Evaluating methods for estimating shark natural mortality rate and management reference points using life‐history parameters

2021 ◽  
Author(s):  
Shijie Zhou ◽  
Roy A. Deng ◽  
Matthew R. Dunn ◽  
Simon D. Hoyle ◽  
Yeming Lei ◽  
...  
Keyword(s):  
Life History ◽  
Mortality Rate ◽  
Reference Points ◽  
Natural Mortality ◽  
Life History Parameters ◽  
Evaluating Methods

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.

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.

scholarly journals The logic of comparative life history studies for estimating key parameters, with a focus on natural mortality rate

2016 ◽  
Vol 73 (10) ◽  
pp. 2453-2467 ◽  
Author(s):  
John M. Hoenig ◽  
Amy Y.-H. Then ◽  
Elizabeth A. Babcock ◽  
Norman G. Hall ◽  
David A. Hewitt ◽  
...  
Keyword(s):  
Life History ◽  
Mortality Rate ◽  
Natural Mortality

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.

The Evolution of Life History Parameters in Teleosts

1984 ◽  
Vol 41 (6) ◽  
pp. 989-1000 ◽  
Author(s):  
Derek A. Roff
Keyword(s):  
Growth Rate ◽  
Life History ◽  
Life History Theory ◽  
Larval Survival ◽  
Reasonable Assumption ◽  
Natural Mortality ◽  
Age At First Reproduction ◽  
Life History Parameters ◽  
First Case ◽  
First Reproduction

Empirical studies have shown that in teleosts there is a significant correlation between the life history parameters, age at first reproduction, natural mortality, and growth rate. In this paper 1 hypothesize that these correlations are the result of evolutionary adjustments due to the trade-off between reproduction, growth, and survival. A simple and reasonable assumption is that the costs of reproduction are sufficient to cause the ltmt function to decrease. A simple expression relating the age at first reproduction is derived from this assumption. This formula accounts for a statistically significant portion (60.6%) of the variation in age at first reproduction in 30 stocks of fish. To extend the model to predict the distribution of life history parameters across all teleosts, an explicit cost function is incorporated. The model is analyzed with respect to two fitness measures, the expected lifetime fecundity and malthusian parameter, r. In the first case it is shown that the optimal age at maturity, T, depends only on the natural mortality rate (M) and the growth rate (k). In the second case, T is a function of k and the logarithm of a parameter, In C; the latter is a product of egg and larval survival, maximum body length (Lx), and the proportionality coefficient of the fecundity/length function. Difficulties of measuring egg and larval survival make the testing of the latter case difficult for particular species. However, this method provides a simple formula for the computation of r; this is shown generally to be approximately zero, thereby adding strength to the assumptions of the first analysis. The distribution patterns of T on k and M on k are predicted and compared with the observed pattern. In general, the predictions are validated: however, certain combinations of k and ln C are shown to occur very infrequently. The prediction of such "empty" regions of the parameter space remains a challenge for future development of life history theory.

Using r-K Selection Theory to Predict Natural Mortality

1980 ◽  
Vol 37 (12) ◽  
pp. 2266-2271 ◽  
Author(s):  
Donald R. Gunderson
Keyword(s):  
Body Weight ◽  
Life History ◽  
Growth Parameters ◽  
Stepwise Multiple Regression ◽  
Natural Mortality ◽  
Instantaneous Rate ◽  
Life History Parameters ◽  
Selection Theory ◽  
Weight Index ◽  
Body Weight Index

Theory on r-K selection is used as a basis for examining correlations between instantaneous rate of natural mortality (M), gonad-body weight index, age at maturity, longevity, and Bertalanffy growth parameters (k, L∞) for 10 species of marine fish. All correlations were consistent with r-K selection theory. The gonad-body weight index was found to be more highly correlated with M than any of the other life history parameters examined (r2 = 0.62), and stepwise multiple regression showed that additional variables added little to the predictive ability of the model. The gonad-body weight index appears to be quite useful in predicting M, and development of an analogous index on an energetics basis might enhance its utility in this regard.Key words: natural mortality, r-K selection, life history parameters

scholarly journals Preliminary Estimates of Age, Growth and Natural Mortality of Margate, Haemulon album, and Black Margate, Anisotremus surinamensis, from the Southeastern United States

Fishes ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 44
Author(s):  
Michael L. Burton ◽  
Jennifer C. Potts ◽  
Andrew D. Ostrowski
Keyword(s):  
United States ◽  
Life History ◽  
Atlantic Coast ◽  
Southeastern United States ◽  
Geographic Region ◽  
Fish Growth ◽  
Natural Mortality ◽  
Von Bertalanffy ◽  
Life History Parameters ◽  
Growth Equations

Ages of margate, Haemulon album (n = 415) and black margate, Anisotremus surinamensis (n = 130) were determined using sectioned sagittal otoliths collected from the Southeastern United States Atlantic coast from 1979 to 2017. Opaque zones were annular, forming between January and June for both species, with peaks in occurrence of otoliths with opaque margins in April for margate and March for black margate. The observed ages for margate were 0–22 years, and the largest fish measured 807 mm TL (total length). Black margate ranged in age from 3 to 17 years, and the largest fish was 641 mm TL. Weight–length relationships were: margate, ln(W) = 2.88 ln(TL) − 10.44 (n = 1327, r2 = 0.97, MSE = 0.02), where W is total weight (grams, g); black margate, ln(W) = 3.02 ln(TL) − 11.10 (n = 451, r2 = 0.95, MSE = 0.01). Von Bertalanffy growth equations were Lt = 731 (1 − e−0.23(t+0.38)) for margate, and Lt = 544 (1 − e−0.13(t+2.61)) for black margate. After re-estimating black margate growth using a bias-correction procedure to account for the lack of younger fish, growth was described by the equation Lt = 523 (1 − e−0.18(t+0.0001)). Age-invariant estimates of natural mortality were M = 0.19 y−1 and M = 0.23 y−1 for margate and black margate, respectively, while age-varying estimates of M ranged from 2.93 −0.23 y−1 for fish aged 0–22 for margate and 7.20 − 0.19 y−1 for fish aged 0–18 for black margate. This study presents the first documentation of life-history parameters for margate from the Atlantic waters off the Southeastern United States, and the first published estimate of black margate life history parameters from any geographic region.

Implications of life-history invariants for biological reference points used in fishery management

2003 ◽  
Vol 60 (6) ◽  
pp. 710-720 ◽  
Author(s):  
Erik H Williams ◽  
Kyle W Shertzer
Keyword(s):  
Life History ◽  
Fishery Management ◽  
Deterministic Model ◽  
A Priori ◽  
Reference Points ◽  
Model Parameters ◽  
Natural Mortality ◽  
Wide Range ◽  
Stock Recruitment ◽  
Harvest Policies

Fish harvest policies typically rely on biological reference points for measures of a stock's status. We examine three common biological reference points based on fishing mortality rates corresponding to maximum sustainable yield with an age-structured deterministic model. We incorporate invariant life-history relationships into the model to maintain parsimony and focus model parameters on biologically plausible parameter space. A wide range of biological and fishery characteristics were used in the model so that our results pertain to the management of virtually any exploited population. Results indicate that two biological reference points based on spawning biomass are insensitive to life-history parameters, whereas one based on natural mortality is highly sensitive. All three depend largely on the choice of a stock–recruitment function and on steepness, a measure of the population growth rate. For each of the three, values have been previously proposed that were intended to safely apply to all fisheries; our results show that no such universal values exist. We recommend determining stock–recruitment functions a priori, establishing biological reference points on steepness explicitly and eliminating harvest policies based on the natural mortality rate altogether.

Identifying spawner biomass per‐recruit reference points from life‐history parameters

2020 ◽  
Vol 21 (4) ◽  
pp. 760-773
Author(s):  
Shijie Zhou ◽  
André E. Punt ◽  
Yeming Lei ◽  
Roy Aijun Deng ◽  
Simon D. Hoyle
Keyword(s):  
Life History ◽  
Reference Points ◽  
Life History Parameters
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