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 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.

Relationships between Common Biological Reference Points Used as Thresholds and Targets of Fisheries Management Strategies

1994 ◽  
Vol 51 (1) ◽  
pp. 110-122 ◽  
Author(s):  
Pamela M. Mace
Keyword(s):  
Life History ◽  
Density Dependence ◽  
Fisheries Management ◽  
Management Strategies ◽  
Mortality Rates ◽  
Reference Points ◽  
Fishing Mortality ◽  
New Methods ◽  
Life History Characteristics ◽  
Target Fishing

Relationships between various biological reference points (BRPs) used to define thresholds and targets of fisheries management strategies were examined for a range of combinations of life history characteristics. Rank orderings of the selected BRPs were strongly influenced by the degree of density dependence in the underlying spawning–recruitment (S–R) relationship. The validity of F0.1, Fmax, F20% (the fishing mortality at which spawning per recruit is 20% of the maximum), and other reference fishing mortality rates as approximations to Fmsy or as thresholds of overfishing is highly dependent on life history characteristics, particular the degree of density dependence in the S–R relationship. It is recommended that F40% be adopted as a target fishing mortality rate when the S–R relationship is unknown and that threshold levels of biomass be related to the estimated or assumed degree of density dependence in the S–R relationship. Two new methods of calculating threshold biomass levels are suggested as alternatives to the familiar 20% B0 rule.

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.

scholarly journals An integrated tagging model to estimate mortality rates of Albemarle Sound – Roanoke River striped bass

2017 ◽  
Vol 74 (7) ◽  
pp. 1061-1076 ◽  
Author(s):  
Julianne E. Harris ◽  
Joseph E. Hightower
Keyword(s):  
Striped Bass ◽  
Stock Assessment ◽  
Mortality Rates ◽  
Natural Mortality ◽  
Fishing Mortality ◽  
Albemarle Sound ◽  
High Reward ◽  
Accuracy And Precision ◽  
Summer Mortality ◽  
Roanoke River

We developed an integrated tagging model to estimate mortality rates and run sizes of Albemarle Sound – Roanoke River striped bass (Morone saxatilis), including (i) a multistate component for telemetered fish with a high reward external tag; (ii) tag return components for fish with a low reward external or PIT tag; and (iii) catch-at-age data. Total annual instantaneous mortality was 1.08 for resident (458–899 mm total length, TL) and 0.45 for anadromous (≥900 mm TL) individuals. Annual instantaneous natural mortality was higher for resident (0.70) than for anadromous (0.21) fish due to high summer mortality in Albemarle Sound. Natural mortality for residents was substantially higher than currently assumed for stock assessment. Monthly fishing mortality from multiple sectors (including catch-and-release) corresponded to seasonal periods of legal harvest. Run size estimates were 499 000–715 000. Results and simulation suggest increasing sample size for the multistate component increases accuracy and precision of annual estimates and low reward tags are valuable for estimating monthly fishing mortality rates among sectors. Our results suggest that integrated tagging models can produce seasonal and annual mortality estimates needed for stock assessment and management.

scholarly journals Making inference from wildlife collision data: inferring predator absence from prey strikes

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3014 ◽  
Author(s):  
Peter Caley ◽  
Geoffrey R. Hosack ◽  
Simon C. Barry
Keyword(s):  
Prey Species ◽  
Red Fox ◽  
Prey Availability ◽  
Numerical Response ◽  
Practical Application ◽  
Ecological Inference ◽  
Predator Species ◽  
New Approach ◽  
Predictive Probability ◽  
Irreducible Uncertainty

Wildlife collision data are ubiquitous, though challenging for making ecological inference due to typically irreducible uncertainty relating to the sampling process. We illustrate a new approach that is useful for generating inference from predator data arising from wildlife collisions. By simply conditioning on a second prey species sampled via the same collision process, and by using a biologically realistic numerical response functions, we can produce a coherent numerical response relationship between predator and prey. This relationship can then be used to make inference on the population size of the predator species, including the probability of extinction. The statistical conditioning enables us to account for unmeasured variation in factors influencing the runway strike incidence for individual airports and to enable valid comparisons. A practical application of the approach for testing hypotheses about the distribution and abundance of a predator species is illustrated using the hypothesized red fox incursion into Tasmania, Australia. We estimate that conditional on the numerical response between fox and lagomorph runway strikes on mainland Australia, the predictive probability of observing no runway strikes of foxes in Tasmania after observing 15 lagomorph strikes is 0.001. We conclude there is enough evidence to safely reject the null hypothesis that there is a widespread red fox population in Tasmania at a population density consistent with prey availability. The method is novel and has potential wider application.

Emergent Facilitation among Predators on Size-Structured Prey

2013 ◽  
Author(s):  
André M. de Roos ◽  
Lennart Persson
Keyword(s):  
Life History ◽  
Prey Species ◽  
The Other ◽  
Positive Interactions ◽  
Predator Species ◽  
Life History Stages ◽  
Specific Predator ◽  
Specialist Predators

This chapter discusses a variety of positive interactions between predators foraging on different stages of the same prey species, which all emerge owing to the biomass overcompensation that may occur in prey life history stages in response to increased mortality. These interactions include emergent facilitation of specialist predators by generalists that forage on the same prey individuals as the specialists, but in addition forage on smaller or larger prey individuals as well. Furthermore, the chapter shows that two predators that specialize on different life-history stages of prey can facilitate each other to the extent that one predator relies on the presence of the other for its persistence. A stage-specific predator may act as a catalyst species, which promotes and in fact is necessary for the invasion of another predator species, but is subsequently outcompeted by the latter.

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 Knowing the Enemy: Inducible Defences in Freshwater Zooplankton

Diversity ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 147 ◽  
Author(s):  
Patricia Diel ◽  
Marvin Kiene ◽  
Dominik Martin-Creuzburg ◽  
Christian Laforsch
Keyword(s):  
Life History ◽  
Chemical Cues ◽  
Prey Species ◽  
Inducible Defences ◽  
Animal Kingdom ◽  
Predator Species ◽  
Freshwater Zooplankton ◽  
Taxonomic Range ◽  
Set Up ◽  
Made In

Phenotypic plasticity in defensive traits is an appropriate mechanism to cope with the variable hazard of a frequently changing predator spectrum. In the animal kingdom these so-called inducible defences cover the entire taxonomic range from protozoans to vertebrates. The inducible defensive traits range from behaviour, morphology, and life-history adaptations to the activation of specific immune systems in vertebrates. Inducible defences in prey species play important roles in the dynamics and functioning of food webs. Freshwater zooplankton show the most prominent examples of inducible defences triggered by chemical cues, so-called kairomones, released by predatory invertebrates and fish. The objective of this review is to highlight recent progress in research on inducible defences in freshwater zooplankton concerning behaviour, morphology, and life-history, as well as difficulties of studies conducted in a multipredator set up. Furthermore, we outline costs associated with the defences and discuss difficulties as well as the progress made in characterizing defence-inducing cues. Finally, we aim to indicate further possible routes in this field of research and provide a comprehensive table of inducible defences with respect to both prey and predator species.

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