| Zusammenfassung: | Aim: Climatic niche modelling of species and community distributions implicitly assumes strong and constant climatic determinism across geographical space. We tested this assumption by assessing how stacked-species distribution models (S-SDMs) perform for predicting plant species assemblages along elevation gradients. Location: The western Swiss Alps. Methods: Using robust presence-absence data, we first assessed the ability of topo-climatic S-SDMs to predict plant assemblages in a study area encompassing a 2800-m wide elevation gradient. We then assessed the relationships among several evaluation metrics and trait-based tests of community assembly rules. Results: The standard errors of individual SDMs decreased significantly towards higher elevations. Overall, the S-SDM overpredicted far more than they underpredicted richness and could not reproduce the humpback curve along elevation. Overprediction was greater at low and mid-range elevations in absolute values but greater at high elevations when standardized by the actual richness. Looking at species composition, overall prediction success, kappa and specificity increased with increasing elevation, while the Jaccard index and sensitivity decreased. The best overall evaluation - as driven by specificity - occurred at high elevation where species assemblages were shown to be subject to significant environmental filtering of small plants. In contrast, the decreased overall accuracy in the lowlands was associated with functional patterns representing any type of assembly rule (environmental filtering, limiting similarity or null assembly). Main conclusions: We provide a thorough evaluation of S-SDM emphasizing the need to carefully interpret standard evaluation metrics, which reflect different aspects of assemblage predictions. We further reported interesting patterns of change in S-SDM errors with changes in assembly rules along elevation. Yet, significant levels of assemblage prediction errors occurred throughout the gradient, calling for further improvement of SDMs, e.g. by adding key environmental filters that act at fine scales and developing approaches to account for variations in the influence of predictors along environmental gradients.
|
|---|