The relative importance of spatial scale variables for explaning macroinvertebrate richness in different aquatic ecological function regions

Abstract

Identifying the key drivers of aquatic fauna structuring at multiple spatial scales is critical in reducing biodiversity loss. Macroinvertebrates are the most sensitive indicators of disturbance and they are used as a cost-effective tool for bioassessment at catchment and site scales. The focus of our study was to identify the key drivers from three classes of environmental variables (geophysical landscape, land use, and site habitat) that influence macroinvertebrate richness in different aquatic ecological function regions (AEFRs) of the Liaohe River Basin. We sampled macroinvertebrate assemblages, extracted geophysical and climate variables from geospatial data, and quantified physical and chemical habitats from 407 randomly distributed sites that belong to the three level-I AEFRs. We analyzed our data through multiple linear regression models by using the three classes of predicted variables alone and in combination. The models that were constructed in the first level-I AEFR explained similar amounts of macroinvertebrate richness and had the maximum ability to explain how macroinvertebrate richness distributed (denoted "explaining ability"; geophysical landscape: RGL2 ≈ 60%, land use and site habitat: RLU2 and RSH2 ≈ 50%, and combined: RCB2 ≈ 75%). The explaining abilities for the third level-I AEFR were as follows: RGL2 ≈ 11%, RLU2 ≈ 14%, RSH2 ≈ 25%, and RCB2 ≈ 38%. The explaining abilities for the 4th level-I AEFR were as follows: RGL2 ≈ 30%, RLU2 ≈ 7%, RSH2 ≈ 40%, and RCB2 ≈ 55%. We conclude that: (1) all of the combined models explained more interaction as compared with the single models; (2) the environmental variables differed among different level-I AEFRs; and, (3) variables in the site habitat scale were the most robust explainers when analyzing the relationship between environmental variables and macroinvertebrate richness and they can be recommended as the optimal candidate explainer. These results may provide cost-effective tools for distinguishing and identifying the drivers of sensitive aquatic organisms at regional scales.