SESAM - a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages

Abstract

Two different approaches currently prevail for predicting spatial patterns of species assemblages. The first approach (macroecological modelling, MEM) focuses directly on realized properties of species assemblages, whereas the second approach (stacked species distribution modelling, S‐SDM) starts with constituent species to approximate the properties of assemblages. Here, we propose to unify the two approaches in a single ‘spatially explicit species assemblage modelling’ (SESAM) framework. This framework uses relevant designations of initial species source pools for modelling, macroecological variables, and ecological assembly rules to constrain predictions of the richness and composition of species assemblages obtained by stacking predictions of individual species distributions. We believe that such a framework could prove useful in many theoretical and applied disciplines of ecology and evolution, both for improving our basic understanding of species assembly across spatio‐temporal scales and for anticipating expected consequences of local, regional or global environmental changes. In this paper, we propose such a framework and call for further developments and testing across a broad range of community types in a variety of environments.

Can Species Richness Patterns Be Interpolated From a Limited Number of Well-Known Areas? Mapping Diversity Using GLM and Kriging

“…Ferrier 2002), it provides a first approximation to the geographic variations of species diversity in the absence of good quality data on species distributions or composition, which is more prone to error (see Hortal et al 2007;Pineda & Lobo 2009;Aranda & Lobo 2011 for comparisons of both kinds of data). Being based in a simpler variable, it also provides a simpler way of analyzing the effects of data quality on model reliability; we assume that the results of this study can be extrapolated to ALM applications based on other aspects of biodiversity (see Hortal & Lobo 2006;Guisan & Rahbek 2011). Thus, here we compare the adequacy of GLM and kriging to interpolate richness values.…”

“…While SDM techniques are widely used to project species distributions into different geographical and temporal scenarios (see Lobo et al 2010), ALMs receive less attention as predictive tools, being more used to study the relationships between diversity and environment (e.g. Lobo et al 2001; see also Guisan & Rahbek 2011).…”

“…Many modelling techniques have been proposed so far, mainly grouped in two kinds of approaches; (i) Species Distribution Models (SDM) intend to predict the spatial distribution of single species from data on their occurrences and an array of-often environmental-predictors (Guisan & Zimmermann 2000); (ii) Assemblage-level Models (ALM)-also called Synecological Models (Hortal & Lobo 2006), Community-level Models (Ferrier & Guisan 2006) or Macroecological Models (Guisan & Rahbek 2011)-aim to represent the spatial variations in the diversity of whole assemblages based on data from a few well-known areas and the corresponding set of predictors (Austin et al 1996). While SDM techniques are widely used to project species distributions into different geographical and temporal scenarios (see Lobo et al 2010), ALMs receive less attention as predictive tools, being more used to study the relationships between diversity and environment (e.g.…”

Modeling distribution and abundance of multiple species: Different pooling strategies produce similar results

“…Drawbacks include the lack of speciesspecific information and the potential smoothing or generalization of spatial predictions due to a loss of biological connection with covariates, depending on how species are grouped. Research comparing the different modeling strategies for v www.esajournals.org estimating species richness from occurrencebased models has yielded mixed results about differences in model predictions (Ferrier et al 2002, Lehmann et al 2002, Guisan and Rahbek 2011, Hallstan et al 2012). To our knowledge, our work represents the first comparison of these different strategies for SAMs.…”

Spatial Conservation Prioritization in Data-Poor Areas of the World

“…This class of analysis is macro-ecological analysis using models for species richness and turnover (Ferrier & Guisan 2006;Arponen et al 2008;Guisan & Rahbek 2011;Mokany & Ferrier 2011). Arponen et al (2008) identified the components needed for successful ecological communitylevel decision analysis, including statistically justified simultaneous handling of species richness and pair-wise similarity of ecological communities, modeled as functions of environmental conditions.…”