Li, J., A. Herlihy, W. Gerth, P.R. Kafumann, S. Gregory, S. Urquhart, and D.P. Larsen. 2001. Variability in stream macroinvertebrates at multiple spatial scales. Freshwater Biology 46:87-97.
1.We intensively sampled 16 western Oregon streams to characterize: (1) the variability in macroinvertebrate assemblages at seven spatial scales; and (2) the change in taxon richness with increasing sampling effort. An analysis of variance (ANOVA) model calculated spatial variance components for taxon richness, total density, percent individuals of Ephemeroptera, Plecoptera and Trichoptera (EPT), percent dominance and Shannon diversity. 2. At the landscape level, ecoregion and among-streams components dominated variance for most metrics, accounting for 43-72% of total variance. However, ecoregion accounted for very little variance in total density and 36%of the variance was attributable to differences between streams. For other metrics, variance components were more evenly divided between stream and ecoregion effects. 3. Within streams, approximately 70% of variance was associated with unstructured local spatial variation and not associated with habitat type or transect position. The remaining variance was typically split about evenly between habitat and transect. Sample position within a transect (left, centre or right) accounted for virtually none of the variance for any metric. 4. New taxa per stream increased rapidly with sampling effort with the first four to eight Surber samples (500-1000 individuals counted), then increased more gradually. After counting more than 50 samples, new taxa continued to be added in stream reaches that were 80 times as long as their mean wetted width. Thus taxon richness was highly dependent on sampling effort, and comparisons between sites or streams must be normalized for sampling effort. 5. Characterization of spatial variance structure is fundamental to designing sampling programmes where spatial comparisons range from local to regional scales. Differences in metric responses across spatial scales demonstrate the importance of designing sampling strategies and analyses capable of discerning differences at the scale of interest.