Choice of predictor variables as a source of uncertainty in continental‐scale species distribution modelling under climate change

By Nicholas W. Synes, Patrick E. Osborne. 2011

249 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/474191b76729df57fe88d7ecf911341b646d1a65

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Citations in the corpus

Collinearity: a review of methods to deal with it and a simulation study evaluating their performance (2013, background): “Selecting the correct variable to retain in a model is more error-prone under collinearity (Faraway 2005), and the resulting reduced data set will also be biased (see Elith et al. (2010) and Synes & Osborne (2011), for more details).”
sPlot – A new tool for global vegetation analyses (2019, methodology): “We also calculated growing degree days for 1°C (GDD1) and 5°C (GDD5), according to Synes and Osborne (2011) and based on CHELSA data, and included the index of aridity and potential evapotranspiration extracted from the CGIAR- CSI website (www.cgiar-csi.org).”
A 40‐year, continent‐wide, multispecies assessment of relevant climate predictors for species distribution modelling (2014, methodology): “However, a few recent studies (Synes & Osborne, 2011; Braunisch et al., 2013) have highlighted that the choice of the environmental predictors to include in the models is very important.”
ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling (2016, background): “…measured with Schoener’s D, was small in the present, but greater in the LGM. Choice of predictor variables has previously been shown to have large impacts on model projections to other time periods or geographic regions (Peterson and Nakazawa 2008, Synes and Osborne 2011, Braunisch et al. 2013).”
Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics (2018, background): “However, the output of SDMs can vary substantially depending on whether a subset of variables is pre-selected or not, especially when models are projected into new environments (R€odder, Schmidtlein, Veith, & L€otters, 2009; Synes & Osborne, 2011).”
Incorporating model complexity and spatial sampling bias into ecological niche models of climate change risks faced by 90 California vertebrate species of concern (2014, background): “The inclusion of different variables can have large effects on model outputs (R€odder et al., 2009; Synes & Osborne, 2011).”
Climate change amplifies plant invasion hotspots in Nepal (2019, methodology): “Furthermore, there are other potential issues such as modelling algorithm (Elith et al., 1999), the choices of environmental variables used (Synes & Osborne, 2011), for future climate, GCMs used (Steen, Sofaer, Skagen, Ray, & Noon, 2017), collinearity (Dormann et al., 2000), model complexity…”
Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates (2011, background): “They include gaps and biases in species occurrence data (Hortal et al. , 2008), the selection of predictors (Synes & Osborne, 2011”
A quantitative synthesis of the importance of variables used in MaxEnt species distribution models (2017, methodology): “Environ- mental variables are usually chosen based on past predictive performance, known relationship with the species of interest, or by using a variable selection process (Synes & Osborne, 2011).”
Testing the ability of species distribution models to infer variable importance (2019, background): “This is somewhat surprising given that the choice of predictors has a direct effect on model performance (Peterson & Cohoon 1999; Petitpierre et al. 2017; Bosh et al. 2018; Guevara et al. 2018; Barbet-Massin & Jetz 2014; for exceptions see Synes & Osborne 2011 and Buklin et al. 2015).”
Climate change‐induced decline in bamboo habitats and species diversity: implications for giant panda conservation (2015, background): “On the other hand, niche-based models project species distributions by analyzing the relationships between species distributions and a number of environmental variables (Synes & Osborne, 2011).”
Selecting predictors to maximize the transferability of species distribution models: lessons from cross‐continental plant invasions (2017, background): “…it is reasonable to 285 assume that such recommendations for building transferrable SDMs in space can be extended more 286 generally to projecting species’ potential habitats under rapid climate changes scenarios, where 287 variable selection can also affect predictions (Synes & Osborne, 2011).”