Dataset Information

Predicting Impacts of Future Climate Change on the Distribution of the Widespread Conifer Platycladus orientalis.

ABSTRACT: Chinese thuja (Platycladus orientalis) has a wide but fragmented distribution in China. It is an important conifer tree in reforestation and plays important roles in ecological restoration in the arid mountains of northern China. Based on high-resolution environmental data for current and future scenarios, we modeled the present and future suitable habitat for P. orientalis, evaluated the importance of environmental factors in shaping the species' distribution, and identified regions of high risk under climate change scenarios. The niche models showed that P. orientalis has suitable habitat of ca. 4.2×106 km2 across most of eastern China and identified annual temperature, monthly minimum and maximum ultraviolet-B radiation and wet-day frequency as the critical factors shaping habitat availability for P. orientalis. Under the low concentration greenhouse gas emissions scenario, the range of the species may increase as global warming intensifies; however, under the higher concentrations of emissions scenario, we predicted a slight expansion followed by contraction in distribution. Overall, the range shift to higher latitudes and elevations would become gradually more significant. The information gained from this study should be an useful reference for implementing long-term conservation and management strategies for the species.

SUBMITTER: Hu XG

PROVIDER: S-EPMC4488561 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting Impacts of Future Climate Change on the Distribution of the Widespread Conifer Platycladus orientalis.

Hu Xian-Ge XG Jin Yuqing Y Wang Xiao-Ru XR Mao Jian-Feng JF Li Yue Y

PloS one 20150701 7

Chinese thuja (Platycladus orientalis) has a wide but fragmented distribution in China. It is an important conifer tree in reforestation and plays important roles in ecological restoration in the arid mountains of northern China. Based on high-resolution environmental data for current and future scenarios, we modeled the present and future suitable habitat for P. orientalis, evaluated the importance of environmental factors in shaping the species' distribution, and identified regions of high ris ...[more]

PMID: 26132163

Similar Datasets

Project description:Plant species distribution is constrained by both dynamic and static environmental variables. However, relative contribution of dynamic and static variables in determining species distributions is not clear and has far reaching implications for range change dynamics in a changing world. Prunus eburnea (Spach) Aitch. & Hemsl. is an endemic and medicinal plant species of Iran. It has rendered itself as ecologically important for its functions and services and is currently in need of habitat conservation measures requiring investigation of future potential distribution range. We conducted sampling of 500 points that cover most of Iran plateau and recorded the P. eburnea presence and absence during the period 2015-2017. In this study, we evaluated impacts of using only climatic variables versus combined with topographic and edaphic variables on accuracy criteria and predictive ability of current and future habitat suitability of this species under climate change (CCSM4, RCP 2.6 in 2070) by generalized linear model and generalized boosted model. Models' performances were evaluated using area under the curve, sensitivity, specificity and the true skill statistic. Then, we evaluated here, driving environmental variables determining the distribution of P. eburnea by using principal component analysis and partitioning methods. Our results indicated that prediction with high accuracy of the spatial distribution of P. eburnea requires both climate information, as dynamic primary factors, but also detailed information on soil and topography variables, as static factors. The results emphasized that environmental variable grouping influenced the modelling prediction ability strongly and the use of only climate variables would exaggerate the predicted distribution range under climate change. Results supported using both dynamic and static variables improved accuracy of the modeling and provided more realistic prediction of species distribution under influence of climate change.

Dataset Information

Predicting Impacts of Future Climate Change on the Distribution of the Widespread Conifer Platycladus orientalis.

Publications

Predicting Impacts of Future Climate Change on the Distribution of the Widespread Conifer Platycladus orientalis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets