A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and Kappa

By Elizabeth A. Freeman, Gretchen G. Moisen. 2008

616 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/5b45910d68f6d5b08706b671eadc2570c32c467b

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Focused identification of germplasm strategy (FIGS) detects wheat stem rust resistance linked to environmental variables (2011, methodology): “Such a plot would have high AUC values indicating favorable model performance (Freeman and Moisen 2008 ).”
The Mediterranean Sea as a ‘cul‐de‐sac’ for endemic fishes facing climate change (2010, methodology): “…criticized (Termansen et al., 2006; Austin, 2007; Peterson et al., 2007; Lobo et al., 2008), we based our evaluation on TSS, as recommended by Allouche et al. (2006) and used in some recent studies (e.g., Allouche et al., 2008; Boitani et al., 2008; Freeman & Moisen, 2008; La Morgia et al., 2008).”
Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors (2009, background): “SDM predictions are often scaled or thresholded in some way for subsequent use in decision making (Freeman and Moisen 2008).”
Use of Maximum Entropy Modeling in Wildlife Research (2009, neutral): “, [16,20,31,32]).”
Challenges of predicting the potential distribution of a slow-spreading invader: a habitat suitability map for an invasive riparian tree (2010, neutral): “This method matches the Freeman and Moisen (2008) required specificity threshold criteria.”
Combining vessel-based surveys and tracking data to identify key marine areas for seabirds (2009, neutral): “…variable based on threshold values in order to identify the suitable potential habitat; however, providing a continuous probability surface may be the most flexible and powerful method, thus allowing managers to select different thresholds depending on the model objective (Freeman & Moisen 2008).”
Role of climate and invasive species in structuring trout distributions in the interior Columbia River Basin, USA (2011, methodology): “In constructing a confusion matrix it is necessary to specify a threshold or cutoff for predicted presence (i.e., the level of predicted probability of occurrence at which a species is assumed to be present) and there are various methods for making this choice (Freeman and Moisen 2008).”
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): “…SDM from a continuous suitability map to a presence/ absence binary map using a threshold criterion (Liu et al. 2005; Jim enez-Valverde & Lobo 2007; Freeman & Moisen 2008) and (4) summation of the binarymaps to build a richness model, and formulation of a presence/absence matrix to represent…”