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A Macroecological Approach to Unders...

Thai, Michael D.

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A Macroecological Approach to Understanding Drivers of Riverine Fish Community Composition.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Macroecological Approach to Understanding Drivers of Riverine Fish Community Composition./
Author:	Thai, Michael D.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	34 p.
Notes:	Source: Masters Abstracts International, Volume: 57-01.
Contained By:	Masters Abstracts International57-01(E).
Subject:	Ecology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10289225
ISBN:	9780355353358

A Macroecological Approach to Understanding Drivers of Riverine Fish Community Composition.
Thai, Michael D.

A Macroecological Approach to Understanding Drivers of Riverine Fish Community Composition. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 34 p.

Source: Masters Abstracts International, Volume: 57-01.

Thesis (M.A.)--University of Kansas, 2017.

Macroecology is an evolving ecological discipline that analyzes regional through global processes whose temporal interactions are especially significant over decades to millennia. Understanding if and how variables acting on rivers at large spatiotemporal scales affect fish communities is key to better river management and ecological theory. Using the American Fisheries Society's standard sampling protocol, we sampled fish communities in contrasting (constricted and wide valley) hydrogeomorphic patches in both upland and lowland areas within terminal basin rivers in the Great Basin USA. We used species and trait-based community composition data, reach scale habitat data, and valley scale hydrogeomorphic data to analyze relationships between community composition and environmental variables. These relationships were evaluated using Mantel and partial Mantel tests to elucidate a causal network between the previously listed elements. Canonical correspondence analysis (CCA) was then used to illuminate specific variables within each environmental scale that may shape the composition of fish communities. Results indicated that valley scale hydrogeomorphic variables had a significant direct effect on fish community composition and explained more variation within the CCA than reach scale habitat variables. Correlations were stronger when based on a trait description of fish community composition with valley scale variables and more variance was explained in CCAs by environmental variables when a trait-based description was used.

ISBN: 9780355353358Subjects--Topical Terms:

516476
Ecology.

A Macroecological Approach to Understanding Drivers of Riverine Fish Community Composition.
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500 $a Adviser: James H. Thorp.
502 $a Thesis (M.A.)--University of Kansas, 2017.
520 $a Macroecology is an evolving ecological discipline that analyzes regional through global processes whose temporal interactions are especially significant over decades to millennia. Understanding if and how variables acting on rivers at large spatiotemporal scales affect fish communities is key to better river management and ecological theory. Using the American Fisheries Society's standard sampling protocol, we sampled fish communities in contrasting (constricted and wide valley) hydrogeomorphic patches in both upland and lowland areas within terminal basin rivers in the Great Basin USA. We used species and trait-based community composition data, reach scale habitat data, and valley scale hydrogeomorphic data to analyze relationships between community composition and environmental variables. These relationships were evaluated using Mantel and partial Mantel tests to elucidate a causal network between the previously listed elements. Canonical correspondence analysis (CCA) was then used to illuminate specific variables within each environmental scale that may shape the composition of fish communities. Results indicated that valley scale hydrogeomorphic variables had a significant direct effect on fish community composition and explained more variation within the CCA than reach scale habitat variables. Correlations were stronger when based on a trait description of fish community composition with valley scale variables and more variance was explained in CCAs by environmental variables when a trait-based description was used.
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