首页|基于公民科学数据测算物种保护优先性的方法优化研究——应用机器学习与智能优化算法

基于公民科学数据测算物种保护优先性的方法优化研究——应用机器学习与智能优化算法

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  • 万方数据
  • 维普
公民科学观测记录是生物多样性保护相关规划研究和实践中常用的数据来源,但存在记录点代表性有限和热点聚集等问题,充分了解其局限性及可能存在的偏差对于有效的保护规划至关重要.选用广州市100条鸟类系统调查样线数据作为基准,分析同时期公民科学数据的偏差情况及不同数据筛选方法和优先性测算方法的改善效果.采用3种数据稀疏方式减小公民科学数据热点聚集产生的影响,对鸟类记录点及其所处环境进行机器学习并构建275种鸟类的分布模型,基于此测算保护优先性.广州市公民科学观测记录数据热点聚集明显,对其进行稀疏有助于减小物种分布模拟偏差,但与基于系统调查数据得出的结果相比仍具有较大差距.对比传统丰富度方法与智能优化算法的保护优先性测算结果显示,智能优化算法可以更有效地识别丰富度不高但对特定物种更重要的区域,且对公民科学数据采样不均问题带来的保护优先性测算结果偏差具有良好的改善效果.因此,在利用公民科学观测记录进行规划研究和实践时,宜采用多种方式进行数据筛选、物种分布模拟及保护优先性测算,以取得更加可靠的结果.
Optimizing the Framework for Species Conservation Priorities Calculation Based on Citizen Science Data:Application of Machine Learning and Intelligent Optimization Algorithms
Citizen science records are commonly used as data sources in planning research and practice.However,there are issues including limited representativeness of observation points and clustering of hotspots,leading to biases in the analysis based on them.It is important to fully understand their limitations and possible deviations for effective conservation planning.We used bird survey data from 100 transects in Guangzhou as a benchmark to assess how various data filtering and prioritization methods improves the results from concurrent citizen science data.Applied machine learning with a max entropy model to simulate 275 bird species'occurrence probabilities according to their records and environments,which then informed conservation priority calculations.The results revealed clustering in Guangzhou's citizen science data.Sparsity can alleviate deviation,yet it still lags significantly in representativeness compared to systematic survey-based results.Compared with the conservation priority estimation results of the traditional richness method and the intelligent optimization algorithm,it is found that the latter can identify habitats with low richness but more important for specific species effectively,and has an obvious alleviation on the bias of conservation priority estimation results caused by the uneven sampling of citizen science data.The results show that it is advisable to use a variety of methods to screen data,simulate species distribution,and estimate conservation priorities in order to obtain relatively reliable results when using citizen science records for planning research and practice.

landscape architecturelandscape planningbiodiversity conservationspecies distribution modelartificial intelligencecitizen science

侯姝彧、尚轩仪、刘彦、李晖、梁健超

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华南农业大学林学与风景园林学院(广州510642)

广州市水生态建设中心(广州510660)

广东省科学院动物研究所广东省动物保护与资源利用重点实验室(广州510642)

风景园林 风景园林规划 生物多样性保护 物种分布模型 人工智能 公民科学

国家自然科学基金面上项目广东省自然科学基金面上项目广东省基础与应用基础研究基金项目2023年度广州市水务科技项目

520782222024A15150107832021A1515110744GZSWKJ2022-008

2024

10.19775/j.cla.2024.09.0029

中国园林
中国风景园林学会

中国园林

CSTPCD北大核心
影响因子：1.108
ISSN：1000-6664
年,卷(期)：2024.40(9)
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