Are niche‐based species distribution models transferable in space?

By C. Randin, T. Dirnböck, S. Dullinger, N. E. Zimmermann, Massimiliano Zappa, Antoine Guisan. 2006

819 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/7866a6dcd7cb6669e1b9736ef2835faa735410b2

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In Ecological Modeling

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Citations in the corpus

Palaeoclimatic models help to understand current distribution of Caucasian forest species (2012, methodology): “The last two factors can be incorporated into SDMs by accounting for spatial or temporal autocorrelation patterns (Lichstein et al., 2002; Araújo et al., 2005; Randin et al., 2006).”
Ground validation of presence‐only modelling with rare species: a case study on barbastelles Barbastella barbastellus (Chiroptera: Vespertilionidae) (2010, methodology): “We started by calculating a two-tailed Wilcoxon-signed rank test using eachmodel’s cell values to test if they generated different predictions (Randin et al. 2006; Phillips et al. 2009).”
Establishing the link between habitat selection and animal population dynamics (2015, background): “…frameworks that, in their majority, make the assumption that populations are at a state of equilibrium (Guisan and Zimmermann 2000, Araújo and Guisan 2006, Randin et al. 2006, Elith and Graham 2009, Elith and Leathwick 2009, Zurell et al. 2009, Hoffman et al. 2010, Matthiopoulos and Aarts 2010).”
Importance of abiotic stress as a range‐limit determinant for European plants: insights from species responses to climatic gradients (2009, background): “another area (Randin et al., 2006) or after climate change (Thuiller et al.”
Testing the predictive performance of distribution models (2013, neutral): “Several studies tested predictions from distribution models on spatially segregated data (Fielding and Haworth 1995, Peterson 2003, Randin et al. 2006, Segurado et al.2006, Peterson et al. 2007, Phillips 2008) or temporarily segregated data (Martinez-Meyer et al. 2004, Araújo et al. 2005a).”
Where, why and how? Explaining the low‐temperature range limits of temperate tree species (2016, neutral): “Yet, when employing a fundamental niche concept, we need to assume that the niche boundary can be identified and had been conserved in space and time (e.g. Huntley 1989; Randin et al. 2006; Pearman et al. 2008).”
Selecting predictors to maximize the transferability of species distribution models: lessons from cross‐continental plant invasions (2017, background): “…distributions of species (Elith & Leathwick, 54 2009), and it is frequent to project models calibrated in one area onto a different geographic area or 55 time period, with an assumption of ecological niche transferability (Randin et al., 2006; Maiorano 56 et al., 2013; Wenger & Olden, 2012).”
Model‐based uncertainty in species range prediction (2006, background): “Comparing each model’s ability to simulate observed presences and absences is an informative step, yet good model fits with observed distributions do not guarantee that a species’ potential range has been accurately captured (Pearson & Dawson, 2003; Hampe, 2004; Araújo et al., 2005a; Randin et al., ”
Applications of species distribution modeling to paleobiology (2011, background): “It is thus important to validate model transferability (its predictive ability on data sets onwhich it has not been calibrated, such as from a different geographic region or time period), to the greatest extent possible (e.g., Randin et al., 2006).”
Predicting species distributions: a critical comparison of the most common statistical models using artificial species (2007, methodology): “However, when studying climate change scenarios, more flexible models, such as GAM, may perform poorly in predicting distribution shifts, due to their reduced performance under new environmental conditions (Araújo et al., 2005; Araújo & Rahbek, 2006; Randin et al., 2006).”