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Lison, F.

Publications and source records attributed to Lison, F..

2 recordsLinked to original sources

A new method to estimate the ecological niche through n-dimensional hypervolumes that combines convex hulls and elliptical envelopes

O_LIMethods that estimate the niche of a species by calculating a convex hull or an elliptical envelope have become popular due to their simplicity and interpretation, given Hutchinsons conception of the niche as an n-dimensional hypervolume. C_LIO_LIIt is well known that convex hulls are sensitive to outliers and do not have the ability to differentiate between regions of low and high concentration of presences, while the elliptical envelopes may contain large regions of niche space that are not relevant for the species. Thus, when the goal is to estimate the realized niche of the species, both methods may overestimate the niche. C_LIO_LIWe present a methodology that combines both the convex hull and the elliptical envelope methods producing an n-dimensional hypervolume that better fits the observed density of species presences, making it a better candidate to model the realized niche. Our method, called the CHE approach, allows defining regions of iso-suitability as a function of the significance levels inherited from the method (Mahalanobis distance model, minimum covariance determinant, or minimum volume ellipsoid) used to fit an initial elliptical envelope from which we then discard regions not relevant for the species by calculating a convex hull. C_LIO_LIWe applied the CHE approach to a case study of twenty-five species of bats present in the Iberian Peninsula, fitting a hypervolume for each species and comparing them to both the convex hulls and elliptical envelopes obtained with the same data and different values of n. We show that as the number of variables used to define the niche space increases, both the convex hull and elliptical envelope models produce overly large hypervolumes, while the size of the hypervolume fitted with the CHE approach remains stable. As a consequence, similarity measures that account for the niche overlap among different species may be inflated when using convex hulls or elliptical envelopes to model the niche; something that does not occur under the CHE approach. C_LI

ecology↗

RUSBoost: A suitable species distribution method for imbalanced records of presence and absence. A case study of twenty-five species of Iberian bats

O_LITraditional Species Distribution Models (SDMs) may not be appropriate when examples of one class (e.g. absence or pseudo-absences) greatly outnumber examples of the other class (e.g. presences or observations), because they tend to favor the learning of observations more frequently. C_LIO_LIWe present an ensemble method called Random UnderSampling and Boosting (RUSBoost), which was designed to address the case where the number of presence and absence records are imbalanced, and we opened the "black-box" of the algorithm to interpret its results and applicability in ecology. C_LIO_LIWe applied our methodology to a case study of twenty-five species of bats from the Iberian Peninsula and we build a RUSBoost model for each species. Furthermore, in order to improve to build tighter models, we optimized their hyperparameters using Bayesian Optimization. In particular, we implemented a objective function that represents the cross-validation loss: [Formula], with [Formula] representing the hyper-parameters Maximum Number of Splits, Number of Learners and Learning Rate. C_LIO_LIThe models reached average values for Area Under the ROC Curve (AUC), specificity, sensitivity, and overall accuracy of 0.84 {+/-} 0.05%, 79.5 {+/-} 4.87%, 74.9 {+/-} 6.05%, and 78.8 {+/-} 5.0%, respectively. We also obtained values of variable importance and we analyzed the relationships between explanatory variables and bat presence probability. C_LIO_LIThe results of our study showed that RUSBoost could be a useful tool to develop SDMs with good performance when the presence/absence databases are imbalanced. The application of this algorithm could improve the prediction of SDMs and help in conservation biology and management. C_LI

ecology↗