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It’s in Their Bones: Ecological Drivers of Kemp’s Ridley Sea Turtle (Lepidochelys kempii) Somatic Growth and Population Dynamics

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  • Somatic growth variation manifests from the cumulative effects of a suite of biological, ecological, and environmental processes and can have profound effects on individual fitness and species population dynamics. As ectotherms whose growth dynamics are greatly influenced by environmental factors, sea turtles display considerable variation in somatic growth within and among individuals, populations, and species. Given the sensitivity of sea turtle population dynamics to small changes in demographic rates, identifying the proximate drivers of somatic growth variation, and subsequent influences on population dynamics, is of high importance to sea turtle conservation and management. This is particularly true for the critically endangered Kemp’s ridley sea turtle (Lepidochelys kempii), which displays regional differences in somatic growth rates and whose recovery is now uncertain given recent changes in population growth. Through the integration of multiple skeletal, geochemical, and quantitative analyses, my dissertation aims to identify ecological factors influential to Kemp’s ridley sea turtle somatic growth variation and the potential influence of life history variation on their population dynamics. In Chapter 2, I used a 20+ year dataset of Kemp’s ridley sea turtle somatic growth rates generated through skeletochronology to quantify the influence of the Deepwater Horizon oil spill, climate change, and changing population density on age- and region-specific somatic growth rates. These analyses revealed a significant reduction in mean somatic growth rates in 2012–2015 for Age 0 and Age 2–5 turtles that stranded in the U.S. Gulf of Mexico and Atlantic Coasts. Additionally, Age 0 and Age 2–5 growth rates were related to regional climate indices and population abundance estimates, respectively. Integrative analysis determined that the 2012 growth shift explained the greatest variation in somatic growth rates, which I hypothesize may be related to long-term deleterious effects of the Deepwater Horizon oil spill. Continued evaluation of growth rates is needed to distinguish the effects of population density and climate indices as drivers of somatic growth variation in this species. In Chapter 3, I sampled bones processed in Chapter 2 for stable isotope ratios (δ13C, δ15N) to characterize regional variation in diet composition and quantify relationships between diet composition and somatic growth rates. Turtle bone stable isotope date were combined with prey stable isotope data collated from the literature into a Bayesian stable isotope mixing model to estimate the proportional contribution of crustaceans, bivalves, gastropods, fish, and seagrass/macroalgae to turtle diets. I found distinct regional differences in model-derived estimates of diet composition that largely follow known diet patterns. My mixing models indicated that northern GoM and Atlantic turtles primarily consumed invertebrates, western GoM turtles consumed equal amounts of invertebrates and fish, and eastern GoM turtles consumed equal amounts of invertebrates and basal resources. Growth rates were poorly correlated with δ15N values and diet composition estimates, suggesting that higher trophic level diets do not cause higher Kemp’s ridley growth rates and that diet composition does not drive the apparent regional differences in somatic growth evident in this species. In Chapter 4, I investigated the ability of complementary lead (Pb) stable isotope, trace element, and growth rate analyses to discriminate regional (GoM vs. Atlantic) Kemp’s ridley sea turtle habitat use. Through multiple quadratic discriminant function analyses, I found that 208Pb:206Pb could be used to classify turtles to stranding region with exceptional accuracy (94.1 %), whereas somatic growth rates in conjunction with Sr:Ca, Cu:Ca, Ba:Ca, Mg:Ca, and Zn:Ca had a correct classification success rate of 79.5 %. These results suggest that Pb stable isotopes, and possibly somatic growth rates, may provide a useful tool for studying Atlantic-to-GoM ontogenetic shifts in this and other sea turtle species in the future. In Chapter 5, I used a spatially explicit, age-structured matrix population model to evaluate the relative contribution of Atlantic Kemp’s ridley sea turtles to population growth and recovery prior to 2010. I specifically evaluated sensitivity to changes in key transition probabilities that describe the movement of turtles among habitats and life stages within the western North Atlantic Ocean. My model simulations suggest that Atlantic turtles were a strong contributor to Kemp’s ridley population growth during the species’ pre-2010 recovery and are unlikely to influence recovery time, even under the most extreme scenarios evaluated. Future work will include simulations under stable or declining population growth rate indicators, as have been observed in the species since 2010. Taken together, this study filled some critical knowledge gaps in our understanding of the relationship between multiple ecological and environmental factors (oil spills, climate, population density, foraging ecology, habitat use) and Kemp’s ridley sea turtle somatic growth and population dynamics. This research also highlighted the importance of continued collection and study of stranded turtle tissues as they provide a means to investigate otherwise intractable research questions in sea turtle ecology.
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