文献类型：期刊文献

英文题名：Using DEM to predict Abies faxoniana and Quercus aquifolioides distributions in the upstream catchment basin of the Min River in southwest China

作者：Zhang, Lei[1] Liu, Shirong[2] Sun, Pengsen[2] Wang, Tongli[3] Wang, Guangyu[4] Wang, Linlin[5] Zhang, Xudong[1]

第一作者：张雷

通信作者：Liu, SR[1]

机构：[1]Chinese Acad Forestry, Res Inst Forestry, Beijing 10091, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Ecol Environm & Protect, Beijing 10091, Peoples R China;[3]Univ British Columbia, Dept Forest Sci, Ctr Forest Conservat Genet, Vancouver, BC V6T 1Z4, Canada;[4]Univ British Columbia, Fac Forestry, Vancouver, BC V6T 1Z4, Canada;[5]Beijing Agr Univ, Beijing 102206, Peoples R China

年份：2016

卷号：69

起止页码：91-99

外文期刊名：ECOLOGICAL INDICATORS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000388785100010)】；

基金：This study was funded by the National Natural Science Foundation of China (41301056), the Ministry of Science and Technology of China (2012BAD22B01, 2015DFA31440) and the Special Foundation of Chinese Academy of Forestry (CAFYBB2014QB006, RIF2012-04).

语种：英文

外文关键词：Digital elevation model; Species distribution model; Topographical variable; Spatial resolution; Forestation

摘要：The species distribution model (SDM), which is used to spatially predict species distributions, can also identify the probable causes of the location of certain species (i.e. the mathematical description of habitat requirements). Therefore, SDM has the potential to guide resource management and biodiversity conservation. In the topographically complex terrain, SDMs are often Complicated by the lack of environmental data; however, the first information that is typically obtained for these analyses is a topographic map. Here, the possibility of using 16 predictor variables derived from the digital elevation model (DEM) to model the distributions of Abies faxoniana and Quercus aquifolioides in the mountainous upstream catchment basin of the Min River (UCBM) in southwest China was investigated. In particular, with the ensemble modeling approach based on eight niche models and nine model-training and-testing datasets, changes in model performance and shifts in the explanatory power of the predictor variable over five different levels of spatial resolution (30 m, 90 m, 120 m, 240 m, 900 m) were assessed. Almost all models succeed in predicting the distributions of both species, although predictive accuracies differed significantly among spatial scales and model classes. On average, model accuracies increased to the highest level at the meso-scale (120 m and 240 m for A. faxoniana and Q, aquifolioides, respectively) and then decreased as resolution became coarser, indicating that high spatial resolution does not imply a better model. The relative importance rankings for each topographical variable were consistent across all spatial scales, but their explanatory powers did differ significantly among spatial scales. Elevation and terrain-distributed solar radiation for growing season (SRG) drive the distributions of A. faxoniana and Q, aquifolioides with a much higher level of confidence than other predictors across all spatial scales; the former tended to decrease, and the latter tended to increase when spatial resolution became coarse. Our findings confirm that DEM can be used exclusively and effectively to predict species distribution. Multi-scale analysis is needed to detect highly subtle variations in species habitat requirements, and to select the spatial scale that corresponds to known spatial characteristics of the species habitat. This has broad implications for distribution modeling of species in rugged terrain. (C) 2016 Elsevier Ltd. All rights reserved.