|Document Type:||Journal Article|
|Title:||Separating intrinsic and environmental contributions to growth and their population consequences|
|Author:||Andrew Olaf Shelton, William H. Satterthwaite, Michael Beakes, Stephan Munch, Susan M. Sogard, Marc Mangel|
|Keywords:||individual heterogeneity, von Bertalanffy, bioenergetics, Bayesian state-space, Oncorhynchus mykiss,|
Among-individual heterogeneity in growth is a commonly observed phenomenon that has clear consequences for population and community dynamics yet has proved difficult to quantify in practice. In particular observed among-individual variation in growth can be difficult to link to any given mechanism. Here we develop a Bayesian state-space framework for modeling growth that bridges the complexity of bioenergetic models and the statistical simplicity of phenomenological growth models. The model allows for intrinsic individual variation in traits, a shared environment, process stochasticity, and measurement error. We apply the model to two populations of steelhead trout (Oncorhynchus mykiss) grown under common but temporally varying food conditions. Models allowing for individual variation match available data better than models that assume a single shared trait for all individuals. Estimated individual variation translated into a ~2-fold range in realized growth rates within populations. Comparisons between populations showed strong differences in trait means, trait variability, and responses to a shared environment. Together, individual- and population-level variation have substantial implications for variation in size and growth rates among and within populations. State-dependent life history models predict this variation can lead to differences in individual life history expression, lifetime reproductive output, and population life history diversity.
|Theme:||Recovery, Rebuilding and Sustainability of Marine and Anadromous Species|
Characterize vital rates and other demographic parameters for key species, and develop and improve methods for predicting risk and viability/sustainability from population dynamics and demographic information.
Develop methods to use physiological and biological information to predict population-level processes.