Catchment and reach‐scale properties as indicators of macroinvertebrate species traits

Abstract

1. We used catchment and reach‐scale physical properties to predict the occurrence of specific species life history and behaviour traits of aquatic insects across fifty‐eight catchments in a mixed land use basin. Catchment‐scale attributes were derived using a geographical information system (GIS). Logistic regression techniques were used to model the relationships. 2. The reach‐scale properties were highly predictive of species traits. Fourteen of the fifteen traits had significant models with concordance values greater than 68%. Cross‐sectional area at bank full discharge, % shallow, slow‐water habitats, and % fines were the most important variables. 3. Life history and behavioural attributes were best related to reach‐scale physical features. This suggests that species traits exhibit strong relationships to local environmental conditions. 4. Catchment‐scale variables had fewer significant models with species traits (four of fifteen), however these variables may have direct or indirect influence on reach‐scale properties. 5. Catchment features, in particular surficial geology, influence macroinvertebrate assemblages through their control over channel morphology and hydrologic patterns. 6. The effects of land use were masked by geology (i.e. lacustrine clay geology and rowcrop agriculture were correlated), lack of detail in land use data and the aggregation of the species data. 7. These models reflect the coupling of local environmental conditions and the set of adaptations among the local taxa. These observations underscore the idea that habitat plays a major role in organizing stream assemblages. 8. Using these approaches, predictions can be made about the ability of various taxonomic groupings to track environmental change through time, or for projecting the impact of alternative land management scenarios. Identifying fundamental life history and other traits can improve the selection and evaluation of such indicators.

Macroinvertebrate community response to inter‐annual and regional river flow regime dynamics

“…This demonstrates that the hydrology and ecology of different river 'types' interact in a variety of ways and also that additional factors (i.e. other than the flow regime such as climatology, catchment characteristics, habitat availability, water chemistry and biological interactions) may be important in structuring instream macroinvertebrate communities (Allan et al, 1997;Doisy and Rabeni, 2001;Hughes and James, 1989;Poff and Ward, 1989;Rabeni and Doisy, 2000;Richards et al, 1997;Richards and Minshall, 1992;Sponseller et al, 2001). In the case of RM2, the 'region' is widely distributed across the study area and the variable LIFE scores recorded and high variance of the 'most significant hydrological descriptor (lnQ 10 /Q 90 -from the magnitude of flow events category within the IHA methodology) (Figure 4c) may reflect these 'additional' factors.…”

Overview and application of the National Aquatic Ecological Monitoring Program (NAEMP) in Korea

“…Lammert and Allan (1999) reported that 50% of the variation in biotic integrity scores of headwater fish communities across seven sub-watersheds was explained by land use in the watersheds, and Roth et al (1996) found that habitat integrity was closely tied to local land uses. It has been widely demonstrated that macroinvertebrate communities are more likely to respond to local-and regionalscale conditions than to catchment land use (Richards et al, 1997;Lammert and Allan, 1999;Sponseller et al, 2001), although watershed-scale land use has been shown to be an important predictor of macroinvertebrate communities in other studies (Li et al, 2001;Townsend et al, 2003;Kratzer et al, 2006). Notably, water chemistry is also generally a more significant variable in explaining the community variance of fish and macroinvertebrates than The classes of biological indicators were re-coded for correlation analysis as "Class A (Excellent)" = 1, "Class B (Good)" = 2, "Class C (Fair)" = 3, and "Class D (Poor)" = 4.…”

Using Diatom Assemblages to Assess Urban Stream Conditions

“…A similar approach was taken but only a 91 m radius was used for this analysis. We realize that stream ecosystems are continuous and that conditions at a particular site on a stream reflect the cumulative inputs upstream in the watershed, however studies have also shown that local environmental conditions have a strong influence on biological assemblages (Carter et al, 1996;Pan et al, 1996;Richards et al, 1997).…”