Search

Sustainability indices are proliferating, both to help synthesize scientific understanding and inform policy. However, it remains poorly understood how such indices are affected by underlying assumptions of the data and modelling approaches used to compute indicator values. Here, we focus on one such indicator, the fisheries goal within the Ocean Health Index (OHI), which evaluates the sustainable provision of food from wild fisheries. We quantify uncertainty in the fisheries goal status arising from the (a) approach for estimating missing data (i.e., fish stocks with no status) and (b) reliance on a data‐limited method (catch‐MSY) to estimate stock status (i.e., B/BMSY). We also compare several other models to estimate B/BMSY, including an ensemble approach, to determine whether alternative models might reduce uncertainty and bias. We find that the current OHI fisheries goal model results in overly optimistic fisheries goal statuses. Uncertainty and bias can be reduced by (a) using a mean (vs. median) gap‐filling approach to estimate missing stock scores and (b) estimating fisheries status using the central tendency from a simulated distribution of status scores generated by a bootstrap approach that incorporates error in B/BMSY. This multitiered approach to measure and describe uncertainty improves the transparency and interpretation of the indicator and allows us to better understand uncertainty around our OHI fisheries model and outputs for country‐level interpretation and use.

Many of the world's fisheries are unassessed, with little information about population status or risk of overfishing. Unassessed fisheries are particularly predominant in developing countries and in small-scale fisheries, where they are important for food security. Several catch-only methods based on time series of fishery catch and commonly available life-history traits have been developed to estimate stock status (defined as biomass relative to biomass at maximum sustainable yield: B/B-MSY). While their stock status performance has been extensively studied, performance of catch-only models as a management tool is unknown. We evaluated the extent to which a superensemble of three prominent catch-only models can provide a reliable basis for fisheries management and how performance compares across management strategies that control catch or fishing effort. We used a management strategy evaluation framework to determine whether a superensemble of catch-only models can reliably inform harvest control rules (HCRs). Across five simulated fish life histories and two harvest-dynamic types, catch-only models and HCR combinations reduced the risk of overfishing and increased the proportion of stocks above B-MSY compared to business as usual, though often resulted in poor yields. Precautionary HCRs based on fishing effort were robust and insensitive to error in catch-only models, while catch-based HCRs caused high probabilities of overfishing and more overfished populations. Catch-only methods tended to overestimate B/B-MSY for our simulated data sets. The catch-only superensemble combined with precautionary effort-based HCRs could be part of a stepping stone approach for managing some data-limited stocks while working towards more data-moderate assessment methods.

Fishery managers must often reconcile conflicting estimates of population status and trend. Superensemble models, commonly used in climate and weather forecasting, may provide an effective solution. This approach uses predictions from multiple models as covariates in an additional "superensemble" model fitted to known data. We evaluated the potential for ensemble averages and superensemble models (ensemble methods) to improve estimates of population status and trend for fisheries. We fit four widely applicable data-limited models that estimate stock biomass relative to equilibrium biomass at maximum sustainable yield (B/BMSY). We combined these estimates of recent fishery status and trends in B/BMSY with four ensemble methods: an ensemble average and three superensembles (a linear model, a random forest and a boosted regression tree). We trained our superensembles on 5,760 simulated stocks and tested them with cross-validation and against a global database of 249 stock assessments. Ensemble methods substantially improved estimates of population status and trend. Random forest and boosted regression trees performed the best at estimating population status: inaccuracy (median absolute proportional error) decreased from 0.42 -0.56 to 0.32 -0.33, rank-order correlation between predicted and true status improved from 0.02 - 0.32 to 0.44 - 0.48 and bias (median proportional error) declined from - 0.22 - 0.31 to - 0.12 - 0.03. We found similar improvements when predicting trend and when applying the simulation-trained superensembles to catch data for global fish stocks. Superensembles can optimally leverage multiple model predictions; however, they must be tested, formed from a diverse set of accurate models and built on a data set representative of the populations to which they are applied.

The exploitation status of marine fisheries stocks worldwide is of critical importance for food security, ecosystem conservation, and fishery sustainability. Applying a suite of data-limited methods to global catch data, combined through an ensemble modeling approach, we provide quantitative estimates of exploitation status for 785 fish stocks. Fifty six percent (439 stocks) are below BMSY and of these, 261 are estimated to be below 80% of the BMSY level. While the 178 stocks above 80% of BMSY are conventionally considered “fully exploited”, stocks staying at this level for many years, forego substantial yield. Our results enable managers to consider more detailed information than simply a categorization of stocks as “fully” or “over” exploited. Our approach is reproducible, allows consistent application to a broad range of stocks, and can be easily updated as new data become available. Applied on an ongoing basis, this approach can provide critical, more detailed information for resource management for more exploited fish stocks than currently available. This article is protected by copyright. All rights reserved

Overfishing and degradation of the marine environment continue to plague coastal communities worldwide, with multiple diverse solutions being proposed. Territorial Use Rights for Fisheries (TURFs) is a fishery management approach that aligns fishers’ incentives with sustainability, while marine reserves have proven effective for ecosystem protection, and in some cases for fishery enhancement. These two management approaches are often used in isolation, leaving the potential utility of integrating them poorly understood. We examine cases where TURFs and marine reserves have been implemented together to create “TURF-reserves”. We compiled a database of 27 TURF-reserves and collected information on the governance, management, enforcement, fishing practices, fishing rights, regulations, and design attributes for each site. We address several research questions including: what species are managed with TURF-reserves, how are TURF-reserves created and who is involved in the process? Our findings show that the majority of surveyed TURF-reserves arose from previously established TURF systems that target a range of fisheries, and multiple entities play a role in TURF-reserve development and management. We also examine the differences between two TURF-reserve archetypes and find that those developed with a strong history of customary tenure share distinct qualities from those created in a more recently established, government-mandated system. Keywords: TURF, Marine reserve, TURF-reserve, Fisheries, Rights-based management

Introduction: Human impacts on Arctic marine ecosystems are increasing in extent and intensity as sea ice shrinks and utilization of marine resources expands. The effects of climate change are being felt across the arctic while stressors such as commercial fishing and shipping continue to grow as the Arctic becomes more accessible. Given these emerging changes, there is need for an assessment of the current cumulative impact of human activities to better anticipate and manage for a changing Arctic. Cumulative human impacts (CHI) assessments have been widely applied around the world in a variety of ecosystem types but have yet to incorporate temporal dynamics of individual stressors. Such dynamics are fundamental to Arctic ecosystems. Outcomes: Here, we present the first CHI assessment of an Arctic ecosystem to incorporate sea ice as a habitat and assess impact seasonality, using the Bering Strait Region (BSR) as a case study. We find that cumulative impacts differ seasonally, with lower impacts in winter and higher impacts in summer months. Large portions of the BSR have significantly different impacts within each season when compared to a mean annual cumulative impact map. Cumulative impacts also have great spatial variability, with Russian waters between 2.38 and 3.63 times as impacted as US waters. Conclusion: This assessment of seasonal and spatial cumulative impacts provides an understanding of the current reality in the BSR and can be used to support development and evaluation of future management scenarios that address expected impacts from climate change and increasing interest in the Arctic.

Reproducibility has long been a tenet of science but has been challenging to achieve-we learned this the hard way when our old approaches proved inadequate to efficiently reproduce our own work. Here we describe how several free software tools have fundamentally upgraded our approach to collaborative research, making our entire workflow more transparent and streamlined. By describing specific tools and how we incrementally began using them for the Ocean Health Index project, we hope to encourage others in the scientific community to do the same-so we can all produce better science in less time.

Coral reef cryptofauna, which live hidden within reef framework structures, are con- sidered to be the most diverse group of coral reef metazoans. They likely comprise more biomass than all surface fauna, providing food sources for fishes and playing important roles as predators, herbivores, detritivores, filter feeders, and scavengers. In an era of global change, it is necessary to determine how these communities are structured across reef habitats as well as to understand how reef framework degradation will impact the cryptofauna and, by extension, ecosystem func- tion. Artificial reef framework units were constructed from coral rubble to approxi mate framework substrates. Forty replicates were subjected to treatments of differing porosity, flow, and coral cover in a fully crossed ANOVA design. After 2 mo in situ, all motile cryptofauna (>2 mm) were counted, weighed, and identified to the lowest possible level. A total of 11 309 specimens were col- lected, comprising >121 species from 6 separate phyla. Cryptofaunal abundances and biomass were higher in low-porosity crypts and biomass was greater in slow-flow environments, highlight- ing the importance of sheltered low-porosity habitats, such as back-reef rubble plains. The pres- ence of live coral was not found to have a significant effect on the motile cryptofauna occupying the dead coral framework below it, suggesting a high degree of resilience in how framework- dwelling fauna respond to coral mortality. These data support the assertion that artificial reefs are capable of facilitating the accumulation of a diverse cryptic community, independent of live coral, provided they contain suitably porous crypts.