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Monitoring fishing activities is crucial for marine conservation but challenging in practice, because the ability in tracking fisheries has been limited on varying spatial and temporal scales. The challenge is for both developed and developing countries, and is outstanding in China which is characterized by the world's largest fishing fleet. The advance of Automatic Identification System (AIS) provides the opportunity to enforce monitoring and to promote the compliance of responsible fisheries. Here, the dynamics of fishing fleets in China Seas were integrally investigated for the first time. We demonstrated heavy fishing efforts widely distributed across China Seas, whereas most vessels were concentrated along the coastal line. The temporal pattern of fishing efforts was dominated by summer moratorium, and intensive fishing occurred immediately before and after the moratorium. We highlight that most fishing activities occurred in a remarkably limited spatial and temporal scope, particularly trawling vessels which spent a few months on fishing every year and covered a small geographical area. Additionally, considerable fishing efforts have transferred from Eastern China Sea to the coastal areas of south China in response to an extension of summer moratorium, which requires the fishing moratorium beginning one month earlier since 2017. We argue that summer moratorium cannot effectively control overall fishing efforts, but rearrange the same level of efforts in space and time. We highlight caveats in the interpretation of AIS data in terms of reception issues, meanwhile the novel information provided by AIS can refine the understanding of fleet dynamics and contribute to adaptive fisheries management over broad spatial and temporal scales., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Species distribution models (SDMs) have been increasingly used to predict the geographic distribution of a wide range of organisms; however, relatively fewer research efforts have concentrated on rare species despite their critical roles in biological conservation. The present study tested whether community data may improve modelling rare species by sharing information among common and rare ones. We chose six SDMs that treat community data in different ways, including two traditional single-species models (random forest and artificial neural network) and four joint species distribution models that incorporate species associations implicitly (multivariate random forest and multi-response artificial neural network) or explicitly (hierarchical modelling of species communities and generalized joint attribute model). In addition, we evaluated two approaches of data arrangement, species filtering and conditional prediction, to enhance the selected models. The model predictions were tested using cross validation based on empirical data collected from marine fisheries surveys, and the effects of community data were evaluated by comparing models for six selected rare species. The results demonstrated that the community data improved the predictions of rare species' distributions to certain extent but might also be unhelpful in some cases. The rare species could be appropriately predicted in terms of occurrence, whereas their abundance tended to be underestimated by most models. Species filtering and conditional predictions substantially benefited the predictive performances of multiple- and single-species models, respectively. We conclude that both the modelling algorithms and community data need to be carefully selected in order to deliver improvement in modelling rare species. The study highlights the opportunity and challenges to improve prediction of rare species' distribution by making the most of community data.

Fishery-independent surveys are essential for collecting high quality data to support fisheries management. For fish populations with low abundance and aggregated distribution in a coastal ecosystem, high intensity bottom trawl surveys may result in extra mortality and disturbance to benthic community, imposing unnecessarily large negative impacts on the populations and ecosystem. Optimization of sampling design is necessary to acquire cost-effective sampling efforts, which, however, may not be straightforward for a survey with multiple goals. We developed a simulation approach to evaluate and optimize sampling efforts for a stratified random survey with multiple goals including estimation of abundance indices of individual species and fish groups and species diversity indices. We compared the performances of different sampling efforts when the target estimation indices had different spatial variability over different survey seasons. This study suggests that sampling efforts in a stratified random survey can be reduced while still achieving relatively high precision and accuracy for most indices measuring abundance and biodiversity, which can reduce survey mortality. This study also shows that optimal sampling efforts for a stratified random design may vary with survey objectives. A postsurvey analysis, such as this study, can improve survey designs to achieve the most important survey goals.

The management of mixed fisheries is complicated by the biological and technical interactions among fish species. We tested a multispecies total allowable catch approach to managing mixed fisheries while accounting for the interactions and changes within fish community. A multispecies size–spectrum model was developed to simulate the dynamic of fish community in North Yellow Sea, China. Three scenarios were evaluated: (1) business-as-usual (BAU); (2) Single-species TAC (SSTAC); and (3) Multispecies TAC (MSTAC, assigning an aggregated total TAC to a selected group of species). Our results showed that BAU reduced biomass of target species to a substantially low level, SSTAC led to loss of fishing opportunity by involving "choke species" with discard ban, while MSTAC had a potential for maintaining long-term yields and community structure. We demonstrated the ecological effects of varying levels of MSTACs on the target and non-target species. Species' responses to MSTAC were stronger when the species had similar feeding and habitat traits to the target species, implying intense competition. Particularly, a 20% decrease in MSTAC could cause an average 17% decline in the biomass of other species through biological interactions in the long-term. MSTAC could be a promising approach to achieving long-term sustainability in data deficient fisheries. [ABSTRACT FROM AUTHOR]

Varying catchability is a common feature in fisheries and has great impacts on fisheries assessments and species distribution models. However, spatial variations in catchability have been rarely evaluated, especially in the multispecies context. We advocate that the need for multispecies models stands for both challenges and opportunities to handle spatial catchability. This study evaluated the influence of spatially varying catchability on the performance of a novel joint species distribution model, namely Hierarchical Modelling of Species Communities (HMSC). We implemented the model under nine simulation scenarios to account for diverse spatial patterns of catchability and conducted empirical tests using survey data from Yellow Sea, China. Our results showed that ignoring variability in catchability could lead to substantial errors in the inferences of species response to environment. Meanwhile, the models' predictive power was less impacted, yielding proper predictions of relative abundance. Incorporating a spatially autocorrelated structure substantially improved the predictability of HMSC in both simulation and empirical tests. Nevertheless, combined sources of spatial catchabilities could largely diminish the advantage of HMSC in inference and prediction. We highlight situations where catchability needs to be explicitly accounted for in modelling fish distributions, and suggest directions for future applications and development of JSDMs. [ABSTRACT FROM AUTHOR]

Age‐structured data are commonly needed in stock assessments but are unavailable for the majority of existing fisheries, while fish size (length or weight) data are most readily accessible and carry critical information of population status. However, the performances of assessment methods based on size‐frequency data have been less well understood. This study evaluated the reliability of two size‐frequency‐based assessment methods in R package, s6model and TropFishR, for estimating the exploitation status of one data‐poor stock, Pholis fangi in Haizhou Bay, China. We used both simulation and empirical approaches to compare the performances of the two assessment methods in estimating F and Fmsy and examined their robustness to the stochasticity and amount of sampling data. In the simulation test, s6model and TropFishR yielded satisfactorily accurate and consistent estimates of F and Fmsy. In the empirical test, the estimated stock exploitation status varied substantially among seasons. TropfishR and s6model estimated the F/Fmsy to be 2.40 and 2.05 in spring, respectively, indicating remarkable overfishing, and 0.43 and 0.41 in fall, respectively, indicating an optimistic exploitation level. The reflected pattern was consistent with the fishery characteristics of this species. The s6model method was substantially influenced by sample size and might lead to unreasonable estimations in small samples, whereas TropFishR was relatively robust on the changes of sample size. This study highlighted the usefulness of size‐frequency methods in guiding fisheries management and provided references for their applications in data‐limited situations. [ABSTRACT FROM AUTHOR]

Crab species are economically and ecologically important in coastal ecosystems, and their spatial distributions are pivotal for conservation and fisheries management. This study was focused on modelling the spatial distributions of three Portunidae crabs (Charybdis bimaculata, Charybdis japonica, and Portunus trituberculatus) in Haizhou Bay, China. We applied three analytical approaches (Generalized additive model (GAM), random forest (RF), and artificial neural network (ANN)) to spring and fall bottom trawl survey data (2011, 2013–2016) to develop and compare species distribution models (SDMs). Model predictability was evaluated using cross-validation based on the observed species distribution. Results showed that sea bottom temperature (SBT), sea bottom salinity (SBS), and sediment type were the most important factors affecting crab distributions. The relative importance of candidate variables was not consistent among species, season, or model. In general, we found ANNs to have less stability than both RFs and GAMs. GAMs overall yielded the least complex response curve structure. C. japonica was more pronounced in southwestern portion of Haizhou Bay, and C. bimaculata tended to stay in offshore areas. P. trituberculatus was the least region-specific and exhibited substantial annual variations in abundance. The comparison of multiple SDMs was informative to understand species responses to environmental factors and predict species distributions. This study contributes to better understanding the environmental niches of crabs and demonstrates best practices for the application of SDMs for management and conservation planning. [ABSTRACT FROM AUTHOR]

• Ocean warming would reduce energy transfer efficiency and recycling capacity within food webs. • There would be a substantial decline in total system biomass and fisheries production under the extreme warming scenario. • Reducing fishing intensity could mitigate warming impacts on fisheries production, but was insufficient to maintain ecological properties. The influences of ocean warming on marine lives have accelerated over the 21st century, greatly altering the structure and function of marine food webs and causing distributional shifts, species invasions, and changes in productivity. It is imperative to clarify the overall ecosystem responses to ocean warming and develop fisheries management strategies adaptive to the ecosystem changes. In this study, the potential impacts of ocean warming on trophic structure, energy flows, and fisheries production of an overexploited ecosystem were examined, and the effectiveness of fisheries management in mitigating warming impacts were also evaluated. We constructed a mass-balance food web model in Haizhou Bay and simulated three climate scenarios (RCPs 2.6, 4.5, and 8.5) along with different levels of fishing pressure, in order to examine the ecosystem responses to the combined changes in fishing and climate changes. Results showed that the total biomass of commercial species and fisheries catches would decline with rising temperature, especially under the RCP8.5 scenario. Ocean warming could induce lower trophic transfer efficiency and decrease energy recycling capacity within the food web, leading to large losses in total biomass and total production. Reducing fishing intensity could help mitigate the negative effects of ocean warming on fisheries productivity, but was insufficient to maintain ecological properties. Moreover, the effectiveness of such alternative measures would be diminished with increased greenhouse gas emissions, especially under the climate scenario of RCP8.5. The findings of this study highlight the need to slow the rise of sea temperature and implement climate-adaptive fisheries management in the future. [ABSTRACT FROM AUTHOR]

• Conger myriaster has length- and condition-driven reproductive strategies. • Fish condition greatly influences the estimates of SPR-related reference points. • Spawning stock biomass can produce 10–25% bias in calculating F 40%. • Monitoring the changes in body conditions in fisheries assessment is needed. Spawning potential ratio (SPR) is a commonly used biological reference point to inform management decisions; however, the fish reproductivity may vary substantially with different body conditions, and the variability has not been well understood. Here, we examined the maturity, fecundity, and SPR of a marine eel Conger myriaster. The results showed that total fecundity increased with length and hepatosomatic indices (HSI), whereas relative fecundity (total fecundity/body weight) decreased with body weight, suggesting length- and condition-driven reproductive strategies. A length-structured per-recruit model was used to estimate SPR and examine the influence of HSI in resultant management decisions. Our results suggested that this stock was subject to a high risk of recruitment overfishing. Fish condition greatly influences the estimates of SPR-related reference points. For example, when HSI increased from 0.6% to 1.8%, F 40% increased by 91%. In addition, using spawning stock biomass to calculate F 40% could produce a bias of 23%. We highlight the need for monitoring the changes in fish fecundity and conditions in fisheries assessment, which may contribute to the robust management of data-poor fisheries. [ABSTRACT FROM AUTHOR]

Many of the world fisheries, especially small-scale fisheries, are in data-limited situations, for which proxy Biological Reference Points (BRPs) derived from per-recruit analyses are widely used to inform fisheries management. However, due to limited information on stock recruitment, per-recruit analyses usually assume constant recruitment and the influences of this key assumption have not been fully understood. In this study, we combined per-recruit analyses and stock-recruit relationship to build equilibrium models for eight species with different life history traits, and used the management strategy evaluation (MSE) framework to assess the uncertainty of proxy BRPs in achieving fisheries sustainability. Our results showed substantial variations in the performances of proxy BRPs on maintaining yield and biomass, and even conservative BRPs, e.g. F 0.1 caused overexploitation in half of the cases (4 out of 8). The divergence of the performance between proxy BRPs and F MSY could be substantial, depending on steepness of stock-recruit relationships (correlation ranged from –0.55 to 0.9 for yield, and 0.53 to 0.96 for biomass). Accordingly, we presented an empirical approach for remediation, using correction coefficient to adjust the traditional proxy BRPs. The empirical approach showed satisfactory performances in the validation processes with an independent dataset. This paper demonstrates that proxy BRPs may introduce risks in achieving fisheries sustainability, whereas the errors can be readily adjusted with empirical estimates of steepness for most data-limited fisheries. [ABSTRACT FROM AUTHOR]

Japanese mantis shrimp (Oratosquilla oratoria) is an economically important species in the coastal fisheries of East Asian countries, but the yield shows a declining trend in recent years under intense fishing pressure. The spatial distribution and biomass of this species vary geographically, sexually and periodically due to its adaptability of diverse environmental factors, which leads to substantial challenges in modelling and predicting the spatio-temporal distribution and adaptive management. In this study, based on distribution data of Japanese mantis shrimp in coastal Shandong trawling surveys from 2016 to 2017, period- and sex-specific generalized additive models (GAM) and geographically weighted regression models (GWR) were built to evaluate influential factors on the occurrence and biomass of Japanese mantis shrimp and to test their performance and predictability. Our results showed that the spatial distribution of Japanese mantis shrimp was significantly associated with depth, sea bottom temperature, and silt and clay content in sediment. Specifically, Japanese mantis shrimp tended to aggregate in higher content of silt and clay, whereas the preference of depth and sea temperature. Following relatively higher bottom temperature, the distribution of Japanese mantis shrimp was concentrated in inshore water in the breeding period and in offshore water in the wintering period. There was remarkable difference in the distribution between male and female, and the influential factors differed between gender groups and changed in different life periods. Regarding the performances of models, GWR showed advantages over GAM in identifying influential factors and predicting distribution in the cross validation, and GWR was recommended in similar applications in the future. This study might contribute to better understanding of the distributional patterns of Japanese mantis shrimp and inform stock monitoring and sustainable management. • We highlighted the life-history and survey data imposed challenges to model the distribution of Japanese mantis shrimp. • We compared multiple variants of GAM and GWR, and structured the two-step model with sexual, periodical variations. • The model predictive performances were tested using cross validation with bootstrapped data. • We propose suggestions for application of the models and guideline for fisheries management of Japanese mantis shrimp. [ABSTRACT FROM AUTHOR]