Since the theory of evolution by natural selection was first postulated, biologists have noted that life histories evolve following broad patterns across all organisms. Understanding the mechanisms causing these relationships is the central focus of life history theory; these insights can also be used to better estimate the biology and extinction risk of data-poor species. Sharks, rays, and chimaeras (class Chondrichthyes) are an ideal taxon to explore these relationships as they have evolved a broad range of life history strategies. In this thesis, I focus on two key time-related life history parameters that are often used as a measure of productivity: growth coefficient k, which is estimated from the von Bertalanffy growth function (VBGF), and maximum intrinsic rate of population increase rmax, estimated by simplifying the Euler-Lotka equation. I begin by clarifying two methodological problems regarding the estimation of growth and productivity. I first show that fixing the y-intercept in the VBGF, a common approach in chondrichthyan age and growth studies, often causes considerable bias in growth coefficient estimates, and recommend using the three-parameter VBGF instead. I then point out an important omission in a method commonly used for estimating r max in chondrichthyans and clarify the correct way to estimate it. Next I explore the effect of uncertainty on the estimation of r max and show that species with low annual reproductive outputs are bound to have very low productivities, thus focus should be placed into accurately estimating litter sizes, breeding intervals, and the variability of these traits. As an example of how these insights can be applied, I better estimate growth and productivity for a data sparse species of conservation concern, the Spinetail Devil Ray (Mobula japanica), and show it has a much lower somatic growth rate than previously thought and one of the lowest productivities among chondrichthyans. Finally, I show that productivity in chondrichthyans varies with temperature as well as depth, and that the scaling of this relationship changes with temperature according to the expectation from Bergman’s rule. My thesis demonstrates that simple insights from life history theory can further our knowledge on the broad patterns that shape the evolution of life histories we see today, which can be used to inform management of data-poor species.
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Thesis advisor: Dulvy, Nicholas
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