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Effect of Methodological and Ecological Approaches on Heterogeneity of Nest-Site Selection of a Long-Lived Vulture

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  • Rubén Moreno-Opo
  • Mariana Fernández-Olalla
  • Antoni Margalida
  • Ángel Arredondo
  • Francisco Guil

Abstract

The application of scientific-based conservation measures requires that sampling methodologies in studies modelling similar ecological aspects produce comparable results making easier their interpretation. We aimed to show how the choice of different methodological and ecological approaches can affect conclusions in nest-site selection studies along different Palearctic meta-populations of an indicator species. First, a multivariate analysis of the variables affecting nest-site selection in a breeding colony of cinereous vulture (Aegypius monachus) in central Spain was performed. Then, a meta-analysis was applied to establish how methodological and habitat-type factors determine differences and similarities in the results obtained by previous studies that have modelled the forest breeding habitat of the species. Our results revealed patterns in nesting-habitat modelling by the cinereous vulture throughout its whole range: steep and south-facing slopes, great cover of large trees and distance to human activities were generally selected. The ratio and situation of the studied plots (nests/random), the use of plots vs. polygons as sampling units and the number of years of data set determined the variability explained by the model. Moreover, a greater size of the breeding colony implied that ecological and geomorphological variables at landscape level were more influential. Additionally, human activities affected in greater proportion to colonies situated in Mediterranean forests. For the first time, a meta-analysis regarding the factors determining nest-site selection heterogeneity for a single species at broad scale was achieved. It is essential to homogenize and coordinate experimental design in modelling the selection of species' ecological requirements in order to avoid that differences in results among studies would be due to methodological heterogeneity. This would optimize best conservation and management practices for habitats and species in a global context.

Suggested Citation

  • Rubén Moreno-Opo & Mariana Fernández-Olalla & Antoni Margalida & Ángel Arredondo & Francisco Guil, 2012. "Effect of Methodological and Ecological Approaches on Heterogeneity of Nest-Site Selection of a Long-Lived Vulture," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-9, March.
    • Handle: RePEc:plo:pone00:0033469
    DOI: 10.1371/journal.pone.0033469
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    1. D. A. Williams, 1987. "Generalized Linear Model Diagnostics Using the Deviance and Single Case Deletions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(2), pages 181-191, June.
    2. T. B. Hallett & T. Coulson & J. G. Pilkington & T. H. Clutton-Brock & J. M. Pemberton & B. T. Grenfell, 2004. "Why large-scale climate indices seem to predict ecological processes better than local weather," Nature, Nature, vol. 430(6995), pages 71-75, July.
    3. Michael Doebeli & Ulf Dieckmann, 2003. "Speciation along environmental gradients," Nature, Nature, vol. 421(6920), pages 259-264, January.
    4. Austin, Mike, 2007. "Species distribution models and ecological theory: A critical assessment and some possible new approaches," Ecological Modelling, Elsevier, vol. 200(1), pages 1-19.
    5. Elena Kulinskaya & Michael B. Dollinger & Kirsten Bjørkestøl, 2011. "Testing for Homogeneity in Meta-Analysis I. The One-Parameter Case: Standardized Mean Difference," Biometrics, The International Biometric Society, vol. 67(1), pages 203-212, March.
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    1. Marta Ezquerro & Marta Pardos & Luis Diaz-Balteiro, 2019. "Sustainability in Forest Management Revisited Using Multi-Criteria Decision-Making Techniques," Sustainability, MDPI, vol. 11(13), pages 1-24, July.
    2. Esther Ortiz-Urbina & Luis Diaz-Balteiro & Carlos Iglesias-Merchan, 2020. "Influence of Anthropogenic Noise for Predicting Cinereous Vulture Nest Distribution," Sustainability, MDPI, vol. 12(2), pages 1-17, January.

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