particular project (in this case, bringing a new antibiotic to the market). They were given a high degree of freedom to address all intellectual and technical realities of the project and be creative, and they also received their own budget. The project had its own processes and infrastructure (M. Springsklee, personal communication, April 25, 2023). Faropenem was geared toward the US market, and the whole team moved to the US in mid-2001. The idea was to have the global pharma headquarters there. It was not only the most important antibiotics market but also for pharmaceuticals in general. Having a presence in the US, Bayer hoped to match other big companies such as Pfizer ( ibid ).

However, on August 8, 2001, the big hope compound back then, Lipobay, was announced for withdrawal from the market due to several reported fatalities associated with its usage. It was a statin - a member of the rapidly expanding and lucrative class of drugs, which held immense promise as a potential high-earning medication. Lipobay’s withdrawal substantially declined Bayer’s sales and profits (Tuffs, 2001 ). In addition, it significantly negatively affected Bayer’s standing within the pharmaceutical sector, tarnishing its reputation considerably. It even led Bayer to consider divesting its entire pharmaceutical division. Coincidentally, around the same period, the anthrax crisis emerged. Notably, Bayer was the sole pharmaceutical firm capable of promptly providing an efficient anti-anthrax medication, namely its antibiotic Cipro. This development played an important role in helping rescue the pharmaceutical division (Jennewein et al., 2010 ). Nonetheless, the Lipobay incident changed many things at Bayer, and many people were laid off. “It was a total implosion.” as described by the former medical lead of anti-infectives at that time (M. Springsklee, personal communication, April 25, 2023). After delisting Lipobay, Bayer shares dropped by 36 percent compared to the previous year (Bayer, 2001 ). “We were in survival mode.” (M. Springsklee, personal communication, April 25, 2023).

From that moment, Bayer needed to find a compound that would save the company, commercially and in the eyes of shareholders. The work on Faropenem was advanced with the development process, being in phase 3, and it continued even after the Lipobay disaster, although still far from bringing the results. Faropenem was even more expensive to produce than moxifloxacin. Therefore, the suggested dose was 200 milligrams, which was challenging in terms of pharmacology and the possibility of not being efficient enough. But the high dose wouldn’t work commercially. Nonetheless, the team tested the substance to get a new type of molecule, the idea of a post-cephalosporin antibiotic. There was still some hope. Nonetheless, the only indication that failed was streptococcal pharyngitis, which was the least expected, not the difficult-to-treat indication (M. Springsklee, personal communication, April 25, 2023). “In line with Bayer’s philosophy not to mix departments, the licensing team did not incorporate experienced medical microbiologists into the team and did simply overlook that H. influenzae should be covered by an RTI antibiotic, but that was not the case […] Thus, the gaps in faropenem’s RTI-spectrum were clinically not relevant.”, pointing out how the infrastructure of personnel and expertise impacted the study design and, thus, the result, according to former Bayer microbiologist (A. Dalhoff, personal communication, November 10, 2023). Moreover, the clinical work on Faropenem was done during a discussion about the specifications of the non-inferiority trials and the margin of antimicrobial agents. The FDA suggested the change from minus 15% to minus 10% in the early 2000s. A non-inferiority trial aims to demonstrate that the test product is not worse than the comparator by more than a pre-specified, small amount. This amount is known as the non-inferiority margin. The discussion about this change included some practical implications, such as increasing the sample size for the trial or that it did not take into account the seriousness of the infection in question (Powers et al., 2002 ; Shlaes and Moellering, 2002 ). The industry was very concerned with this change, especially in rare, serious infections, since the sample size, cost, and time implications could be enormous (D’Agostino et al., 2002 ). Thus, a non-inferiority margin minus 15% was no longer acceptable for faropenem. The team had to increase the patient numbers to go with the higher non-inferiority margin. It turned out that faropenem had a very low minimum inhibitory concentration against streptococcal pharyngitis indication. Unfortunately, that was the one indication for which the highest commercial value was predicted. Therefore, the whole commercial value and the idea of a ‘new cephalosporin’ imploded. Still in the crisis after Lipobay, Bayer had no more money to invest in multilevel studies (M. Springsklee, personal communication, April 25, 2023). Eventually, the project was discontinued in 2002 (Shelton, 2005 ). “We have discontinued the development of Faropenem since the results of clinical trials indicated that it was unlikely that the product could be marketed successfully.” as stated in Bayer’s SEC Form 20-F (SEC Form 20-F, 2003 ).

‘The next best thing’ had a profound effect on the commercial model. The conservative approach to research and development, which primarily emphasizes improving existing molecular scaffolds, proved ineffective in a business environment driven by the need for continual sales growth. This model struggled to significantly boost sales of a new antibiotic that offered only marginal improvements over existing products. In the words of the Bayer global medical lead at the time: “So that whole commercial model, oh here is the next generation, here is the next best thing since … sliced bread. You know, all these types of simple stories didn’t fly [anymore]. […] I mean, in our commercial models, any new compound, to be able to be commercially attractive, […] we could not make any compound commercially work with peak sales below 500 million […] euros […] per year. But that was kind of the threshold. You know, putting the odds ratios, the odds in the equation that you have to start so and so many programs for so and so long time is the one that will make it into phase 2 and 3 […] also would have to do some human PK [pharmacokinetics]. Because predicting human PK from animal data as the way it can be done right now was a bit more difficult back then.” (M. Springsklee, personal communication, April 25, 2023).

Bayer also investigated the anti-Tumor Necrosis Factor programs, the big hope for sepsis, and natural product options. “The development of anti-inflammatory cytokines in their broadest sense is highly complex - or better, should have been highly complex, but it wasn’t. Clinical studies have been designed in the same way as antibiotic studies have been designed in those times. Bacteria have to be killed. Thus, cytokines have to be totally inactivated. But this approach is unphysiological and not meaningful” described by the former Bayer microbiologist (A. Dalhoff, personal communication, November 10, 2023). Nonetheless, none of these programs was convincing enough to pursue them further. “And also, it was not commercially viable.” (M. Springsklee, personal communication, April 25, 2023). However, the company still did not give up on antibiotics and, in 2003, signed a contract with Paratek Pharmaceuticals to collaborate and develop the novel aminomethylcycline antibiotic. “Bayer has a long history in antibiotics, and we are committed to further antibiotic development and to strengthening our hospital franchise. BAY 73-6944/PTK 0796 would complement our existing antibiotic portfolio and is expected to fill an important medical need,” said Dr. Wolfgang Plischke, head of Bayer HealthCare’s Pharmaceuticals Division, in 2003 for the magazine Infection Control Today (Infection Control Today, 2003 ). Thus, Bayer, even being in ‘survival mode,’ trusted its most notable area in the pharmaceutical segment. The compound was the first in its class to be selected for development. The pre-clinical research showed promising results (Pharmabiz, 2004 ). The team from Bayer worked with the molecule for a while. However, a complex array of technical issues led to the decision to return the compound (M. Springsklee, personal communication, April 25, 2023). In 2005, Bayer stopped further work on this antibiotic due to restructuring its pharmaceutical interests (Bayer, 2005 ).

Sacrificing the ‘holy cow’

Bayer’s efforts to develop and introduce the next antibiotic to the market were failing internally and in collaboration with externals. Moxifloxacin was doing well after its introduction to the market. Still, it never reached the sales of Cipro, and “There were no new molecules.” recalled the former Bayer medical lead for anti-infectives (M. Springsklee, personal communication, April 25, 2023). Moreover, there was no breakthrough antibiotic in Bayer’s anti-infective program. The option was to aim toward outbreak situations. However, “[…] in such a situation, having a new antibiotic just in case is great. Who’s paying for that antibiotic ‘just in case’? Who’s paying?” (M. Springsklee, personal communication, April 25, 2023). In light of the ongoing post-Lipobay crisis, pharmaceutical research and development needed to be readjusted. The budget for investment was limited, so the company had to decide and pick the most attractive program only. And that’s where Rivaroxaban [Xarelto] was picked.” (M. Springsklee, personal communication, April 25, 2023). Bayer had a big oxazolidinone program during the 1990s, and it was aimed at discovering new antibiotics.

Interestingly, Rivaroxaban (also known as Xarelto) was sorted out from this anti-infective program. It was a new, highly selective, and potent Factor Xa inhibitor, an antithrombotic drug (Agnelli et al., 2007 ). With its intended use as an anticoagulant, giving this compound over prolonged periods was essential. It was important to ensure that this oxo-compound has no antibacterial activity when used as an anticoagulant. The exact opposite of an anti-infective drug. To continue developing Rivaroxaban, the company needed substantial resources at that time. Therefore, in 2005, Bayer teamed up with Johnson & Johnson to work on this late-stage drug jointly (Bayer, 2005 ; Johnson and Johnson, 2005 ). During the development phase, 65,000 people participated in the clinical studies at the time, making it the most intensively researched oral, direct Factor Xa inhibitor (Bayer, 2010 ). Compared with an antibiotic program of moxifloxacin, where there were about 8000 participants.

Rivaroxaban was launched in 2008 in Europe and 2011 in the US. The estimations for the sales in 2008 by Bayer were that a global peak sale of Rivaroxaban would reach 2 billion euros (roughly corresponding to $2.8 billion) a year for all indications (Reuters Staff, 2008 ). A few years after its introduction, in 2014, it reached 1.6 billion euros in annual sales peak (roughly $ 2.3 billion) (Bayer, 2014 ). Rivaroxaban helped Bayer to get ‘back on track’ after Lipobay’s withdrawal. Eventually, Bayer’s anti-infective program did save the company again. This time, it was in a different sense, though.

In the mid-2000s, the internal competition for innovation in pharmaceuticals at Bayer was high. In anti-infective, antibacterial, or antivirus programs, there are no promising compounds to compete for the limited resources. “[…] if we had a good enough anti-HIV compound, that possibly could have saved us. But we hadn’t. It was nothing.” (M. Springsklee, personal communication, April 25, 2023). In the 2005 Bayer annual report, we learn: “Bayer HealthCare has carved its anti-infective research activities out of the Pharmaceuticals Division and placed them into a new company in which Santo Holding AG […] will hold a majority interest and Bayer HealthCare a minority interest of 12 percent. The anti-infectives research activities are expected to be fully independent by March 2006.” (Bayer, 2005 ). Later, in 2006, Bayer concluded its divestment of anti-infectives. At the time, Bayer kept the minority in this new spin-off called AiCuris (Rübsamen-Waigmann, 2006 ).

“Would Bayer really dare to slaughter the Holy Cow? Because it was our lifeline. You know, it was a lot of our identity. It was a lot of Bayer’s identity was anti-infectives. And now come a few guys … saying this has to change.” (M. Springsklee, personal communication, April 25, 2023). Bayer did close its endeavors in the research and development of new antibiotics in-house. “It was cheaper to outsource this work than actually have it in-house.” as described by the former Bayer microbiologist (A. Dalhoff, personal communication, January 31, 2023).

Nonetheless, the company continued with ideas of extending the indications for existing compounds. For instance, in 2005, moxifloxacin was researched for its potential against tuberculosis. Bayer also entered into a partnership with Nektar Thrapeutics Inc. to develop an inhaled formulation of the antibiotic amikacin to treat pneumonia in 2007 (Bayer, 2007 ). So, a company with its strong tradition of anti-infectives could not easily let go and still kept its ‘foot’ in the field. “[…] we still had in-licensed compounds that we were able … were working in development till 2018 actually.” (M. Springsklee, personal communication, April 25, 2023).

This paper carries out a historical case study of the notion of the ‘dry antibiotic pipeline’ by focusing on the Bayer company during the 1990s and the early 2000s when many big pharmaceutical companies abandoned antibiotic research and development. The work explores scientific advancements, regulatory changes, and economic models that influenced the development of new antibiotics, the most discussed obstacles in the field. The approach was to explore the concept of the dry pipeline by employing these most discussed challenges and seeing them through the example of one renowned company, Bayer, with a long history in the field of developing antibiotics.

Bayer’s extensive experience in antibiotics culminated in its most successful product, Cipro, in the late 1980s. In the 1990s, the company sought to replicate this success by investing in developing another Cipro-like antibiotic. Bayer’s approach to innovation involved improving proven concepts, and despite numerous research programs exploring various molecules, few progressed to the development stage. Additionally, Bayer’s evaluation of innovation was heavily influenced by potential future profits. The model faced significant challenges due to antibiotic resistance, highlighting the necessity for restricted use of these medications and, thus, less sales volume. Nonetheless, Bayer still invested in the new antibiotics programs, which were in-licensed. However, it faced significant challenges related to Bayer’s expert infrastructure at the time, study design, and the newly introduced clinical study regulations. One program failed because Bayer lacked the resources to correct the issues and comply with the updated clinical study rules. The other program was unsuccessful due to technical challenges that Bayer’s experts could not solve. Lastly, in the early 2000s, Bayer faced a major problem with its drug Lipobay, which impacted the distribution of the investment within the company. This incident underscored a broader issue within Bayer characterized by the interplay of internal crises and strategic errors.

By examining Bayer’s trajectory, this paper questions the common understanding of the dry antibiotic pipeline. In the case of Bayer, we see many different crises that accumulated in the company by the end of the 1990s and early 2000s. The most prominent argument in the field about the unprofitability of antibiotics was not the most important factor for Bayer. In this case, we see an internal accumulation of challenges resulting in a drying up of new ideas for scientific advances, economic expectations, and poor management decisions faced by externalities, like new clinical trial demands, which led to the demise of the antibiotic pipeline.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available. Archival material (referenced in the text as BAL with the file number) is available in the Bayer AG archive in Leverkusen, Germany, and from the corresponding author upon reasonable request and with permission from the Bayer AG archive. Interview material is available from the corresponding author on reasonable request and with the participant’s agreement. Note about archival material, the author translated quotes from the Bayer AG archive documents (BAL) and research text written in German. All errors remain mine.

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Acknowledgements

I would like to thank to Christoph Gradmann, Jørgen J. Leisner, Helen Lambert, Cyriac George, Mingyuan Zhang, and Laura Daniela Martinenghi for their insightful feedback and invaluable comments on the manuscript. I would also like to thank all former and present Bayer employees who spent time talking to me formally and informally, which helped me gain insights into past events. Finally, I am thankful to be granted access to the Bayer AG archive in Leverkusen, Germany, and to the employees who helped me navigate it. This research is part of the “DryAP: How did the Antibiotic Pipeline Run Dry?” project (nr: 314490), funded by the Research Council of Norway. A version of the article was presented at The European Association for the History of Medicine and Health in Oslo, Norway, in September 2023.

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Skender, B. The demise of the antibiotic pipeline: the Bayer case. Humanit Soc Sci Commun 11 , 1069 (2024). https://doi.org/10.1057/s41599-024-03584-3

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Deciphering physiological and transcriptional mechanisms of maize seed germination

• Published: 30 August 2024
• Volume 114 , article number  94 , ( 2024 )

Cite this article

• Yaqi Jie 1   na1 ,
• Wei Wang 1   na1 ,
• Zishan Wu 1   na1 ,
• Zhaobin Ren 1 ,
• Yuyi Zhou 1 ,
• Mingcai Zhang 1 ,
• Zhaohu Li 1 ,
• Fei Yi   ORCID: orcid.org/0000-0001-5329-8195 1 &
• Liusheng Duan 1 , 2  

Maize is a valuable raw material for feed and food production. Healthy seed germination is important for improving the yield and quality of maize. Seed aging occurs relatively fast in crops and it is a process that delays germination as well as reduces its rate and even causes total loss of seed viability. However, the physiological and transcriptional mechanisms that regulate maize seeds, especially aging seed germination remain unclear. Coronatine (COR) which is a phytotoxin produced by Pseudomonas syringae and a new type of plant growth regulator can effectively regulate plant growth and development, and regulate seed germination. In this study, the physiological and transcriptomic mechanisms of COR-induced maize seed germination under different aging degrees were analyzed. The results showed that 0.001–0.01 μmol/L COR could promote the germination of aging maize seed and the growth of primary roots and shoots. COR treatment increased the content of gibberellins (GA 3 ) and decreased the content of abscisic acid (ABA) in B73 seeds before germination. The result of RNA-seq analysis showed 497 differentially expressed genes in COR treatment compared with the control. Three genes associated with GA biosynthesis ( ZmCPPS2 , ZmD3 , and ZmGA2ox2 ), and two genes associated with GA signaling transduction ( ZmGID1 and ZmBHLH158 ) were up-regulated. Three genes negatively regulating GA signaling transduction ( ZmGRAS48 , ZmGRAS54 , and Zm00001d033369 ) and two genes involved in ABA biosynthesis ( ZmVP14 and ZmPCO14472 ) were down-regulated. The physiological test results also showed that the effects of GA and ABA on seed germination were similar to those of high and low-concentration COR, respectively, which indicated that the effect of COR on seed germination may be carried out through GA and ABA pathways. In addition, GO and KEGG analysis suggested that COR is also highly involved in antioxidant enzyme systems and secondary metabolite synthesis to regulate maize seed germination processes. These findings provide a valuable reference for further research on the mechanisms of maize seed germination.

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Data availability

The original contributions presented in the study are publicly available. The transcriptome data can be found at: NCBI, BioProject, PRJNA1046141. Further inquiries can be directed to the corresponding author.

Abbreviations

Abscisic acid

Brassinosteroid

Calcium-dependent protein kinases

Coronafacic acid

β-Carotene hydroxylase

Coronamic acid

ent -Copalyl diphosphate synthase

Differentially expressed gene

Enzyme-linked immunosorbent assay

False discovery rate

Fragments per kilobase of transcript per million mapped reads

Gibberellin

GA20oxidase

Gibberellin insensitive dwarf1

Gene ontology

Jasmonic acid

ent -Kaurenoic acid oxidase

9-Cis-epoxycarotenoid dioxygenase

Principal component analysis

Protein phosphatase 2C

Salicylic acid

Short-chain dehydrogenase/reductase

Triphenyltetrazolium chloride

Triphenyl formazone

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Acknowledgements

We would like to acknowledge the help of Ruihong Tang in the preliminary trial process.

This work was supported by the National Key Research & Development Program (Grant Number: 2023YFD1700600).

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Yaqi Jie, Wei Wang and Zishan Wu are share first authorship.

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State Key Laboratory of Plant Physiology and Biochemistry, Engineering Research Center of Plant Growth Regulator, Ministry of Education & College of Agronomy and Biotechnology, China Agricultural University, No.2 Yuanmingyuan West Road, Haidian, Beijing, 100193, China

Yaqi Jie, Wei Wang, Zishan Wu, Zhaobin Ren, Lu Li, Yuyi Zhou, Mingcai Zhang, Zhaohu Li, Fei Yi & Liusheng Duan

College of Plant Science and Technology, Beijing University of Agriculture, Beijing, 102206, China

Liusheng Duan

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L.D. and F.Y. designed and conceived the experiments. Y.J., W.W., Z.W., Z.R., and L. L. carried out the experiments and analyzed and interpreted the data. Y.J., W.W., Z.W., and F.Y. prepared the manuscript. Y.Z., M.Z., and Z.L. conceived the study and participated in its design. L.D. and F.Y revised the manuscript. All authors have read and agreed to the published version of the manuscript.

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Jie, Y., Wang, W., Wu, Z. et al. Deciphering physiological and transcriptional mechanisms of maize seed germination. Plant Mol Biol 114 , 94 (2024). https://doi.org/10.1007/s11103-024-01486-1

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Received : 21 January 2024

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DOI : https://doi.org/10.1007/s11103-024-01486-1

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