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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Using Fine-scale Aquatic Habitat Data to Construct Dreissenid SDMs in the Laurentian Great Lakes./
作者:	Henderson, Grace Cecile.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	47 p.
附註:	Source: Masters Abstracts International, Volume: 83-11.
Contained By:	Masters Abstracts International83-11.
標題:	Ecology. -
電子資源:	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.
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500 $a Source: Masters Abstracts International, Volume: 83-11.
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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.
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