Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR, 97331, USA; Department of Biology (Area 18), University of York, Heslington, York, YO10 5DD, UK.
Glob Chang Biol. 2014 Nov;20(11):3351-64. doi: 10.1111/gcb.12642. Epub 2014 Jun 30.
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.
预测生物多样性对气候变化的响应仍然是一个具有挑战性的难题,尤其是在降水是限制因素的气候复杂地区。尽管统计气候包络模型经常被用于预测物种在气候变化下的未来分布情景,但这些模型很少使用经验数据进行测试。我们使用了美国西部五个州和加拿大不列颠哥伦比亚省的鸟类分布和数量的长期数据,以测试统计模型预测鸟类种群在 32 年内随时间变化的能力。使用增强回归树,我们构建了存在-缺失和丰度模型,将 132 种鸟类的存在和丰度与气候条件的空间变化联系起来。使用 1970-1974 年数据构建的存在/缺失模型预测了后期时期(1998-2002 年)大多数物种的分布(平均 AUC=0.79±0.01)。回溯模型的表现同样(平均 AUC=0.82±0.01)。对于大多数物种,观察到的和预测到的丰度之间的相关性也具有统计学意义(预测的平均 Spearman ρ=0.34±0.02,回溯=0.39±0.02)。最严格的检验是通过时间检验地理模式的预测变化。对于 59%的物种(所有物种的平均 Spearman ρ=0.28±0.02),观察到的丰度模式变化与预测的变化呈显著正相关。该地区鸟类分布和丰度的最重要预测因子是三个降水变量(最湿润月、繁殖季节和最干燥月)和最冷月的最低温度,因此也是丰度随时间变化的预测因子。我们的研究结果表明,描述气候变量与丰度模式之间关系的模型可以预测某些物种的时间变化,而且降水和冬季温度的变化似乎已经导致了北美西部鸟类种群丰度地理模式的变化。