Incorporating the impacts of climate variability on growth in fish population dynamics models
Resumen
Variations in ocean conditions influenced by climate fluctuations may impact fish populations by changing their spatial distribution, physiology, survival, and other ecological features. Somatic growth is a crucial aspect of the biology of fishes and an important contributor to biomass fluctuations. Climate variability also affects somatic growth rates along the fish life span, impacting the fish ecology and the fishery. In this dissertation, I present multiple analyses on how fish somatic growth variability may be incorporated in population dynamics models, a quantitative approach to studying climate impacts on fish populations. Chapter 1 introduces fish somatic growth variability, population dynamics models and their importance, current strategies to incorporate growth in population dynamics models, and an introduction to the ecology of the Pacific cod in the eastern Bering Sea, fish stock used as a case study in this dissertation. Chapter 2 investigated how projected climate under two emission scenarios (RCP4.5 and RCP8.5) might impact the growth and survival of Pacific cod early life stages in the eastern Bering Sea. Specifically, I evaluated the impacts on the following biological variables hatch success, survival probability, growth, and spatial distribution. I implemented a mechanistic individual based cod larvae model that quantifies changes and the larval behavioral response to the physical and biological environment. The results indicate that, under the RCP8.5 scenario, the temperature in the larvae habitat will increase by 2 C while pCO2 will triple by 2100 compared to present day values. These changes will increase hatch success and standard length (mm), however, survival probability will decrease by 85% and recruitment by 50%. A shallow retention area was detected in the southeast part of the eastern Bering Sea, being this area also the most vulnerable to the future climate. Chapter 3 explored how somatic growth variability impacts the age composition estimation, a critical data input to stock assessment models. I compared the performance of four methods for estimating age compositions of a simulated fish population based on the Pacific cod biology two methods based on age-length keys (ALK, pooled and annual) and two model-based approaches (generalized additive models-GAM and continuation ratio logits-CRL). CRL was the most robust and precise method followed by annual ALKs, mainly when significant growth variability was present. I applied these methods to survey age subsample data for Pacific cod (Gadus macrocephalus) in the eastern Bering Sea, estimating age compositions that were then incorporated in its stock assessment model. The model that included age compositions estimated by CRL displayed the highest consistency with other data in the model. Chapter 4 used a simulation-estimation framework to expand previous analyses and examine the consequences of spatial and temporal (year- and cohort-specific) variability in somatic growth in stock assessment models. The study included three life history types small pelagic (e.g., sardine), gadids (e.g., cod), and long-lived (e.g., rockfish). In general, ignoring any variability in somatic growth led to biased and imprecise estimates of stock spawning biomass (SSB) and management quantities. Unequal distribution of fishing mortality across space had large impacts on the performance of estimation models as well. Conversely, accounting for somatic growth variability, either by including an environmental index, estimating annual deviates, or implementing a spatially explicit model, produced unbiased and precise results. Finally, Chapter 5 includes the main conclusions of this dissertation, the pros and cons of population dynamics models, and discusses the limitations of my research and extensions that future investigations could undertake.
