Prediction, Detection, and Management of Aquatic Invasive Species

Doctoral Dissertation
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Abstract

Biological invasion, the establishment of organisms beyond their native ranges, represents powerful global change, disturbing native communities and nutrient cycles, disrupting human land use, and threatening human health. My research has addressed methodological and technological uncertainties associated with the ecology and management of biological invasions, focusing on prediction and detection of aquatic invasions.

I considered aquatic plants as a case study to improve predictions of species transport and establishment. In laboratory experiments, plant fragment mass loss due to air exposure accurately predicted viability upon reintroduction to an aquatic environment, but similar periods of air exposure differentially affected different species. Understanding species traits like desiccation response can contribute to predicting dispersal. To improve predictions of where dispersing propagules may establish, I used the species distribution modeling program Maxent to predict potential North American distribution of the invasive aquatic plant Hydrilla verticillata. I demonstrated that information gaps due to non-reporting of native occurrences alter model accuracy and transferability. I concluded that distribution modeling efforts must consider potential spatial biases in occurrence data.

I have also advanced current knowledge about genetic surveillance of freshwater fish using environmental DNA (eDNA). In a laboratory experiment, biotic and abiotic environmental factors influenced Common Carp (Cyprinus carpio) eDNA degradation. Logistic regression accurately predicted detection vs. nondetection over time, while eDNA concentration decayed exponentially. I explored the influence of environmental factors on eDNA particle size distribution (PSD) in experimental ponds. PSD did not differ between Common Carp and bigheaded carps (genus Hypophthalmichthys), but abiotic and biotic factors influenced PSD differences between ponds. Quantifying the influence of local environmental conditions on eDNA degradation and PSD rates will improve eDNA surveillance.

Overall, the science of prediction and detection of aquatic invasions continues to progress rapidly. In my dissertation, I have evaluated uncertainties in current predictive methodologies and technologies to increase understanding of the ecology of invasive species and improve management and mitigation of damages due to aquatic invasions. In an era of global change, advancing science will allow a transition from simply reacting to the latest environmental crises to instead averting future challenges.

Attributes

Attribute NameValues
URN
  • etd-07152013-142228

Author Matthew Alexander Barnes
Advisor David Lodge
Contributor David Lodge, Committee Chair
Contributor Jeffrey Feder, Committee Member
Contributor Gary Lamberti, Committee Member
Contributor Jason McLachlan, Committee Member
Degree Level Doctoral Dissertation
Degree Discipline Biological Sciences
Degree Name PhD
Defense Date
  • 2013-06-10

Submission Date 2013-07-15
Country
  • United States of America

Subject
  • eDNA

  • environmental DNA

  • model

  • dispersal

  • biological invasion

Publisher
  • University of Notre Dame

Language
  • English

Record Visibility and Access Public
Content License
  • All rights reserved

Departments and Units

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