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Urban Planning and Green Landscape Management Drive Plant Diversity in Five Tropical Cities in China

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  • Jianpeng Cui

    (Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China
    Collaborative Innovation Center of Nanfan and High-Efficiency Tropical Agriculture, School of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, China)

  • Meihui Zhu

    (Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China
    Collaborative Innovation Center of Nanfan and High-Efficiency Tropical Agriculture, School of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, China)

  • Linyuan Guo

    (Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China
    Collaborative Innovation Center of Nanfan and High-Efficiency Tropical Agriculture, School of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, China)

  • Haili Zhang

    (Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China
    Collaborative Innovation Center of Nanfan and High-Efficiency Tropical Agriculture, School of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, China)

  • Alice C. Hughes

    (School of Biological Sciences, University of Hong Kong, Hong Kong, China)

  • Huafeng Wang

    (Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China
    Collaborative Innovation Center of Nanfan and High-Efficiency Tropical Agriculture, School of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, China)

Abstract

Green space is essential in urban areas to maintain, and improve human well-being. To better understand the impact of environmental and socioeconomic changes on the sustainability of tropical urban green space landscapes, work is needed to explore the patterns of plant diversity and its drivers in urban green spaces. We explore urban floristic diversity patterns based on samples from 826 urban functional units located in five cities in the tropical coastal region of China. Field surveys were conducted to obtain data on plant diversity, land use types, socioeconomic characteristics, and environmental characteristics within these units. Plant diversity within the cities varied significantly among land-use types, with high-rise housing, parks, and universities exhibiting higher diversity. The diversity of cultivated plant species and the regional economy showed a significant positive correlation (β coefficient = 0.15, p -value < 0.05), while an increase in the diversity of spontaneously growing plant species and native species was linked to building age (β coefficient = 0.12, p -value < 0.01; β coefficient = 0.13, p -value < 0.01). Management also affected plant species diversity, with watering and maintenance frequency having a positive association. Urban plant diversity patterns result from a combination of multiple factors, and different drivers affect native vs. non-native plant diversity Socio-economic factors are the primary driver of urban plant diversity patterns, as space management and maintenance determine what can grow in different areas. This study has important practical significance for policymakers and managers in developing and managing urban green spaces more sustainably.

Suggested Citation

  • Jianpeng Cui & Meihui Zhu & Linyuan Guo & Haili Zhang & Alice C. Hughes & Huafeng Wang, 2023. "Urban Planning and Green Landscape Management Drive Plant Diversity in Five Tropical Cities in China," Sustainability, MDPI, vol. 15(15), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:12045-:d:1211720
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    References listed on IDEAS

    as
    1. Teodoro Semeraro & Aurelia Scarano & Riccardo Buccolieri & Angelo Santino & Eeva Aarrevaara, 2021. "Planning of Urban Green Spaces: An Ecological Perspective on Human Benefits," Land, MDPI, vol. 10(2), pages 1-26, January.
    2. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
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