Fisheries Science Cheatsheet

Cheatsheet Content

1. Unit Stock vs. Mixed Stock Unit Stock: A self-contained, reproductively isolated group of fish that does not mix significantly with other groups. Managed as a single entity. Example: A specific salmon run returning to a single river system. Mixed Stock: Two or more distinct stocks that are harvested together, often on shared feeding or migration grounds, making it difficult to manage them individually. Example: Several herring stocks from different spawning areas intermingling on a common feeding ground in the North Sea. Differences: Management challenges are higher for mixed stocks due to varying productivity and vulnerability among constituent stocks. Overfishing one stock could occur while others are sustainably harvested. 2. Fish Stock Assessment: Data & Principles Data Requirements: Catch Data: Total weight/number of fish removed (commercial, recreational, discards). Effort Data: Amount of fishing activity (e.g., number of boat days, hooks set, net hours). Biological Data: Length, weight, age, sex, maturity, diet, fecundity (from samples). Abundance Indices: CPUE (Catch Per Unit Effort), survey data (e.g., trawl surveys, acoustic surveys). Environmental Data: Temperature, salinity, habitat quality (influences productivity). Principles: Accuracy & Precision: Data must be reliable and consistent. Timeliness: Data should be collected and analyzed promptly to inform management. Representativeness: Samples must reflect the true population structure. Long-term Perspective: Assessments require historical data to understand trends. Uncertainty: Acknowledging and quantifying uncertainty in estimates. 3. Von Bertalanffy Growth Model (VBGM) Description: A widely used model to describe the average growth in length (or weight) of fish over time. It assumes that growth rate is highest when young and decreases as the fish approaches its maximum size. Formula (Length): $L_t = L_\infty (1 - e^{-K(t - t_0)})$ $L_t$: Length at age $t$ $L_\infty$: Asymptotic (maximum theoretical) length $K$: Growth coefficient (rate at which $L_\infty$ is approached) $t$: Age of the fish $t_0$: Theoretical age at which length is zero (often negative, representing initial growth before hatching) Importance in Fisheries Science: Estimates growth parameters crucial for age-structured models. Predicts size structure of a population under different fishing scenarios. Informs minimum landing sizes and mesh size regulations. Used in stock assessment models to estimate biomass and yield. 4. Age Structure in Fish Population Studies Concept: The distribution of individuals within a fish population across different age classes. It shows the proportion of young, reproductive, and old individuals. Role: Population Health: A healthy, stable population typically has a balanced age structure. Dominance of very young or very old fish can indicate issues. Recruitment Success: The proportion of young fish reflects past recruitment events. Mortality Rates: Changes in age structure over time can help estimate natural and fishing mortality. Reproductive Potential: Older, larger fish often contribute disproportionately to spawning stock biomass. Management Decisions: Informs setting catch limits, minimum landing sizes, and understanding the impact of fishing on different age groups. Forecasting: Used to project future population size and yield. 5. Mortality Types & Estimation Total Mortality ($Z$): The overall rate at which fish are removed from a population. Estimation: Catch curve analysis (from age frequency data), $Z = F + M$. Fishing Mortality ($F$): Mortality caused by fishing activity. Estimation: Catch-Effort Models: $F = qE$ where $q$ is catchability and $E$ is effort. Virtual Population Analysis (VPA) / Cohort Analysis: Reconstructs historical stock sizes and fishing mortality by age. Tagging Studies: Recapture rates of tagged fish. Natural Mortality ($M$): Mortality due to all other natural causes (predation, disease, starvation, old age). Estimation: Often difficult to estimate directly, commonly derived from empirical relationships or assumed values. Pauly's Formula: $\log_{10} M = -0.0066 - 0.279 \log_{10} L_\infty + 0.6543 \log_{10} K + 0.463 \log_{10} T$ (where $T$ is mean annual temperature in $^{\circ}C$). Empirical Methods: Based on growth parameters ($K$, $L_\infty$) and maximum age. Assumptions: Often assumed to be constant across ages or estimated from unfished populations. 6. Trawl vs. Gillnet Selectivity Selectivity: The ability of a fishing gear to catch fish of certain sizes or species while allowing others to escape. Trawl Net Selectivity: Mechanism: Fish are herded into a large, cone-shaped net. Selection primarily occurs at the codend (the very end of the net) through mesh size. Smaller fish can escape through the meshes. Selectivity Curve: Typically a "bell-shaped" or S-shaped curve, with a range of sizes caught, peaking at an optimal size, and declining for very large or very small individuals. Factors: Codend mesh size, mesh shape (diamond vs. square), towing speed, fish behavior. Example: A large-mesh codend in a demersal trawl will allow juvenile cod to escape, reducing bycatch of undersized fish. Gillnet Selectivity: Mechanism: Fish swim into a wall of netting and become entangled, typically by their gills, head, or body. Selection is based on the fish's girth relative to the mesh size. Selectivity Curve: Usually a unimodal (single peak) curve, strongly selecting for a narrow range of sizes. Fish much smaller or much larger than the optimal size for a given mesh will tend to escape. Factors: Mesh size, twine thickness, net material, hanging ratio, fish species and shape. Example: A gillnet with a 100mm mesh size will primarily catch adult salmon of a specific size range, allowing smaller juveniles and very large, old individuals to pass through or avoid entanglement. Comparison: Trawls are generally less size-selective than gillnets for a given mesh size and often have higher bycatch. Gillnets are highly size-selective but can lead to "ghost fishing" if lost. Both can be modified to improve selectivity (e.g., square mesh in trawls, escape panels). 7. Yield Per Recruit (YPR) Definition: A theoretical model that calculates the expected total yield (weight) that can be obtained from a single fish entering the fishery (a "recruit") over its lifetime, given specific fishing mortality and natural mortality rates, and a growth pattern. Concept: It helps evaluate the trade-offs between catching many small, young fish versus fewer, larger, older fish. It assumes recruitment is constant. Formula (simplified concept): $Y/R = \sum_{t=t_c}^{t_{max}} N_t \cdot W_t \cdot F$ $Y/R$: Yield per recruit $N_t$: Number of fish alive at age $t$ (from survival curve) $W_t$: Average weight of fish at age $t$ (from growth model) $F$: Fishing mortality rate $t_c$: Age at first capture $t_{max}$: Maximum age Importance: Identifies the optimal fishing mortality rate ($F_{opt}$ or $F_{max}$) that maximizes the yield per recruit. Helps determine the optimal age/size at first capture to maximize yield. Evaluates the impact of different fishing strategies (e.g., mesh size regulations) on the total yield. Used as a basis for setting management targets, though it doesn't consider recruitment variability. 8. Maximum Sustainable Yield (MSY) & Maximum Economic Yield (MEY) Maximum Sustainable Yield (MSY): Definition: The largest average catch that can be taken from a fish stock over an indefinite period under existing environmental conditions. It represents the maximum biological productivity of the stock. Concept: Based on logistic growth models, MSY occurs at the point where the population is at about half its carrying capacity, allowing for maximum net production. Goal: To maximize the biological output of the fishery in the long term. Formula (simplified Schaefer model): $Y = rK/4$ (where $r$ is intrinsic growth rate, $K$ is carrying capacity) at $B = K/2$. Limitations: Often difficult to estimate precisely, assumes constant environment, can lead to overfishing if recruitment is poor. Does not consider economic factors. Maximum Economic Yield (MEY): Definition: The catch level that maximizes the net economic returns (profits) from a fishery, considering both the value of the catch and the cost of fishing effort. Concept: MEY is achieved when the difference between total revenue and total cost is maximized. It usually occurs at a lower fishing effort and lower catch than MSY. Goal: To maximize the economic efficiency and profitability of the fishery. Relationship to MSY: MEY is typically found at a lower fishing mortality and higher stock size than MSY. This means the stock is healthier, and fishing is more efficient. Why MEY Beyond MEY, the cost of catching additional fish increases faster than the revenue generated, reducing overall profit. Comparison: MSY: Biological focus, maximizes catch quantity. MEY: Economic focus, maximizes profit. MEY is generally considered a more conservative and sustainable target for management, as it maintains a larger stock size and promotes economic viability. 9. Role of ECOPATH Model in Ecosystem-Based Fisheries Management ECOPATH: A software package and modeling approach used to construct static (snapshot in time) mass-balanced trophic models of aquatic ecosystems. It describes the feeding relationships and biomass flows between different functional groups (species or groups of species) within an ecosystem. Components: Biomass (B): Weight of living organisms per unit area/volume. Production/Biomass (P/B): Turnover rate of biomass. Consumption/Biomass (Q/B): Consumption rate relative to biomass. Ecotrophic Efficiency (EE): Proportion of production consumed by predators or caught by fisheries. Diet Composition (DC): What each group eats. Role in Ecosystem-Based Fisheries Management (EBFM): Holistic View: Shifts from single-species management to understanding the entire food web. Trophic Interactions: Quantifies energy flow and predator-prey relationships. Impact Assessment: Helps predict the ecosystem-wide effects of fishing on target species, bycatch, and their predators/prey. Identifying Keystones: Helps identify functionally important species in the food web. Scenario Testing: Can be extended (with Ecosim and Ecospace) to simulate the effects of different fishing policies, environmental changes, or protected areas on the ecosystem. Communication Tool: Provides a visual and quantitative representation of complex ecosystems for stakeholders. 10. Monte Carlo Simulation in Fisheries Concept: A computational technique that uses random sampling to obtain numerical results. In fisheries, it's used to model systems with inherent uncertainty by running many simulations with different random inputs drawn from probability distributions. Application in Fisheries: Stock Assessment Uncertainty: Estimating confidence intervals for stock size, fishing mortality, or MSY by varying input parameters (e.g., natural mortality, growth rates, recruitment) within their plausible ranges. Example: Instead of using a single value for $M$, a Monte Carlo simulation might draw $M$ from a normal distribution with a specified mean and standard deviation. Risk Assessment: Evaluating the probability of exceeding a biological limit (e.g., spawning stock biomass falling below a critical threshold) under different management strategies. Assessing the risk of fishery closures or economic losses. Management Strategy Evaluation (MSE): Simulating the long-term performance of different harvest control rules (HCRs) or management procedures under various sources of uncertainty (observation error, process error, model uncertainty). Bioeconomic Modeling: Analyzing the economic outcomes (e.g., profit, revenue) of a fishery while accounting for uncertainty in fish prices, fishing costs, and stock dynamics. Process: Define the system model and its parameters. Assign probability distributions to uncertain parameters (e.g., normal, lognormal, uniform). Generate a large number of random samples for each uncertain parameter. Run the model for each set of sampled parameters. Analyze the distribution of output results (e.g., mean, median, percentiles, probabilities) to understand the range of possible outcomes and associated risks. Benefits: Provides a more realistic and robust assessment of uncertainty compared to deterministic models, leading to more precautionary and adaptive management decisions.