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Zhu Hong, Lin Hai-Jiao, Yang Le, Li He-Peng, Yue Chun-Lei, Jiang Bo. Geographical distribution pattern and environmental explanation of Kandelia obovata Sheue, H.Y. Liu & J. Yong populations along the Southeast coast of China[J]. Plant Science Journal, 2021, 39(5): 476-487. DOI: 10.11913/PSJ.2095-0837.2021.50476
Citation: Zhu Hong, Lin Hai-Jiao, Yang Le, Li He-Peng, Yue Chun-Lei, Jiang Bo. Geographical distribution pattern and environmental explanation of Kandelia obovata Sheue, H.Y. Liu & J. Yong populations along the Southeast coast of China[J]. Plant Science Journal, 2021, 39(5): 476-487. DOI: 10.11913/PSJ.2095-0837.2021.50476

Geographical distribution pattern and environmental explanation of Kandelia obovata Sheue, H.Y. Liu & J. Yong populations along the Southeast coast of China

  • 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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