Date of Award
2023
Degree Type
Open Access Thesis
Degree Name
Master of Science (MS)
Department
Biological Sciences
First Advisor
Cy L. Mott
Abstract
Body size is a critical aspect of an organism’s biological identity mediated by various biotic and abiotic factors. Body size historically been approached through “single optima” approaches, but body size variation modifies both inter- and intraspecific interactions and impacts competition-driven fitness outcomes within populations. Understanding optimal levels of body size variation will help illuminate how variation influences competitive outcomes and reproductive fitness. Populations may be structured through resource partitioning or competitive hierarchies, yet these structures predict contradictory size variation optima. This study sought to determine optimal levels of intraspecific body size variation in populations of larval salamanders to evaluate these mechanisms. Larval Ambystoma maculatum were raised in mesocosm populations along a continuum of body size variation at both high and low densities. Rates of larval growth, survival, size at metamorphosis, and length of the larval period were used to characterize optimum levels of body size variation. High density populations exhibited 38% lower survival than low density mesocosms, and metamorphs from high density populations were 11% smaller. Population size structures experienced shifts throughout the larval period, generally becoming less variable, though populations with higher initial levels of size variation exhibited shorter larval periods. Increased fitness through shorter larval periods in highly variable populations demonstrates fitness optima occur at high levels of intraspecific variation in body size, which is indicative of resource partitioning. However, the fitness benefits of this partitioning appear to be concentrated among larger individuals.
Recommended Citation
Kratzer, Ian, "Using Mesocosms to Model Optimal Levels of Body Size Variation in Larval Spotted Salamanders (Ambystoma maculatum)" (2023). Online Theses and Dissertations. 797.
https://encompass.eku.edu/etd/797