Selecting thresholds of occurrence in the prediction of species distributions

By Canran Liu, P. Berry, Terence P. Dawson, Richard G. Pearson. 2005

2,750 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/9f49d245cb9ee9d6edaec32a2caf4b15be92b0c5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Does the interpolation accuracy of species distribution models come at the expense of transferability (2012, methodology): “The characteristics of the species data used here are described in Virkkala et al. (2005), Luoto et al.”
Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges. (2009, methodology): “The output of correlative SDMs typically involve dimensionless indices of habitat suitability or estimates of probability of occurrence, and a variety of approaches are used to assess the threshold at which the distribution limit should be placed ( Liu et al. 2005 ).”
The spatially varying influence of humans on fire probability in North America (2016, methodology): “As a measure of overall of model performance, the AUC was computed from the true positives and false positives (Liu et al 2005).”
Defining and evaluating predictions of joint species distribution models (2020, background): “The value of thresholding continuous predictions has been debated in the SDM literature (Freeman & Moisen, 2008; Guillera-Arroita et al., 2015; Liu et al., 2005), for prediction and evaluation (Lawson et al., 2014).”
A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels (2019, background): “We note that many previous applications of SDMs have evaluated them based on either the predicted speciesspecific marginal occurrence probabilities, or occurrences derived by thresholding the occurrence probabilities (Liu et al. 2005, Jim enez-Valverde and Lobo 2007, Lawson et al. 2014).”
Use of predictive habitat modelling to assess the distribution and extent of the current protection of ‘listed’ deep‐sea habitats (2013, methodology): “Five thresholding methods assessed by Liu et al. (2005) as ‘good’ were considered for each model because of their tolerance to low prevalence build data comprising sensitivity-specificity equality, sensitivity-specificity sum maximization, and ROC-plot-based approaches (Cantor et al., 1999), and…”
Using species distribution modelling to disentangle realised versus potential distributions for rare species conservation (2013, background): “value is needed (Fielding and Bell, 1997; Liu et al., 2005; JiménezValverde and Lobo, 2007).”
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): “the SDM literature (Liu et al. 2005; Jim enez-Valverde & Lobo 2007; Freeman & Moisen 2008). However, such literature is oriented mainly to the selection of thresholds when the input data are presence/absence, and therefore the threshold criteria for presence-only based SDMs are not well-developed. A”