Abstract
Understanding species' distributions often requires taking into consideration the characterization of the environment at different spatial scales. The habitat characteristics of the endangered fire salamander, S. infraimmaculata, have received little attention. In this study, at this species' most peripheral and xeric limit (Mt. Carmel, Israel), we examined predictors of the larval distribution of S. infraimmaculata at aquatic-breeding sites at both local and landscape scales. We investigated the predictive power of environmental variables using two methods: generalized linear models and conditional inference trees (CTREE). Both multi-model approaches yielded similar results. At the local site scale, hydroperiod predicted breeding site use. At the landscape scale, Salamandra presence was best predicted by proximity to other breeding sites. In addition, our study indicates that sites selected for breeding are far from roads and agricultural fields. Overall, this study demonstrates that ultimately, both local and landscape scale predictors are necessary to understand properly a species' habitat requirements and thus can help in planning future management around the breeding sites.
Original language | English |
---|---|
Pages (from-to) | 229-244 |
Number of pages | 16 |
Journal | Hydrobiologia |
Volume | 726 |
Issue number | 1 |
DOIs | |
State | Published - Mar 2014 |
Bibliographical note
Funding Information:Acknowledgments This study was funded by the Israel Science Foundation grant 961-2008 awarded to Leon Blaustein, Deustche-Israel Project DIP 10 awarded to Leon Blaustein, Alan R. Templeton, Sebastian Steinfartz and Arne Nolte, and a scholarship provided by the Israel Council for Higher Education awarded to LiorBlank.WethankAlanR.Templeton, JuhaMerilä, IftahSinai, Arik Kershenbaum, Asaf Sadeh, and Ori Segev for fruitful discussion and Arik Kershenbaum for comments on the manuscript. Field surveys of S. infraimmaculata larvae were conducted with permission from the Israel Nature and Parks Authority (permit 2009/36565).
Keywords
- Conditional inference trees
- Generalized linear models
- Land-use
- Scale
ASJC Scopus subject areas
- Aquatic Science