| 摘要: |
| 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。 |
| 关键词: 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 |
| DOI：10.19775/j.cla.2024.09.0029 |
| 投稿时间：2024-05-04修订日期：2024-06-26 |
| 基金项目:国家自然科学基金面上项目(52078222)；广东省
自然科学基金面上项目(2024A1515010783)；广东省基础
与应用基础研究基金项目(2021A1515110744)；2023年度
广州市水务科技项目(GZSWKJ2022-008) |
|
| Optimizing the Framework for SpeciesConservation Priorities Calculation Based onCitizen Science Data: Application of MachineLearning and Intelligent Optimization Algorithms |
| HOU Shuyu,SHANG Xuanyi,LIU Yan,LI Hui,LIANG Jianchao |
| Abstract: |
| 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. |
| Key words: landscape architecture landscape planning biodiversity
conservation species distribution model artificial intelligence citizen science |
