Growth standards for nine North American fish species

Ecological Modelling, 2004

Variability in animal growth from one population to another is of keen interest to population ecologists wishing to understand the inherent within species variability and explore meaningful environmental covariates. Yet most studies investigating growth of animals within a population are usually analyzed in isolation from, or at best, compared qualitatively across populations. Here, we introduce statistical methods that permit simultaneous quantitative analysis of the growth of 245 populations of largemouth bass (Micropterus salmoides) across the North American Continent. Growth in length at age is modeled using a nonlinear mixed effect model and we used Bayesian hierarchical meta-analysis as a natural approach to estimate parameters, investigate growth variability among populations and to elucidate meaningful biological covariates for this species. Growth of M. salmoides across North America varied by more than 120% in terms of maximum attainable size (L ∞ ; 36-80 cm) and by more than 88% in terms of instantaneous growth rates (K; 0.091 to 0.670 per year). Results from this method also confirm the theoretical, but often untested, view that growth parameters L ∞ and K are negatively correlated in fish populations; Bayesian credibility intervals ranged from −0.56 to −0.72 with the posterior mode of −0.65. The Bayesian hierarchical growth model showed less variability in growth parameters and lower correlations among parameters than those from standard techniques used in population ecology suggesting that the absolute value of the correlation between L ∞ and K may be lower than the general perception in the ecological literature, often in the range of −0.8 to −0.9. Finally, growth parameters were negatively correlated with latitude suggesting that population productivity most likely declines the higher in latitude a population is found for this species. Published by Elsevier B.V. dynamics models that are used to derive sustainable harvests for managing stocks . Since von Bertalanffy's (1938) pioneering work on the theory of organic growth, the relationship between fish length and age has been often described by a three-parameter nonlinear relationship, the functional form of which is attributed to that author. The von Bertalanffy growth model still presides as the dominant paradigm of 0304-3800/$ -see front matter. Published by Elsevier B.V.

Journal of Freshwater Ecology, 1999

Standards for the assessment of age and growth data for channel catfish (Ictalurus punctatus) were developed using published data. A representative set of 102 studies of individual populations from across the geographic range of channel catfish was selected. Percentile values (5, 10, 25, 50, 75, 90, and 95") were computed from the distribution of estimated mean total lengths for fish three to ten years old. This approach for development of standards to assess data from age and growth studies of fish is proposed for consideration in development of standards for other species in addition to channel catfish.

Journal of Fish Biology, 1983

The relationship between growth rate and fish size is described by the equation log, G,=a-0.4 log, W , where G, is the specific growth rate and W is fish weight. Since the intercept (a) represents the log, G , of a fish unit size, the relationship presents a method allowing comparison of data from experiments involving fish of different sizes. The application of the method is demonstrated by examining the effects ofenvironmental temperature on growth rates of cod, Gadus morhua, and it is suggested that the optimum temperature for growth of cod is 13-1 5" C.

Journal of the World Aquaculture Society, 1992

Aquaculturists typically report growth using absolute (g/d), relative (Vo increase in body weight), and specific growth rates (Told). Less frequently, von Bertalanffy Growth Functions (VBGF) are used. Each of these rates is a numerical representation of growth which assumes a specific relationship between size and time (linear, exponential, or asymptotic). Aquaculturists typically determine size at time throughout their experiments. Unfortunately, the intermediate data points are usually ignored when computing growth rates (except for VBGF) and the appropriateness of the method for calculating growth for a particular data set is not tested. This paper reviews the basis and computation of each of the growth rates in an effort to encourage aquaculturists to use the appropriate growth rates. I Contribution #I044 of the USAID-funded Collaborative Research Support Program in Pond Dynamicsf Aquaculture.

North American Journal of Fisheries Management, 2005

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2012

Comparison of different methods of regression analysis between scale radius and fork length and (c) values for inclusion in Fraser-Lee (1920) for 19 species, where the number of rivers and individuals used in the analysis are the same as in Table 2.2. 157 APPENDIX 2 Growth curves (calculated according to Hickley and Dexter (1979)) for the individual populations of A. brama used to create the standard growth curve (black) for the Environment Agency Anglian region. 160 APPENDIX 3 Growth curves (calculated according to Hickley and Dexter (1979)) for the individual populations of A. brama used to create the standard growth curve (black) for the Environment Agency Midlands region. 161 APPENDIX 4 Growth curves (calculated according to Hickley and Dexter (1979)) for the individual populations of A. brama used to create the standard growth curve (black) for the Environment Agency North East region. 162 APPENDIX 5 Growth curves (calculated according to Hickley and Dexter (1979)) for the individual populations of A. brama used to create the standard growth curve (black) for the Environment Agency Southern region. 163 APPENDIX 6 Growth curves (calculated according to Hickley and Dexter (1979)) for the individual populations of A. brama used to 164 create the standard growth curve (black) for the Environment Agency South West region.

Journal of Fish Biology, 2005

Total length (L T ) and its inter annual variation of walleye Sander vitreus from Oneida Lake, New York, based on 51 years of data for ages 1 to 7 years were analysed. Growth increased over time at young ages, did not change at intermediate ages and decreased at old ages. Total length at age increased over time to age 4 or 5 years, but was stable at older ages. Principle component analysis was used to study the pattern of variations in annual L T increments among years. More than 92 and 91% of inter annual variability in growth was described by the first three principal components for males and females, respectively. The first principal component was a general indicator of annual growth at all ages, but was dominated by annual growth at intermediate ages. The second and third principal components represented contrasts among yearling L T , yearling growth and growth at older ages. Therefore, changes in the three stage-specific parameters, yearling L T , yearling growth and asymptotic L T , explained most of the variance in observed growth. Using these three stage-specific parameters for the von Bertalanffy growth function facilitated interpretations of growth comparisons.

Journal of Fish Biology, 2008

A combination of a dynamic energy budget (DEB) model, field data on Atlantic salmon Salmo salar and brown trout Salmo trutta and laboratory data on Atlantic salmon was used to assess the underlying assumptions of three different metrics of growth including specific growth rate (G), standardized mass-specific growth rate (G S) and absolute growth rate in length (G L) in salmonids. Close agreement was found between predictions of the DEB model and the assumptions of linear growth in length and parabolic growth in mass. Field data comparing spring growth rates of age 1þ year and 2þ year Atlantic salmon demonstrated that in all years the larger age 2þ year fish exhibited a significantly lower G, but differences in growth in terms of G S and G L depended on the year examined. For brown trout, larger age 2þ year fish also consistently exhibited slower growth rates in terms of G but grew at similar rates as age 1þ year fish in terms of G S and G L. Laboratory results revealed that during the age 0þ year (autumn) the divergence in growth between future Atlantic salmon smolts and non-smolts was similar in terms of all three metrics with smolts displaying higher growth than non-smolts, however, both G S and G L indicated that smolts maintain relatively fast growth into the late autumn where G suggested that both smolts and non-smolts exhibit a sharp decrease in growth from October to November. During the spring, patterns of growth in length were significantly decoupled from patterns in growth in mass. Smolts maintained relatively fast growth though April in length but not in mass. These results suggest G S can be a useful alternative to G as a size-independent measure of growth rate in immature salmonids. In addition, during certain growth stanzas, G S may be highly correlated with G L. The decoupling of growth in mass from growth in length over ontogeny, however, may necessitate a combination of metrics to adequately describe variation in growth depending on ontogenetic stage particularly if life histories differ.

Size-selective fish sampling can strongly bias von Bertalanffy growth parameter estimates. Biascorrection methods have been developed, but they often require previous knowledge of selectivity pattern, capture-recapture data or intensive age-growth sampling over multiple consecutive years. When corrective measures are not feasible, investigators have attempted a number of biologically based procedures to reduce this bias. We evaluated several existing biologically based procedures that could potentially remove bias from growth parameter estimates. We built an age and length structured population model and tested the utility of four procedures to remove bias: 1) fixing t 0 at zero, 2) deleting data associated with ages that are not fully vulnerable to sampling, 3) deleting less than fully vulnerable ages and fixing t 0 at zero, and 4) fixing L ∞ at the maximum value observed in the data. We considered sampling gears that had no size selectivity, asymptotic selectivity, and dome-shaped selectivity patterns for all procedures. Results suggested that none of these procedures would eliminate bias in growth parameters across all three gear selectivity patterns. Investigators should attempt to use methods to correct growth parameters for size selectivity of sampling gears (e.g., mark recapture methods). If such corrections are not feasible, prior information about the size selectivity pattern of sampling gears is necessary for informed selection of biologically based von Bertalanffy fitting procedures.

North American Journal of Fisheries Management, 1999

Weight-length data for 78 populations of yellow perch Perca flavescens in 20 states and 6 Canadian provinces were used to develop a standard weight (W s) equation. We used the regression-line-percentile (RLP) technique, which provides a 75-percentile standard, to develop the W s relationship. The proposed equation in metric units is \og\QW 5 =-5.386 + 3.230 logioL; W s is weight in grams and L is total length in millimeters. The English equivalent of this equation is logic w s =-3.506 + 3.230 logic/-; w s is weight in pounds and L is total length in inches. These equations are proposed for use with 100-mm (4-in) and longer yellow perch. Relative weight (W r) values calculated with the proposed W s equation did not consistently increase or decrease with increasing fish length. Mean population W r values were significantly correlated with growth and size structure of yellow perch populations, but correlation coefficients were generally low. 374