Predicting species distributions: a critical comparison of the most common statistical models using artificial species

By C. Meynard, James Quinn. 2007

263 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/a2b1745c6576d403511d2556ff347b90c2b0d491

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In Ecological Modeling

In Ecology, Evolution, Behavior and Systematics

In Nature and Landscape Conservation

Citations in the corpus

The effect of species response form on species distribution model prediction and inference (2009, methodology): “Simulated data have increasingly been used for evaluating the performance of species distribution modelling methods (Hirzel et al., 2001; Austin et al., 2006; Meynard and Quinn, 2007).”
Spatial autocorrelation in predictors reduces the impact of positional uncertainty in occurrence data on species distribution modelling (2011, methodology): “As a result, simulated data have increasingly been used as a tool for developing and evaluating SDMs (Austin et al., 2006; Meynard & Quinn, 2007; Elith & Graham, 2009; JiménezValverde et al., 2009; Santika & Hutchinson, 2009; Beale et al., 2010).”
A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels (2019, background): “…variation in SDM performance 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…”
Where is positional uncertainty a problem for species distribution modelling (2014, methodology): “The link between global spatial autocorrelation in predictors and robustness of SDMs has been already demonstrated by Osborne and Leitão (2009) and Naimi et al. (2011). The methodology presented in this study extends this by using the local spatial association in predictors.”
Equilibrium or not? Modelling potential distribution of invasive species in different stages of invasion (2012, methodology): “The final probability was rescaled to values between 0 and 1 using linear transformation to preserve the original shape of response functions (Meynard & Quinn, 2007).”
The virtual ecologist approach: simulating data and observers (2010, background): “Different names have emerged throughout the literature for the very same approach: “artificial data” or “artificial species” (Austin et al. 2006, Meynard and Quinn 2007, Cabral and Schurr 2009), “virtual species” (Hirzel et al. 2001), “virtual ecologist” (Grimm et al. 1999, Tyre et al. 2001, Zurell…”
Not as good as they seem: the importance of concepts in species distribution modelling (2008, background): “…the quality of predictors, quality of the distributional data, and of the adequacy of the species distribution modelling techniques can be analysed using virtual species, in order to avoid the effects of confusing factors (Austin et al ., 2007; JiménezValverde & Lobo, 2007b; Meynard & Quinn, 2007).”
No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation (2015, methodology): “…richness of methods has encouragedmany comparative studies (Guisan & Zimmermann 2000; Brotons et al. 2004; Segurado & Ara ujo 2004; Allouche, Tsoar & Kadmon 2006; Elith et al. 2006; Meynard & Quinn 2007; Ortega-Huerta & Peterson 2008), often attempting to find a ‘best method’ (Elith et al. 2006).”
Habitat suitability modelling of an invasive plant with advanced remote sensing data (2009, background): “…et al., 2006; Peterson & Nakazawa, 2008; Prates-Clark et al., 2008; Wiegand et al., 2008), biomass (Edwards et al., 2006), net primary production (Meynard & Quinn, 2007), vegetation moisture and leaf/wood density (Prates-Clark et al., 2008), topography (Beck et al., 2005; Gutierréz et al.,…”
Minimum required number of specimen records to develop accurate species distribution models (2016, background): “Studies using simulated species offer unique opportunities to assess SDM accuracy for different sample sizes and species’ prevalence classes (Meynard and Quinn 2007, Jiménez-Valverde et al. 2009).”