Controlling for hydrologic connectivity to assess the importance of catchment- and reach-scale factors on macroinvertebrate community structure
Understanding landscape influences on stream ecosystems is a challenging task due to the spatial complexity and connectedness of stream networks. Here, we control for longitudinal connectivity to provide a robust test of the relative importance of reach- and catchment-scale factors in determining ma...
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Published in | Hydrobiologia Vol. 763; no. 1; pp. 285 - 299 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Cham
Springer International Publishing
01.01.2016
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | Understanding landscape influences on stream ecosystems is a challenging task due to the spatial complexity and connectedness of stream networks. Here, we control for longitudinal connectivity to provide a robust test of the relative importance of reach- and catchment-scale factors in determining macroinvertebrate community structure in southwestern Pennsylvania streams. We determined that sites separated by ≤510 m along the stream network had significantly correlated macroinvertebrate community scores. After controlling for this spatial autocorrelation, a partial least squares regression identified two factors that together accounted for 32% of the variation in community scores. In this model, two reach-scale factors—habitat assessment score and stream pH—were the most important factors for predicting a stream’s macroinvertebrate community score. However, landscape diversity was also important. Landscape diversity is a catchment-scale factor that was highly correlated with percent pasture/hay and measures of habitat fragmentation. Our results provide support for the idea that stream communities in undisturbed areas are heavily influenced by reach-scale characteristics. Furthermore, our results indicate that Pennsylvania natural resource managers should consider habitat score and stream pH after accounting for spatial autocorrelation when identifying restoration targets for impacted streams. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0018-8158 1573-5117 |
DOI: | 10.1007/s10750-015-2385-z |