Geographical distribution pattern and environmental explanation of Kandelia obovata Sheue, H.Y. Liu & J. Yong populations along the Southeast coast of China
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摘要: 为揭示我国秋茄树（Kandelia obovata Sheue，H.Y.Liu&J.Yong）地理分布格局及其与主要环境因子的关系，本研究整合天然和引种的地理分布信息，采用生态位模型模拟其适生区范围，评估两个类型分布区的空间分布多样性指标，并结合对应的气候、水文资料，利用生态统计学方法，对影响秋茄树地理分布的各层次环境因子开展定量分析。结果显示，BIOCLIM模型预测的准确度很高，Kappa系数与受试者工作特征曲线下的面积（AUC值）分别为0.952和0.976，并获取3个多样性热点区域。主成分分析（PCA）结果表明温度是限制秋茄树分布的主导气候因子，其中年均温（17.68℃）、最冷季均温（8.22℃）和极端最低温（4.04℃）为其最适分布阈值。典范对应分析（CCA）显示秋茄树地理分布受经、纬度的双重控制，但纬向效应相对更显著；水文局域尺度上，平均海面温度和平均潮差对秋茄树分布有显著影响，而平均海面pH值和平均海面盐度对其影响不大。除Pielou指数外，我国秋茄树天然林的各α多样性指数均高于人工林，主要省份间多样性排序为浙江（人工林）>广东>福建>海南>广西>台湾。非加权组平均聚类分析结果可将48个秋茄树种源划分成3大类群，具有明显的地理区域性与生态位分化。
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关键词:
- 秋茄树 /
- 地理分布格局 /
- BIOCLIM模型 /
- 空间多样性 /
- 典范对应分析(CCA)
Abstract: To reveal the relationship between the geographical distribution patterns of Kandelia obovata Sheue, HY Liu & J. Yong and its major environmental factors in China, we integrated species geographic distribution records of natural and introduced ranges and applied an ecological niche model to simulate the range of suitable areas and compare the spatial distribution diversity indices between the distribution areas. Combined with climatological and hydrological data, we performed quantitative analysis of the environmental factors that affect the geographical distribution of K. obovata. Results showed that the prediction accuracy of the BIOCLIM model was very high, as indicated by the Kappa coefficient (0.952) and area under the receiver operating characteristic curve (0.976). In addition, three diversity hotspots were identified. Principal component analysis (PCA) showed that temperature was the dominant climatic factor limiting the distribution of K. obovata, with average annual temperature (17.68℃), the coldest season average temperature (8.22℃), and the lowest extreme temperature (4.04℃) found to be the optimal distribution thresholds. Canonical correspondence analysis (CCA) showed that the geographical distribution of K. obovata was controlled by both longitude and latitude, but the latitudinal effect was more significant. At the local hydrological scale, mean sea surface temperature (MSST) and mean tidal range (MTR) significantly influenced K. obovata distribution (P < 0.05), while mean sea surface water pH (MSSWP) and mean sea surface salinity (MSSS) had little influence. Except for Pielou's index (J), the population (alpha) diversity indices of K. obovata in natural forests were higher than that in introduced forests, and diversity in the major provinces was ranked Zhejiang (introduced) > Guangdong > Fujian > Hainan > Guangxi > Taiwan. Based on cluster analysis using the unweighted pair-group method with arithmetic means (UPGMA), the 48 distribution records of K. obovata could be divided into three groups, with obvious geographic regionalization and niche differentiation. The above research enriches our understanding of the geographical and ecological theories of K. obovata and provides a scientific basis for its introduction and development in China. -
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