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Determination of spatio-temporal distribution of Rastrelliger kanagurta using modelling techniques for optimal fishing
Journal of Coastal Conservation ( IF 2.1 ) Pub Date : 2021-01-19 , DOI: 10.1007/s11852-020-00796-y
Syazwani Mohd Yusop , Muzzneena Ahmad Mustapha , Tukimat Lihan

The commercial Indian mackerel, Rastrelliger kanagurta (R. kanagurta) is widely distributed on the east coast of the Peninsular of Malaysia. Monsoon variations influence its occurrences and abundance. Understanding the variability of oceanographic physical processes and formation of habitats is vital in exploring fish resources. Temperature differences can have an important impact on ocean circulation and mechanisms that can affect the distribution and availability of fish. This study used fishing locations data of 2008 and 2009 and derived chlorophyll-a (chl-a) and sea surface temperature (SST) from satellite data (MODIS-Aqua). The objectives of this study were to determine potential fishing grounds of R. kanagurta using a presence-absence data model, Generalized Additive Model (GAM) and presence-only data model, maximum entropy (Maxent) and to assess the impact of temperature rise on its seasonal distribution based on the IPCC-AR5-RCP temperature forecast. Results showed that the GAM model had higher prediction accuracy. The constructed model predicted larger distribution areas of R. kanagurta. Maxent model, however, predicted limited distribution range concentrated only areas surrounding the point of presence. Increase of SST projected across all RCPs resulted in a decreased extent of suitable fishing habitats. Potential habitats were observed to shift out of the EEZ. Applicability of the GAM model to understand the spatio-temporal distribution of fish habitat positively contributes to optimal fishing and sustainability in the management of marine resources.



中文翻译:

利用最佳捕捞技术建模技术确定红腹蛙的时空分布

商业印度鲭鱼Rastrelliger kanagurta(R. kanagurta)广泛分布在马来西亚半岛的东海岸。季风变化影响其发生和丰度。了解海洋物理过程的变异性和栖息地的形成对于探索鱼类资源至关重要。温差会对海洋环流和影响鱼类分布和供应的机制产生重要影响。本研究中使用的钓鱼地点2008和2009和数据导出叶绿素一个(chl-一个)和海面从卫星数据(MODIS-水族)温度(SST)。这项研究的目的是确定kanagurta的潜在渔场使用在场数据模型,广义加法模型(GAM)和仅在场数据模型,最大熵(Maxent),并根据IPCC-AR5-RCP温度预测评估温度上升对其季节性分布的影响。结果表明,GAM模型具有较高的预测精度。所构建的模型预测了卡纳古特河的更大分布区域。然而,Maxent模型预测的有限分布范围仅集中在存在点周围的区域。预计所有RCP的SST都将增加,从而导致合适的捕鱼生境减少。观察到潜在的栖息地移出了专属经济区。GAM模型对了解鱼类栖息地的时空分布的适用性,对优化捕捞和海洋资源管理的可持续性有积极作用。

更新日期:2021-01-19
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