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Integration of landscape metric surfaces derived from vector data improves species distribution models

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  • Ortner, Olivia
  • Wallentin, Gudrun

Abstract

A species’ distribution across the landscape is not random, but it is affected by distribution, size, abundance and connectivity of landscape patches. This spatial configuration of the landscape shapes ecological processes, for example the location of home ranges, migration routes and migration ability. Landscape metrics describe the configuration of a landscape quantitatively. While traditional approaches in habitat modelling only consider environmental attributes at a specific location, the integration of landscape metrics adds more functional information. In this paper we evaluated a method of directly incorporating a set of landscape metrics as covariates into a Maxent habitat model. Specifically, we used hexagons as statistical units for the calculation of landscape metrics. With this method also landscape metrics calculated with vector data sets can be used for SDM. We tested this approach for the smooth snake (Coronella austriaca) in the Austrian Alps. The experimental designs resulted in an improvement of the habitat models. Moreover, the results demonstrated the benefits of landscape metrics for the model outcomes at different scales.

Suggested Citation

  • Ortner, Olivia & Wallentin, Gudrun, 2020. "Integration of landscape metric surfaces derived from vector data improves species distribution models," Ecological Modelling, Elsevier, vol. 431(C).
    • Handle: RePEc:eee:ecomod:v:431:y:2020:i:c:s0304380020302313
    DOI: 10.1016/j.ecolmodel.2020.109160
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    References listed on IDEAS

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    1. Boria, Robert A. & Olson, Link E. & Goodman, Steven M. & Anderson, Robert P., 2014. "Spatial filtering to reduce sampling bias can improve the performance of ecological niche models," Ecological Modelling, Elsevier, vol. 275(C), pages 73-77.
    2. Shcheglovitova, Mariya & Anderson, Robert P., 2013. "Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes," Ecological Modelling, Elsevier, vol. 269(C), pages 9-17.
    3. Anderson, Robert P. & Gonzalez, Israel, 2011. "Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent," Ecological Modelling, Elsevier, vol. 222(15), pages 2796-2811.
    4. Ian W. Renner & David I. Warton, 2013. "Equivalence of MAXENT and Poisson Point Process Models for Species Distribution Modeling in Ecology," Biometrics, The International Biometric Society, vol. 69(1), pages 274-281, March.
    5. Valerio Amici & Britta Eggers & Francesco Geri & Corrado Battisti, 2015. "Habitat Suitability and Landscape Structure: A Maximum Entropy Approach in a Mediterranean Area," Landscape Research, Taylor & Francis Journals, vol. 40(2), pages 208-225, February.
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    Cited by:

    1. Haider, Saira M. & Benscoter, Allison M. & Pearlstine, Leonard & D'Acunto, Laura E. & Romañach, Stephanie S., 2021. "Landscape-scale drivers of endangered Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis) presence using an ensemble modeling approach," Ecological Modelling, Elsevier, vol. 461(C).

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