Geostatistical modelling of regional bird species richness: exploring environmental proxies for conservation purpose |
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Authors: | Giovanni Bacaro Elisa Santi Duccio Rocchini Francesco Pezzo Luca Puglisi Alessandro Chiarucci |
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Institution: | 1. BIOCONNET, BIOdiversity and CONservation NETwork, Dipartimento di Scienze Ambientali“G. Sarfatti”, Università di Siena, Via P. A. Mattioli 4, 53100, Siena, Italy 3. TerraData s.r.l. Environmetrics, Dipartimento di Scienze Ambientali“G. Sarfatti”, Università di Siena, Via P.A. Mattioli 4, 53100, Siena, Italy 2. IRPI-CNR, Via Madonna Alta 126, 06128, Perugia, Italy 4. Department of Biodiversity and Molecular Ecology, GIS and Remote Sensing Unit, Fondazione Edmund Mach, Research and Innovation Centre, Via E. Mach 1, 38010, S. Michele all’Adige, TN, Italy 5. Centro Ornitologico Toscano, C.P. 470, 57100, Livorno, Italy
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Abstract: | Identifying spatial patterns in species diversity represents an essential task to be accounted for when establishing conservation
strategies or monitoring programs. Predicting patterns of species richness by a model-based approach has recently been recognised
as a significant component of conservation planning. Finding those environmental predictors which are related to these patterns
is crucial since they may represent surrogates of biodiversity, indicating in a fast and cheap way the spatial location of
biodiversity hotspots and, consequently, where conservation efforts should be addressed. Predictive models based on classical
multiple linear regression or generalised linear models crowded the recent ecological literature. However, very often, problems
related with spatial autocorrelation in observed data were not adequately considered. Here, a spatially-explicit data-set
on birds presence and distribution across the whole Tuscany region was analysed. Species richness was calculated within 1 × 1 km
grid cells and 10 environmental predictors (e.g. altitude, habitat diversity and satellite-derived landscape heterogeneity
indices) were included in the analysis. Integrating spatial components of variation with predictive ecological factors, i.e.
using geostatistical models, a general model of bird species richness was developed and used to obtain predictive regional
maps of bird diversity hotspots. A meaningful subset of environmental predictors, namely habitat productivity, habitat heterogeneity,
combined with topographic and geographic information, were included in the final geostatistical model. Conservation strategies
based on the predicted hotspots as well as directions for increasing sampling effort efficiency could be extrapolated by the
proposed model. |
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