DOI: 10.5061/dryad.28d4k
title: Data from: Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches

description: 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 hump-shaped
 patterns of SR along the elevational gradient. Main conclusions:
 Macroecological approach and S-SDM have complementary strengths. We
 suggest that both could be used in combination to obtain better SR
 predictions by following the suggestion of constraining S-SDM by MEM
 predictions.
description: Plant distribution and
 environmental data in the Swiss AlpsPlant species
 (presence-absence records) and environmental data in each of the 912
 plots. The Dubuis et al. (2011, Diversity and Distributions) paper uses
 the associated Dryad dataset of plant species distribution data and
 environmental variables to compare two approaches traditionally used to
 model plant species richness: stacked species distribution models (S-SDMs)
 and macroecological richness models (MEMs). Subsets of these data have
 also been used in previous or later papers by the same research group:
 Randin et al. (2006, J. Biogeography), Randin et al. (2009, Global Change
 Biology), Randin et al. (2009, Arctic, Antarctic and Alpine Research),
 Randin et al. (2009, J. Vegetation Science), Guisan & Rahbeck (2011,
 J. Biogeography), Pottier et al. (2013, Global Ecology and Biogeography),
 Ndiribe et al. (2013, Ecology & Evolution) and others (see full
 publication list under http://www.unil.ch/ecospat).plant_env_data.txt