The impact of climate change on potential distribution of species in semi-arid region: A case study of Qinghai spruce (Picea crassifolia) in Qilian Mountain, Gansu province, China

To restore the human-disturbed natural ecosystem and to assess the impact of the projected future climatic change on the natural ecosystem at a plant community level or at a plant species level, the potential distribution of the community and the species under current climate conditions need to be understood. Therefore many methods have recently been developed to simulate the potential distribution of a particular community or species [1]. However, very little has been done to assess the potential distribution of Qinghai spruce (Picea crassifolia) in Qilian Mountains where the spruce forest is extremely important ecologically and hydrologically. This study used Maximum Entropy model to simulate the potential distribution of Qinghai spruce under current climatic conditions. The validity of the model was verified by comparing the simulated potential distribution with the observed distribution of the spruce. The result shows the model is feasible to simulate the potential distribution of Qinghai spruce. Then this model was used to assess the impact of the projected climatic changes on the distribution of the spruce. The distribution of the spruce under current climate condition was compared with that under the projected climatic change scenario. The areal extent of the potential distribution may increase by 1% under the projected climatic change scenario. In addition, this study revealed that Mean Maximum Temperature of Warmest Month and Mean Temperature of Wettest Quarter are the most important factors which controlling the potential distribution of Qinghai spruce among the 19 environmental and climatic factors used in this model.

[1]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

[2]  E. Welk,et al.  Present and potential distribution of invasive garlic mustard (Alliaria petiolata) in North America , 2002 .

[3]  P. Harrison,et al.  Modelling climate change impacts on the distribution of breeding birds in Britain and Ireland , 2003 .

[4]  J. Franklin Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients , 1995 .

[5]  J. Dutoit The Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) , 2007 .

[6]  Hughes,et al.  Biological consequences of global warming: is the signal already apparent? , 2000, Trends in ecology & evolution.

[7]  J. P. Mccarty Ecological Consequences of Recent Climate Change , 2001 .

[8]  N. Gotelli Predicting Species Occurrences: Issues of Accuracy and Scale , 2003 .

[9]  P. Harrison,et al.  Modelling climate change impacts on species’ distributions at the European scale: implications for conservation policy , 2006 .

[10]  D. Gray The gypsy moth life stage model: landscape-wide estimates of gypsy moth establishment using a multi-generational phenology model , 2004 .

[11]  Luigi Boitani,et al.  A Large‐Scale Model of Wolf Distribution in Italy for Conservation Planning , 1999 .

[12]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[13]  L. Beaumont,et al.  Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions , 2005 .

[14]  Louis R. Iverson,et al.  Potential redistribution of tree species habitat under five climate change scenarios in the eastern US , 2002 .

[15]  A. Peterson,et al.  Sensitivity of distributional prediction algorithms to geographic data completeness , 1999 .

[16]  M. J. Salinger,et al.  Reducing Vulnerability of Agriculture and Forestry to Climate Variability and Change: Workshop Summary and Recommendations , 2005 .

[17]  O. Olfert,et al.  Impact of climate change on potential distributions and relative abundances of Oulema melanopus, Meligethes viridescens and Ceutorhynchus obstrictus in Canada , 2006 .