Document Type
Paper
Start Date
16-10-2024 9:50 AM
End Date
16-10-2024 11:10 AM
Abstract
Biodiversity is essential for maintaining ecosystem balance and functionality, providing vital services such as climate regulation. The rapid decline in biodiversity, driven by habitat loss, habitat fragmentation, and climate change, poses significant threats to ecosystems. Climate change, in particular, is fundamentally altering habitats, leading to shifts in species distributions. However, existing research often lacks decomposed contribution analyses, particularly spatially, for a changing individual environmental attributes when modeling species distribution as an aggregate result of all factors and their interactions. Such analyses are crucial for identifying climate refugia and prioritizing conservation efforts. Taking endangered mammal species as an example, this study addresses this gap by employing species distribution modeling (SDM) and the post-hoc interpretability method, Shapley values, to analyze how future environmental variables are likely to reshape habitat suitability spatially. Our findings indicate that by 2070, some regions in North America, Europe, and Australia will become suitable for many species due to changes in annual mean temperature, while extensive areas in the Amazon and Congo rainforests will become less suitable. Annual mean precipitation is also projected to drive worsening conditions for local species, particularly in South America and central Africa. Our analysis demonstrates the effectiveness of explainable AI (xAI) techniques, such as Shapley values, in elucidating the future impacts of climate change by accounting for the interactions between environmental attributes. We identify a spatial analysis tool to develop conservation strategies targeted at the environmental attribute level, aimed at mitigating the diverse impacts of climate change on global biodiversity.
DOI
10.5703/1288284317811
Explainable artificial intelligence to interpret spatially-explicit impacts of future climate change on species distribution
Biodiversity is essential for maintaining ecosystem balance and functionality, providing vital services such as climate regulation. The rapid decline in biodiversity, driven by habitat loss, habitat fragmentation, and climate change, poses significant threats to ecosystems. Climate change, in particular, is fundamentally altering habitats, leading to shifts in species distributions. However, existing research often lacks decomposed contribution analyses, particularly spatially, for a changing individual environmental attributes when modeling species distribution as an aggregate result of all factors and their interactions. Such analyses are crucial for identifying climate refugia and prioritizing conservation efforts. Taking endangered mammal species as an example, this study addresses this gap by employing species distribution modeling (SDM) and the post-hoc interpretability method, Shapley values, to analyze how future environmental variables are likely to reshape habitat suitability spatially. Our findings indicate that by 2070, some regions in North America, Europe, and Australia will become suitable for many species due to changes in annual mean temperature, while extensive areas in the Amazon and Congo rainforests will become less suitable. Annual mean precipitation is also projected to drive worsening conditions for local species, particularly in South America and central Africa. Our analysis demonstrates the effectiveness of explainable AI (xAI) techniques, such as Shapley values, in elucidating the future impacts of climate change by accounting for the interactions between environmental attributes. We identify a spatial analysis tool to develop conservation strategies targeted at the environmental attribute level, aimed at mitigating the diverse impacts of climate change on global biodiversity.