High-resolution bioclimatic dataset derived from future climate projections for plant species distribution modeling |
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| Authors: | Kou Xiaojun Li Qin Liu Shirong |
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| Institution: | 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, PR China;2. College of Life Sciences, Beijing Normal University, Beijing 100875, PR China;3. Institute of Forest Environment and Ecology, Chinese Academy of Forestry, Beijing 100091, PR China;1. School of Architecture, Xi’an University of Architecture and Technology, Xi’an 710055, China;2. School of Architectural and Artistic Design, Henan Polytechnic University, Jiaozuo 454000, China;1. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China;2. Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China;3. Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China;4. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China;1. College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China;2. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;3. Faculty of Geo-Information Science and Earth Observation, University of Twente, 7500AE Enschede, The Netherlands;4. School of Environment, Tsinghua University, Beijing 100084, China;5. Shenzhen Key Laboratory of Southern Subtropical Plant Diversity, Fairylake Botanical Garden, Shenzhen & Chinese Academy of Sciences, Shenzhen 518004, China;6. School of Life Sciences, East China Normal University, Shanghai 200062, China;1. Departamento de Sistemas y Recursos Naturales, Universidad Politécnica de Madrid, Ciudad Universitaria s/n, 28040 Madrid, Spain;2. Departamento de Biología Vegetal I, Facultad de Ciencias Biológicas, Universidad Complutense de Madrid, C/ José Antonio Novais, 12, 28040 Madrid, Spain;3. Institute of Plant Sciences and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland |
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| Abstract: | Species distribution modeling is playing an increasingly prominent role in ecology and global change biology, owing to its potential to predict species range shifts, biodiversity losses, and biological invasion risks for future climates. Such models are now well-established as important tools for biological conservation. However, the lack of high-resolution data for future climate scenarios has seriously limited their application, particularly because of the scale gap between general circulation models (GCMs) and species distribution models (SDMs). A recently introduced change-factor downscaling technique provides a convenient way to build high-resolution datasets from GCM projections. Here, we present a high-resolution (10’ × 10’) global bioclimatic dataset (BioPlant) for plant species distribution. The 15 bioclimatic variables we select are considered those most eco-physiologically relevant. They can be easily calculated from climatic variables common to all GCM projections. In addition to the traditional classes of variables regarding temperature and precipitation, the BioPlant dataset emphasizes the interactions between temperature and precipitation, particularly within plant growing seasons. A preliminary visual analysis shows that variations among GCMs are more significant on a species range scale than on a global scale. Thus, the formerly advocated ensemble modeling method should be applied not only to different SDMs, but also to various GCMs. Statistic analysis suggests that divergent behavior among GCM variations for temperature class variables and classes of precipitation variables requires special attention. Our dataset may provide a common platform for ensemble modeling, and can serve as an example to develop higher-resolution regional datasets. |
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