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

Abstract

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 andindependent factors influence the underlying process of growth (i.e., growth = anabolic factorscatabolic 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.

Modernising fish and shark growth curves with Bayesian length-at-age models

“…Examples already exist where biphasic or Schnute models have been used in Bayesian growth models [ 17 , 23 , 63 ]. Likewise, more sophisticated models can incorporate hierarchical structures [ 16 , 19 , 22 , 64 ], identify growth morphs [ 54 ], account for autocorrelation in back calculations [ 65 ], or examine environmental drivers [ 64 , 66 ]. The framework we have presented sits alongside this existing body of research by providing an entry point to Bayesian growth modelling that can be applied to standard growth modelling scenarios.…”

Population dynamics of Mylossoma albiscopum (Characiformes: Serrasalmidae) in the Ucayali River

“…albiscopum is a fast‐growing species that reaches gonadal maturity at a theoretical age of 1.13 years in females and 1.23 years in males; however, it has a similar longevity value (7.46 years) in both sexes. These growth parameters can generally be influenced by genetic, environmental and population factors as density (Matthias et al, 2018). According to Batista and Isaac (2012), this characteristic enables the population to recover quickly despite fishing pressure.…”

Variable Length at Age for Black Crappies and White Crappies in Missouri Small Impoundments

“…Future studies on life history and feeding ecology, especially during the juvenile stage, may provide insight into mechanisms leading to the variability in TL at age of adult crappies. Although several studies have documented spawning phenology and the growth, mortality, and diets of larval crappies (e.g., Pope and Willis 1998; Sammons et al 2001; Dubuc and DeVries 2002; Bunnell et al 2003, 2006), few have included juveniles beyond the postlarval stage (but see Matthias et al 2018). For example, how do hatch dates, cohort densities, water temperature, and the spatial and temporal changes in invertebrate abundance and taxonomic composition influence growth rates of juvenile crappies?…”