2019
DOI: 10.1111/ddi.12893
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Modelling bat distributions and diversity in a mountain landscape using focal predictors in ensemble of small models

Abstract: Aim: Bats are important components of mammalian biodiversity and strong bioindicators, but their fine-scale distributions often remain less known than other taxa (e.g., plants, birds). Yet as highly mobile species with multiple needs in the landscape, bats impose serious modelling challenges, such as advanced use of neighbourhood analyses. The aims of this study were to test the use of a designed sampling of bats for biodiversity and conservation assessments, and to find appropriate modelling solutions for pro… Show more

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Cited by 38 publications
(29 citation statements)
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References 92 publications
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“…Model calibration. Nine different modelling techniques were calibrated and evaluated 126129 : Artificial Neural Networks – ANN 130 (Ripley, 1996); Classification Tree Analysis - CTA 131 , Flexible Discriminant Analyses – FDA 132 ; Generalised Additive Models – GAM 133 ; Generalised Boosted Models – GBM 134 ; Generalised Linear Models – GLM 135 ; Multivariate Adaptative Regression Splines – MARS 136 ; MAXimum ENThropy – MAXENT 1 ; Random Forests – RF 137 . For each set of presence/pseudo-absence, model calibration was realized with 70% of all data.…”
Section: Methodsmentioning
confidence: 99%
“…Model calibration. Nine different modelling techniques were calibrated and evaluated 126129 : Artificial Neural Networks – ANN 130 (Ripley, 1996); Classification Tree Analysis - CTA 131 , Flexible Discriminant Analyses – FDA 132 ; Generalised Additive Models – GAM 133 ; Generalised Boosted Models – GBM 134 ; Generalised Linear Models – GLM 135 ; Multivariate Adaptative Regression Splines – MARS 136 ; MAXimum ENThropy – MAXENT 1 ; Random Forests – RF 137 . For each set of presence/pseudo-absence, model calibration was realized with 70% of all data.…”
Section: Methodsmentioning
confidence: 99%
“…To integrate the output of the performed distribution models and develop the ensemble predictions, we used a weighted-averaging approach through which each single distribution model was weighted according to its predictive accuracy as independently assessed [43]. The performance of the ensemble models was evaluated using the AUC of the ROC [44,45,46,47,48]. Based on the results obtained from the models run for the two study areas, we developed two ensemble predictive maps for the whole landscape.…”
Section: Building the Predictive Ensemble Modelsmentioning
confidence: 99%
“…To develop the ensemble prediction by integrating the output of the single distribution models, we used a weighted-averaging approach through which the distribution models were weighted according to their predictive accuracy (Thuiller et al 2009;Rodrıguez-Soto et al 2011). The performance of the ensemble model was evaluated using the AUC of the ROC (Fernandes et al 2019;Mohammadi et al 2019;Scherrer et al 2019).…”
Section: Habitat Suitability Analysismentioning
confidence: 99%