Modeling the Distribution of Woodpecker Species in the Jura France and in Switzerland Using Atlas Data
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University of Montana ScholarWorks at University of Montana Graduate Student Theses, Dissertations, & Professional Papers Graduate School 1998 Modeling the distribution of woodpecker species in the Jura France and in Switzerland using atlas data Claudine Tobalske The University of Montana Follow this and additional works at: https://scholarworks.umt.edu/etd Let us know how access to this document benefits ou.y Recommended Citation Tobalske, Claudine, "Modeling the distribution of woodpecker species in the Jura France and in Switzerland using atlas data" (1998). Graduate Student Theses, Dissertations, & Professional Papers. 10533. https://scholarworks.umt.edu/etd/10533 This Dissertation is brought to you for free and open access by the Graduate School at ScholarWorks at University of Montana. It has been accepted for inclusion in Graduate Student Theses, Dissertations, & Professional Papers by an authorized administrator of ScholarWorks at University of Montana. 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FRANCE, AND IN SWITZERLAND, USING ATLAS DATA by Claudine Tobalske Diplome d'Etudes Universitaires Generates, Universite de Besanqon. France. 1988 Maitrise Biophysiologie Appliquee aux Productions Vegetates, Universite d ’Angers. France. 1990 Diplome d ’Etudes Superieures Specialisees Ressources Nature lies et Environnement. Universite de Nancy 1, France, 1991 Presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Montana Spring 1998 Approved Chairman, Board of Examiners Dean, Graduate School S-igr-9 ar Date Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. UMI Number: 9840101 UMI Microform 9840101 Copyright 1998, by UMI Company. AH rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. UMI 300 North Zeeb Road Ann Arbor, MI 48103 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Maureen and Mike MANSFIELD LIBRARY The University ofMONTANA Permission is granted by the author to reproduce this material in its entirety, provided that this material is used for scholarly purposes and is properly cited in published works and reports. * * Please check "Yes" or "No" and provide signature ** Yes, I grant permission No, I do not grant permission ____ Author’s Signature Date 5/12 ______________ Any copying for commercial purposes or financial gain may be undertaken only with the author's explicit consent: Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Tobalske, Claudine, Ph.D., Spring 1998 Wildlife Biology Modeling the distribution of woodpecker species in the Jura, France, and in Switzerland, using atlas data (137 pp.) Director: Roland L. Redmond QjJ- ABSTRACT Wildlife-habitat relationship (WHR) models, that relate the distribution of species to characteristics of their environment, have become a tool commonly used in wildlife management and conservation. To assess the usefulness and identify some of the limitations associated with using breeding bird atlas data to generate such models, I used maps from a local atlas and extracted simple, broad-scale habitat variables using a GIS to develop logistic regression models for seven woodpecker species in the Jura, France: the Black (Dryocopus martius), Green ( Picus viridis), Grey-headed (P. canus), Great Spotted ( Dendrocopos major), Middle Spotted (D. medius), and Lesser Spotted (D. minor) woodpeckers, and the Wryneck (Jynx torquilla). Although the selected independent variables were in general agreement with known ecological requirements of the species, the reliability of the models was species-specific. Despite limitations inherent to working in a grid format, the models could be used to identify areas with high probability of woodpecker presence as targets for future censuses and fine-scale habitat studies. However, an independent validation performed by applying the models to two sites in Switzerland showed that model performance outside of the Jura was low. This poor predictive generality was probably the result of important differences between the variables used for model development, and those used for the validation, although additional factors may have contributed to model failure. To further explore this issue of model generality, new logistic models were developed for the green woodpecker using the atlas distribution maps from the two Swiss sites. The effect of scale on predictive accuracy was assessed by enlarging the mesh size of the breeding bird atlas maps and increasing the minimum mapping unit of the habitat map. Cross-area predictions were not successful, possibly because the variables used in the models did not reflect differences in landscape configuration between the sites. Model composition, classification results, and predictive generality were all affected by scale. This variability suggests that caution should be used when applying a model developed in one part of a species’ range to another, and that particular attention should be given to the choice of the scale of analysis. n _2a Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. PREFACE Models, because they represent a simplified view of reality, can help us understand natural patterns and processes, and thereby provide useful tools for the protection and management of our environment. The need to manage wildlife habitat has been recognized for many years (e.g., Leopold 1933). However, unprecedented rates of species extinctions in recent decades, caused by fragmentation and loss of habitat worldwide, have made conservation measures all the more urgent. In this context, the advent of computer technology over the past decade has led to the rapid growth of wildlife-habitat relationship (WHR) models (Vemer et al. 1986). WHR models are developed for two main reasons: 1) to improve our understanding of the relationships between wildlife species and their habitat (i.e., to identify which factors affect the distribution and abundance of species; and 2) to predict the distribution and/or abundance of species (Morrison et al. 1992). In both cases, the ultimate goal of the model usually is related to management and/or protection of species and their habitat. WHR models can be categorized as either deductive or inductive. The deductive approach relies on previous knowledge of species-habitat associations to derive model rules, and the actual species’ distributions are not required. Examples of deductive models include Gap Analysis, the broad-scale mapping of potential habitat of vertebrate species across the United States (Scott et al. 1993); Habitat Evaluation Procedures, that assess environmental conditions at the species level (Rood et al. 1977); and Habitat Suitability Index models, that denote habitat suitability of a species as the geometric mean of environmental variables (Schamberger et al. 1982). With inductive models, habitat variables are gathered at or around known species’ locations, and some type of statistical analysis is used to select which environmental parameters best account for any observed variation in the distribution and abundance of the species. Multivariable iii Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. statistical approaches, such as multiple regression, principal component analysis, discriminant function analysis, and logistic regression are commonly used to develop inductive WHR models (Capen 1981). Inductive models are most useful to help formalize our understanding of a species’ relation to its habitat, but they require that the species’ distributions must be known. Because