Using species distribution models to predict new occurrences for rare plants

By John N. Williams, Changwan Seo, James H. Thorne, J. Nelson, S. Erwin, J. O’Brien, M. D. Schwartz. 2009

459 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/3dce61b0108032afc873e51a7f53d755ccd60551

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In Ecological Modeling

In Ecology, Evolution, Behavior and Systematics

In Nature and Landscape Conservation

Citations in the corpus

Climate change vulnerability assessment of species (2018, background): “Efforts to fill data gaps and use conventional CCVA methods can rely on inferences from data for related species (Foden et al., 2013), expert opinion (Martin et al., 2015; Murray et al., 2009), data imputation techniques, or a combination of literature and targeted fieldwork (Williams et al., 2009).”
A statistical explanation of MaxEnt for ecologists (2011, unknown): “California Rare plants Williams et al. (2009)”
A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling (2013, background): “…or among the best (Hernandez et al. 2006, Guisan et al. 2007, Sérgio et al. 2007, Wang et al. 2007, Wisz et al. 2008, Roura-Pascual et al. 2009, Tognelli et al. 2009, Václávik & Meentemeyer 2009, Veloz 2009, Williams et al. 2009, Mateo et al. 2010, Rebelo & Jones 2010, Rupprecht et al. 2011).”
Assessing the Relevance of Herbarium Collections as Tools for Conservation Biology (2017, background): “On another hand, studies on rare plants (e.g., Williams et al., 2009; Marcer et al., 2013; McCune, 2016; Vroh et al., 2016; Tang et al., 2017) or threatened plants (e.g., Adhikari et al., 2012; Aguirre-Gutiérrez et al., 2015; Hassemer et al., 2016; Sántiz et al., 2016) facilitate to identify and…”
Crop wild relatives of pigeonpea [Cajanus cajan (L.) Millsp.]: Distributions, ex situ conservation status, and potential genetic resources for abiotic stress tolerance (2015, background): “Further field exploration of climatic extremes beyond the edges of the distributions created through these methods may therefore lead to the discovery of new populations with particularly valuable adaptations to abiotic stress (Williams et al., 2009).”
The impact of modelling choices in the predictive performance of richness maps derived from species‐distribution models: guidelines to build better diversity models (2013, methodology): “Following the guidelines provided by Williams et al. (2012), we selected a comprehensive set of environmental variables which have been shown to influence the distribution of tropical trees in the study”
The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models (2013, background): “This paper explores the consequences of correcting for geographical sampling bias and non-automatically selecting model functional forms on the predictive ability of MaxEnt, one of the best performing species distribution modelling techniques for analysis of presence-only data [10,11,12,13].”
Species distribution models in conservation biogeography: developments and challenges (2013, background): “(Williams et al., 2009) 31 Application new occurrences: compared SDM methods for predicting undiscovered populations of”
Using species distribution models to inform IUCN Red List assessments (2014, methodology): “MaxEnt MaxEnt is among the best-performing of the different presence-only correlative SDM approaches available (Elith et al. 2006, Mateo et al. 2010, Williams et al. 2009).”
Using species distribution modelling to disentangle realised versus potential distributions for rare species conservation (2013, unknown): “, 2009) and other Mediterranean-climate zones (Loarie et al., 2008; Riordan and Rundel, 2009; Williams et al., 2009).”