Application of pre-fishery abundance modelling and Bayesian hierarchical stock and recruitment analysis to the provision of precautionary catch advice for Irish salmon ( L.) fisheries (original) (raw)

Incorporating conservation limit variability and stock risk assessment in precautionary salmon catch advice at the river scale

ICES Journal of Marine Science

International wild Atlantic salmon management priorities have moved from exploitation to conservation since the 1990s, recognizing the need to protect diversity and abundance at individual river levels amid widespread declines. Here we review international salmon-stock assessments and describe a simple, transferable catch-advice framework, established for management of fisheries that conforms to international obligations. The risk assessment approach, applied at the river scale, jointly incorporates uncertainty in estimated and forecasted returning salmon numbers with the level of uncertainty around spawning requirements (Conservation Limits). Outputs include quantification of risk of stocks not attaining conservation limits (CL) and surpluses above CL on stocks able to support sustainable exploitation via total allowable catches (TAC), with monitoring by rod catch or fish counter. Since management implementation and cessation of at-sea mixed-stock fisheries, there has been a deteri...

Incorporating natural variability in biological reference points and population dynamics into management of Atlantic salmon ( Salmo salar L.) stocks returning to home waters

ICES Journal of Marine Science: Journal du Conseil, 2016

Following advice from the International Council for the Exploration of the Seas and North Atlantic Salmon Conservation Organization, Irish salmon stocks have been managed on a river-by-river basis since 2007 with biological reference points (BRPs) based on maximum sustainable yield (MSY). A method for estimating BRPs at the river scale and the associated variability arising from observed variability in population structures and fecundities is presented here. Calculations of BRPs (referred to as conservation limits, CLs) were updated and their natural variability was included. Angling logbooks provided new river-specific weight data to give sea age and fecundity ranges, and improved estimates of river-wetted areas, to account for available nursery habitat for juveniles and river-specific carrying capacities, were introduced. To transport BRPs, Bayesian stock–recruitment analysis was re-run with an updated list of monitored rivers and smolt ages. Results were converted to salmon numbe...

A Bayesian approach to estimating Atlantic salmon fry densities using a rapid sampling technique

Removal sampling by electric fishing is widely used for assessing fish population size. As it is manpower consuming, less-demanding techniques have been proposed to increase the number of sites covered with the same human resources. These techniques essentially provide relative abundance measures. To be used for absolute abundance estimation, they need to be inter-calibrated with another method such as removal sampling. Here, hierarchical Bayesian modelling framework is used for this inter-calibration because it allows an estimate of absolute abundance from abundance index data alone while accounting for the main sources of uncertainty. It is applied to a 0+ juvenile Atlantic salmon, Salmo salar L. data set from 21 sites on the River Faughan, Northern Ireland. A positive relationship was found between the abundance index (number of fish caught in 5 min of actual electric fishing) and the density. The estimates from the index of abundance alone are fairly imprecise, but still allow differentiation of contrasting levels of fish density. K E Y W O R D S : density estimates, electric fishing, hierarchical Bayesian model, Salmo salar.

Separating wild versus stocking components in fish recruitment without identification data: a hierarchical modelling approach

Canadian Journal of Fisheries and Aquatic Sciences

Salmonid juvenile stocking programs are often poorly monitored due to the lack of identification between stocked and wild fish. In this study, a hierarchical Bayesian model is developed to take advantage of spatiotemporal variations of stocking and wild recruitment for estimating these two components despite the absence of identification data. It is first tested by means of simulated data and then applied to the 37 year abundance data set of the Atlantic salmon (Salmo salar) population of the Allier catchment (France). Despite the absence of identification data, juvenile densities could be estimated and split into wild and stocked components. We found that the stocked juveniles contributed significantly to the total juvenile production, while the wild reproduction continued to provide an important contribution. This approach is encouraging and promising from a management advice perspective. It is flexible enough to accommodate for case study specificities and shows that long-term mo...

Evaluation of chum salmon fishery performance using Ricker and Beverton-Holt stock recruitment approaches in a Bayesian framework

To improve the ability of managers to make appropriate decisions, we analyzed chum salmon Oncorhynchus keta data from central British Columbia and evaluated alternative escapement goals and the potential impacts of environmental change on fishery performance.Fishery performance was expressed in terms of mean catch and the probability of annualcatches exceeding 100,000 fish. We investigated Ricker and Beverton–Holt stock–recruitmentrelationships and tested if environmental variation as reflected by sea surface temperature anomalies (SSTA) improved these relationships. Uncertainties associated with stock–recruitment model parameters were evaluated using a Bayesian approach. Posterior distributions of the parameters were generated using Monte Carlo Markov Chain procedures. Our data set was relatively informative as reflected by the well defined probability distributions of the stock– recruitment parameters. Fishery performance was assessed through stochastic projections over three gene...

Embedding stock assessment within an integrated hierarchical Bayesian life cycle modelling framework: an application to Atlantic salmon in the Northeast Atlantic

ICES Journal of Marine Science, 2014

ABSTRACT We developed a hierarchical Bayesian integrated life cycle model for Atlantic salmon that improves on the stock assessment approach currently used by ICES and provides some interesting insights about the population dynamics of a stock assemblage. The model is applied to the salmon stocks in eastern Scotland. It assimilates a 40-year (1971–2010) time-series of data compiled by ICES, including the catches in the distant water fisheries at Faroes and West Greenland and estimates of returning fish abundance. Our model offers major improvements in terms of statistical methodology for A. salmon stock assessment. Uncertainty about inferences is readily quantified in the form of Bayesian posterior distributions for parameters and abundance at all life stages, and the model could be adapted to provide projections based on the uncertainty derived from the estimation phase. The approach offers flexibility to improve the ecological realism of the model. It allows the introduction of density dependence in the egg-to-smolt transition, which is not considered in the current ICES assessment method. The results show that this modifies the inferences on the temporal dynamics of the post-smolt marine survival. In particular, the overall decrease in the marine survival between 1971 and 2010 and the sharp decline around 1988–1990 are dampened when density dependence is considered. The return rates of smolts as two-sea-winter (2SW) fish has declined in a higher proportion than return rates as one-sea-winter (1SW) fish. Our results indicate that this can be explained either by an increase in the proportion maturing as 1SW fish or by an increase in the mortality rate at sea of 2SW fish, but the data used in our analyses do not allow the likelihood of these two hypotheses to be gauged.

ICES CM 2003/Theme session V: Mixed and Multi-Stock Fisheries - Challenges and Tools for Assessments, Prediction, and Management. Paper 13. Not to be cited without prior reference to the author

The goal of our probabilistic model is to provide a formal framework which uses all relevant information about the juvenile population and ex- presses this knowledge in terms of probability distributions for relevant pop- ulation parameters. One important focus is the estimation and prediction of the size of the smolt run. For this task, biological understanding of the life- cycle of salmon, as well as measurements of parr and smolt abundances are all relevant information. The probability model which combines these sources of information can be divided to three sub models, which can be developed independently: 1) a parr abundance model that describes the rela- tionship between the true abundance of parr and corresponding measure- ments, 2) a smolt abundance model that connects the true abundance of smolts to the corresponding mark-recapture measurements and 3) a popu- lation model, based on the knowledge about the life-cycle of salmon, de- scribing the linkages between parr and smol...

Linking habitat and population dynamics to inform conservation benchmarks for data-limited salmon stocks

bioRxiv, 2021

Management of data-limited populations is a key challenge to the sustainability of fisheries around the world. For example, sockeye salmon (Oncorhynchus nerka) spawn and rear in many remote coastal watersheds of British Columbia (BC), Canada, making population assessment a challenge. Estimating conservation and management targets for these populations is particularly relevant given their importance to First Nations and commercial fisheries. Most sockeye salmon have obligate lake-rearing as juveniles, and total abundance is typically limited by production in rearing lakes. Although methods have been developed to estimate population capacity based on nursery lake photosynthetic rate (PR) and lake area or volume, they have not yet been widely incorporated into stock-recruit analyses. We tested the value of combining lake-based capacity estimates with traditional stock-recruit based approaches to assess population status using a hierarchical-Bayesian stock-recruit model for 70 populatio...

Experiences in Bayesian Inference in Baltic Salmon Management

Statistical Science, 2014

We review a success story regarding Bayesian inference in fisheries management in the Baltic Sea. The management of salmon fisheries is currently based on the results of a complex Bayesian population dynamic model, and managers and stakeholders use the probabilities in their discussions. We also discuss the technical and human challenges in using Bayesian modeling to give practical advice to the public and to government officials and suggest future areas in which it can be applied. In particular, large databases in fisheries science offer flexible ways to use hierarchical models to learn the population dynamics parameters for those by-catch species that do not have similar large stock-specific data sets like those that exist for many target species. This information is required if we are to understand the future ecosystem risks of fisheries.