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Changes of Major Terrestrial Ecosystems in China Since 1960

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Global and Planetary Change 48 (2005) 287–302 www.elsevier.com/locate/gloplacha Changes of major terrestrial ecosystems in China since 1960 Tian Xiang YueT, Ze Meng Fan, Ji Yuan Liu Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Jia No. 11, Datun, Anwai, 100101 Beijing, China Abstract Daily temperature and precipitation data since 1960 are selected from 735 weather stations that are scattered over China. After comparatively analyzing relative interpolation methods, gradient-plus-inverse distance squared (GIDS) is selected to create temperature surfaces and Kriging interpolation method is selected to create precipitation surfaces. Digital elevation model of China is combined into Holdridge Life Zone (HLZ) model on the basis of simulating relationships between temperature and elevation in different regions of China. HLZ model is operated on the created temperature and precipitation surfaces in ARC/ INFO environment. Spatial pattern of major terrestrial ecosystems in China and its change in the four decades of 1960s, 1970s, 1980s and 1990s are analyzed in terms of results from operating HLZ model. The results show that HLZ spatial pattern in China has had a great change since 1960. For instance, nival area and subtropical thorn woodland had a rapid decrease on an average and they might disappear in 159 years and 96 years, respectively, if their areas would decrease at present rate. Alpine dry tundra and cool temperate scrub continuously increased in the four decades and the decadal increase rates are, respectively, 13.1% and 3.4%. HLZ patch connectivity has a continuous increase trend and HLZ diversity has a continuous decrease trend on the average. Warm temperate thorn steppe, subtropical wet forest and cool temperate wet forest shifted 1781.45 km, 1208.14 km and 977.43 km in the four decades, respectively. These HLZ types are more sensitive to climate change than other ones. These changes reflect the great effects of climate change on terrestrial ecosystems in China. D 2005 Elsevier B.V. All rights reserved. Keywords: Holdridge Life Zone; digital elevation model; interpolation method; spatial pattern; mean-center shift; geographical information system 1. Introduction world’s environments since early 1880s (Schouw, 1823; Griesebach, 1872; Merriam, 1892; Clements, A continuing endeavor had been made in 1916; Koeppen and Geiger, 1930; Thornthwaite, different earth surface sciences to classify the 1931). To improve the drawback of the categories of the prior systems that are too coarse and T Corresponding author. Tel.: +86 10 64889633; fax: +86 10 inapplicable to the whole world, Holdridge (1947) 64889630. devised a classification. The Holdridge Life Zone E-mail address: [email protected] (T.X. Yue). (HLZ) classification relates the distribution of major 0921-8181/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.gloplacha.2005.03.001 288 T.X. Yue et al. / Global and Planetary Change 48 (2005) 287–302 terrestrial ecosystems (termed life zones) to the seem more important than direct physiological ones at bioclimatic variables. It is a scheme that uses the the life zone level. The vegetation distribution is three bioclimatic variables derived from standard essentially controlled by climate at large scale (Wood- meteorological data to formulate the relation of ward, 1987). Application of HLZ model was validated climate patterns and broad-scale vegetation distri- by comparing the modeled HLZ distribution for the bution. It has been widely accepted in projecting current climate with the real life zone distribution map impacts of climate change on vegetation distribu- in China (Chen et al., 2003, 2005). It is indicated that tions (Chen et al., 2003, 2005; Chinea and Helmer, HLZ model could be applied to simulate changes of 2003; Kerr et al., 2003; Yang et al., 2002; Yue et major terrestrial ecosystems in China. In this paper, al., 2001; Xu and Yan, 2001; Peng, 2000; Kirilenko spatial pattern of major terrestrial ecosystems in China et al., 2000; Powell et al., 2000; Dixon et al., 1999; and its changes in the four decades of 1960s, 1970s, Metternicht and Zinck, 1998; Belotelov et al., 1996; 1980s and 1990s are analyzed on the basis of digital Smith et al., 1992; Post et al., 1982). elevation model of China by operating HLZ model on Although no consideration of physiological meteorological data from 735 weather stations that are changes is a drawback of HLZ model, climatic effects scattered over China (Fig. 1). N WE S 3000 300 600 km Fig. 1. Spatial distribution of the weather observation stations in China. T.X. Yue et al. / Global and Planetary Change 48 (2005) 287–302 289 2. Methods 2.1. The HLZ model The HLZ model divides the world into over 100 life zones in terms of mean annual biotemperature in degrees centigrade (MAB), average total annual precipitation in millimeters (TAP), and potential evapotranspiration ratio (PER) logarithmically. Biotemperature is defined as the mean of unit-period temperatures with substitution of zero for all unit-period values below 0 8C and above 30 8C(Holdridge et al., 1971). Evapotranspiration is the total amount of water that is returned directly to the atmosphere in the form of vapor through the combined processes of evaporation and transpiration. Potential evapotranspiration is the amount of water that would be transpired under constantly optimal conditions of soil moisture and plant cover. The potential evapotranspiration ratio is the ratio of mean annual potential evapotranspiration to average total annual precipitation, which provides an index of biological humidity conditions. In other words, MAB, TAP and PER at site (x,y) and in the t-th year have the following formulation: 1 X365 MABðÞ¼x; y; t TEMðÞj; x; y; t ð1Þ 365 j¼1 X365 TAPðÞ¼x; y; t PjðÞ; x; y; t ð2Þ j¼1 58:93MABðÞx; y; t PERðÞ¼x; y; t ð3Þ TAPðÞx; y; t where TEM( j,x,y,t) is the value summing the hourly temperature above 0 8C and below 30 8C on the j-th day and dividing by 24; P( j,x,y,t) is the mean of precipitation on the j-th day. Suppose ; ; ; ; MxðÞ¼y t log2MABðÞx y t ð4Þ ; ; ; ; TxðÞ¼y t log2TAPðÞx y t ð5Þ ; ; ; ; PxðÞ¼y t log2PERðÞx y t ð6Þ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 2 diðÞ¼x; y; t ðMxðÞÀ; y; t Mi0Þ þðTxðÞÀ; y; t Ti0Þ þðPxðÞÀ; y; t Pi0Þ ð7Þ where Mi0, Ti0 and Pi0 are standards of MAB logarithm, TAP logarithm and PER logarithm at the central point of the i-th life zone in the hexagonal system of HLZs. When dk ðÞ¼x; y; t mini fdiðÞgx; y; t , the site (x,y) is classified into the k-th life zone. 2.2. Mean center model The model of the mean center was introduced in the USA census of population in 1870 (Shaw and Wheeler, 1985). Such spatial statistics has been used in geography since 1950s (Hart, 1954; Warntz and Neft, 1960; Ebdon, 1978; Shaw and Wheeler, 1985; Yue et al., 2003). The major applications were those concerned with central tendency, especially the weight mean center of spatial distribution. The model is formulated as (Yue et al., 2003), 290 T.X. Yue et al. / Global and Planetary Change 48 (2005) 287–302 XIj s ðÞt X ðÞt x ðÞ¼t ij ij ð8Þ j S t i¼1 jðÞ XIj s ðÞt Y ðÞt y ðÞ¼t ij ij ð9Þ j S t i¼1 jðÞ where t is the variable of time; Ij(t) is patch number of HLZ type j; sij(t) is area of the i-th patch of HLZ type j; Sj(t) is total area of HLZ type j;(Xij(t), Yij(t)) is longitude and latitude coordinate of the geometric center of the i-th patch of HLZ type j;(xj(t), yj(t)) is the mean center of HLZ type j. 2.3. Ecological diversity index On the basis of analyzing all diversity indexes and conducting theoretical demonstration, the ecological diversity index is expressed as (Yue et al., 1998, 2001, 2003, in press-b), !2 mXðÞe 1 ln ðÞpiðÞt 2 i 1 dtðÞ¼ À ¼ ð10Þ lnðÞe where t is the variable of time; pi(t) is probability of the i-th ecotope; m(e) is total number of the 1 A investigation ecotopes; e ¼ e þ s , A is area of studied region in hectares or area of the sampling quadrat, s is spatial resolution of land cover data or the smallest crown diameter of the sampled individuals, and e equals 2.71828. 2.4. Patch connectivity index Landscape connectivity is distinguished into patch connectivity, line connectivity, vertex connectivity and network connectivity (Yue et al., 2003). The patch connectivity index is formulated as (Yue et al., 2003, 2004), XmtðÞ nXiðÞt COðÞ¼t pijðÞt SijðÞt ð11Þ i¼1 j¼1 AijðÞt where t is the variable of time; pijðÞ¼tffiffi A , Aij(t) the area of the j-th patch in the i-th HLZ type and A the total p pffiffiffi 8 3AijðÞt area under investigation; SijðÞ¼t 2 ,Prij(t) is the perimeter of the j-th patch in the i-th HLZ type and 8 3 the ðPrijðÞt Þ ratio of the square of perimeter to the area of a hexagon; 0VC(t)V1.1 and when all patches have the shape of hexagon (6-gon), C(t)=1.0. 3. Simulation process 3.1. Interpolation Our analyses on data from the 735 weather observation stations show that there is no statistically significant correlation between precipitation and elevation. Therefore, Kriging interpolation method is selected T.X. Yue et al. / Global and Planetary Change 48 (2005) 287–302 291 to create 1 kmÂ1 km precipitation surfaces.
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