Making better biogeographical predictions of species’ distributions

Abstract

Summary1. Biogeographical models of species' distributions are essential tools for assessing impacts of changing environmental conditions on natural communities and ecosystems. Practitioners need more reliable predictions to integrate into conservation planning (e.g. reserve design and management). 2. Most models still largely ignore or inappropriately take into account important features of species' distributions, such as spatial autocorrelation, dispersal and migration, biotic and environmental interactions. Whether distributions of natural communities or ecosystems are better modelled by assembling individual species' predictions in a bottom-up approach or modelled as collective entities is another important issue. An international workshop was organized to address these issues. 3. We discuss more specifically six issues in a methodological framework for generalized regression: (i) links with ecological theory; (ii) optimal use of existing data and artificially generated data; (iii) incorporating spatial context; (iv) integrating ecological and environmental interactions; (v) assessing prediction errors and uncertainties; and (vi) predicting distributions of communities or collective properties of biodiversity. 4. Synthesis and applications. Better predictions of the effects of impacts on biological communities and ecosystems can emerge only from more robust species' distribution models and better documentation of the uncertainty associated with these models. An improved understanding of causes of species' distributions, especially at their range limits, as well as of ecological assembly rules and ecosystem functioning, is necessary if further progress is to be made. A better collaborative effort between theoretical and functional ecologists, ecological modellers and statisticians is required to reach these goals.

Incorporating uncertainty in predictive species distribution modelling

“…For example, calculating extinction risk requires knowing when the last population goes extinct: smooth modelled distributions with no outliers are more likely to predict total extinction than a real distribution with populations existing beyond the main climate envelope. An important additional source of uncertainty in niche-based distribution models is in the assessment of model performance and goodness-of-fit [12,48,49]. Ultimately, all models must be assessed for performance and predictive ability, but this is not as straightforward as implied by many papers in this field.…”

An overview of recent remote sensing and GIS based research in ecological informatics

“…Species distribution modelling has also benefited from the increased provision of data arising from the opening-up of archive resources and data sharing activities as well as the availability of a suite of modelling tools (Guisan et al, 2006;Austin, 2007;Graham et al, 2008). Additionally, much modelling is based upon presence or presence-absence data which are relatively easy to acquire and less sensitive than other data sets, such as those relating to abundance or cover, to variations in surveyor expertise (Ringvall et al, 2005).…”

“…None-the-less many challenges and issues remain to be addressed. For example, further work to help accommodate for the effects interactions between variables and a greater incorporation of theoretical knowledge may be required (Guisan et al, 2006;Austin, 2007). Additionally there are many factors that may influence a modelling study.…”

Linking density, productivity and trends of an endangered species: The Bonelli's eagle in Spain

“…Much effort has been made on modelling species distribution and determining which variables explained the variation of species' abundances (Guisan et al, 2006;Rodríguez, et al, 2007), predicting abundance from presence-absence data (e.g., He and Gaston 2000;Holt et al, 2002;Conlisk et al 2009), or testing different methods for modelling species distributions (e.g., Segurado and Araújo 2003).…”