Item talk:Q251471
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{
"USGS Publications Warehouse": { "@context": "https://schema.org", "@type": "Article", "additionalType": "Journal Article", "name": "The relative importance of physicochemical factors to stream biological condition in urbanizing basins: Evidence from multimodel inference", "identifier": [ { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse IndexID", "value": "70192887", "url": "https://pubs.usgs.gov/publication/70192887" }, { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse Internal ID", "value": 70192887 }, { "@type": "PropertyValue", "propertyID": "DOI", "value": "10.1899/10-131.1", "url": "https://doi.org/10.1899/10-131.1" } ], "journal": { "@type": "Periodical", "name": "Freshwater Science", "volumeNumber": "31", "issueNumber": "1" }, "inLanguage": "en", "isPartOf": [ { "@type": "CreativeWorkSeries", "name": "Freshwater Science" } ], "datePublished": "2011", "dateModified": "2017-11-12", "abstract": "Many physicochemical factors potentially impair stream ecosystems in urbanizing basins, but few studies have evaluated their relative importance simultaneously, especially in different environmental settings. We used data collected in 25 to 30 streams along a gradient of urbanization in each of 6 metropolitan areas (MAs) to evaluate the relative importance of 11 physicochemical factors on the condition of algal, macroinvertebrate, and fish assemblages. For each assemblage, biological condition was quantified using 2 separate metrics, nonmetric multidimensional scaling ordination site scores and the ratio of observed/expected taxa, both derived in previous studies. Separate linear regression models with 1 or 2 factors as predictors were developed for each MA and assemblage metric. Model parsimony was evaluated based on Akaike\u2019s Information Criterion for small sample size (AICc) and Akaike weights, and variable importance was estimated by summing the Akaike weights across models containing each stressor variable. Few of the factors were strongly correlated (Pearson |r| > 0.7) within MAs. Physicochemical factors explained 17 to 81% of variance in biological condition. Most (92 of 118) of the most plausible models contained 2 predictors, and generally more variance could be explained by the additive effects of 2 factors than by any single factor alone. None of the factors evaluated was universally important for all MAs or biological assemblages. The relative importance of factors varied for different measures of biological condition, biological assemblages, and MA. Our results suggest that the suite of physicochemical factors affecting urban stream ecosystems varies across broad geographic areas, along gradients of urban intensity, and among basins within single MAs.", "description": "13 p.", "publisher": { "@type": "Organization", "name": "University of Chicago Press" }, "author": [ { "@type": "Person", "name": "Carlisle, Daren dcarlisle@usgs.gov", "givenName": "Daren", "familyName": "Carlisle", "email": "dcarlisle@usgs.gov", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0002-7367-348X", "url": "https://orcid.org/0000-0002-7367-348X" }, "affiliation": [ { "@type": "Organization", "name": "WMA - Earth System Processes Division", "url": "https://www.usgs.gov/mission-areas/water-resources" }, { "@type": "Organization", "name": "Kansas Water Science Center", "url": "https://www.usgs.gov/centers/kansas-water-science-center" }, { "@type": "Organization", "name": "National Water Quality Assessment Program", "url": "https://www.usgs.gov/programs/national-water-quality-program" }, { "@type": "Organization", "name": "Office of Water Quality", "url": "https://www.usgs.gov/mission-areas/water-resources" }, { "@type": "Organization", "name": "National Water Quality Program", "url": "https://www.usgs.gov/programs/national-water-quality-program" } ] }, { "@type": "Person", "name": "Bryant, Wade L. 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