Digitized Honors Theses (2002-2017)

Date of Award

5-2006

Document Type

Undergraduate Thesis

Degree Name

BS

Department

Biology

Faculty Mentor

Kelly Major, Ph.D.

Advisor(s)

Clinton S. Major, M.S., Anne Boettcher, Ph.D.

Abstract

Second only to habitat loss, non-native invasions have become the leading cause of species endangerment and extinction. Potential ecologic and economic costs of such invasions are significant Exotic (non-indigenous) plant invaders (invasives) are of particular interest because they exhibit abilities to rapidly establish, spread and exclude competitors that are unparalleled by other organisms (Houlahan and Findlay, 2004). This project is part of a large exotic plant survey, the first and most comprehensive of its kind to be conducted in Mobile and Baldwin Counties, Alabama. The vulnerability of south Alabama to plant invasion is directly tied to a subtropical climate and fragile ecosystems characterized by intense human impact Herein, I describe a preliminary analysis of species occurrence and habitat data from Mobile and Baldwin Counties, to assess their usefulness as predictors of plant invasion. The analysis focused on the highly invasive species, Imperata cylindrica, Lygodium Japonicum, and Triadica sebifera. Three methods were used to obtain data for this analysis: 1) searches of the primary literature were used to gather basic information regarding the biology of these species (e.g., growth rate and mode of reproduction), 2) block surveys were used to document the abundance and distribution of these and other exotic invasive plants and 3) route surveys were used to obtain site-specific plant community data for the assessment of habitat invasion. Analysis of abundance and distribution data revealed significant correlations between the occurrence of each target species and latitude, longitude and elevation.

Furthermore, herbaceous height and soil type were found to be good predictors of plant invasion. This preliminary analysis suggests that with additional data and modification of traditional predictive models, scientists and land managers will be able to rapidly identify at-risk habitats.

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