Affiliation:
1. Institute for Biochemistry and Biology, University of Potsdam Potsdam Germany
2. Land Change Science, Swiss Federal Research Institute WSL Birmensdorf Switzerland
Abstract
Abstract
Species distribution models (SDMs) are widely used to infer species–environment relationships, predict spatial distributions and characterise species' environmental niches. While the importance of space and spatial scales is widely acknowledged in SDM applications, temporal components of the niche are rarely addressed.
We discuss how phenology and demographic stages affect model inference in plant SDMs. Ignoring conspicuousness and timing of phenological stages may bias niche estimates through increased observer bias, while ignoring stand age may bias niche estimates through temporal mismatches with environmental variables, especially during times of rapid global warming.
We present different methods to consider phenology and demographic stages in plant SDMs, including the selection of causal, spatiotemporally explicit predictors and the calibration of stage‐specific SDMs. Based on a case study with citizen science data, we illustrate how spatiotemporal SDMs provide deeper insights on the coincidence of range and phenological shifts under climate change.
The proliferation of digitally available biodiversity and citizen science data increasingly allows considering time explicitly in SDMs. This offers a more mechanistic understanding of plant distributions, and more robust predictions under global change, especially if the reporting of phenological stages and age is facilitated and promoted by relevant data portals.
Funder
Deutsche Forschungsgemeinschaft
Cited by
1 articles.
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