A simulation-based comparison of confidence interval coverage, bias, and variance of alternative spatial biomass estimation methods used for the evaluation of Northern Shrimp biomass

• Main Author: Williams, John-Philip
• Format: Thesis
• Language: English
• Published: 2024
• Subjects: Newfoundland; northern shrimp; Pandalus borealis
• Online Access: https://spectrum.library.concordia.ca/id/eprint/993390/; https://spectrum.library.concordia.ca/id/eprint/993390/1/Williams_MSc_S2024.pdf

In fisheries management, reliable estimates of population abundance metrics such as standing biomass are crucial for sustainability and balanced decision-making. The OGive MAPping (OGMAP) method, used by the Department of Fisheries and Oceans (DFO) in Newfoundland and Labrador for biomass estimation, addresses non-normally distributed populations but raises concerns about handling spatial data variations. I conducted a simulation-based comparative analysis comparing OGMAP against Generalized Additive Models (GAMs) and STRAtified Programs (STRAP) to answer the following question: “are the uncertainties of the estimates calculated from these different methods reliable?” Using Northern Shrimp, Pandalus borealis, as a reference, I simulated biomass landscapes, exploring parameters like landscape roughness, sampling intensity, and model settings. The analysis consistently showed OGMAP's failure to capture nominal confidence intervals (CIs) compared to alternatives, regardless of the treatment. OGMAP exhibited tighter intervals, raising concerns about overfitting and its inability to reflect the true landscape biomass. However, halving the automatically optimized bandwidths for OGMAP's probability distribution fields significantly improved its realized coverage. These findings underscore OGMAP's variability, shedding light on its limitations in decision-making by the Department of Fisheries and Oceans. I stress the pivotal role of reliable estimates in fisheries management. Additionally, I suggest that alternative methods, like GAMs, may offer more dependable forecasts given OGMAP's underperformance. This research prompts a review of the fisheries management framework relying on OGMAP, suggesting potential inadequacies in capturing the true uncertainty associated with spatially distributed stocks.