Predicting soil micro-variables and the distribution of an endogeic earthworm species through a model based on large-scale variables |
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| Affiliation: | 1. Departamento de Zoología y Antropología Física, Facultad de Biología, Universidad Complutense de Madrid, C/José Antonio Nováis 2, 28040, Madrid, Spain;2. Cardiff School of Biosciences, Cardiff University, BIOSI 1, Museum Avenue, Cardiff CF10 3AT, UK;3. Museum of Comparative Zoology, Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, USA;1. Department of Natural Resource Sciences, Macdonald Campus, McGill University, Ste Anne de Bellevue, QC H9X 3V9, Canada;2. Leibniz Center for Agricultural Landscape Research (ZALF), Eberswalder Straße 84, 15374 Müncheberg, Germany;3. State Environmental Protection Key Laboratory of Urban Ecological Environment Simulation and Protection, South China Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Guangzhou, 510535, China;4. Department of Botany, University of Balochistan, Saryab Road, Quetta, Balochistan, Pakistan;5. Département de biologie, Université de Sherbrooke, 2500 Boulevard de l’Université, Sherbrooke, QC J1K 2R1, Canada;1. Université Paris Est Créteil, Université Pierre et Marie Curie, CNRS, INRA, IRD, Université Paris-Diderot, Institut d’écologie et des Sciences de l''environnement de Paris (iEES-Paris), Créteil, France;2. UMR ECOSYS, INRA, AgroParisTech, Université Paris-Saclay, 78026 Versailles, France;1. Depto. de Zoología y Antropología Física, Facultad de Ciencias Biológicas, Universidad Complutense de Madrid, José Antonio Nováis, 2, 28040, Madrid, Spain;2. Grupo de Toxicología ambiental y Biología, Universidad Nacional de Educación a Distancia, Senda del Rey, 9, 28040, Madrid, Spain |
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| Abstract: | Studies on spatial patterns of distributions of soil dwelling animals have usually relied on soil micro-variables or statistical analyses based on presence/absence data. Geographic Information Systems (GIS) allow easy access to large-scale variables to build species distribution models. In this study, we used MaxEnt to model the distribution of the endogeic earthworm Hormogaster elisae. Significant differences were found between the predicted suitability values of localities where the species was present and those where it was absent, validating the predictive model. Most of the large-scale training variables showed significant correlation with soil micro-variables known to influence the biology of the species, proving the ability of the model to predict (to an extent) soil variables from environmental ones. The methodology could be extended to other soil fauna. |
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| Keywords: | Species distribution model MaxEnt Hormogastridae |
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