Five (or so) challenges for species distribution modelling

By Miguel B. Araújo, Antoine Guisan. 2006

1,925 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Genetics

View Paper: https://www.semanticscholar.org/paper/a6159d6c0bdae867b09f529364e9b0515f64acf6

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Citations in the corpus

Detecting the influence of climatic variables on species distributions: a test using GIS niche‐based models along a steep longitudinal environmental gradient (2008, background): “However, these techniques do not account for species interactions or historical factors (Araújo & Guisan, 2006).”
Uncertainty associated with survey design in Species Distribution Models (2014, background): “Despite the potential effects of spatial biases in the design of the surveys on SDM prediction ability (Ara ujo & Guisan, 2006; Phillips et al., 2009), this issue has not been addressed in the literature.”
Global warming will affect the genetic diversity and uniqueness of Lycaena helle populations (2011, methodology): “These models have frequently been applied to predict changes in species’ potential distributions under current, past and future climate scenarios (Araújo et al., 2004; Araújo & Guisan, 2006; Heikkinen et al., 2006; Hijmans & Graham, 2006; Pearman et al., 2008).”
ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models (2014, background): “A number of recent studies have demonstrated the sensitiv- ity of ENMperformance to model specification (e.g. Ara ujo & Guisan 2006; Elith, Kearney & Phillips 2010; Anderson & Gonzalez 2011; Elith et al. 2011; Warren & Seifert 2011; Ara- ujo & Peterson 2012; Merow, Smith & Silander 2013;…”
Fine‐scale environmental variation in species distribution modelling: regression dilution, latent variables and neighbourly advice (2011, background): “…2010) and the ability of current SDMmethods to make plausible predictions is frequently regarded as limited (see: Austin 2002; Gaston 2003; Hampe 2004; Guisan&Thuiller 2005; Araújo &Rahbek 2006; Araújo & Guisan 2006; Heikkinen et al. 2006; Austin 2007; Soberón 2007; Elith & Leathwick 2009).”
When and how should biotic interactions be considered in models of species niches and distributions? (2017, background): “Despite these advances, consideration of biotic interactions constitutes an outstanding problem even for single-species correlative models (Ara ujo & Guisan, 2006; Ara ujo & Luoto, 2007; Holt, 2009; Hellmann et al., 2012; Anderson, 2013; Ara ujo & Rozenfeld, 2014).”
Predictor complexity and feature selection affect Maxent model transferability: Evidence from global freshwater invasive species (2020, background): “One aspect that has received less attention, however, is the selection of environmental predictors, despite predictor selection being fundamental to any modelling effort (Araújo & Guisan, 2006).”
Understanding what controls the spatial distribution of fish populations using a multi-model approach (2011, methodology): “…2006) and their aim is to relate observations of animal presence or abundance to various attributes of the environment using a statistical method (Randin et al., 2006, Austin, 2002, Rushton et al., 2004, Araújo and Guisan, 2006, Guisan et al., 2006, Austin, 2007, MacNally, 2000, Boisclair, 2001).”
Estimating demographic models for the range dynamics of plant species (2010, background): “…limitations of phenomenological models: their static nature precludes predictions for species in disequilibrium with the environment (e.g. Thuiller et al., 2008), and they are poorly linked to ecological theory because they do not represent ecological processes (e.g. Araújo & Guisan, 2006).”
Comparing species distribution models constructed with different subsets of environmental predictors (2015, background): “A common challenge encountered in species distribution modelling (SDM) is the selection of environmental variables to use as predictors (Ara ujo & Guisan, 2006).”
Effects of the number of presences on reliability and stability of MARS species distribution models: the importance of regional niche variation and ecological heterogeneity (2010, background): “Species distribution models (SDM) have increased in importance in recent years (Guisan & Zimmermann 2000; Araújo & Guisan 2006).”