Search

Selecting thresholds to convert continuous predictions of species distribution models proves critical for many real‐world applications and model assessments. Prevalent threshold selection methods for presence‐only data require unproven pseudo‐absence data or subjective researchers' decisions. This study proposes a new method, Boyce‐Threshold Quantile Regression (BTQR), to determine thresholds objectively without pseudo‐absence data. We summarize that the mutation point is a typical shape feature of the predicted‐to‐expected (P/E) curve after reviewing relevant articles. Analysis based on source‐sink theory suggests that this mutation point may represent a transition in habitat types and serve as an appropriate threshold. Threshold regression is introduced to accurately locate the mutation point. To validate the effectiveness of BTQR, we used four virtual species of varying prevalence and a real species with reliable distribution data. Six different species distribution models were employed to generate continuous suitability predictions. BTQR and nine other traditional methods transformed these continuous outputs into binary results. Comparative experiments show that BTQR has advantages in terms of accuracy, applicability, and consistency over the existing methods. [ABSTRACT FROM AUTHOR]

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 (max SSS and max Fpb) 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 max SSS and max Fpb with four presence-only datasets with different ratios of the number of known presences to the number of random points ( KP- RPratio). 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 max Fpb varied as the KP- RPratio of the threshold selection datasets changed. Datasets with the KP- RPratio 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, max SSS 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 max Fpb is affected by the KP- RPratio of the threshold selection datasets, but max SSS is almost unaffected by this ratio. Unbiased estimations of prevalence are difficult to be determined using the threshold-based approach. [ABSTRACT FROM AUTHOR]