Using between‐model comparisons to fine‐tune linear models of species ranges

By Graeme Cumming. 2000

144 citations recorded by the source corpus.

Subfields: Insect Science, Parasitology, Plant Science

View Paper: https://www.semanticscholar.org/paper/4ab1c3a44f49ca0d7939d1f42775b12a63574744

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Citations in the corpus

Evaluating resource selection functions (2002, methodology): “From this ROC curve, we can integrate the area under the curve (AUC) as an assessment of model performance or predictive power ( Cumming, 2000 ).”
A PCA‐based modelling technique for predicting environmental suitability for organisms from presence records (2001, background): “Fielding & Bell (1997) reviewed a number of model assessment measures for quantitatively assessing a model's prediction success.”
The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact? (2004, methodology): “Both sampling prevalence (Fielding & Haworth 1995; Manel, Dias & Ormerod 1999; Cumming 2000; Olden, Jackson & Peres-Neto 2002) and sample size (Hendrickx 1999; Cumming 2000; Pearce & Ferrier 2000b; Stockwell & Peterson 2002) have been shown to influence the performance of distribution models…”
Comparing sample bias correction methods for species distribution modeling using virtual species (2021, background): “…on variable importance and provides a measure of a model’s ability to discriminate between presence and absence localities, independent of an arbitrary cutoff threshold (Cumming 2000), although it has been criticized for its sensitivity to geographic extent, among other factors (Lobo et al. 2008).”
An evaluation of methods for modelling species distributions (2004, unknown): “Sensitivity is based on the concept of true-presences misclassification (false negatives or type II error), and is calculated as 1 – percentage of false negatives (Fielding & Bell, 1997; Cumming, 2000).”
Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS) (2006, unknown): “Similar patterns have been shown to characterize AUC as well ( Cumming 2000a; McPherson, Jetz & Rogers 2004).”
The effect of prevalence and its interaction with sample size on the reliability of species distribution models (2009, methodology): “In the absence of a general rule, it is generally assumed that the greater the sample size, the more accurate the model (Cumming 2000, Olden and Jackson 2000, McPherson et al. 2004, Martínez-Meyer 2005, Reese et al. 2005).”
Effects of the number of presences on reliability and stability of MARS species distribution models: the importance of regional niche variation and ecological heterogeneity (2010, methodology): “Others have merely mentioned the potential drawbacks of small data sets (Stockwell & Peters 1999; Reese et al. 2005), while some, whose research aims were different, expressed concern about minimum sample size (Cumming 2000b; Pearce & Ferrier 2000; Drake et al. 2006; Guisan et al. 2007).”
Effect of species rarity on the accuracy of species distribution models for reptiles and amphibians in southern California (2009, background): “It is not affected by changes in species’ prevalence (Manel et al ., 2001) and is reliable for model comparisons (Cumming, 2000).”
Techniques for cetacean-habitat modeling (2006, methodology): “Receiver operating characteristic (ROC) curves provide a tool for selecting the ‘optimal’ threshold, minimizing both errors of omission and commission, or selecting alternative thresholds to minimize a particular source of error (Pearce & Ferrier 2000, Pontius 2000, Cummings 2001).”
IDENTIFYING BREEDING HABITAT FOR THE IBERIAN LYNX: INFERENCES FROM A FINE‐SCALE SPATIAL ANALYSIS (2003, background): “This step is needed to provide causal insights into the habitat selection process, a goal difficult to address from the sole application of statistical models (Boyce and McDonald 1999, Cumming 2000).”
Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation (2006, methodology): “The accuracy of different models can be compared on the same ROC diagram (e.g., Cumming 2000; Zheng et al. 2006).”
Predicting the spatial distribution of an invasive plant species (Eupatorium adenophorum) in China (2007, methodology): “by some researchers (Cumming 2000; Mcpherson et al. 2004). The Kappa statistic is a reliability metric that uses the classification probabilities of the model and of reality to compute the likelihood of agreement by chance. In other words, the Kappa value is useful for testing the null hypothesis th”
Modelling non-equilibrium distributions of invasive species: a tale of two modelling paradigms (2009, methodology): “We repeat the appeal (Sutherst and Maywald 2005) for less concern with precision of model fits to species geographical distributions ( Cumming 2000a, b; Anderson and Lew 2003; Elith et al. 2006), which are usually of poor quality and more emphasis on avoiding the systemic errors that arise from fau”
Characterizing and predicting species distributions across environments and scales: Argentine ant occurrences in the eye of the beholder (2009, methodology): “…measurements of model accuracy: area under the curve (AUC) of receiver operating characteristic (ROC) plots, Cohen’s kappa and sensitivity and specificity to quantify the predictive ability of every model in each region with the remaining third of the data (Cumming, 2000; McPherson et al ., 2004).”
A Climate Model of the Red Imported Fire Ant, Solenopsis invicta Buren (Hymenoptera: Formicidae): Implications for Invasion of New Regions, Particularly Oceania (2005, background): “They also provide minimal insight into the biology of the species, and the use of sophisticated statistical tests of goodness-of-Þt (Cumming 2000, Anderson and Lew 2003) gives false impressions of precision because it ignores systemic errors in the model paradigms.”