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Precipitation and winter temperature predict long‐term range‐scale abundance changes in Western North American birds
Authors:Javier Gutiérrez Illán  Chris D. Thomas  Julia A. Jones  Weng‐Keen Wong  Susan M. Shirley  Matthew G. Betts
Affiliation:1. Department of Forest Ecosystems and Society, Oregon State University, , Corvallis, OR 97331 USA;2. Department of Biology (Area 18), University of York, , York, YO10 5DD UK;3. Department of Geography, College of Earth, Ocean and Atmospheric Sciences, Oregon State University, , Corvallis, OR, 97331 USA;4. School of Electrical Engineering and Computer Science, Kelley Engineering Center, Oregon State University, , Corvallis, OR, 97331 USA
Abstract:Predicting biodiversity responses to climate change remains a difficult challenge, especially in climatically complex regions where precipitation is a limiting factor. Though statistical climatic envelope models are frequently used to project future scenarios for species distributions under climate change, these models are rarely tested using empirical data. We used long‐term data on bird distributions and abundance covering five states in the western US and in the Canadian province of British Columbia to test the capacity of statistical models to predict temporal changes in bird populations over a 32‐year period. Using boosted regression trees, we built presence‐absence and abundance models that related the presence and abundance of 132 bird species to spatial variation in climatic conditions. Presence/absence models built using 1970–1974 data forecast the distributions of the majority of species in the later time period, 1998–2002 (mean AUC = 0.79 ± 0.01). Hindcast models performed equivalently (mean AUC = 0.82 ± 0.01). Correlations between observed and predicted abundances were also statistically significant for most species (forecast mean Spearman′s ρ = 0.34 ± 0.02, hindcast = 0.39 ± 0.02). The most stringent test is to test predicted changes in geographic patterns through time. Observed changes in abundance patterns were significantly positively correlated with those predicted for 59% of species (mean Spearman′s ρ = 0.28 ± 0.02, across all species). Three precipitation variables (for the wettest month, breeding season, and driest month) and minimum temperature of the coldest month were the most important predictors of bird distributions and abundances in this region, and hence of abundance changes through time. Our results suggest that models describing associations between climatic variables and abundance patterns can predict changes through time for some species, and that changes in precipitation and winter temperature appear to have already driven shifts in the geographic patterns of abundance of bird populations in western North America.
Keywords:bird populations  boosted regression trees  climate‐envelope models  global change  niche models  Pacific Northwest  species distributions
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