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Performance of methods to select landscape metrics for modelling species richness

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  • Schindler, Stefan
  • von Wehrden, Henrik
  • Poirazidis, Kostas
  • Hochachka, Wesley M.
  • Wrbka, Thomas
  • Kati, Vassiliki

Abstract

Landscape metrics are commonly used indicators of ecological pattern and processes in ecological modelling. Numerous landscape metrics are available, making the selection of appropriate metrics a common challenge in model development. In this paper, we tested the performance of methods for preselecting sets of three landscape metrics for use in modelling species richness of six groups of organisms (woody plants, orchids, orthopterans, amphibians, reptiles, and small terrestrial birds) and overall species richness in a Mediterranean forest landscape. The tested methods included expert knowledge, decision tree analysis, principal component analysis, and principal component regression. They were compared with random choice and optimal sets, which were evaluated by testing all possible combinations of metrics. All pre-selection methods performed significantly worse than the optimal sets. The statistical approaches performed slightly better than random choice that in turn performed slightly better than sets derived by expert knowledge. We concluded that the process of selecting the most appropriate landscape metrics for modelling biodiversity is not trivial and that shortcuts to systematic evaluation of metrics should not be expected to identify appropriate indicators.

Suggested Citation

  • Schindler, Stefan & von Wehrden, Henrik & Poirazidis, Kostas & Hochachka, Wesley M. & Wrbka, Thomas & Kati, Vassiliki, 2015. "Performance of methods to select landscape metrics for modelling species richness," Ecological Modelling, Elsevier, vol. 295(C), pages 107-112.
    • Handle: RePEc:eee:ecomod:v:295:y:2015:i:c:p:107-112
    DOI: 10.1016/j.ecolmodel.2014.05.012
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    References listed on IDEAS

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    1. Ana Paula Dias Turetta & Rachel Bardy Prado & Gustavo de Souza Valladares, 2013. "Evaluating the Potential of Landscape Metrics in Supporting Landscape Planning in Atlantic Forest: Rio de Janeiro, Brazil," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 4(1), pages 55-67, January.
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    Cited by:

    1. Hossard, Laure & Gosme, Marie & Souchère, Véronique & Jeuffroy, Marie-Hélène, 2015. "Linking cropping system mosaics to disease resistance durability," Ecological Modelling, Elsevier, vol. 307(C), pages 1-9.
    2. Miaomiao Liu & Guishan Chen & Guanhua Li & Yingyu Huang & Kaiwei Luo & Changfa Zhan, 2023. "Landscape Evolution and Its Driving Forces in the Rapidly Urbanized Guangdong–Hong Kong–Macao Greater Bay Area, a Case Study in Zhuhai City, South China," Sustainability, MDPI, vol. 15(17), pages 1-23, August.
    3. Qinghui Wang & Yu Peng & Min Fan & Zheng Zhang & Qingtong Cui, 2018. "Landscape Patterns Affect Precipitation Differing across Sub-climatic Regions," Sustainability, MDPI, vol. 10(12), pages 1-17, December.
    4. Jinming Yang & Shimei Li & Huicui Lu, 2019. "Quantitative Influence of Land-Use Changes and Urban Expansion Intensity on Landscape Pattern in Qingdao, China: Implications for Urban Sustainability," Sustainability, MDPI, vol. 11(21), pages 1-18, November.
    5. Patrícia Abrantes & Jorge Rocha & Eduarda Marques da Costa & Eduardo Gomes & Paulo Morgado & Nuno Costa, 2019. "Modelling urban form: A multidimensional typology of urban occupation for spatial analysis," Environment and Planning B, , vol. 46(1), pages 47-65, January.

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