Model‐based uncertainty in species range prediction

By Richard G. Pearson, Wilfried Thuiller, Miguel B. Araújo, E. Martínez‐Meyer, Lluís Brotons, Colin J. McClean, L. Miles, Pedro Segurado, Terence P. Dawson, D. Lees. 2006

1,016 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/5e6b18e3c345f64e8c8d778aa313d7c7e4969280

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Citations in the corpus

Potential Impacts of Climate Change on the Distribution of North American Trees (2007, background): “…in which populations are able to migrate entirely into their future climate habitat, and a “no dispersal” situation, in which they are unable to migrate quickly enough and thus survive only in areas that overlap with their current climatic range (Peterson et al. 2002, Thuiller et al. 2006).”
The ability of climate envelope models to predict the effect of climate change on species distributions (2006, background): “We found considerable variation between CEMs in their ability to reproduce these predictions, as was to be expected given previously reported variation in such predictions (Thuiller, 2003, 2004; Pearson et al., 2006).”
Projecting climate change impacts on species distributions in megadiverse South African Cape and Southwest Australian Floristic Regions: Opportunities and challenges (2009, methodology): “…be prescribed with reasonable certainty (e.g. Protea Atlas in South Africa), modellers have favoured the presence/absence techniques such as GLM, GAM, and CART, because these seem to give better predictions than techniques designed for presence-only data (Midgley et al. 2003; Pearson et al. 2006).”
A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD (2019, methodology): “However, no one method is consistently superior in perfor‐ mance across species, regions and applications (Elith et al., 2006; Pearson et al., 2006; Segurado & Araujo, 2004).”
The art of modelling range‐shifting species (2010, methodology): “This sensitivity of outcome to method has been demonstrated elsewhere, especially in the context of climate change (e.g. Pearson et al. 2006), and some authors have argued that consensus (ensemble) methods are an appropriate way to deal with the issue (Thuiller 2004; Araújo, Thuiller & Pearson…”
Species’ Distribution Modeling for Conservation Educators and Practitioners (2010, methodology): “These include (1) whether the model uses presence-absence or presence-only data (Brotons et al., 2004; Pearson et al., 2006), (2) if the model does not use absence data, whether the model uses solely presence records, ‘background’ data, or ‘pseudo-absences’ (Elith et al.”
Predicting the past distribution of species climatic niches. (2009, background): “Also, different model classes may well produce completely different projections ( Pearson et al., 2006 ) and the ability to model the distribution of the climatic niche of species along time differs widely (Hijmans & Graham, 2006).”
Areas of Climate Stability of Species Ranges in the Brazilian Cerrado: Disentangling Uncertainties Through Time (2012, methodology): “2012) involves the use of Ecological Niche Models (ENMs; also called Species Distribution Models – but see Araújo & Peterson (2012) and Peterson & Soberón (2012)).”