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Assessment report for northern shrimp Pandalus borealis in the Barents Sea ICES Subareas 1 and 2

The shape parameter (n) was chosen based on the empirical evidence from the fishery (ICES, 2022c).

Introduction

The Surplus Production in Continuous Time (SPiCT) model is a dynamic model that simulates the dynamics of fish populations in the ocean.

Catchability is key to sustainable fisheries management.

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Introduction

The world of fisheries management is complex and multifaceted, with various factors influencing the sustainability of fish populations. One crucial aspect of this management is the assessment of catchability, which refers to the ability of a fish population to be caught. In this article, we will delve into the concept of catchability, its measurement, and its significance in fisheries management.

What is Catchability? Catchability is a measure of the ease with which a fish population can be caught. It is a critical factor in fisheries management, as it affects the sustainability of fish populations and the effectiveness of fishing gear. Catchability is often measured using various indices, including the Catchability CPUE (Catchability Per Unit Effort) and the Catchability BESS (Catchability Based on Estimated Stock Size). ### Catchability CPUE

The Catchability CPUE is a widely used index to measure catchability.

Introduction

The study focuses on the impact of fishing gear on the marine ecosystem, specifically on the biomass of fish populations. The researchers used a combination of historical data from fisheries-dependent logbook data and trawl-survey biomass indices to analyze the effects of fishing gear on fish populations.

Methods

The researchers used a combination of two types of data: fisheries-dependent logbook data and trawl-survey biomass indices. The logbook data provided information on the catch of fish populations, while the trawl-survey biomass indices provided information on the biomass of fish populations. The logbook data consisted of standardized stock indices from time series of fisheries-dependent logbook data for 1980–2024. The trawl-survey biomass indices consisted of standardized stock indices from time series of trawl-survey biomass indices for 1982–2004, 1984–2005, and 1984–2023.*

Results

The analysis of the data revealed a significant correlation between the use of fishing gear and the biomass of fish populations. The results showed that the use of fishing gear was associated with a decrease in the biomass of fish populations. The correlation was strongest for the species that were most heavily targeted by fishing gear.

Discussion

The findings of the study suggest that the use of fishing gear has a significant impact on the marine ecosystem.

Understanding the Benchmark for West Greenland Shrimp

The benchmark for West Greenland shrimp was established in 2022, aiming to provide a scientific basis for fisheries management. The benchmark is built on a comprehensive analysis of the species’ ecological characteristics, including its carrying capacity and initial depletion. To determine these parameters, prior knowledge on stock density and historic fishing pressure was used as a starting point.

Key Parameters of the Benchmark

  • Carrying Capacity (K): The carrying capacity of a species refers to the maximum number of individuals that can be sustainably supported by the environment. For West Greenland shrimp, this parameter was defined based on a comparison with the Barents Sea shrimp, which has a similar environment. This comparison provided a reference value for likely densities at carrying capacity. * Initial Depletion (B0/K): The initial depletion parameter represents the proportion of the carrying capacity that is depleted at the start of a fishing season. This parameter is crucial in understanding the impact of fishing pressure on the stock. ### Implications of the Benchmark**

    • Implications of the Benchmark

    The benchmark has significant implications for fisheries management in West Greenland. By providing a scientific basis for fisheries management, the benchmark enables managers to make informed decisions about fishing quotas, gear restrictions, and other conservation measures.

    This approach allowed the assessment model to incorporate the uncertainty of the stock indices into the assessment process, providing a more accurate representation of the fishery’s status.

    Understanding the Assessment Model

    The assessment model used in this year’s assessment is a Bayesian hierarchical model, which is a type of statistical model that combines the strengths of both Bayesian and hierarchical models. This model is particularly useful for modeling complex relationships between variables, such as the relationships between fish populations, environmental factors, and fishing practices.

    Key Components of the Assessment Model

  • Bayesian inference: This approach uses Bayes’ theorem to update the probability of a hypothesis based on new data. In the context of the assessment model, Bayesian inference allows the model to incorporate prior knowledge and update it with new data, providing a more accurate representation of the fishery’s status. Hierarchical modeling: This approach allows the model to account for the relationships between different levels of the data, such as the relationships between fish populations, environmental factors, and fishing practices. Hierarchical modeling is particularly useful for modeling complex relationships between variables. Extrinsic information: The assessment model incorporates extrinsic information from the index estimation, which provides information about the uncertainty of the stock indices. This information is used to define the priors for the observation errors in SPiCT, allowing the assessment model to incorporate the uncertainty of the stock indices into the assessment process.

    The model was found to be robust and reliable, with a high degree of accuracy in predicting the outcomes of various scenarios. The results showed that the model was able to capture the underlying dynamics of the system, including the interactions between different components and the impact of external factors.

    The Validation of the Model

    The validation of the model was a crucial step in the benchmarking process. It involved a thorough examination of the model’s performance in predicting the outcomes of various scenarios.

    The CPUE index is a widely used metric to assess the health of a fishery, but it has limitations. It primarily focuses on the number of fish caught per unit of effort, without considering the size or weight of the catch. This can lead to inaccurate assessments of fishery health.

    The Problem with CPUE Index

    The CPUE index has been criticized for its narrow focus on the quantity of fish caught, rather than the quality of the catch. This can result in overestimation of fishery health, particularly in fisheries with high levels of bycatch or discarding. For example, a fishery with a high catch rate may have a large number of small fish, which can lead to an overestimation of the fishery’s health.

    The Need for a More Comprehensive Approach

    To address the limitations of the CPUE index, a more comprehensive approach is needed. This could involve incorporating additional metrics, such as the size and weight of the catch, as well as the number of bycatch and discarding.

    However, the sensitivity of the parameter estimates was found to be high, indicating that the parameter estimates were highly sensitive to the choice of prior distribution for the age-structured model.

    Introduction

    The North Atlantic cod fishery has been a subject of intense scrutiny and debate in recent years. The fishery’s sustainability has been a major concern, with many stakeholders questioning the effectiveness of the current management framework. In response to these concerns, the International Council for the Exploration of the Sea (ICES) conducted a benchmark analysis in 2022 to assess the sustainability of the cod fishery.

    The Benchmark Analysis

    The benchmark analysis was conducted using an age-structured model that takes into account the complex dynamics of the cod population. The analysis considered various parameters, including the initial depletion, fishing mortality, and recruitment rates. The results of the analysis showed that the cod stock was considered to be overfished, with a biomass of approximately 1.3 million tonnes.

    Key Findings

  • The analysis found that the cod stock was considered to be overfished, with a biomass of approximately 3 million tonnes. The initial depletion had no impact on the perception of stock status, but the sensitivity of the parameter estimates was found to be high. The parameter estimates were highly sensitive to the choice of prior distribution for the age-structured model. ## Sensitivity of Parameter Estimates*

    • Sensitivity of Parameter Estimates

    The sensitivity of the parameter estimates was explored during the benchmark in 2022 (ICES, 2022c).

    The Impact of Overfishing on Stock Biomass

    Overfishing has been a persistent issue in the world’s oceans, with many fish stocks facing significant declines in biomass due to excessive fishing pressure. The consequences of overfishing can be far-reaching, affecting not only the health of the fish populations but also the livelihoods of people who depend on them.

    The Effects of Overfishing on Fish Populations

  • Decline in biomass: Overfishing leads to a decline in the total biomass of a fish population, making it more vulnerable to environmental changes and reducing its ability to sustain itself. Changes in population structure: Overfishing can alter the age and size structure of a fish population, leading to a loss of genetic diversity and making it more susceptible to disease and other threats. Reduced reproduction: Overfishing can reduce the number of fish that reach reproductive age, leading to a decline in population growth rates and making it more difficult for the population to recover. ## The Relationship Between Fishing Mortality and Stock Biomass**

    • The Relationship Between Fishing Mortality and Stock Biomass

    Fishing mortality, which is the rate at which fish are caught and removed from the population, plays a critical role in determining the health of a fish stock.

    Forecasting the Catch Scenarios

    The process of forecasting catch scenarios involves several steps, which are outlined below:

  • Step 1: Data Collection**
      • Gather data on the current stock levels, fishing effort, and environmental factors that may impact the fish population. This data is used to inform the forecasting model and ensure that the predictions are as accurate as possible. * Step 2: Model Selection**
      • Choose a suitable forecasting model that takes into account the relevant factors and is capable of producing accurate predictions.

        Introduction

        The Barents Sea, located in the Arctic Ocean, is one of the most productive fisheries in the world. Shrimp fishing is a significant component of this fishery, with a large number of vessels operating in the area. However, the shrimp fishery in the Barents Sea has faced several challenges in recent years, including overfishing, habitat degradation, and climate change. In response to these challenges, a management strategy evaluation was conducted in 2024 to assess the effectiveness of the current harvest control rules (HCRs) and to propose new rules that could help to sustain the shrimp fishery.

        Background

        The Barents Sea shrimp fishery is a complex system that involves multiple stakeholders, including fishermen, processors, and regulatory agencies. The fishery is managed by the Norwegian-Russian fisheries commission, which is responsible for setting catch limits and monitoring fishing activities. However, the commission has faced challenges in managing the fishery effectively, including the need to balance the interests of different stakeholders and to address the impacts of climate change on the fishery.

        Harvest Control Rules (HCRs)

        The HCRs are a set of rules that are used to manage the shrimp fishery in the Barents Sea. The HCRs are designed to ensure that the fishery is managed in a sustainable way, by limiting the amount of shrimp that can be caught and by monitoring fishing activities.

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