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Modelling of landscape variables at multiple extents to predict fine sediments and suitable habitat for Tubifex tubifex in a stream system
Author(s) -
ANLAUF KARA J.,
MOFFITT CHRISTINE M.
Publication year - 2010
Publication title -
freshwater biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.297
H-Index - 156
eISSN - 1365-2427
pISSN - 0046-5070
DOI - 10.1111/j.1365-2427.2009.02323.x
Subject(s) - tubifex tubifex , habitat , tubifex , ecology , environmental science , hydrology (agriculture) , land cover , streams , biology , geology , land use , computer science , computer network , geotechnical engineering
Summary 1. Aggregations of fine sediments are a suitable proxy for the presence and abundance of Tubifex tubifex , one of the obligate hosts in the parasitic life cycle that causes salmonid whirling disease ( Myxobolus cerebralis ). 2. To determine and evaluate practical approaches to predict fine sediments (<2 mm diameter) that could support Tubifex spp. aggregations, we measured habitat features in a catchment with field measures and metrics derived from digital data sets and geospatial tools at three different spatial extents (m 2 ) within a hierarchical structure. 3. We used linear mixed models to test plausible candidate models that best explained the presence of fine sediments measured in stream surveys with metrics from several spatial extents. 4. The percent slow water habitat measured at the finest extent provided the best model to predict the likely presence of fine sediments. The most influential models to predict fine sediments using landscape metrics measured at broader extents included variables that measure the percentage land cover in conifer or agriculture, specifically, decreases in conifer cover and increases in agriculture. 5. The overall best‐fitting model of the presence of fine sediments in a stream reach combined variables measured and operating at different spatial extents. 6. Landscape features modelled within a hierarchical framework may be useful tools to evaluate and prioritise areas with fine sediments that may be at risk of infection by Myxobolus cerebralis .