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

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.