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Assessing the Suitability of the worldclim Dataset for Ecological Studies in Southern Kenya

Journal of Geographic Information System 2018年第6期10卷 643-658页

作者： Tim J. L. Wango Douglas Musiega Charles N. Mundia Department of Geomatic Engineering & Geospatial Information Systems Jomo Kenyatta University of Agriculture and Technology Juja Kenya Department of Geomatics and Geospatial Information Systems Dedan Kimathi University Nyeri Kenya

There have been numerous efforts to generate freely available climatic datasets for use in species distribution models, the most popular being the global climatic dataset known as worldclim. The availability of such datasets is invaluable to scientists as many studies are performed in remote areas where no weather stations are found. However, many users do not critically assess the suitability of these datasets for their applications, and errors associated with global datasets are often assumed to be negligible. Understanding what a climate dataset can or cannot deliver requires the user to have a working knowledge of what the basic spatial climate-forcing factors are at the scale of his/her study, and to have a good understanding of the uncertainty in the dataset. In geographic studies, uncertainty is often described by the degree of error (uncertainty), or degree of accuracy (certainty) in data, and thematic uncertainty refers to the uncertainty in measures made for each variable, whereas temporal uncertainty refers to the uncertainty in time period represented by each variable. Here, we used climatic data from weather stations to investigate the climate-forcing factors in southern Kenya, and then used this weather station data to investigate the uncertainty in the worldclim dataset. Results indicated that the nineteen core worldclim variables, known as bioclimatic variables, accurately depicted the local variations in climate in the study area. However, whereas the monthly and seasonal temperature variables represented the same time period in different locations, the same was not true for the monthly and seasonal precipitation variables. The onset of rains is a key biological indicator, and scientists studying phenomena tied to the onset of rains need to keep in mind the temporal variations represented in the worldclim dataset.

关键词： worldclim Bioclimatic Variables Thematic Uncertainty Temporal Uncertainty

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未来气候条件下蒙古草原植被的预测分布

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Journal of Plant Ecology 2024年第3期17卷 16-27页

作者： Kohei Suzuki Ikutaro Tsuyama Radnaakhand Tungalag Amartuvshin Narantsetseg Tsagaanbandi Tsendeekhuu Masato Shinoda Norikazu Yamanaka Takashi Kamijo Department of Regional Regeneration Science Faculty of Regional Environment ScienceTokyo University of AgricultureTokyo 156-8502Japan Hokkaido Research Center Forestry and Forest Products Research InstituteHokkaido 062-8516Japan Department ofBiology School of Arts and SciencesNational University of MongoliaUlaanbaatar 210646Mongolia Botanic Garden and ResearchInstitute Mongolian Academy of ScienceUlaanbaatar 51Mongolia Department of Earth and Environmental Sciences GraduateSchool of Environmental StudiesNagoya UniversityNagoya 464-8601Japan Arid Land Research Center Tottori UniversityTottori680-0001Japan Faculty of Life and Environmental Sciences University of TsukubaIbaraki 305-8572Japan

蒙古牧民家庭通过适应当地植被分布的特点来维持牲畜的健康状况。因此,预测未来植被变化对于稳定的牲畜放牧和可持续的草原利用至关重要。本研究基于前期研究获得的植被数据预测了2081年至2100年间草原植被,尤其是沙漠草原、草原和草甸... 详细信息

蒙古牧民家庭通过适应当地植被分布的特点来维持牲畜的健康状况。因此,预测未来植被变化对于稳定的牲畜放牧和可持续的草原利用至关重要。本研究基于前期研究获得的植被数据预测了2081年至2100年间草原植被,尤其是沙漠草原、草原和草甸草原的分布范围。我们首先基于在蒙古(43–50°N,87–119°E)收集的草原植被数据(2012至2016年,278个样地)划分了不同的群落类型。其次,采用最大熵(MaxEnt)模型进行物种分布建模,其中以荒漠草原、草原和草甸草原的分布数据作为目标变量,使用worldclim的生物气候数据作为解释变量。最后,采用worldclim提供的CMIP6下缩放的未来气候预测数据进行未来预测。结果发现,荒漠草原、草原和草甸草原模型的曲线下面积值分别为0.850、0.847和0.873。根据所有情景和所有群落的气候变化,适宜栖息地预计将缩小。潜在适宜区域减少的程度在草甸草原群落中最为显著。上述结果表明,未来气候变化情景下草甸草原群落将转变为草原群落。

关键词：气候变化群落最大熵物种分布建模草原 worldclim

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