Robbing Peter to pay Paul: replacing unintended cross-taxa conflicts with intentional tradeoffs by moving from piecemeal to integrated fisheries bycatch management

Gilman, Eric; Chaloupka, Milani; Dagorn, Laurent; Hall, Martín; Hobday, Alistair J.; Musyl, Michael K.; Pitcher, Tony J.; Poisson, François; Restrepo, Victor; Suuronen, Petri

doi:10.1007/s11160-019-09547-1

Cited by 53 publications

(60 citation statements)

References 212 publications

(249 reference statements)

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“…, Gilman et al. ). In fisheries where conventional management methods have failed, the underlying causes for failure (management measures do not follow scientific advice, lack of compliance, high levels of illegal fishing, etc.)…”

Section: Discussionmentioning

confidence: 99%

“…MPAs designed to mitigate the bycatch of a species of conservation concern may, in some cases, cause the displacement of fishing effort to areas or periods that inadvertently exacerbate bycatch rates of this species or of other at‐risk taxa (Gilman et al. ). In some cases, marine area closures have resulted in the spatial or temporal displacement of bycatch of an individual species or age classes (Murray et al.…”

Section: Theoretical and Observed Evidence Of Pelagic Mpas Achieving mentioning

confidence: 99%

“…Although less well documented, there are also examples of closed areas designed to reduce the bycatch of one species of conservation concern that then lead to an increase in the bycatch of another species of conservation concern (Abbott and Haynie , Gilman et al. ). For example, Baum et al.…”

Section: Theoretical and Observed Evidence Of Pelagic Mpas Achieving mentioning

confidence: 99%

“…, Hall and Roman , Gilman et al. ). School sets, however, have higher catch rates of mobulid rays and leatherback sea turtles (Dagorn et al.…”

Section: Theoretical and Observed Evidence Of Pelagic Mpas Achieving mentioning

confidence: 99%

“…, Hall and Roman , Gilman et al. ). Therefore, if restrictions on associated sets (i.e., sets on FADs and other floating objects) increased school set effort, this would result in cross‐taxa conflicts by displacing bycatch issues onto other species of conservation concern (Gilman et al.…”

Section: Theoretical and Observed Evidence Of Pelagic Mpas Achieving mentioning

confidence: 99%

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Do static and dynamic marine protected areas that restrict pelagic fishing achieve ecological objectives?

2019

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There has been a recent proliferation of large‐scale marine protected areas (MPAs) containing pelagic habitats. These contribute substantially toward meeting the area‐based goal of Aichi Biodiversity Target 11 and to managing pelagic ecosystem pressures, including fishing. We assessed theoretical and empirical evidence for the achievement of ecological objectives by static and dynamic spatial management of pelagic fisheries. Exceptionally few studies have assessed ecological responses to MPAs that constrain pelagic fisheries, leaving substantial uncertainty over their efficacy. Assessments have provided a limited basis for causal inferences and have not evaluated whether other management tools would be more effective. Pelagic MPAs have relatively high promise to mitigate fisheries bycatch of species of conservation concern with “slow” life history traits and that form temporally and spatially predictable hotspots, and for some species, to protect habitats important for critical life history stages. It would be challenging to design MPAs to maintain absolute biomass levels of target stocks near targets and above limits: MPAs would need to be extensive to account for broad and variable distributions, and account for catch risk outside of the MPA, including from displaced fishing effort and fishing‐the‐line. For non‐overexploited stocks, which is the status of most target pelagic species and their prey, there would likely be little response in absolute stock biomass to an MPA. While pelagic MPAs have a higher promise of increasing target stocks’ local abundance, evidence with a robust basis for inferring causality is needed. Reducing fishing mortality of prey species might not affect the biomass of their pelagic predators because prey species experience light fishing pressure and because there may be a weak correlation between the absolute abundance of forage fish and their predators. There is an especially limited basis for predicting the effects of MPAs on fisheries‐induced evolution (FIE) in pelagic species. We describe how pelagic MPAs could be designed to achieve five ecological objectives without causing cross‐taxa conflicts and exacerbating FIE. To fill substantial gaps in knowledge, we prescribe counterfactual‐based modeling of time series data of standardized catch records to infer causation in assessments of ecological responses to pelagic MPAs.

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Section: Discussionmentioning

confidence: 99%

Section: Theoretical and Observed Evidence Of Pelagic Mpas Achieving mentioning

confidence: 99%

Section: Theoretical and Observed Evidence Of Pelagic Mpas Achieving mentioning

confidence: 99%

“…, Hall and Roman , Gilman et al. ). School sets, however, have higher catch rates of mobulid rays and leatherback sea turtles (Dagorn et al.…”

Section: Theoretical and Observed Evidence Of Pelagic Mpas Achieving mentioning

confidence: 99%

Section: Theoretical and Observed Evidence Of Pelagic Mpas Achieving mentioning

confidence: 99%

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Do static and dynamic marine protected areas that restrict pelagic fishing achieve ecological objectives?

2019

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Evidence to inform spatiotemporal management of a western Pacific Ocean tuna purse seine fishery

Gilman,

Chaloupka,

Posanau

et al. 2024

Ecological Applications

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Fisheries can profoundly impact co‐occurring species exposed to incidental capture. Spatiotemporal fisheries management holds substantial potential to balance socioeconomic benefits with ecological costs to threatened bycatch species. This study estimated the effect of the spatial and temporal distribution of effort by a western Pacific Ocean tuna purse seine fishery on catch rates of target and at‐risk species by fitting spatially explicit generalized additive multilevel regression models within a Bayesian inference framework to observer data. Mean field prediction surfaces defined catch rate hotspots for tunas, silky sharks, rays, and whale sharks, informing the design of candidate area‐based management strategies. Due to limited sample sizes, odontocete and marine turtle catch rate geospatial patterns were summarized using simple 2D hexagonal binning. Effort could be focused in two areas within core fishing grounds to reduce overlap with hotspots for silky sharks, rays, and whale sharks without affecting target catch. Effort could be shifted outside of core fishing areas to zones with higher target tuna catch rates to reduce overlap with hotspots for at‐risk species. Sparse and small marine turtle and whale shark hotspots occurred across the fishing grounds. Results did not identify opportunities for temporally dynamic spatial management to balance target and at‐risk catch rates. Research on the economic and operational viability of alternative spatial management strategies is a priority. A small subset of sets had disproportionately large odontocete captures. Real‐time fleet communication, move‐on rules, and avoiding sets on dolphin schools might reduce odontocete catch rates. Managing set association type and mesh size present additional opportunities to balance catch rates of at‐risk and target species. Employing output controls that effectively constrain the fishery would alter the spatial management strategy to focus fishing within zones with the lowest ratio of at‐risk bycatch to target tuna catch. Findings inform the design of alternative spatial management strategies to avoid catch rate hotspots of at‐risk species without compromising the catch of principal market species.

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Evidence from interpretable machine learning to inform spatial management of Palau's tuna fisheries

Gilman,

Chaloupka

2024

Ecosphere

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Static and dynamic area‐based management tools hold substantial potential to balance socioeconomic benefits derived from fisheries and costs from bycatch mortality of at‐risk species. Palau longline fisheries have high bycatch of at‐risk species including the olive ridley marine turtle and silky and blue sharks. This study analyzed a two decades‐long time series of observer and electronic monitoring datasets from the Palau distant‐water and locally‐based pelagic longline fisheries. An interpretable or explainable machine learning‐based modeling approach was used to derive spatially resolved species‐specific catch rate predictions. These models were conditioned on a suite of potentially informative environmental, bathymetric, ocean‐climate metric, vessel, monitoring system, and set‐specific operational predictors. Overall, there would be limited ecological tradeoffs from focusing fishing effort within primary catch rate hotspots for target bigeye and yellowfin tunas. Mean field prediction surfaces also defined catch rate hotspots for at‐risk species of silky and blue sharks, olive ridley turtle, and pelagic stingray, which did not overlap the hotspots for target species. The predicted target species hotspots, however, overlap olive ridley and pelagic stingray warmspots. Results also identify opportunities for temporally dynamic spatial management to control catch rates of target and bycatch species. Management of fishery operational predictors of fishing depth and soak duration present additional opportunities to balance catch rates of at‐risk bycatch and target species. A transition to employing fleetwide or vessel‐based output controls that effectively constrain the fishery would alter the spatial management strategy to focus on zones with the lowest ratio of at‐risk bycatch to commercial catch. Our findings support evidence‐informed evaluation of spatial management strategies and complementary measures to meet objectives for balancing socioeconomic benefits derived from target species catch with costs to threatened species.

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Robbing Peter to pay Paul: replacing unintended cross-taxa conflicts with intentional tradeoffs by moving from piecemeal to integrated fisheries bycatch management

Cited by 53 publications

References 212 publications

Do static and dynamic marine protected areas that restrict pelagic fishing achieve ecological objectives?

Do static and dynamic marine protected areas that restrict pelagic fishing achieve ecological objectives?

Evidence to inform spatiotemporal management of a western Pacific Ocean tuna purse seine fishery

Evidence from interpretable machine learning to inform spatial management of Palau's tuna fisheries

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