Abundance within species' ranges: understanding species' responses to environmental change

加速的气候变化和环境退化要求我们提高对物种对环境变化的反应的理解和预测能力。由于有了大规模的物种分布数据库和环境数据（例如来自卫星的数据），因此进行了数千项研究，利用物种地理分布范围的数据来估计其环境生境，并预测物种为应对未来环境变化而进行的迁移。然而，这种方法越来越受到批评，因为关键假设是有问题的：1。假设物种的丰度从其地理范围的中间到边缘逐渐减少，反映出这些物种的环境适宜性平行下降。2.假设限制物种分布的关键环境因素（a）被测量的环境变量捕获，（B）它们的影响在地理上是固定的。 3.物种的地理分布图被认为足以确定这些物种的基本环境生态位，完全忽略了生物相互作用等重要因素，这些因素将许多物种的分布限制在其环境耐受范围内。 虽然所有3个假设都受到了批评，但在经验上测试它们的进展甚微，这主要是由于缺乏关于植物丰度，植物功能特征和大地理范围内精细尺度环境变量的可靠数据。结合来自遥感和最近开发的物种分布模型的最先进的高分辨率环境数据，新的sPlot数据库（www.idiv.de/sPlot）使我们能够克服这些限制。sPlot包含全球约200万个植被样地中植物物种丰度的记录，以及有关植物功能性状和植物发生的相关最新数据。在世界各地的植物园中，还有其他丰富的数据来源，未经数字化。 该项目的主要目的是利用最近开发的建模方法，利用上述新数据源提供的巨大新机会，解决植物对环境变化反应的基本问题。

Accelerated climate change and environmental degradation require us to improve our understanding of, and ability to predict, species' responses to environmental change. Availability of large-scale species distribution databases and environmental data (e.g. from satellites) have resulted in thousands of studies using data on species' geographical ranges to estimate their environmental niches and predict species' movements in response to future environmental change. However, this approach is increasingly criticised because key assumptions are problematic:1. Species' abundances are assumed to decrease from the middle of their geographical ranges to the edges, reflecting parallel decreases in environmental suitability for those species. 2. It is assumed that the key environmental factors limiting species' distributions are (a) captured by the environmental variables measured, and (b) their effects are geographically stationary. 3. Maps of species' geographical ranges are assumed to be sufficient to determine those species' fundamental environmental niches, completely ignoring important factors such as biological interactions which limit the distributions of many species well within their environmental tolerances. Whilst all 3 assumptions have been criticised, there has been little progress in testing them empirically, largely due to a lack of reliable data on plant abundance, plant functional characteristics and fine-scale environmental variables over large geographical extents. In combination with state-of-the-art, high-resolution environmental data from remote sensing and recently developed species distribution models, the novel sPlot database (www.idiv.de/sPlot) allows us to overcome these limitations. sPlot contains records of plant species' abundances in ~2 million vegetation plots worldwide and associated state-of-the-art data on the plants' functional traits and phylogeny. Additional rich sources of data are available, un-digitized, in botanical gardens across the world. The project's main aim is to address fundamental questions about plant responses to environmental change by harnessing the immense new opportunities offered by the novel data sources listed above, using recently developed modelling approaches.