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Noguera-Urbano, E. A.

Publications and source records attributed to Noguera-Urbano, E. A..

3 recordsLinked to original sources

Forest cover is more important than its integrity or configuration of the landscape in determining the habitat use of mammals in a modified landscape in Colombia

Human activities shape the structure of landscapes in different ways and hence modify animal communities depending on the type and intensity of these activities. The Montes de Maria subregion of Colombia has experienced a heavy transformation of most areas despite covering one of the last remnants of dry forest, a critically endangered ecosystem. However, the effects of this transformation have been little explored. Here, we used a multispecies occupancy model (MSOM) to understand the relative influence of three components of land-use change - deforestation (remaining habitat amount), degradation of habitat quality (habitat quality) and fragmentation (landscape configuration) on mammalian habitat use across a mosaic of tropical dry forest in Colombia. Our data suggest that the percentage of forest cover was substantially important for herbivores, and consistently showed a moderate effect on the entire community and some individual species. High variability in species-specific responses to the examined variables hindered broad taxonomic generalizations, nevertheless, we detected a moderate positive effect of forest cover in both diet specialists and generalists species, as well as in species with small home ranges. Although omnivores responses, tended to use less complex landscapes (mosaics of land uses), there was high uncertainty in this response. The lack of substantial effects on most species, and the absence of threatened species across this anthropogenic landscape, suggests that the current community is composed of species tolerant to habitat modifications, but not only diet generalist species. This is most likely the result of a long filtering process caused by land use transformation and hunting which could have caused non-sensitive species to distribute relatively homogenously across this landscape. Our results suggest that conservation strategies in the study area should focus on conserving and expanding as much forest as possible rather than only improving the quality of already existing forest patches.

ecology↗

Machine learning techniques to derive bioclimatic classifications for Colombia

Bioclimatic classifications seek to divide a study region into geographic areas with similar bioclimatic characteristics. In this study we proposed two bioclimatic classifications for Colombia using machine learning techniques. We firstly characterized the precipitation space of Colombia using principal component analysis. Based on Lang classification, we then projected all background sites in the precipitation space with their corresponding categories. We sequentially fit logistic regression models to reclassify all background sites in the precipitation space with six redefined Lang categories. New categories were the used to define a new modified Lang and Caldas-Lang classifications.

ecology↗

A supervised learning algorithm to evaluate occurrence records in virtual species

Digital and open access of occurrence data have encouraged the development of tools to improve biodiversity conservation and management. In this study, we proposed a methodology to evaluate point-occurrence records based on expert knowledge. We firstly generated virtual data to test our methodology without confounding factors by simulating geographical distributions, virtual sampling, and expert checking of occurrence records. We used a set of non-linear bioclimatic variables and principal component analysis (PCA) to define a duality function between niche and biotope spaces. Subsequently, a supervised-learning model was fit to classify records between true and doubtful presence based on the virtual expert checking. We then tested our methodology using three virtual species and 10-fold cross validation. Also, we evaluated the prediction performance of the supervise model compared with the virtual observer using a virtual external database of occurrence data.

ecology↗