This study presents a Monte Carlo method (CMSY) for estimating fisheries reference points from catch, resilience and qualitative stock status information on data‐limited stocks. It also presents a ...Bayesian state‐space implementation of the Schaefer production model (BSM), fitted to catch and biomass or catch‐per‐unit‐of‐effort (CPUE) data. Special emphasis was given to derive informative priors for productivity, unexploited stock size, catchability and biomass from population dynamics theory. Both models gave good predictions of the maximum intrinsic rate of population increase r, unexploited stock size k and maximum sustainable yield MSY when validated against simulated data with known parameter values. CMSY provided, in addition, reasonable predictions of relative biomass and exploitation rate. Both models were evaluated against 128 real stocks, where estimates of biomass were available from full stock assessments. BSM estimates of r, k and MSY were used as benchmarks for the respective CMSY estimates and were not significantly different in 76% of the stocks. A similar test against 28 data‐limited stocks, where CPUE instead of biomass was available, showed that BSM and CMSY estimates of r, k and MSY were not significantly different in 89% of the stocks. Both CMSY and BSM combine the production model with a simple stock–recruitment model, accounting for reduced recruitment at severely depleted stock sizes.
•We present JABBA: a new open-source software for biomass dynamic stock assessment.•JABBA rapidly generates reproducible stock status estimates of interest for fisheries management.•Exclusive visual ...diagnostic tools to aid in identifying data conflicts.•Option to automatically produce future projections and retrospective analysis.
This study presents a new, open-source modelling software entitled ‘Just Another Bayesian Biomass Assessment’ (JABBA). JABBA can be used for biomass dynamic stock assessment applications, and has emerged from the development of a Bayesian State-Space Surplus Production Model framework, already applied in stock assessments of sharks, tuna, and billfishes around the world. JABBA presents a unifying, flexible framework for biomass dynamic modelling, runs quickly, and generates reproducible stock status estimates and diagnostic tools. Specific emphasis has been placed on flexibility for specifying alternative scenarios, achieving high stability and improved convergence rates. Default JABBA features include: 1) an integrated state-space tool for averaging and automatically fitting multiple catch per unit effort (CPUE) time series; 2) data-weighting through estimation of additional observation variance for individual or grouped CPUE; 3) selection of Fox, Schaefer, or Pella-Tomlinson production functions; 4) options to fix or estimate process and observation variance components; 5) model diagnostic tools; 6) future projections for alternative catch regimes; and 7) a suite of inbuilt graphics illustrating model fit diagnostics and stock status results. As a case study, JABBA is applied to the 2017 assessment input data for South Atlantic swordfish (Xiphias gladius). We envision that JABBA will become a widely used, open-source stock assessment tool, readily improved and modified by the global scientific community.
Overfishing is the primary cause of marine defaunation, yet declines in and increasing extinction risks of individual species are difficult to measure, particularly for the largest predators found in ...the high seas
. Here we calculate two well-established indicators to track progress towards Aichi Biodiversity Targets and Sustainable Development Goals
: the Living Planet Index (a measure of changes in abundance aggregated from 57 abundance time-series datasets for 18 oceanic shark and ray species) and the Red List Index (a measure of change in extinction risk calculated for all 31 oceanic species of sharks and rays). We find that, since 1970, the global abundance of oceanic sharks and rays has declined by 71% owing to an 18-fold increase in relative fishing pressure. This depletion has increased the global extinction risk to the point at which three-quarters of the species comprising this functionally important assemblage are threatened with extinction. Strict prohibitions and precautionary science-based catch limits are urgently needed to avert population collapse
, avoid the disruption of ecological functions and promote species recovery
.
Minimizing the impact of fishing Froese, Rainer; Winker, Henning; Gascuel, Didier ...
Fish and fisheries (Oxford, England),
September 2016, Letnik:
17, Številka:
3
Journal Article
Recenzirano
Minimizing the impact of fishing is an explicit goal in international agreements as well as in regional directives and national laws. To assist in practical implementation, three simple rules for ...fisheries management are proposed in this study: 1) take less than nature by ensuring that mortality caused by fishing is less than the natural rate of mortality; 2) maintain population sizes above half of natural abundance, at levels where populations are still likely to be able to fulfil their ecosystem functions as prey or predator; and 3) let fish grow and reproduce, by adjusting the size at first capture such that the mean length in the catch equals the length where the biomass of an unexploited cohort would be maximum (Lopt). For rule 3), the basic equations describing growth in age‐structured populations are re‐examined and a new optimum length for first capture (Lc_opt) is established. For a given rate of fishing mortality, Lc_opt keeps catch and profit near their theoretical optima while maintaining large population sizes. Application of the three rules would not only minimize the impact of fishing on commercial species, it may also achieve several goals of ecosystem‐based fisheries management, such as rebuilding the biomass of prey and predator species in the system and reducing collateral impact of fishing, because with more fish in the water, shorter duration of gear deployment is needed for a given catch. The study also addresses typical criticisms of these common sense rules for fisheries management.
•Fish recruitment is highly variable, and variation is often not modeled in data-moderate or -poor models.•Recruitment variation can be estimated and bias-corrected using marginal or penalized ...likelihood.•A simulation experiment shows that estimating recruitment variation reduces bias and imprecision regardless of data quantity.•Similarly, uncertainty estimates are reliable when estimating recruitment variation and not otherwise.•Estimating recruitment variation is a clear “best practice” for modeling recruitment regardless of data availability.
The birth and survival of juveniles (termed recruitment) is highly variable in marine populations, and this variation is not precisely predicted by spawning output or environmental effects in isolation. Variation in recruitment after accounting for differences in spawning output (termed “recruitment deviations”) is commonly estimated in age-structured populations models that are fitted to data including samples of length or age-composition (“age-structured integrated models”, ASIM). However, recruitment deviations often are not estimated when fitting only to catch and abundance-index data (“age-structured production models”, ASPM), or catch data given an assumed value for final stock status (“age-structured stock-reduction analysis”, ASSRA). We use a simulation and real-world example to illustrate the potential benefits of estimating recruitment deviations in data-rich (ASIM), data-moderate (ASPM), and data-poor (ASSRA) models, while using either marginal likelihood or tuning algorithms with penalized likelihood estimation. The simulation experiment shows that marginal and penalized likelihood methods are both unbiased and precise for ASIM when estimating recruitment deviations, but biased and imprecise when not. The ASPM is somewhat biased both with or without estimating recruitment deviations, but is more precise when estimating recruitment deviations than not. The ASPM with recruitment deviations estimated using marginal likelihood has somewhat better performance than using penalized likelihood, although maximum likelihood also sometimes does not converge. The ASSRA has similar precision with or without estimating recruitment deviations, but estimating recruitment deviations is important to have reasonable estimates of confidence intervals. Both marginal and penalized likelihood can accurately estimate the variance of recruitment deviations for ASIM and ASPM. Estimating recruitment deviations is necessary to accurately estimate uncertainty in spawning output, and results are robust to mis-specifying the recruitment model (e.g., when simulating data with regime-shift dynamics). A sensitivity analysis shows mis-specifying maturity-at-age causes bias in all models, but bias is somewhat mitigated when estimating recruitment deviations. Finally, a case-study involving Pacific hake off the US West Coast emphasizes that it is necessary to estimate recruitment deviations to closely match results from the original assessment.
Estimating trends in abundance from fishery catch rates is one of the oldest endeavors in fisheries science. However, many jurisdictions do not analyze fishery catch rates due to concerns that these ...data confound changes in fishing behavior (adjustments in fishing location or gear operation) with trends in abundance. In response, we developed a spatial dynamic factor analysis (SDFA) model that decomposes covariation in multispecies catch rates into components representing spatial variation and fishing behavior. SDFA estimates spatiotemporal variation in fish density for multiple species and accounts for fisher behavior at large spatial scales (i.e., choice of fishing location) while controlling for fisher behavior at fine spatial scales (e.g., daily timing of fishing activity). We first use a multispecies simulation experiment to show that SDFA decreases bias in abundance indices relative to ignoring spatial adjustments and fishing tactics. We then present results for a case study involving petrale sole (Eopsetta jordani) in the California Current, for which SDFA estimates initially stable and then increasing abundance for the period 1986-2003, in accordance with fishery-independent survey and stock assessment estimates.
The spatial distribution of marine fishes can change for many reasons, including density-dependent distributional shifts. Previous studies show mixed support for either the proportional-density model ...(PDM; no relationship between abundance and area occupied, supported by ideal-free distribution theory) or the basin model (BM; positive abundance–area relationship, supported by density-dependent habitat selection theory). The BM implies that fishes move towards preferred habitat as the population declines. We estimate the average relationship using bottom trawl data for 92 fish species from six marine regions, to determine whether the BM or PDM provides a better description for sea-bottom-associated fishes. We fit a spatio-temporal model and estimate changes in effective area occupied and abundance, and combine results to estimate the average abundance–area relationship as well as variability among taxa and regions. The average relationship is weak but significant (0.6% increase in area for a 10% increase in abundance), whereas only a small proportion of species–region combinations show a negative relationship (i.e. shrinking area when abundance increases). Approximately one-third of combinations (34.6%) are predicted to increase in area more than 1% for every 10% increase in abundance. We therefore infer that population density generally changes faster than effective area occupied during abundance changes. Gadiformes have the strongest estimated relationship (average 1.0% area increase for every 10% abundance increase) followed by Pleuronectiformes and Scorpaeniformes, and the Eastern Bering Sea shows a strong relationship between abundance and area occupied relative to other regions. We conclude that the BM explains a small but important portion of spatial dynamics for sea-bottom-associated fishes, and that many individual populations merit cautious management during population declines, because a compressed range may increase the efficiency of harvest.
Geographic variation can be an indicator of still poorly understood evolutionary processes such as adaptation and drift. Sensory systems used in communication play a key role in mate choice and ...species recognition. Habitat-mediated (i.e. adaptive) differences in communication signals may therefore lead to diversification. We investigated geographic variation in echolocation calls of African horseshoe bats, Rhinolophus simulator and R. swinnyi in the context of two adaptive hypotheses: 1) James' Rule and 2) the Sensory Drive Hypothesis. According to James' Rule body-size should vary in response to relative humidity and temperature so that divergence in call frequency may therefore be the result of climate-mediated variation in body size because of the correlation between body size and call frequency. The Sensory Drive Hypothesis proposes that call frequency is a response to climate-induced differences in atmospheric attenuation and predicts that increases in atmospheric attenuation selects for calls of lower frequency. We measured the morphology and resting call frequency (RF) of 111 R. simulator and 126 R. swinnyi individuals across their distributional range to test the above hypotheses. Contrary to the prediction of James' Rule, divergence in body size could not explain the variation in RF. Instead, acoustic divergence in RF was best predicted by latitude, geography and climate-induced differences in atmospheric attenuation, as predicted by the Sensory Drive Hypothesis. Although variation in RF was strongly influenced by temperature and humidity, other climatic variables (associated with latitude and altitude) as well as drift (as suggested by a positive correlation between call variation and geographic distance, especially in R. simulator) may also play an important role.
Abstract The approach to fisheries termed “balanced harvesting” (BH) calls for fishing across the widest possible range of species, stocks, and sizes in an ecosystem, in proportion to their natural ...productivity, so that the relative size and species composition is maintained. Such fishing is proposed to result in higher catches with less negative impact on exploited populations and ecosystems. This study examines the models and the empirical evidence put forward in support of BH. It finds that the models used unrealistic settings with regard to life history (peak of cohort biomass at small sizes), response to fishing (strong compensation of fishing mortality by reduced natural mortality), and economics (uniform high cost of fishing and same ex-vessel price for all species and sizes), and that empirical evidence of BH is scarce and questionable. It concludes that evolutionary theory, population dynamics theory, ecosystem models with realistic assumptions and settings, and widespread empirical evidence do not support the BH proposal. Rather, this body of evidence suggests that BH will not help but will hinder the policy changes needed for the rebuilding of ecosystems, healthy fish populations, and sustainable fisheries.
Global forage-fish landings are increasing, with potentially grave consequences for marine ecosystems. Predators of forage fish may be influenced by this harvest, but the nature of these effects is ...contentious. Experimental fishery manipulations offer the best solution to quantify population-level impacts, but are rare. We used Bayesian inference to examine changes in chick survival, body condition and population growth rate of endangered African penguins Spheniscus demersus in response to 8 years of alternating time–area closures around two pairs of colonies. Our results demonstrate that fishing closures improved chick survival and condition, after controlling for changing prey availability. However, this effect was inconsistent across sites and years, highlighting the difficultly of assessing management interventions in marine ecosystems. Nevertheless, modelled increases in population growth rates exceeded 1% at one colony; i.e. the threshold considered biologically meaningful by fisheries management in South Africa. Fishing closures evidently can improve the population trend of a forage-fish-dependent predator—we therefore recommend they continue in South Africa and support their application elsewhere. However, detecting demographic gains for mobile marine predators from small no-take zones requires experimental time frames and scales that will often exceed those desired by decision makers.
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