Predicting Winter Potential Fishing Zones of Albacore Tuna (Thunnus Alalunga) Using Maximum Entropy Models and Remotely Sensed Data in The South Indian Ocean

Lee, Ming-an; Yang, Wan-Chen; I-Cheng, Hung; Teng, Sheng-Yuan

doi:10.1109/igarss.2018.8517359

Cited by 6 publications

(1 citation statement)

References 6 publications

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“…We found that tropical estuary-dependent finfish and elasmobranch abundance and distribution were influenced by changes in the environmental parameters from the variations of SST, SSS, DO and photosynthetic cover of the shoreline across a large coastal region (2641 km 2 ) adjacent to the Great Barrier Reef (GBR) in Australia. In particular, the results showed that the spatial habitat patterns were explained predominantly by bathymetry and SST (Lee et al 2018). Changes in environmental temperature may lead to non-linear responses in abundance among marine species; therefore, small increases in temperature can have large impacts on predicted outcomes (Flanagan et al 2019).…”

Section: Discussionmentioning

confidence: 93%

Predicting the current and future suitable-habitat distribution of tropical adult and juvenile targeted fishes in multi-sector fisheries of central Queensland, Australia

2023

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Context Coastal and estuarine finfish species are responding to human-induced climate change by altering their distributions. In tropical regions, the species mostly affected by warming have limited acclimation capacity or live close to their upper thermal limits. Consequently, coastal fish assemblages may dramatically contract in range, experience declining population abundance or local extinction. Aim Here we use two different predictive modelling techniques that cope with non-linear empirical relationships between responses and environmental predictors to investigate distribution change. Methods The habitat-suitability models we use are the maximum entropy model (MaxEnt) and the generalised additive model (GAM). We built the models for the period 2004–2019 with environmental data relevant to coastal systems. We incorporated climate change at current conditions, near future (2015–2054) and distant future (2055–2100) from CMIP6 climate models. Key results We identified bathymetry and sea-surface temperature to be key variables explaining the current and future distribution of coastal finfish and elasmobranchs of the Great Barrier Reef coast in central Queensland. Conclusions We showed how the distributions of valuable fisheries species will change under future warming conditions. Implications The objective is to inform fisheries management supporting the restructure of existing fisheries or the development of new resources for the dual purposes of conservation and food security.

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Section: Discussionmentioning

confidence: 93%

Predicting the current and future suitable-habitat distribution of tropical adult and juvenile targeted fishes in multi-sector fisheries of central Queensland, Australia

2023

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Leveraging Data from Vast Unexplored Seas: Positive Unlabeled Learning for Refining Prediction Area in Good Fishing Ground Prediction

Konii,

Nakano,

Miyazawa

et al. 2024

Lecture Notes in Computer Science

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Region Proposal and Regression Network for Fishing Spots Detection From Sea Environment

Patil

Iiyama

2021

IEEE Access

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In this paper, a two-stage method is proposed for predicting the catch of skipjack tuna (Katsuwonus pelamis) from a 2D sea environmental pattern. Following the assumption that sea water temperature and sea surface height (SSH) which fishermen often use for finding fishing spots has a correlation with the skipjack tuna catch, a new approach of using Faster R-CNN in object detection is proposed. The proposed method consists of two part. In the first part, taking a sea temperature map as input, Faster R-CNN extracts the candidates of where skipjack tuna would be on the map in order to imitate the behaviors of fishers. In the second part, Support Vector Regression (SVR) estimates the catch amount in each candidate. Faster R-CNN is applied to several sea environmental patterns with three different loss functions and compares each performance. The proposed model is evaluated by comparing the result with average fishers' ability on the skipjack tuna catches and several criteria for evaluating the proposed model. The results show that the proposed method is able to outperform the average fishers' ability by an average of 3%.

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Predicting Winter Potential Fishing Zones of Albacore Tuna (Thunnus Alalunga) Using Maximum Entropy Models and Remotely Sensed Data in The South Indian Ocean

Cited by 6 publications

References 6 publications

Predicting the current and future suitable-habitat distribution of tropical adult and juvenile targeted fishes in multi-sector fisheries of central Queensland, Australia

Predicting the current and future suitable-habitat distribution of tropical adult and juvenile targeted fishes in multi-sector fisheries of central Queensland, Australia

Leveraging Data from Vast Unexplored Seas: Positive Unlabeled Learning for Refining Prediction Area in Good Fishing Ground Prediction

Region Proposal and Regression Network for Fishing Spots Detection From Sea Environment

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