文章摘要
李宝贤,李国梁,姚海芹,申欣,鲁晓萍,梁洲瑞,刘福利,张朋艳,汪文俊.基于MaxEnt模型和ArcGIS对巨藻在我国适生情况的分析.渔业科学进展,2023,44(2):118-126
基于MaxEnt模型和ArcGIS对巨藻在我国适生情况的分析
Potential geographic distribution of Macrocystis pyrifera in China based on MaxEnt model and ArcGIS
投稿时间:2021-12-14  修订日期:2022-01-13
DOI:10.19663/j.issn2095-9869.20211214002
中文关键词: 巨藻  MaxEnt  ArcGIS  适生区域
英文关键词: Macrocystis pyrifera  MaxEnt  ArcGIS  Potential suitable area
基金项目:
作者单位
李宝贤 江苏海洋大学 江苏省海洋生物技术重点实验室 江苏 连云港 222005中国水产科学研究院黄海水产研究所 农业农村部海洋渔业可持续发展重点实验室 山东 青岛 266071 
李国梁 中国水产科学研究院黄海水产研究所 农业农村部海洋渔业可持续发展重点实验室 山东 青岛 266071 
姚海芹 中国水产科学研究院黄海水产研究所 农业农村部海洋渔业可持续发展重点实验室 山东 青岛 266071 
申欣 江苏海洋大学 江苏省海洋生物技术重点实验室 江苏 连云港 222005 
鲁晓萍 中国水产科学研究院黄海水产研究所 农业农村部海洋渔业可持续发展重点实验室 山东 青岛 266071 
梁洲瑞 中国水产科学研究院黄海水产研究所 农业农村部海洋渔业可持续发展重点实验室 山东 青岛 266071 
刘福利 中国水产科学研究院黄海水产研究所 农业农村部海洋渔业可持续发展重点实验室 山东 青岛 266071 
张朋艳 中国水产科学研究院黄海水产研究所 农业农村部海洋渔业可持续发展重点实验室 山东 青岛 266071 
汪文俊 中国水产科学研究院黄海水产研究所 农业农村部海洋渔业可持续发展重点实验室 山东 青岛 266071 
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中文摘要:
      发展新养殖对象及异地栽培需首先掌握物种的生态适应性。有关巨藻(Macrocystis pyrifera)在我国海区适应性的研究甚少,海区养殖效果不理想,我国巨藻养殖业发展欠佳。本研究采用MaxEnt构建了巨藻的物种分布模型,当特征组合为乘积型特征(product features)、二次型特征(quadratic features)和片段化特征(hinge features),正则化参数为0.8时,模型预测性能最佳;综合考虑环境因子的相关性及对模型的贡献率,筛选出6项环境因子用于模型构建,其中,光强与温度对巨藻自然分布的影响最大,在光强不低于2 μmol/(m2·s)、年均温度范围10.5~17.0℃条件下,巨藻的适生概率较高。采用所构模型结合ArcGIS预测巨藻在我国的适生区:主要分布于黄渤海,约占该海域面积的13.17%,其中,边缘适生区为5.46%,低适生区为2.85%,中适生区为1.20%,高适生区为3.66%,表明辽东湾、渤海湾是巨藻引种养殖和藻场建设的适宜海域。
英文摘要:
      Macrocystis pyrifera is a large perennial brown alga used as a raw material in the chemical, energy, and medicine industries. It is also a high-quality material for the construction of seaweed beds with extremely high economic and ecological value. In the 1980s, M. pyrifera was introduced to China, and many experiments on its seedling and cultivation technologies were undertaken. However, research on its ecological adaptability is relatively challenging, and the aquaculture industry has not yet developed due to bottleneck problems. In the present study, the MaxEnt model was used to predict the suitability and potential invasion risk of M. pyrifera in China to lay a foundation for M. pyrifera culture industry development and marine ecological construction. Parameter optimization showed that the predictive performance of the model was the best when the feature combination was product, quadratic, and hinge features and the regularization multiplier was 0.8. Considering the correlation between environmental attributes and their contribution to the model, six environmental factors were selected to construct a prediction model for the suitability of M. pyrifera. Among these, light intensity and temperature produced the greatest impact on the natural distribution of M. pyrifera. For high suitable growth probability, the optimal light intensity was > 2 μmol/(m2·s) and the optimum temperature range was 10.5~17℃. Combined with ArcGIS, the modeling results showed that the suitable habitats for M. pyrifera in China are mainly distributed in the Yellow Sea and Bohai Sea, accounting for approximately 13.17% of the sea area, with a marginal suitability of 5.46%, low suitability of 2.85%, moderate suitability of 1.20%, and high suitability of 3.66%. Furthermore, Liaodong Bay and Bohai Bay are suitable sea areas for the introduction and cultivation of M. pyrifera as well as the construction of M. pyrifera farms. Some areas in Liaodong Bay are highly suitable, indicating a certain risk of invasion. Therefore, ecological safety evaluations should be strengthened if M. pyrifera cultivation is promoted near this area.
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