Graduation Year

2022

Document Type

Thesis

Degree

M.S.

Degree Name

Master of Science (M.S.)

Degree Granting Department

Biology (Integrative Biology)

Major Professor

Andrew M. Kramer, Ph.D.

Committee Member

David B. Lewis, Ph.D.

Committee Member

Joni A. Downs, Ph.D.

Keywords

invasive species, bioclim, machine learning, spatial modeling

Abstract

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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