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Ecological barriers mediate spatiotemporal shifts of bird communities at a continental scale.

Emma-Liina MarjakangasLaura BoscoMartijn VersluijsYanjie XuAndrea SantangeliSari HolopainenSanna MäkeläinenSergi HerrandoVerena KellerPetr VoříšekLluís BrotonsAlison JohnstonKarine PrincéStephen G WillisKaren AghababyanVitalie AjderDawn E BalmerTaulant BinoKerem Ali BoylaTomasz ChodkiewiczJuan Carlos Del MoralVlatka Dumbović MazalAlessandro FerrariniCarlos GodinhoMarco GustinMikhail KalyakinPeter KnausTatiana KuzmenkoÅke LindströmQenan MaxhuniBlas MolinaKároly NagyDimitrije RadišićSaša RajkovDraženko Z RajkovićLiutauras RaudonikiJovica SjeničićStoycho StoychevTibor SzépNorbert TeufelbauerSilvia UrsulChris A M van TurnhoutMetodija VelevskiThomas VikstrømTomasz WilkOlga VoltzitIngar Jostein ØienChristoph SudfeldtBettina GerlachAleksi Lehikoinen
Published in: Proceedings of the National Academy of Sciences of the United States of America (2023)
Species' range shifts and local extinctions caused by climate change lead to community composition changes. At large spatial scales, ecological barriers, such as biome boundaries, coastlines, and elevation, can influence a community's ability to shift in response to climate change. Yet, ecological barriers are rarely considered in climate change studies, potentially hindering predictions of biodiversity shifts. We used data from two consecutive European breeding bird atlases to calculate the geographic distance and direction between communities in the 1980s and their compositional best match in the 2010s and modeled their response to barriers. The ecological barriers affected both the distance and direction of bird community composition shifts, with coastlines and elevation having the strongest influence. Our results underscore the relevance of combining ecological barriers and community shift projections for identifying the forces hindering community adjustments under global change. Notably, due to (macro)ecological barriers, communities are not able to track their climatic niches, which may lead to drastic changes, and potential losses, in community compositions in the future.
Keyphrases
  • climate change
  • human health
  • mental health
  • healthcare
  • machine learning
  • artificial intelligence
  • data analysis
  • case control