基于最大熵模型模拟西北太平洋柔鱼潜在栖息地分布

为模拟西北太平洋柔鱼(Ommastrephes bartramii)潜在栖息地分布,分析柔鱼渔场时空变化和环境变化规律。利用2011—2015年中国鱿钓船在西北太平洋海域获得的柔鱼渔业生产数据,结合该海域海洋环境遥感数据,包括海表面温度(sea surface temperature, SST)、叶绿素a (Chlorophyll-a, Chl a)浓度、净初级生产力(net primary productivity, NPP)、混合层深度(mixed layer depth, MLD)及海平面异常(sea level anomaly, SLA),采用最大熵模型对柔鱼潜在栖息地进行模拟,并利用ArcGIS软件对栖息地适宜性进行评价。结果显示,7月柔鱼最适宜区主要分布在39°N～43°N, 150°E～163°E。8月柔鱼最适宜区向东移动,较适宜区向北扩张至46°N。9月柔鱼最适宜区和较适宜区面积向西缩小,主要集中在40°N～46°N, 150°E～160°E。10月最适宜区和较适宜区向南移动,主要分布在40°N～45°N,150°E～165°E。各月影响柔鱼潜在分布的重要环境因子有所差异,7—8月为SST,9月为MLD和SST,10月为NPP和SST。研究表明西北太平洋柔鱼分布受海洋环境因子的影响,时空变化明显,最大熵模型对西北太平洋柔鱼潜在栖息地分布的模拟精度非常高。

Modeling the potential distribution of the neon flying squid (Ommastrephes bartramii) in the Northwest Pacific Ocean based on a MaxEnt model

The neon flying squid (), found in the Northwest Pacific Ocean, is a species of great economic importance. It is mainly caught by fishing fleets from and consumed in China (including Taiwan), Japan, and other countries and regions. The winter-spring cohort of is a short-lived species with a lifespan of about one year. The abundance and distribution of are influenced by global climate change and local marine environmental changes, such as El Nia events, Pacific interdecadal oscillation, currents, sea surface temperature (SST), chlorophyll-a (Chl a) concentrations, etc. The monthly potential habitat distribution of neon flying squid in the Northwest Pacific Ocean during the peak fishing season (July-October) was explored by using maximum entropy (MaxEnt) models; these models were fitted with squid fishery data obtained from Chinese commercial squid-jigging vessels from 2011 to 2015 and five oceanographic environmental factors derived from remote sensing data (SST; Chl a concentration; net primary productivity, NPP; mixed layer depth, MLD; sea level anomaly, SLA). The accuracy of the monthly MaxEnt models was evaluated by the area under receiver operating characteristic (ROC) curve (AUC). The simulated by a different MaxEnt model for each month, were imported into ArcGIS for visual analysis. The values obtained from probabilistic modeling were defined as habitat suitability index (HSI) and divided manually. When HSI>0.6, the sea area was considered as the most suitable area for ; when 0.4