Effect of species rarity on the accuracy of species distribution models for reptiles and amphibians in southern California

By Janet Franklin, K. Wejnert, S. Hathaway, C. Rochester, Robert N. Fisher. 2009

162 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/8b4afd002cdd635cbba4255d8068bef0f0703e0f

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Using species distribution models in paleobiogeography: A matter of data, predictors and concepts (2011, background): “SDM techniques include a diverse array of statistical and mathematical procedures with the only common characteristic being their general purpose: relating data on the observed species distribution to a set of spatially-defined variables – which usually are environmental factors – (Guisan and Zimmer”
Climatic niche shifts between species' native and naturalized ranges raise concern for ecological forecasts during invasions and climate change (2014, methodology): “Conversely, when tested using current native data alone, SDMs appear to be accurate for species with small native ranges, suggesting they are climate specialists (Franklin et al., 2009; Marmion et al., 2009).”
Overcoming limitations of modelling rare species by using ensembles of small models (2015, background): “Models of rare species, characterized by narrow ecological ranges, often show higher accuracy compared to common species characterized by broad ecological ranges (Franklin et al. 2009 and literature mentioned there).”
What ' s on the horizon for macroecology? (2012, background): “Diversity & Distributions Grey, EK (2009): Scale-dependent relationships between native richness, resource stability and exotic cover in dock fouling communities of Washington, USA.”
A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling (2013, background): “…that it is not spatial autocorrelation in the observed presence data and/or the explanatory variables as such that is important but spatial autocorrelation remaining in the residuals of the model (Segurado et al. 2006, Dormann et al. 2007, Bini et al. 2009, Franklin et al. 2009, Naimi et al. 2011).”
Comparing sample bias correction methods for species distribution modeling using virtual species (2021, background): “…that rare specialists would show erratic responses to differing levels of sampling bias because, while rare species may require fewer samples to adequately estimate their distributions (Franklin et al. 2009), they may also be missed entirely or sampled sparsely at extreme levels of sampling bias.”