TY - JOUR
T1 - Species composition drives macroinvertebrate community classification
AU - de Vries, Jip
AU - Kraak, Michiel H.S.
AU - Verdonschot, Ralf C.M.
AU - Verdonschot, Piet F.M.
PY - 2020/12
Y1 - 2020/12
N2 - Community classification enables us to simplify, communicate, track and assess complex distribution patterns. Yet, the distribution of organisms may not coincide with predefined geographical and environmental boundaries, and therefore, biology itself should be leading the classification. In this study, we showed how to arrive at such a biology-based classification by clustering locations based on similarity in species composition. A hierarchical classification structure allowed for the selection of classification levels that suit multiple scales of analysis. We also showed how to objectively identify the number of clusters present in a dataset based on the distribution of specific indicator species, allowing to identify clear boundaries in species composition on multiple scales. The resulting biology-based clusters were identified and characterized by local and regional environmental conditions, showing the limited explanatory power of these environmental conditions and the added value of taking biology itself as a starting point of the classification. By departing community classification from species composition, the unknown environmental, geographical, and biotic drivers influencing species composition are accounted for.
AB - Community classification enables us to simplify, communicate, track and assess complex distribution patterns. Yet, the distribution of organisms may not coincide with predefined geographical and environmental boundaries, and therefore, biology itself should be leading the classification. In this study, we showed how to arrive at such a biology-based classification by clustering locations based on similarity in species composition. A hierarchical classification structure allowed for the selection of classification levels that suit multiple scales of analysis. We also showed how to objectively identify the number of clusters present in a dataset based on the distribution of specific indicator species, allowing to identify clear boundaries in species composition on multiple scales. The resulting biology-based clusters were identified and characterized by local and regional environmental conditions, showing the limited explanatory power of these environmental conditions and the added value of taking biology itself as a starting point of the classification. By departing community classification from species composition, the unknown environmental, geographical, and biotic drivers influencing species composition are accounted for.
KW - Community classification
KW - Hierarchical clustering
KW - Indicator species
KW - Multiple scales
KW - Species composition
U2 - 10.1016/j.ecolind.2020.106780
DO - 10.1016/j.ecolind.2020.106780
M3 - Article
AN - SCOPUS:85089526259
SN - 1470-160X
VL - 119
JO - Ecological Indicators
JF - Ecological Indicators
M1 - 106780
ER -