Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors

By Alexandra D. Syphard, Janet Franklin. 2009

154 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/94b585c6c7198ee406bd34090a45b9d32527f43a

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

A 40‐year, continent‐wide, multispecies assessment of relevant climate predictors for species distribution modelling (2014, background): “However, the question of the more relevant climate variables remains open despite its proven influence on SDMs results (Syphard & Franklin, 2009; Synes & Osborne, 2011).”
Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds (2012, background): “S1–S17) are likely to explain differences between the machine-learning and the parametric modelling approaches in our study (Dormann et al., 2008; Syphard and Franklin, 2009), but a detailed analysis of the specific model differences is beyond the scope of this contribution.”
Does the interpolation accuracy of species distribution models come at the expense of transferability (2012, background): “2008, 2011), while other papers have continued to advocate AUC as a useful measure of comparative model performance despite its limitations (Syphard and Franklin 2009).”
Forest structure and species traits mediate projected recruitment declines in western US tree species (2015, background): “We examine how apparent demographic niche differences vary with species traits such as drought and shade tolerance as these are associated with suites of functional traits that may increase or reduce the sensitivity of species to climatic changes (Syphard & Franklin, 2009).”
A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels (2019, background): “…is the structure of the data (Fielding and Haworth 1995), especially the prevalence of species (Leathwick et al. 2006, Meynard and Quinn 2007, Syphard and Franklin 2009, Santika 2011, Madon et al. 2013) and the strength and shape of the environmental gradient (Thuiller et al. 2003, Austin et…”