4.8 Article

Species distribution models have limited spatial transferability for invasive species

期刊

ECOLOGY LETTERS
卷 23, 期 11, 页码 1682-1692

出版社

WILEY
DOI: 10.1111/ele.13577

关键词

Accuracy; Bayesian inference; biogeographical experiments; biological invasions; ecological niche models; invasive alien species; model extrapolation; model interpolation; model parameterisation; quantitative synthesis

类别

资金

  1. Second Tibetan Plateau Scientific Expedition and Research Program (STEP) [2019QZKK0501]
  2. National Natural Science Foundation of China [31872568, 31472016]
  3. AlienScenarios Project - FEDER/Ministerio de Ciencia e Innovacion - Agencia Estatal de Investigacion [PCI2018-092966]
  4. Deutsche Forschungsgemeinschaft (DFG) [JE 288/9-2]
  5. Natural Science Foundation of China-Shandong Joint Fund for Marine Ecology and Environmental Sciences [U1606404]
  6. Projekt DEAL
  7. International IGB Fellowship Program in Freshwater Science

向作者/读者索取更多资源

The reliability of transferring species distribution models (SDMs) to new ranges and future climates has been widely debated. Biological invasions offer the unique opportunity to evaluate model transferability, as distribution data between species' native and introduced ranges are geographically independent of each other. Here, we performed the first global quantitative synthesis of the spatial transferability of SDMs for 235 invasive species and assessed the association of model transferability with the focal invader, model choice and parameterisation. We found that SDMs had limited spatial transferability overall. However, model transferability was higher for terrestrial endotherms, species introduced from or to the Southern Hemisphere, and species introduced more recently. Model transferability was also positively associated with the number of presences for model calibration and evaluation, respectively, but negatively with the number of predictors. These findings highlight the importance of considering the characteristics of the focal invader, environment and modelling in the application and assessment of SDMs.

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