Understanding the effects of density and environmental variability on the process of fish growth

For many fish species, variation in somatic growth can drive changes in population productivity through the dependence of survival, fecundity, and reproductive schedules on size. Changes in growth arise from many density-dependent and -independent sources. Many assessments of temporal variation in somatic growth rely on methods that lack biological underpinning in the model structure to describe observed relationships between size and environmental conditions. However, biologically-based growth models are needed to examine how density-dependent and −independent factors influence the underlying process of growth (i.e., growth = anabolic factors − catabolic factors). Our objective was to extend biologically-based growth models to estimate temporal variation in somatic growth patterns. A set of hierarchical non-linear mixed effects models based off the von Bertalanffy model and length-weight relationship were developed. We applied the models to a Black Crappie (BC; Pomoxis nigromaculatus) population to assess the impacts of density, chlorophyll A concentration (Chl-a), water level, and temperature on somatic growth. Growth in length was influenced by temperature, with fastest growth at optimal temperatures and slower growth when temperatures were coldest (48% slower) or hottest (82% slower), and was negatively related to density, with 25% slower growth at high density. Weight of age-0 BC was negatively related to chlorophyll A, individuals were 18% lighter at high Chl-a, and positively to temperature, individuals were 10% lighter when water was cooler. Finally, growth in weight of age-1+ BC was negatively related to all factors, with 5–11% lighter fish at high densities, Chl-a, water levels, and temperatures. The model structure developed in this manuscript has broad applicability to populations that have time series data of size-at-age observations, growth increments, or back-calculated sizes and adequate environmental data.