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Using Fine-scale Aquatic Habitat Data to Construct Dreissenid SDMs in the Laurentian Great Lakes.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Using Fine-scale Aquatic Habitat Data to Construct Dreissenid SDMs in the Laurentian Great Lakes./
Author:	Henderson, Grace Cecile.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	47 p.
Notes:	Source: Masters Abstracts International, Volume: 83-11.
Contained By:	Masters Abstracts International83-11.
Subject:	Ecology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29060949
ISBN:	9798426825024

Using Fine-scale Aquatic Habitat Data to Construct Dreissenid SDMs in the Laurentian Great Lakes.
Henderson, Grace Cecile.

Using Fine-scale Aquatic Habitat Data to Construct Dreissenid SDMs in the Laurentian Great Lakes. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 47 p.

Source: Masters Abstracts International, Volume: 83-11.

Thesis (M.S.)--University of South Florida, 2022.

This item must not be sold to any third party vendors.

The invasion of the Laurentian Great Lakes by aquatic invasive species (AIS) has been the subject of investigation for decades, due to their dramatic alterations to the ecosystem and high economic costs. Two AIS with the largest impacts are dreissenid zebra and quagga mussels, and though these species have been studied extensively, questions remain about what factors control their distributions, and whether lake warming will alter these distributions. Species distribution models (SDMs) offer a powerful tool to examine the relationship between species presences and environmental variables, which are typically bioclimactic data. The creation of the Aquatic Habitat (AqHab) dataset containing biological, chemical, geomorphological, hydrological variables within the Great Lakes provides a novel opportunity to build models that do not rely on atmospheric climate proxies. We hypothesized that the high-resolution AqHab dataset would produce SDMs with improved predictive capabilities for dreissenids than SDMs constructed with BioClim covariates. We also predicted niche differences between the dreissenids would be reflected by the models. SDM models were fitted using two algorithms: Maximum Entropy (MaxENT) and Boosted Regression Trees (BRT). AqHab models better predicted quagga mussel presence than BioClim models. Dreissenid niche differences, such as zebra preferring shallow, warm waters and quaggas preferring deep, cold waters, were apparent from all models. Our results imply that aquatic species distribution models may be improved with the addition of aquatic habitat environmental layers.

ISBN: 9798426825024Subjects--Topical Terms:

516476
Ecology.
Subjects--Index Terms:

Aquatic invasive species

Using Fine-scale Aquatic Habitat Data to Construct Dreissenid SDMs in the Laurentian Great Lakes.
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245 1 0 $a Using Fine-scale Aquatic Habitat Data to Construct Dreissenid SDMs in the Laurentian Great Lakes.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 47 p.
500 $a Source: Masters Abstracts International, Volume: 83-11.
500 $a Advisor: Kramer, Andrew M.
502 $a Thesis (M.S.)--University of South Florida, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a The invasion of the Laurentian Great Lakes by aquatic invasive species (AIS) has been the subject of investigation for decades, due to their dramatic alterations to the ecosystem and high economic costs. Two AIS with the largest impacts are dreissenid zebra and quagga mussels, and though these species have been studied extensively, questions remain about what factors control their distributions, and whether lake warming will alter these distributions. Species distribution models (SDMs) offer a powerful tool to examine the relationship between species presences and environmental variables, which are typically bioclimactic data. The creation of the Aquatic Habitat (AqHab) dataset containing biological, chemical, geomorphological, hydrological variables within the Great Lakes provides a novel opportunity to build models that do not rely on atmospheric climate proxies. We hypothesized that the high-resolution AqHab dataset would produce SDMs with improved predictive capabilities for dreissenids than SDMs constructed with BioClim covariates. We also predicted niche differences between the dreissenids would be reflected by the models. SDM models were fitted using two algorithms: Maximum Entropy (MaxENT) and Boosted Regression Trees (BRT). AqHab models better predicted quagga mussel presence than BioClim models. Dreissenid niche differences, such as zebra preferring shallow, warm waters and quaggas preferring deep, cold waters, were apparent from all models. Our results imply that aquatic species distribution models may be improved with the addition of aquatic habitat environmental layers.
590 $a School code: 0206.
650 4 $a Ecology. $3 516476
650 4 $a Water resources management. $3 794747
650 4 $a Biostatistics. $3 1002712
650 4 $a Aquatic sciences. $3 3174300
650 4 $a Geographic information science. $3 3432445
650 4 $a Artificial intelligence. $3 516317
653 $a Aquatic invasive species
653 $a Dreissena
653 $a Great Lakes
653 $a Spatial modeling
653 $a Species distribution models
653 $a BioClim
653 $a Machine learning
653 $a Speciesdistribution models
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690 $a 0800
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710 2 $a University of South Florida. $b Biology (Integrative Biology). $3 2101399
773 0 $t Masters Abstracts International $g 83-11.
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791 $a M.S.
792 $a 2022
793 $a English
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