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This study assesses spatiotemporal and sex-specific growth of Atlantic cod Gadus morhua in Icelandic waters. We use a Bayesian approach which lends itself to fitting and comparing nested models such as these. We then compare fitted parameters of these models to potential explanatory variables using a redundancy analysis (RDA) to look for drivers of growth in G. morhua. Results indicate that models that incorporate differences in growth among time, space and sex are the best-fitting models according to deviance information criterion (DIC). Results from RDA indicate that capelin Mallotus villosus recruitment and biomass is highly correlated with deviations in the von Bertalannfy growth parameter k and that L∞ is correlated with G. morhua landings in the model that uses year to account for time-varying growth and estimated G. morhua recruitment in the model that uses cohort to account for time-varying growth.

Selectivity curves for sampling size-at-age data are somewhat infamous for biasing growth curve parameter estimates. However, aspects of growth itself can also feed into this phenomenon and further increase bias into parameter estimates. We performed a simulation study by producing size-at-age data under a variety of growth curve parameter values and length-based selectivity curve parameter values for two selectivity functions by altering parameters both independently and in conjunction. In tests where parameters were altered one at a time the greatest amount of bias in growth curve parameter estimates came from sampling the data using dome-shaped selectivity. In addition, certain aspects of growth (namely variance in size-at-age) enhanced the biasing effect of both dome-shaped and asymptotic selectivity. When all parameters were altered together the greatest bias occurred in instances where length distributions overlapped to a greater degree and when asymptotic selectivity curves were more mildly sloped. We conclude that bias in growth curve parameter estimates is a result of the statistical weighting of both the probability of being a certain size at a given age and the probability of being captured at that size.

Frater P, Mikulyuk A, Barton M, Nault M, Wagner K, Hauxwell J, Kujawa E. 2017. Relationships between water chemistry and herbicide efficacy of Eurasian watermilfoil management in Wisconsin lakes. Lake Reserve Manage. 33:1–7.Eurasian watermilfoil (EWM) is an invasive aquatic plant with the potential to disrupt native aquatic plant communities, decrease property values, and affect recreational uses of lakes. Much time, effort, and money is put forth every year to combat the spread of this invader. We examined the long-term efficacy of EWM management using herbicide and derived an empirical equation to quantify long-term treatment success for the lakes in this study. We found that lakes with lower pH (

Several techniques have been proposed and tested to alleviate the common problem of selectivity bias in fish size-at-age data used for fitting growth models. Many of these techniques have not been directly compared to each other, though. Here, we test six methods for correcting selectivity bias in fish growth models using two selectivity shapes and across three life history types. We also include an example of fitted growth curve parameters from each method using data on Atlantic cod (Gadus morhua L.) from standardized surveys in the waters surrounding Iceland. Lastly, we present simulations that incorporate three types of misspecification on each method, selectivity type, and life-history strategy to discern the impacts of incorrect assumptions stemming from each method. Age structured populations are simulated, and the basic von Bertalanffy growth curve is used as the mean to assign normally-distributed length-at-age data which are sampled under two selectivity shapes. Growth parameters are then estimated using these data for each of six bias-correction methods. In general, likelihood-based bias-correction methods resulted in growth parameter estimates that are closer to true values used as well as more similar to parameter estimates derived from data sampled with no selectivity. The method introduced by Troynikov (1999) performed best for both simulation and misspecification scenarios. This result is true of both dome-shaped and asymptotic selectivity as well as across life-history types.