Understanding the interactions between climate change and biodiversity is crucial for planetary resilience. The integration of new remote sensing technologies and climate reanalysis data is transforming our understanding of the effects of compound extreme weather events (EWE) on ecosystem stability and resilience. EWEs are altering forest ecosystem properties worldwide, including their reorganization into shorter and younger stands (e.g., McDowell et al., 2020). Structural, functional, and genetic similarity across communities (a.k.a. biotic homogenization) is increasing at all scales and reducing the multifunctionality of the ecosystem (Oden et al. 2004; van der Plas et al., 2016).

The BioExtremes Python tool V1 was created to help scientists better understand the impacts of extreme weather events on ecosystem structural homogenization, from local to global scales. The Bioextremes tool allows users to download and extract metrics from large amounts of NASA’s Global Ecosystem Dynamics Investigation (GEDI) laser scanner footprints and ERA-5 reanalysis climate gridded files. The user can analyze the relationships between historical extreme weather events (EWEs) and vegetation structural metrics from the landscape to global scales. We developed versatile GEDI granule-level and shot-level filter tools to improve the management of GEDI data (Figure 1). The EWE metrics (e.g., intensity, duration, frequency, and time since the last event) calculation admits varying climate variables, time windows, and extremeness thresholds and is transferable across ecosystems (Figure 2). 

Figure 1.  The Spherical folder in the BioExtremes GitHub repository contains Python code to perform simple geometric computations on the globe; these are the basis of the granule-level constraints used for GEDI downloads. Currently supported functions include: 1) Determining points of intersection between geodesic arcs and polygons; 2) Arc-length parameterizations of geodesic arcs and polygons; and 3) Polygon containment queries. Credits: Frank Sield
 

Figure 2. The Extreme Weather Event (EWE) metrics (e.g., intensity, duration, frequency, and time since the last event) calculation admits varying climate variables, time windows, and extremeness thresholds and is transferable across ecosystems. Credits: Juan David González Trujillo (Trujillo et al., 2023).

Case Study on Global Mangroves

Mangroves are one of the most critical ecosystems in the world, providing multiple services such as carbon storage, fisheries, and coastal protection (Spalding et al., 2010). Studies have indicated mangroves are shorter where rainfall is reduced and cyclones are more frequent (Simard et. al. 2019). However, while it is already known that drought events make mangroves less resilient to cyclones (Amaral et al., 2023), understanding wind speed and rainfall thresholds that make global mangroves shorter, inducing ecosystem biotic homogenization, is still unknown. Here, we explored thresholds of sustained wind speed and rainfall that affect mangrove ecosystem structure globally.

The data used for our case study were: 

• 0.25-degree-resolution ERA-5 mean total precipitation rate and 10m wind gust from 1979 to 2022 (Hersbach et al., 2020)
• International Best Track Archive for Climate Stewardship (IBTrACS) from 1979 to 2022 (Knapp et al., 2010) 25-m-resolution GEDI forest height (RH98) from 2019 to 2022 (Dubayah et al., 2020)
• 2020 mangrove mask from the Global Mangrove Watch – GMW (Bunting et al., 2018)

For the rainfall EWE metric, we used statistical thresholds, specifically the 1st and 5th percentiles. In contrast, for windspeed, we applied climatological EWE thresholds based on the Saffir-Simpson Hurricane Wind Scale, which classifies hurricanes according to their sustained wind speeds. We first used the International Best Track Archive (IBTrACS) dataset to estimate any bias apparent in the ERA5 reporting of wind speeds (Figure 3) and generate coefficients to calibrate ERA5 data to IBTrACS and the Saffir-Simpson Hurricane Wind Scale. 
 

Figure 3. Correlation between IBTrACS and ERA5 wind speed records (n=600) observed over 20 years across all global basins with the best-fit linear regression.

We downloaded all GEDI L2A mangrove height (RH98) from over 40,000 granules, and applied quantile regressions to understand how EWE metrics affect different (tall and short) mangroves globally. Our preliminary results show that different thresholds lead to varying structural responses in mangroves. Here, we highlight that the intensity of wind gusts and the frequency of category 4 and 5 hurricanes (≥ 58.1 m/s) (Figure 4), as well as reduced rainfall EWEs (1st percentile/3-month event) (Figure 5), contribute to mangrove structural homogenization.

Figure 4. Mangrove structural homogenization effect associated with increased wind speed intensity (top) and frequency of category 4 and 5 hurricanes (≥ 58.1 m/s) (bottom).

Figure 5. Mangrove structural homogenization effect associated with EWE of decreased rainfall intensity (top) and frequency of rainfall EWE (i.e., 1st percentile of rainfall during a 3-month window) (bottom).

Using the BioExtremes Tool, we anticipate that scientists will enhance our understanding of extreme weather event (EWE) thresholds and their effects on biotic homogenization. Processes that can diminish the multifunctionality of ecosystems and the provision of services to society. With this knowledge, scientists will be better equipped to inform decision-makers on how to manage ecosystems effectively to preserve their multifunctionality as the frequency of extreme weather events continues to rise.
 

How to access and cite the BioExtremes Python Tool V1

Find the BioExtremes V1 tool at: https://github.com/earthlab/BioExtremes

Citation: Verleye, E., Seidl, F., & Amaral, C. (2025). BioExtremes open-source tool. Zenodo. https://doi.org/10.5281/zenodo.14816537

References

Amaral, C. H., Poulter, B., Lagomasino, D., Fatoyinbo, T., Taillie, P., Lizcano, G., ... & Roman-Cuesta, R. M. (2023). Drivers of Mangrove Vulnerability and Resilience to Tropical Cyclones in the North Atlantic Basin. Science of the Total Environment, 898, 165413. https://doi.org/10.1016/j.scitotenv.2023.165413
Bunting P., Rosenqvist A., Lucas R., Rebelo L-M., Hilarides L., Thomas N., Hardy A., Itoh T., Shimada M. & Finlayson C.M. (2018). The Global Mangrove Watch – a New 2010 Global Baseline of Mangrove Extent. Remote Sensing 10(10): 1669.
Dubayah R., Hofton, M., Blair, J. B., Armston, J., Tang, H., Luthcke, S. (2020). GEDI L2A Elevation and Height Metrics Data Global Footprint Level V001. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/GEDI/GEDI02_A.001
González-Trujillo, J. D., Román-Cuesta, R. M., Muñiz-Castillo, A. I., Amaral, C. H., & Araújo, M. B. (2023). Multiple dimensions of extreme weather events and their impacts on biodiversity. Climatic Change, 176(11), 155. https://doi.org/10.1007/s10584-023-03622-0
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Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone best track data. Bulletin of the American Meteorological Society, 91, 363-376. doi:10.1175/2009BAMS2755.1
McDowell, N. G., Allen, C. D., Anderson-Teixeira, K., Aukema, B. H., Bond-Lamberty, B., Chini, L., ... & Xu, C. (2020). Pervasive shifts in forest dynamics in a changing world. Science, 368(6494), eaaz9463.
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Simard, M., Fatoyinbo, L., Smetanka, C., Rivera-Monroy, V. H., Castañeda-Moya, E., Thomas, N., & Van der Stocken, T. (2019). Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nature Geoscience, 12(1), 40-45.
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Acknowledgements

The development of the BioExtremes tool was supported by the project “BioExtremes - the role of Biodiversity in mangrove ecosystem response to Extreme events” (PI: Cibele Amaral), funded 
by the Cooperative Institute of Research in Environmental Sciences (CIRES) Innovative Research Program 2023.