Simulation of spatio-temporal distribution of swordfish habitat in the western Indian Ocean based on maximum entropy model
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摘要: 剑鱼（Xiphias gladius）是一种高度洄游性鱼类，其迁徙和栖息地利用受海洋环境影响明显，理解其空间分布格局形成的机制对于资源的养护和管理具有重要意义。本研究利用2017−2019年中国印度洋延绳钓渔业观察员数据中剑鱼的渔获物信息作为物种出现数据，结合西印度洋海域的海表温度、海面高度、叶绿素a浓度、混合层深度、海表盐度等环境数据，采用最大熵模型对剑鱼的栖息地适宜性分布进行了模拟。结果表明：（1）模型对印度洋西部剑鱼栖息地适宜性分布的模拟精度非常高，各个季节受试者工作特征曲线的曲线下面积都大于0.9，可用于模拟剑鱼潜在的栖息地适宜性分布；（2）研究区域内剑鱼适宜栖息地分布变化与实际作业位置变动基本一致，干季和湿季剑鱼栖息地高适宜性的区域分布都较为集中，但湿季分布范围要大于干季；（3）海表温度、海表盐度和混合层深度是影响西印度洋剑鱼栖息地适宜性分布的重要环境因子，在干季和湿季的最适范围分别为25.8～31.6℃、34.4～35.9、0.1～24.9 m和25.6～30.5℃、34.8～36.4、13.1～54.1 m。研究结果可为西印度洋海域剑鱼种群的可持续利用和科学管理提供必要参考信息。Abstract: Swordfish (Xiphias gladius) is a highly migratory fish whose habitat suitability is significantly influenced by the marine environment, and the prediction of its habitat using changes in the marine environment is of great scientific importance. In this study, we used the catch information of swordfish in the Chinese Indian Ocean Longline Fisheries Observer Data from 2017 to 2019 as species occurrence data, combined with the environmental data in the western Indian Ocean waters, including sea surface temperature (SST), sea surface height (SSH), chlorophyll a (Chl a) concentration, mixed layer depth (MLD), and sea surface salinity (SSS), the habitat suitability distribution of swordfish in the western Indian Ocean is simulated by using a maximum entropy model (MaxEnt). Model results show that: (1) the model has very high accuracy in simulating the habitat suitability distribution of swordfish in the western Indian Ocean, with AUC values greater than 0.9 in all seasons, and can be used to simulate the potential habitat suitability distribution of swordfish; (2) changes in the distribution of suitable habitat for swordfish in the study area are generally consistent with changes in the actual operational location, and the distribution of areas with high habitat suitability for swordfish is more concentrated in both the dry and rainy seasons, but the distribution range is greater in the wet season than in the dry season; (3) SST, SSS and MLD are important environmental factors affecting the habitat suitability distribution of swordfish in the western Indian Ocean. The optimum ranges of SST, SSS and MLD in the dry and rainy seasons are 25.8−31.6°C, 34.4−35.9 and 0.1−24.9 m, and 25.6−30.5°C, 34.8−36.4 and 13.1−54.1 m, respectively. The results of the study provide essential reference information for the sustainable use and scientific management of swordfish populations in the western Indian Ocean.
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图 1 2017−2019年渔业观察员记录的西印度洋剑鱼渔获站点（黑点）的分布状况
Fig. 1 Distribution of swordfish fishing stations (black dots) in the western Indian Ocean recorded by fishery observers during 2017 to 2019
图 2 2017−2019年西印度洋剑鱼实际出现点与潜在栖息地的分布
a. 2017年干季；b. 2018年干季；c. 2019年干季；d. 2017年湿季； e. 2018年湿季； f. 2019年湿季
Fig. 2 Distribution of actual occurrence points and potential habitat of swordfish in the western Indian Ocean from 2017 to 2019
a. Dry season in 2017; b. dry season in 2018; c. dry season in 2019; d. rainy season in 2017; e. rainy season in 2018; f. rainy season in 2019
图 3 2017−2019年西印度洋剑鱼栖息地适宜性指数分布
a. 2017年干季；b. 2018年干季；c. 2019年干季；d. 2017年湿季； e. 2018年湿季； f. 2019年湿季
Fig. 3 Distribution of swordfish habitat suitability index in the western Indian Ocean from 2017 to 2019
a. Dry season in 2017; b. dry season in 2018; c. dry season in 2019; d. rainy season in 2017; e. rainy season in 2018; f. rainy season in 2019
图 4 2017−2019年干季和湿季西印度洋剑鱼栖息地适宜性指数比较
Fig. 4 Comparison of habitat suitability index for swordfish in the western Indian Ocean during the dry and rainy seasons from 2017 to 2019
图 5 主要环境因子对剑鱼栖息地适宜性指数的响应曲线
Fig. 5 Response curves of main environmental factors to swordfish habitat suitability index
表 1 模型预测评价及主要验证参数
Tab. 1 Main parameters for model evaluation and test
2017年干季 2017年湿季 2018年干季 2018年湿季 2019年干季 2019年湿季 训练数据（AUC值） 0.965 7 0.9343 0.987 2 0.933 9 0.976 9 0.969 4 测试数据 （AUC值） 0.965 4 0.9247 0.981 8 0.925 3 0.965 6 0.965 2 AUC值标准差 0.005 4 0.0136 0.009 8 0.014 4 0.011 0 0.009 4 ESS值 0.251 2 0.3509 0.349 7 0.323 1 0.285 1 0.154 0 遗漏率/% 9.8 14.2 5.6 15.0 5.6 9.4 
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表 2 2017−2019年各季最大熵模型中环境因子的贡献率 (%)
Tab. 2 The contribution rate (%) of environmental factors in the seasonal maximum entropy model from 2017 to 2019
环境因子 2017年
干季2017年
湿季2018年
干季2018年
湿季2019年
干季2019年
湿季SST 41.39 25.87 32.34 46.76 54.93 46.85 SSS 17.75 33.67 15.18 31.67 20.73 13.69 SSH 18.77 17.84 6.40 3.55 3.18 3.23 Chl a浓度 1.99 1.71 28.44 9.06 14.78 28.07 MLD 20.10 20.91 17.64 8.96 6.38 8.16
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表 3 2017−2019年各季Jackknife检验结果得分
Tab. 3 The seasonal result score of Jackknife test from 2017 to 2019
2017年
干季2017年
湿季2018年
干季2018年
湿季2019年
干季2019年
湿季不包含MLD 2.238 1.471 2.221 1.534 2.433 2.485 不包含Chl a浓度 2.259 1.587 2.852 1.410 2.064 2.394 不包含SSH 2.244 1.478 2.679 1.537 2.379 2.491 不包含SSS 2.115 1.202 2.436 0.970 2.312 2.054 不包含SST 2.160 1.581 2.564 1.205 2.247 2.285 只包含MLD 1.416 0.784 1.462 0.679 0.985 0.959 只包含Chl a浓度 0.690 0.206 1.189 0.407 1.063 0.161 只包含SSH 1.079 0.877 0.367 0.566 0.636 1.015 只包含SSS 1.044 0.805 1.512 0.547 0.998 0.874 只包含SST 1.145 0.420 0.924 0.574 1.128 1.338
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