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Geostatistical modelling of regional bird species richness: exploring environmental proxies for conservation purpose

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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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Acknowledgments

The Monitoring Program of Breeding Birds was funded by the “Regione Toscana”. We would like to acknowledge Noam Levin who provided constructive comments to a previous version of this manuscript. Part of this work was done by the first author (GB) during a visiting research period at the Institute of Hazard, Risk and Resilience, Department of Geography, University of Durham (UK), founded by the “Luigi and Francesca Brusarosco” Foundation. DR is partially funded by the Autonomous Province of Trento (Italy), ACE-SAP project (No. 23, June 12, 2008, of the University and Scientific Research Service).

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Authors and Affiliations

  1. BIOCONNET, BIOdiversity and CONservation NETwork, Dipartimento di Scienze Ambientali“G. Sarfatti”, Università di Siena, Via P. A. Mattioli 4, 53100, Siena, Italy

    Giovanni Bacaro, Francesco Pezzo & Alessandro Chiarucci

  2. IRPI-CNR, Via Madonna Alta 126, 06128, Perugia, Italy

    Elisa Santi

  3. TerraData s.r.l. Environmetrics, Dipartimento di Scienze Ambientali“G. Sarfatti”, Università di Siena, Via P.A. Mattioli 4, 53100, Siena, Italy

    Giovanni Bacaro, Duccio Rocchini & Alessandro Chiarucci

  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

    Duccio Rocchini

  5. Centro Ornitologico Toscano, C.P. 470, 57100, Livorno, Italy

    Francesco Pezzo & Luca Puglisi

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  1. Giovanni Bacaro
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  2. Elisa Santi
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  3. Duccio Rocchini
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  6. Alessandro Chiarucci
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Correspondence to Giovanni Bacaro.

Appendix

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Table 4 Matrix of correlation coefficients (Pearson’s product moment) for the set of variables used as bird species richness predictors
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Bacaro, G., Santi, E., Rocchini, D. et al. Geostatistical modelling of regional bird species richness: exploring environmental proxies for conservation purpose. Biodivers Conserv 20, 1677–1694 (2011). https://doi.org/10.1007/s10531-011-0054-8

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