Data from: Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches

• Main Authors: Guisan, Antoine, Dubuis, Anne, Vittoz, Pascal
• Format: Dataset
• Language: unknown
• Published: 2014
• Subjects: Trifolium medium; Anthriscus sylvestris; Pulsatilla alpina sl; environmental maps; Juncus effusus; Luzula alpinopilosa; Vaccinium gaultherioides; Scabiosa lucida; Saxifraga paniculata; Phyteuma spicatum; Ligusticum mutellina; Ranunculus aconitifolius; Achillea millefolium; Poa supina; Galium megalospermum; Geranium sylvaticum; Vaccinium vitis-idaea; Veronica arvensis; Heracleum sphondylium sl; Erigeron uniflorus; Leontodon helveticus; Bromus hordeaceus; Rumex acetosa; Polygala chamaebuxus; Pimpinella saxifraga aggr; Helictotrichon versicolor; Calamagrostis varia; Luzula multiflora; Pritzelago alpina sstr; Ajuga reptans; Trisetum flavescens; Viola hirta; Anthoxanthum odoratum aggr; Vicia sativa sl; Sedum atratum; Rhinanthus alectorolophus; Arrhenatherum elatius; Poa minor; Prunella vulgaris; Gentiana verna; Trifolium badium; Pedicularis foliosa; Stachys officinalis sl; Cirsium acaule; Daucus carota; Salix herbacea; Plantago atrata sstr; Ranunculus acris sl; Knautia arvensis; Primula elatior sstr
• Online Access: https://zenodo.org/record/4974552; https://doi.org/10.5061/dryad.28d4k

PLEASE NOTE, THESE DATA ARE ALSO REFERRED TO IN TWO OTHER PUBLICATIONS. PLEASE SEE http://dx.doi.org/10.1111/j.1365-2486.2008.01766.x AND http://dx.doi.org/10.1111/2041-210X.12222 FOR MORE INFORMATION. Aim: This study compares the direct, macroecological approach (MEM) for modelling species richness (SR) with the more recent approach of stacking predictions from individual species distributions (S-SDM). We implemented both approaches on the same dataset and discuss their respective theoretical assumptions, strengths and drawbacks. We also tested how both approaches performed in reproducing observed patterns of SR along an elevational gradient. Location: Two study areas in the Alps of Switzerland. Methods: We implemented MEM by relating the species counts to environmental predictors with statistical models, assuming a Poisson distribution. S-SDM was implemented by modelling each species distribution individually and then stacking the obtained prediction maps in three different ways – summing binary predictions, summing random draws of binomial trials and summing predicted probabilities – to obtain a final species count. Results: The direct MEM approach yields nearly unbiased predictions centred around the observed mean values, but with a lower correlation between predictions and observations, than that achieved by the S-SDM approaches. This method also cannot provide any information on species identity and, thus, community composition. It does, however, accurately reproduce the hump-shaped pattern of SR observed along the elevational gradient. The S-SDM approach summing binary maps can predict individual species and thus communities, but tends to overpredict SR. The two other S-SDM approaches – the summed binomial trials based on predicted probabilities and summed predicted probabilities – do not overpredict richness, but they predict many competing end points of assembly or they lose the individual species predictions, respectively. Furthermore, all S-SDM approaches fail to appropriately reproduce the observed ...