Linking fishing mortality reference points to life history traits: an empirical study

2012 ◽  
Vol 69 (8) ◽  
pp. 1292-1301 ◽  
Author(s):  
Shijie Zhou ◽  
Shaowu Yin ◽  
James T. Thorson ◽  
Anthony D.M. Smith ◽  
Michael Fuller
Keyword(s):  
Life History ◽  
Meta Analysis ◽  
Maximum Sustainable Yield ◽  
Reference Points ◽  
Natural Mortality ◽  
Fishing Mortality ◽  
Bayesian Hierarchical ◽  
Taxonomic Class ◽  
Large Measurement ◽  
The Relationship

The rule of thumb that fishing mortality to achieve maximum sustainable yield (FMSY) equals natural mortality (M) has been both criticised and supported by theoretical arguments. However, the relationship has been rarely investigated using empirical data. We carried out a meta-analysis on 245 fish species worldwide and linked three types of reference points (FBRP: FMSY, Fproxy, and F0.5r) to M and other life history parameters (LHP). We used Bayesian hierarchical errors-in-variables models to investigate the relationships and included the effect of taxonomic class and order. We compared various models and found that natural mortality is the most important LHP affecting FBRP. Other covariates, such as von Bertalanffy growth coefficient, asymptotic length, maximum age, and habitat types, add little to the relationship, partially because of correlation and large measurement and process errors. The best model results in FMSY = 0.87M (standard deviation (SD) = 0.05) for teleosts and FMSY = 0.41M (SD = 0.09) for chondrichthyans. Fproxy based on per-recruit analysis is about 15% smaller than FMSY. Results could be used to estimate FBRP from LHP in data-poor situations.

scholarly journals Can stock–recruitment points determine which spawning potential ratio is the best proxy for maximum sustainable yield reference points?

2013 ◽  
Vol 70 (6) ◽  
pp. 1075-1080 ◽  
Author(s):  
Christopher M. Legault ◽  
Elizabeth N. Brooks
Keyword(s):  
Life History ◽  
Mortality Rate ◽  
Maximum Sustainable Yield ◽  
Reference Points ◽  
Sustainable Yield ◽  
Marine Science ◽  
Fishing Mortality ◽  
Scatter Plots ◽  
Stock Recruitment ◽  
Spawning Potential Ratio

Abstract Legault, C. M., and Brooks, E. N. 2013. Can stock–recruitment points determine which spawning potential ratio is the best proxy for maximum sustainable yield reference points? – ICES Journal of Marine Science, 70: 1075–1080. The approach of examining scatter plots of stock–recruitment (S–R) estimates to determine appropriate spawning potential ratio (SPR)-based proxies for FMSY was investigated through simulation. As originally proposed, the approach assumed that points above a replacement line indicate year classes that produced a surplus of spawners, while points below that line failed to achieve replacement. In practice, this has been implemented by determining Fmed, the fishing mortality rate that produces a replacement line with 50% of the points above and 50% below the line. A new variation on this approach suggests FMSY proxies can be determined by examining the distribution of S–R points that are above or below replacement lines associated with specific SPRs. Through both analytical calculations and stochastic results, we demonstrate that this approach is fundamentally flawed and that in some cases the inference is diametrically opposed to the method's intended purpose. We reject this approach as a tool for determining FMSY proxies. We recommend that the current proxy of F40% be maintained as appropriate for a typical groundfish life history.

Biological reference points for fish stocks in a multispecies context

2001 ◽  
Vol 58 (11) ◽  
pp. 2167-2176 ◽  
Author(s):  
Jeremy S Collie ◽  
Henrik Gislason
Keyword(s):  
Life History ◽  
Growth Rates ◽  
Prey Species ◽  
Prey Availability ◽  
Mortality Rates ◽  
Reference Points ◽  
Natural Mortality ◽  
Fishing Mortality ◽  
Predator Species ◽  
Life History Parameters

Biological reference points (BRPs) are widely used to define safe levels of harvesting for marine fish populations. Most BRPs are either minimum acceptable biomass levels or maximum fishing mortality rates. The values of BRPs are determined from historical abundance data and the life-history parameters of the fish species. However, when the life-history parameters change over time, the BRPs become moving targets. In particular, the natural mortality rate of prey species depends on predator levels; conversely, predator growth rates depend on prey availability. We tested a suite of BRPs for their robustness to observed changes in natural mortality and growth rates. We used the relatively simple Baltic Sea fish community for this sensitivity test, with cod as predator and sprat and herring as prey. In general, the BRPs were much more sensitive to the changes in natural mortality rates than to growth variation. For a prey species like sprat, fishing mortality reference levels should be conditioned on the level of predation mortality. For a predator species, a conservative level of fishing mortality can be identified that will prevent growth overfishing and ensure stock replacement. These first-order multispecies interactions should be considered when defining BRPs for medium-term (5–10 year) management decisions.

Detecting mortality variation to enhance forage fish population assessments

2018 ◽  
Vol 76 (1) ◽  
pp. 124-135 ◽  
Author(s):  
Nis S Jacobsen ◽  
James T Thorson ◽  
Timothy E Essington
Keyword(s):  
Test Performance ◽  
Size Structure ◽  
Stock Assessment ◽  
Reference Points ◽  
Forage Fish ◽  
Natural Mortality ◽  
Time Varying ◽  
Fishing Mortality ◽  
Catch Data ◽  
Over Time

Abstract Contemporary stock assessment models used by fisheries management often assume that natural mortality rates are constant over time for exploited fish stocks. This assumption results in biased estimates of fishing mortality and reference points when mortality changes over time. However, it is difficult to distinguish changes in natural mortality from changes in fishing mortality, selectivity, and recruitment. Because changes in size structure can be indicate changes in mortality, one potential solution is to use population size-structure and fisheries catch data to simultaneously estimate time-varying natural and fishing mortality. Here we test that hypothesis, using a simulation experiment to test performance for four alternative estimation models that estimate natural and fishing mortality from size structure and catch data. We show that it is possible to estimate time-varying natural mortality in a size-based model, even when fishing mortality, recruitment, and selectivity are changing over time. Finally, we apply the model to North Sea sprat, and show that estimates of recruitment and natural mortality are similar to estimates from an alternative multispecies population model fitted to additional data sources. We recommend exploring potential trends in natural mortality in forage fish assessments using tools such as the one presented here.

scholarly journals Time-varying natural mortality in fisheries stock assessment models: identifying a default approach

2014 ◽  
Vol 72 (1) ◽  
pp. 137-150 ◽  
Author(s):  
Kelli F. Johnson ◽  
Cole C. Monnahan ◽  
Carey R. McGilliard ◽  
Katyana A. Vert-pre ◽  
Sean C. Anderson ◽  
...  
Keyword(s):  
Stock Assessment ◽  
Assessment Method ◽  
Reference Points ◽  
Natural Mortality ◽  
Time Varying ◽  
Fishing Mortality ◽  
Total Allowable Catch ◽  
Robust Approach ◽  
Structured Population Models ◽  
Trade Offs

Abstract A typical assumption used in most fishery stock assessments is that natural mortality (M) is constant across time and age. However, M is rarely constant in reality as a result of the combined impacts of exploitation history, predation, environmental factors, and physiological trade-offs. Misspecification or poor estimation of M can lead to bias in quantities estimated using stock assessment methods, potentially resulting in biased estimates of fishery reference points and catch limits, with the magnitude of bias being influenced by life history and trends in fishing mortality. Monte Carlo simulations were used to evaluate the ability of statistical age-structured population models to estimate spawning-stock biomass, fishing mortality, and total allowable catch when the true M was age-invariant, but time-varying. Configurations of the stock assessment method, implemented in Stock Synthesis, included a single age- and time-invariant M parameter, specified at one of the three levels (high, medium, and low) or an estimated M. The min–max (i.e. most robust) approach to specifying M when it is thought to vary across time was to estimate M. The least robust approach for most scenarios examined was to fix M at a high value, suggesting that the consequences of misspecifying M are asymmetric.

Eggs-per-recruit model for management of the California market squid (Loligo opalescens) fishery

2005 ◽  
Vol 62 (7) ◽  
pp. 1640-1650 ◽  
Author(s):  
Michael R Maxwell ◽  
Larry D Jacobson ◽  
Ramon J Conser
Keyword(s):  
Mortality Rate ◽  
Egg Laying ◽  
Reference Points ◽  
Model Parameters ◽  
Natural Mortality ◽  
Fishing Mortality ◽  
Loligo Opalescens ◽  
Maturation Rate ◽  
Sampling Procedures ◽  
Yield Per Recruit

We develop a per-recruit model for the management of the California market squid (Loligo opalescens) fishery. Based on recent confirmation of determinate fecundity in this species, we describe how catch fecundity (i.e., eggs remaining in the reproductive tracts of harvested females) can be used to simultaneously infer fishing mortality rate along with management reference points such as yield-per-recruit, spawned eggs-per-recruit, and proportional egg escapement. Rates of mortality and egg laying have important effects on these reference points. Somewhat surprisingly, increasing the rate of natural mortality decreased spawned eggs-per-recruit while increasing proportional egg escapement. Increasing the rate of egg laying increased both spawned eggs-per-recruit and egg escapement. Other parameters, such as the maturation rate and gear vulnerability of immature females, affected the reference points. In actual practice, the influence of these parameters for immature squid may go undetected if immature squid are excluded from analysis of the catch. Application of this model to routine management is feasible but requires refinement of sampling procedures, biological assumptions, and model parameters. This model is useful because it is grounded on empirical data collected relatively inexpensively from catch samples (catch fecundity) while allowing for the simultaneous calculation of instantaneous fishing mortality rate and egg escapement.

scholarly journals A method for calculating a meta-analytical prior for the natural mortality rate using multiple life history correlates

2014 ◽  
Vol 72 (1) ◽  
pp. 62-69 ◽  
Author(s):  
Owen S. Hamel
Keyword(s):  
Life History ◽  
Mortality Rate ◽  
Stock Assessment ◽  
Meta Analysis ◽  
Assessment Model ◽  
Natural Mortality ◽  
Uncertainty Interval ◽  
Free Data ◽  
Stock Assessments ◽  
Measure Of Uncertainty

Abstract The natural mortality rate M is an important parameter for understanding population dynamics, and is extraordinarily difficult to estimate for many fish species. The uncertainty associated with M translates into increased uncertainty in fishery stock assessments. Estimation of M within a stock assessment model is complicated by its confounding with other life history and fishery parameters which are also uncertain, some of which are typically estimated within the model. Ageing error and variation in growth, which may not be fully modelled, can also affect estimation of M, as can various assumptions, including the form of the stock–recruitment function (e.g. Beverton–Holt, Ricker) and the level of compensation (or steepness), which may be fixed (or limited by a prior) in the model. To avoid these difficulties, stock assessors often assume point estimates for M derived from meta-analytical relationships between M and more easily measured life history characteristics, such as growth rate or longevity. However, these relationships depend on estimates of M for a great number of species, and those estimates are also subject to errors and biases (as are, to a lesser extent, the other life history parameters). Therefore, at the very least, some measure of uncertainty in M should be calculated and used for evaluating uncertainty in stock assessments and management strategy evaluations. Given error-free data on M and the covariate(s) for a meta-analysis, prediction intervals would provide the appropriate measure of uncertainty in M. In contrast, if the relationship between the covariate(s) and M is exact and the only error is in the estimates of M used for the meta-analysis, confidence intervals would appropriate. Using multiple published meta-analyses of M’s relationship with various life history correlates, and beginning with the uncertainty interval calculations, I develop a method for creating combined priors for M for use in stock assessment.

Relationship of Fishing Mortality to Natural Mortality and Growth at the Level of Maximum Sustainable Yield

1982 ◽  
Vol 39 (7) ◽  
pp. 1054-1058 ◽  
Author(s):  
R. B. Deriso
Keyword(s):  
Logistic Model ◽  
Mortality Rates ◽  
Maximum Sustainable Yield ◽  
Sustainable Yield ◽  
Natural Mortality ◽  
Fishing Mortality ◽  
Difference Model ◽  
Relationship Of ◽  
Yield Per Recruit ◽  
Delay Difference

Fishing mortality constraints are derived for fishes harvested at the maximum sustainable yield (MSY) determined by a delay-difference population model. Those constraints depend upon rates of natural mortality and growth as well as a simple constraint placed on abundance of the exploited population. The results are generalized for a wider class of population models where it is shown that MSY fishing mortality is constrained often to be less than the fishing mortality which maximizes yield per recruit. Fishing mortality rates are lower in the delay difference model in comparison to MSY fishing rates in the logistic model, when a quadratic spawner–recruit curve is applied.Key words: delay-difference model, logistic model, fishing mortality, maximum sustainable yield, yield per recruit

Modelling fishing-induced adaptations and consequences for natural mortality

2010 ◽  
Vol 67 (7) ◽  
pp. 1086-1097 ◽  
Author(s):  
Christian Jørgensen ◽  
Øyvind Fiksen
Keyword(s):  
Life History ◽  
Body Size ◽  
Life Histories ◽  
Life History Traits ◽  
Reproductive Behaviour ◽  
Natural Mortality ◽  
Fishing Mortality ◽  
Trade Offs ◽  
Wide Range ◽  
Investment In Reproduction

When trade-offs involving predation and mortality are perturbed by human activities, behaviour and life histories are expected to change, with consequences for natural mortality rates. We present a general life history model for fish in which three common relationships link natural mortality to life history traits and behaviour. First, survival increases with body size. Second, survival declines with growth rate due to risks involved with resource acquisition and allocation. Third, fish that invest heavily in reproduction suffer from decreased survival due to costly reproductive behaviour or morphology that makes escapes from predators less successful. The model predicts increased natural mortality rate as an adaptive response to harvesting. This extends previous models that have shown that harvesting may cause smaller body size, higher growth rates, and higher investment in reproduction. The predicted increase in natural mortality is roughly half the fishing mortality over a wide range of harvest levels and parameter combinations such that fishing two fish kills three after evolutionary adaptations have taken place.

scholarly journals A simple length-structured model based on life history ratios and incorporating size-dependent selectivity: application to spawning potential ratios for data-poor stocks

2016 ◽  
Vol 73 (12) ◽  
pp. 1787-1799 ◽  
Author(s):  
Adrian R. Hordyk ◽  
Kotaro Ono ◽  
Jeremy D. Prince ◽  
Carl J. Walters
Keyword(s):  
Life History ◽  
Growth Parameter ◽  
Mortality Rates ◽  
Life History Strategies ◽  
Natural Mortality ◽  
Fishing Mortality ◽  
Computationally Efficient ◽  
Size Dependent ◽  
Spawning Potential Ratio ◽  
Recursive Equations

Selectivity in fish is often size-dependent, which results in differential fishing mortality rates across fish of the same age, an effect known as “Lee’s Phenomenon”. We extend previous work on using length composition to estimate the spawning potential ratio (SPR) for data-limited stocks by developing a computationally efficient length-structured per-recruit model that splits the population into a number of subcohorts, or growth-type-groups, to account for size-dependent fishing mortality rates. Two simple recursive equations, using the life history ratio of the natural mortality rate to the von Bertalanffy growth parameter (M/K), were developed to generate length composition data, reducing the complexity of the previous approach. Using simulated and empirical data, we demonstrate that ignoring Lee’s Phenomenon results in overestimates of fishing mortality and negatively biased estimates of SPR. We also explored the behaviour of the model under various scenarios, including alternative life history strategies and the presence of size-dependent natural mortality. The model developed in this paper may be a useful tool to estimate the SPR for data-limited stock where it is not possible to apply more conventional methods.

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