Variability in stream macroinvertebrates at multiple spatial scales

Abstract

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.

Effect of spatial scale on macroinvertebrate assemblages along a Mediterranean river

“…In particular, our findings are based on observations from a single river, hindering generalisations at larger spatial scales Probability is reported as **B0.05; **B0.01; ***B0.001; ns C 0.05 Hydrobiologia or across different eco-regions. Also, the different levels of resolution at which invertebrates were identified (species, genus, family) could have potentially limited our ability to quantify community variation, especially over small spatial gradients such as the micro-habitat scale (Li et al, 2001;Heino et al, 2004). Nonetheless, as explained in the introduction, the Aniene river is a fair representative of many Apennine rivers in central Italy that show similar longitudinal gradients.…”

Estimation of nested spatial patterns and seasonal variation in the longitudinal distribution of Sicyopterus japonicus in the Datuan Stream, Taiwan by using geostatistical methods

“…Gresswell et al (2006) found that nested spatial variation in the relative abundance of coastal cutthroat trout in their variograms displayed the degree of spatial autocorrelation among samples. Peterson (Labbe and Fausch 2000;Li et al 2001;Thompson et al 2001;Fausch et al 2002;Durance et al 2006) reveals that the relative importance of ecological processes at different scales, even in multiple scale studies, appears to vary between examples (Durance et al 2006) because of different biotic and abiotic conditions in each case. However, according to the variogram modeling results, the relative importance of both multiple scales and multiple scale spatial variation can be structured by nested variogram models related to the stream's features and fish distributions However, fish require different habitats during their life cycle, often migrating long distances to reach sites for feeding, spawning, and early development.…”

“…The lack of spatial independence in ecological data has typically been viewed as obscuring an understanding the biology of organisms under study (Liebhold and Gurevitch 2002). Stream systems can be described hierarchical subsystems and exhibit variation on multiple scales and can help to understand why fish are nested in space and time owing to their ability to respond to biological and physical patterns throughout their life cycles (Frissell et al 1986;Schlosser 1991;Imhoff et al 1996;Allan and Johnson 1997;Maddock 1999;Labbe and Fausch 2000;Li et al 2001; Thompson et al 2001;Fausch et al 2002;Durance et al 2006). Recent studies have demonstrated that stream fish respond to habitat features on a substantially larger scale than previously assumed, primarily owing to movements to various habitats to satisfy the requirements of different life history stages.…”

Influence of environmental factors on the distribution of caddisfly (Trichoptera) communities in medium-sized lowland streams in Latvia

“…Streambed substratum also affects the distribution and abundance of lotic invertebrates (Gurtz & Wallace, 1986). It is generally assumed that the scale at which communities exhibit the greatest variation is the scale over which important physical/chemical gradients or biotic interactions control assemblage composition (Li et al, 2001).…”