title: On the selection of thresholds for predicting species occurrence with presence-only data.
abstract: Presence-only data present challenges for selecting thresholds to transform species distribution modeling results into binary outputs. In this article, we compare two recently published threshold selection methods (maxSSS and maxF pb) and examine the effectiveness of the threshold-based prevalence estimation approach. Six virtual species with varying prevalence were simulated within a real landscape in southeastern Australia. Presence-only models were built with DOMAIN, generalized linear model, Maxent, and Random Forest. Thresholds were selected with two methods maxSSS and maxF pb with four presence-only datasets with different ratios of the number of known presences to the number of random points (KP-RP ratio). Sensitivity, specificity, true skill statistic, and F measure were used to evaluate the performance of the results. Species prevalence was estimated as the ratio of the number of predicted presences to the total number of points in the evaluation dataset. Thresholds selected with maxF pb varied as the KP-RP ratio of the threshold selection datasets changed. Datasets with the KP-RP ratio around 1 generally produced better results than scores distant from 1. Results produced by We conclude that maxFpb had specificity too low for very common species using Random Forest and Maxent models. In contrast, maxSSS produced consistent results whichever dataset was used. The estimation of prevalence was almost always biased, and the bias was very large for DOMAIN and Random Forest predictions. We conclude that maxF pb is affected by the KP-RP ratio of the threshold selection datasets, but maxSSS is almost unaffected by this ratio. Unbiased estimations of prevalence are difficult to be determined using the threshold-based approach.
authors.affiliation: Arthur Rylah Institute for Environmental Research Department of Environment, Land, Water and Planning Heidelberg Victoria 3084 Australia.
authors.forename: Canran
authors.initials: C
authors.lastname: Liu
authors.affiliation: Arthur Rylah Institute for Environmental Research Department of Environment, Land, Water and Planning Heidelberg Victoria 3084 Australia.
authors.forename: Graeme
authors.initials: G
authors.lastname: Newell
authors.affiliation: Arthur Rylah Institute for Environmental Research Department of Environment, Land, Water and Planning Heidelberg Victoria 3084 Australia.
authors.forename: Matt
authors.initials: M
authors.lastname: White
keywords: F measure; presence‐only; prevalence; species distribution modeling; specificity; threshold
references.citation: Ecology. 2007 Nov;88(11):2783-92
references.pmid: 18051647
references.citation: Ecology. 2013 Jun;94(6):1409-19
references.pmid: 23923504
references.citation: Science. 2015 Jun 5;348(6239):1135-8
references.pmid: 26045436
references.citation: PLoS One. 2014 Apr 30;9(4):e88798
references.pmid: 24785293
references.citation: Ecography (Cop.). 2013 Aug 1;36(8):864-867
references.pmid: 25492992
references.citation: PLoS One. 2014 May 05;9(5):e96261
references.pmid: 24797792
references.citation: J Anim Ecol. 2013 Nov;82(6):1183-91
references.pmid: 24499379