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Aim: The abundances and distributions of some species are more closely matched to variations in climate than others. Species traits that might influence how well the distribution and abundance of a species are matched to climatic variation include life history (e.g., body size and dispersal ability), ecology (e.g., habitat specialization and territoriality) and demography (e.g., population size). Here, we used a survey of bird abundances across the USA to assess the extent to which species abundances and distributions are predicted by climate (i.e., climate matching) and how species traits relate to interspecific variation in climate matching. Location: USA. Time period: 1983–2018. Major taxa studied: Birds. Methods: Species abundances were obtained from the North American Breeding Bird Survey. Climate matching was estimated as the predictive performance of species–climate models fitted using boosted regression trees and generalized additive models and modelled as a function of species traits. Results: Species traits explained 56% of the variation in climate matching among species. Intermediate-sized species were more well matched to climate than smaller or larger species, as were species that lived primarily in forested compared with open habitats, species that were locally more abundant and species that were more territorial. Alternatively, species that were more specialized or had high variability in abundance among sites were less well matched to climate. We also found that species classified as “near threatened” were more well matched to climate, suggesting that these species might be more vulnerable to climate change. However, species classified as “vulnerable” were more decoupled from climate than those of “least concern”, possibly owing to ecological drift associated with progressive population declines. Main conclusions: Our findings provide an ecological basis for understanding the extent to which species abundances and distributions match broad climatic gradients, which can provide the groundwork to improve our ability to predict distributions under global change. |
