Performance of methods to select landscape metrics for modelling species richness
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In: Ecological Modelling, Vol. 295, 10.01.2015, p. 107-112.
Research output: Journal contributions › Journal articles › Research › peer-review
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TY - JOUR
T1 - Performance of methods to select landscape metrics for modelling species richness
AU - Schindler, Stefan
AU - von Wehrden, Henrik
AU - Poirazidis, Kostas
AU - Hochachka, Wesley M.
AU - Wrbka, Thomas
AU - Kati, Vassiliki
PY - 2015/1/10
Y1 - 2015/1/10
N2 - 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.
AB - 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.
KW - Ecosystems Research
KW - Biodiversity indicator
KW - Dadia National Park
KW - Ecological indicator
KW - Greece
KW - Landscape structure
KW - Variable selection
KW - variable selection
KW - biodiversity indicator
KW - ecological indicator
KW - landscape structure
KW - Dadia National Park
KW - Greece
UR - http://www.scopus.com/inward/record.url?scp=84910075581&partnerID=8YFLogxK
U2 - 10.1016/j.ecolmodel.2014.05.012
DO - 10.1016/j.ecolmodel.2014.05.012
M3 - Journal articles
VL - 295
SP - 107
EP - 112
JO - Ecological Modelling
JF - Ecological Modelling
SN - 0304-3800
ER -