Incorporating egg-transporting pathways into conservation plans of spawning areas: An example of small yellow croaker (Larimichthys polyactis) in the East China Sea zone

Liu, Zunlei; Jin, Yan; Lan, Yang; Yan, Liping; Zhang, Yi; Xu, Min; Tang, Jianhua; Zhou, Yongdong; Hu, Fang; Cheng, Jiahua

doi:10.3389/fmars.2022.941411

Cited by 2 publications

(1 citation statement)

References 79 publications

(72 reference statements)

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“…There are several spatial regression type ML methods to explicitly account for spatial covariance (see Georganos & Kalogirou, 2022), including spatial RF with eigenvector spatial filtering (Liu, Jin, et al, 2022;Liu, Kounadi, et al, 2022;Reisinger et al, 2022)-an approach also used in a more conventional GLM(M)-based model of coral bleaching on the Great Barrier Reef (Hughes et al, 2021). We used the spatialRF R package (Benito, 2021) with eigenvector spatial filtering (Dray et al, 2006) and a matrix of the distance (in kilometers) between all sets to further explore species-specific spatial effects using the top 10 SHAP summary predictors determined previously using the species-specific ML models.…”

Section: Spatial ML Modelsmentioning

confidence: 99%

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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