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The accuracy of plant assemblage prediction from species distribution models varies along environmental gradients
Author(s) -
Pottier Julien,
Dubuis Anne,
Pellissier Loïc,
Maiorano Luigi,
Rossier Leila,
Randin Christophe F.,
Vittoz Pascal,
Guisan Antoine
Publication year - 2013
Publication title -
global ecology and biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.164
H-Index - 152
eISSN - 1466-8238
pISSN - 1466-822X
DOI - 10.1111/j.1466-8238.2012.00790.x
Subject(s) - species richness , elevation (ballistics) , jaccard index , range (aeronautics) , ecology , niche , species distribution , null model , environmental science , environmental gradient , physical geography , statistics , biology , geography , mathematics , habitat , composite material , materials science , geometry , cluster analysis
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 S wiss A lps. 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.