Deconstructing the geography of human impacts on species’ natural distribution

• Published in: Nature communications 2024-10, Vol.15 (1), p.8852-15, Article 8852
• Main Authors: Waldock, Conor, Wegscheider, Bernhard, Josi, Dario, Calegari, Bárbara Borges, Brodersen, Jakob, Jardim de Queiroz, Luiz, Seehausen, Ole
• Format: Article
• Language: English
• Subjects: 704/158; 704/158/670; 704/158/672; 704/158/851; Animal Distribution; Animals; Anthropogenic Effects; Anthropogenic factors; Artificial Intelligence; Biodiversity; Conservation; Conservation of Natural Resources; Ecosystem; Endangered Species - statistics & numerical data; Environmental gradient; Explainable artificial intelligence; Fishes; Fresh Water; Freshwater fish; Geographical distribution; Geography; Human influences; Humanities and Social Sciences; Humans; Introduced species; Mapping; multidisciplinary; Science; Science (multidisciplinary); Shadows; Switzerland; Threat evaluation; Threatened species
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-024-52993-0

It remains unknown how species’ populations across their geographic range are constrained by multiple coincident natural and anthropogenic environmental gradients. Conservation actions are likely undermined without this knowledge because the relative importance of the multiple anthropogenic threats is not set within the context of the natural determinants of species’ distributions. We introduce the concept of a species ‘ shadow distribution ’ to address this knowledge gap, using explainable artificial intelligence to deconstruct the environmental building blocks of current species distributions. We assess shadow distributions for multiple threatened freshwater fishes in Switzerland which indicated how and where species respond negatively to threats — with negative threat impacts covering 88% of locations inside species’ environmental niches leading to a 25% reduction in environmental suitability. Our findings highlight that conservation of species’ geographic distributions is likely insufficient when biodiversity mapping is based on species distribution models, or threat mapping, without also quantifying species’ expected or shadow distributions. Overall, we show how priority actions for nature’s recovery can be identified and contextualised within the multiple natural constraints on biodiversity to better meet national and international biodiversity targets. Explainable Artificial Intelligence can improve conservation decisions by revealing hidden insights to where human impacts on biodiversity are greatest. In this investigation of freshwater fish in Switzerland, around 90% of potentially habitable areas were negatively impacted human influences - these areas form the species’ “shadow distribution”.