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Modeling Potentially Suitable Freshwater Mussel Habitat Using Remote Data for the Duck River Drainage, Tennessee.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling Potentially Suitable Freshwater Mussel Habitat Using Remote Data for the Duck River Drainage, Tennessee./
作者:	Bajo-Walker, Brittany.
面頁冊數:	1 online resource (101 pages)
附註:	Source: Masters Abstracts International, Volume: 84-06.
Contained By:	Masters Abstracts International84-06.
標題:	Aquatic sciences. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29327903click for full text (PQDT)
ISBN:	9798363504150

Modeling Potentially Suitable Freshwater Mussel Habitat Using Remote Data for the Duck River Drainage, Tennessee.
Bajo-Walker, Brittany.

Modeling Potentially Suitable Freshwater Mussel Habitat Using Remote Data for the Duck River Drainage, Tennessee. - 1 online resource (101 pages)

Source: Masters Abstracts International, Volume: 84-06.

Thesis (M.S.)--Tennessee Technological University, 2022.

Includes bibliographical references

The Duck River in central Tennessee is one of the most species rich systems in all of North America, containing over 50 species of freshwater mussels. Freshwater mussels are generally difficult to detect and often require timely, in-depth surveys due to their cryptic nature. Surveys are further complicated by their unique, clustered nature into distinct, multi-species assemblages or beds. The location of mussel beds is often difficult to predict due to the variability of mussel bed species composition and the variability between watershed systems. Species distribution models can narrow survey efforts by associating landscape level hydrogeomorphological variables to a species' known distribution. For particularly fragile and valuable systems like the Duck River, understanding the distribution of the aquatic fauna can lead to more successful conservation and management. My overarching goal was to determine if mussel bed presence corresponds to particular hydrogeomorphological features at the riverscape scale, and to identify areas most similar to these documented mussel beds throughout this system. Sixty-six survey points were used from previous surveys to describe mussel communities in the system in a hierarchical and spatially explicit manner. I used the nested nature of mussel communities in this system, the availability of data, and the particularly high richness of the Duck River mussel fauna to construct three separate habitat models, each of which addressed a different subset of occurrence points based on species richness ranges. MaxEnt, a machine-learning, presence-only modeling technique, was used to run algorithms for the three models. Models one, two, and three used occurrence records with 8 or more, 15 or more, and 23 or more species, respectively. Seven GIS layers representing presence of crops in the catchment, minimum air temperature, hydraulic conductivity, base flow, Kf factor (also known as soil erodibility), percent clay in the catchment, and geological unit name were used in the MaxEnt modeling framework. Output of these models were three choropleth maps that illustrated areas from most to least suitable for freshwater mussels based on the occurrence points and the input variables. The model representing general mussel beds (8 or more species) showed an association with limestone geology while the more narrow, species rich model showed associations with hydraulic conductivity and minimum air temperature. Overall, it appears that mussel beds with different species compositions may be behaving differently and may have slightly different thresholds for riverine and landscape features. While mussel beds can be used as the response variable in species distribution models it is important to acknowledge that not all beds should be treated equally.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798363504150Subjects--Topical Terms:

3174300
Aquatic sciences.
Subjects--Index Terms:

Duck RiverIndex Terms--Genre/Form:

542853
Electronic books.

Modeling Potentially Suitable Freshwater Mussel Habitat Using Remote Data for the Duck River Drainage, Tennessee.
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500 $a Source: Masters Abstracts International, Volume: 84-06.
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520 $a The Duck River in central Tennessee is one of the most species rich systems in all of North America, containing over 50 species of freshwater mussels. Freshwater mussels are generally difficult to detect and often require timely, in-depth surveys due to their cryptic nature. Surveys are further complicated by their unique, clustered nature into distinct, multi-species assemblages or beds. The location of mussel beds is often difficult to predict due to the variability of mussel bed species composition and the variability between watershed systems. Species distribution models can narrow survey efforts by associating landscape level hydrogeomorphological variables to a species' known distribution. For particularly fragile and valuable systems like the Duck River, understanding the distribution of the aquatic fauna can lead to more successful conservation and management. My overarching goal was to determine if mussel bed presence corresponds to particular hydrogeomorphological features at the riverscape scale, and to identify areas most similar to these documented mussel beds throughout this system. Sixty-six survey points were used from previous surveys to describe mussel communities in the system in a hierarchical and spatially explicit manner. I used the nested nature of mussel communities in this system, the availability of data, and the particularly high richness of the Duck River mussel fauna to construct three separate habitat models, each of which addressed a different subset of occurrence points based on species richness ranges. MaxEnt, a machine-learning, presence-only modeling technique, was used to run algorithms for the three models. Models one, two, and three used occurrence records with 8 or more, 15 or more, and 23 or more species, respectively. Seven GIS layers representing presence of crops in the catchment, minimum air temperature, hydraulic conductivity, base flow, Kf factor (also known as soil erodibility), percent clay in the catchment, and geological unit name were used in the MaxEnt modeling framework. Output of these models were three choropleth maps that illustrated areas from most to least suitable for freshwater mussels based on the occurrence points and the input variables. The model representing general mussel beds (8 or more species) showed an association with limestone geology while the more narrow, species rich model showed associations with hydraulic conductivity and minimum air temperature. Overall, it appears that mussel beds with different species compositions may be behaving differently and may have slightly different thresholds for riverine and landscape features. While mussel beds can be used as the response variable in species distribution models it is important to acknowledge that not all beds should be treated equally.
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653 $a Tennessee
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