Measuring fisheries management performance

Abstract

How do we assess the performance of national and international fisheries management organizations? Many organizations produce measures of the extent of overfishing, typically classifying individual stocks as overfished if they are below some biomass threshold. Most agencies then report their overall status (i.e. percentage overfished, fully exploited, etc.) by giving equal weight to all stocks, regardless of stock size or potential yield. We review the range of indices used to assess overfishing levels and apply them to the data from US fisheries to show how they depict very different performance of fisheries. Given that overfishing is a concept imbedded in the maximization of long-term harvest, we evaluate how well these indices reflect the extent to which fisheries have maximized sustainable yield. Indices that are weighted by the potential yield of the stock much better reflect the regional performance of fisheries but are still limited by the arbitrary use of a threshold abundance. For the United States, weighting by maximum sustainable yield or value suggests that the losses from overfishing are less than existing methods using equal weighting and that underfishing is much more common than overfishing.

Conservation risks and portfolio effects in mixed‐stock fisheries

“…We used Kobe plots to illustrate the distributions of individual stock status based on harvest rates ( U ) and abundances ( S ) relative to those predicted to maximize long‐term sustainable yield (MSY) ( U / U MSY ~ 1; S / S MSY ~1). Following the Food and Agricultural Organization (FAO) of the United Nations (Hilborn, 2020), we considered stocks to have achieved harvest rate and abundance management targets if they were within 20% of these values ( U / U MSY = 0.8 – 1.2; S / S MSY = 0.8–1.2).…”

“…Fisheries could harvest only a single stock or many stocks (Bieg et al., 2018), and in the latter case, those stocks could vary in their productivity, evenness, and asynchrony. The objectives and metrics used to asses fisheries performance may be associated with harvest, such as the average annual yield of catch or the inter‐annual stability of catch (Hilborn, 2020; Hilborn et al., 2015). Alternatively, fishery performance can be based on biologically‐based objectives such as reducing conservation risks, with metrics such as the probability that a stock is overfished to depletion or extinction (Hilborn, 2020; Hilborn et al., 2015).…”

“…The objectives and metrics used to asses fisheries performance may be associated with harvest, such as the average annual yield of catch or the inter‐annual stability of catch (Hilborn, 2020; Hilborn et al., 2015). Alternatively, fishery performance can be based on biologically‐based objectives such as reducing conservation risks, with metrics such as the probability that a stock is overfished to depletion or extinction (Hilborn, 2020; Hilborn et al., 2015). Biodiversity may influence fisheries performance via different processes.…”

Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey

“…More-over, humans are more prone to error than a well-designed machine-centred monitoring algorithm. Therefore, an automated, comprehensive monitoring system could significantly reduce labour expenses while improving throughput and accuracy, increasing the precision in estimates of fish stocks, fish distribution and biodiversity in general [7,8]. Implementing such systems necessitates effective Computer Vision (CV) processes.…”

Assessment of the stock status for greenland halibut (<i>Reinhardtius hippoglossoides matsuurae</i>) in the Okhotsk Sea

“…моделей [Hilborn, 2020]. Даже в самых обеспеченных информацией оценках все равно могут находиться большие неопределенности в связи с ошибками модельных допущений [Punt, 2008].…”