acta oecologica 33 (2008) 345–354 available at www.sciencedirect.com journal homepage: www.elsevier.com/locate/actoec Original article Spatial pattern of diversity in an old-growth temperate forest in Northeastern China Xugao Wang, Zhanqing Hao*, Ji Ye, Jian Zhang, Buhang Li, Xiaolin Yao Institute of Applied Ecology, Chinese Academy of Science, P.O. Box 417, Shenyang 110016, China article info abstract Article history: Species diversity has attracted particular attention because of its significance for helping Received 26 March 2007 determine present species performance and likely future community composition. The Accepted 23 January 2008 spatial pattern of species diversity (species richness, abundance and Shannon diversity) Published online 12 March 2008 in Changbai temperate forest in Northeastern China was studied to investigate the present and likely causes for the formation of spatial patterns. To fulfill this goal, three aspects of Keywords: diversity were addressed: 1) changes in the relationships of the diversity variables, species Changbai Mountain richness, abundance and Shannon diversity, to sampling area and sampling design. The Temperate forest three diversity variables were found to respond to sampling area in a dissimilar way. Species diversity Sampling design had no significant effect on the diversity variable-area curves. The power Spatial pattern function, which was derived under the assumption that the forest was in equilibrium, did not fit the observed species-area curves, indicating that the Changbai temperate forest was probably not in equilibrium. 2) Variograms, used to examine the spatial structure of species diversity, showed that the spatial structure of species diversity in the Changbai temperate forest was weakly anisotropic. 3) Partitioning the variation of species diversity into spatial and environmental factors indicated that the spatial pattern of the Changbai forest community was unpredictable, probably because there were many undetermined processes controlling its development. ª 2008 Elsevier Masson SAS. All rights reserved. 1. Introduction Species diversity usually refers to the species richness, abundance, or a combination of both, of a community, and is the result of species interaction or community adaptation to its environment over evolutionary time (Rice and Westoby, 1982). It has attracted particular attention, in large part because of its significance in helping determine the present species performance, and likely future community composition. Ecologists have long sought to explain why numbers of species can coexist at small spatial scales, and how these species are distributed, especially in species-rich tropical rain forests (He et al., 1996; Hubbell et al., 2001; Valencia et al., 2004; Condit et al., 2006). Various hypotheses: niche differentiation (Ashton, 1969), species competition (MacArthur, 1969) and disturbance (Denslow, 1987) have been proposed as driving mechanisms to account for high diversity. Species diversity patterns should emerge as the consequence of any and all of these mechanisms. As a result, studying species diversity patterns should help understand the mechanisms that have generated the observed diversity in the community. * Corresponding author. Tel.: þ86 24 8397 0209; fax: þ86 24 8397 0300. E-mail address: [email protected] (Z. Hao). 1146-609X/$ – see front matter ª 2008 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.actao.2008.01.005 346 acta oecologica 33 (2008) 345–354 Recent studies on diversity pattern have been concentrated on tropical rain forests where species diversity reaches particularly high levels (He et al., 1996; Hubbell et al., 1999; de Oliveira and Mori, 1999; Condit et al., 2002, 2006). For example, a 52 ha plot in Borneo and a 25 ha plot in Ecuador support 1175 and 1104 tree species, respectively (Wright, 2002). In contrast, the 4.2 106 km2 of temperate forests in Europe, North America and Asia support only 1166 tree species (Latham and Ricklefs, 1993). In other words, tree species diversity in just one small area in the tropics is comparable to the diversity of the entire North Temperate Zone. However, because species diversity of one community often differs in composition, structure and species attributes from another community, species diversity in temperate forests has also been a fertile area of research for many ecologists. For example, Latham and Ricklefs (1993) suggested that regional effects caused species diversity of temperate forests to differ between eastern Asia and North America. Busing and White (1997) demonstrated small-scale disturbances created by tree falls enhance plant species diversity in an Appalachian oldgrowth temperate forest. Chen and Bradshaw (1999) suggested the importance of scale and gap-phase regeneration in the spatial patterning of a temperate coniferous forest. Lundholm and Larson (2003) showed the positive correlations between spatial environmental heterogeneity and plant species diversity in southern Ontario, Canada. Getzin et al. (2006) identified tree competition as having significant influence on species spatial pattern in a Douglas-fir forest of the Pacific Northwest region. Wang et al. (2006a,b) suggested that catastrophic fires decreased species abundances dramatically and caused more fragmented spatial pattern, whereas post-fire anthropogenic activities (harvest and reforestation) could differently influence species abundance and distribution patterns in Northeastern China. Although these studies have led ecologists to better understand the species diversity of temperate forests, few studies have focused specifically on how species diversity is distributed spatially over a temperate forest, and to what extent the diversity pattern is regulated by spatial and environmental factors. Since most ecological processes are pattern-generating (Legendre and Fortin, 1989; Legendre, 1993), analyzing the resulting spatial patterns may provide important clues as to the processes that have generated them (Borcard and Legendre, 1994). The objective of this study was to study the spatial pattern of species diversity in an old-growth temperate forest in Northeastern China based on a large-scale, intensively sampled plot. Three groups of questions were addressed: (1) How does species diversity change with plot size? What is the relationship between species diversity and area in different locations? What mathematical function best describes the species-area relationships? (2) How is species diversity distributed spatially in the old-growth temperate forest? Is the distribution of diversity isotropic or anisotropic? (3) Can the diversity pattern be predicted by environmental and/or spatial factors? To what extent do the environmental and the spatial factors (extracted from the spatial coordinate data) contribute to the observed spatial patterns? 2. Materials and methods 2.1. Study site The study site is in the Changbai Nature Reserve, located along the border of China and North Korea extending from 127 420 to 128 170 E and 41 430 to 42 260 N. The reserve, which was first established in 1960 and is one of the largest biosphere reserves in China, has been spared from logging and other severe human disturbances. Furthermore, the Changbai Nature Reserve joined the World Biosphere Reserve Network under the UNESCO Man and the Biosphere Programme in 1980. The reserve is about 200,000 ha in size with an elevation ranging from 740 m to 2691 m at the summit of Changbai Mountain. Changbai Mountain is the highest mountain in Northeastern China and is the head of three large rivers (the Songhua, Yalu and Tumen). The topography of the northern slope is relatively moderate (average slope <3%), whereas the other slopes are relatively steep (average 10%). The area has a temperate continental climate with long cold winters and warm summers. Annual mean temperatures vary from 7.3 C in the lowest elevations to 2.8 C near Sky Lake (a volcanic crater lake) on the mountaintop, and annual mean precipitation varies from 750 to 1340 mm. Even before the reserve was established, forest harvesting and other human disturbances inside the reserve had been minor compared to that at lower elevations, due in part to difficulties of access. A major volcanic eruption occurred between 1000 and 1410; while more recent eruptions in 1597 and 1668 were not destructive (Zhao, 1981; Liu et al., 1992). Forest vegetation inside the reserve is largely the result of natural succession (Zhao, 1981). Topographic and climatic variations result in vertical zones of major forest types that are especially distinct along the northern slope. The forests between 750 and 1100 m are temperate, composed of Korean pine and hardwood species. Common hardwood species include aspen (Poplus davidiana), white birch (Betula platyphylla), Amur linden (Tilia amuresis), oak (Quercus mongolica), Mono maple (Acer mono) and white ash (Fraxinus mandshurica). Between 1100 and 1700 m, the forest changes to an evergreen coniferous forest, dominated by spruce (Picea koraiensis) and fir (Abies nephrolepis) with typical characteristics of boreal forests. From 1700 to 2000 m, the vegetation is sub-alpine forest, dominated by mountain birch (Betula ermanii) and larch (Larix olgensis). Above 2000 m are tundra, bare rock, and the volcanic lake. Hardwoods are located in the temperate forest zone areas that extend 0.8 km outside the nature reserve (lower than 750 m in elevation) and where human activities have transformed the pine-hardwood forests into those mainly composed of hardwoods (Shao, 1996). In 2004, a 25 ha broad-leaved Korean pine mixed forest plot of 500 m 500 m was established in Changbai Nature Reserve (Fig. 1a). Within the plot, all free-standing trees and shrubs at least 1 cm in DBH were identified, tagged, and mapped, and their geographic coordinates recorded following a standard field protocol (Condit, 1998). In the study site, the terrain is relatively gentle. The elevation ranges from 791.8 m to 809.5 m, and mean elevation is 801.5 m. acta oecologica 33 (2008) 345–354 2.2. Fig. 1 – a. Contour map of the 25-ha Changbai temperate plot. b. The sampling designs for the study of diversityarea relations commenced from 10 m 3 10 m quadrats at five different locations (a–e), and the quadrat size was doubled until the entire plot was covered. The total number of living individuals in the first census (2004) was 38902, comprising 52 species, 32 genera and 18 families. The main tree species include P. koraiensis, T. amurensis, Q. mongolica, F. mandshurica, Ulmus japonica, and Acer mono. Unlike tropical rain forests without obvious dominant species, there were 8 species with more than 1000 individuals, which accounted for 83.4% of all individuals in the plot. Species-specific tree abundances in the plot ranged from 1 (3 species: Sorbus pohuashanensis, Actinidia kolomakta, and Rosa dovurica) to 7381 individuals of Corylus mandshurica, the most abundant species. In addition, by virtue of the criterion that species with 1 individual per ha was considered as rare species, there were 18 rare species, accounting for 34.6% of the total number of species in the plot. Mean stand density was 1556 living trees ha1. Mean basal area was 43.2 m2 ha1 (Hao et al., 2008; Wang et al., in press). 347 Data analyses In this paper, diversity refers to richness, abundance and the Shannon diversity index. Richness is defined as the number of species in the study area and abundance as the number of all individuals. The Shannon diversity index has been suggested by Margalef (1958) as a synthetic measure of community structure. Different methods were used to answer the questions stated in the Introduction. For question 1, richness, abundance and Shannon diversity were measured in square plots that ranged from 10 m 10 m to the complete 25-ha plot for five starting locations shown in Fig. 1b (a, b, c, d and e). The expected species-area curve (null model) was also computed under the assumption that all species in the study area were randomly distributed (Coleman et al., 1982; He and Legendre, 2002). The species-area relations were fitted by three models: the power model, the exponential model and the logistic model. The statistical criterion for the fit of a species-area curve is the sum of squares of the residuals. The simplest way to test whether models are significantly different is to check the 95% confidence intervals of the model parameters (Sokal and Rohlf, 1981). If there is no overlap in the confidence intervals for corresponding parameters, then we conclude they are significantly different. All the statistical tests of significance and confidence intervals in this paper were computed at the a ¼ 0.05 level. For question 2, variogram analysis was used to detect the spatial distribution of species diversity, because the semivariance is evaluated from the differences between pairs of observations over predetermined distance classes and emphasizes heterogeneity (Legendre and Legendre, 1998). A typical variogram can be described using three basic parameters: (1) the range is the distance at which the semi-variance ceases to increase (i.e. the spatial influence disappears); (2) the sill is the semi-variance value that the variogram reaches at the range; in theoretical variograms, the sill equals the overall variance of a variable; and (3) the nugget effect is the ordinate value of the variogram at distance zero; it need not be equal to zero. It corresponds to the local variation occurring at scales finer than the sampling interval, such as sampling error, fine-scale spatial variability, and measurement error. The ratio of the nugget effect to the sill is referred to as the relative nugget effect; it can be used to evaluate sampling error and short-scale spatial effect. To determine the strength of anisotropy, variograms of richness, abundance and Shannon diversity were computed in four geographic directions: 0 (south-north: SN), 90 (west-east: WE), 45 (SW to NE) and 135 (SE to NW). For question 3, the spatial patterns of diversity in Changbai temperate forest were explored through environmental and spatial factors, following a polynomial trend-surface analysis (Borcard et al., 1992). The ‘spatial’ data matrix was constructed from all quadrat locations (x and y coordinates) in the Changbai plot, by including all terms of a cubic trend-surface polynomial equation (the x and y geographic coordinates were centred on their respective means before computing the other terms of the geographic polynomial). A stepwise selection procedure was used to discard the terms of the trend 348 acta oecologica 33 (2008) 345–354 2 2 S ¼ b1 x þ b2 y þ b3 x þ b4 xy A ¼ b1 y þ b2 xy2 þ b3 y3 D ¼ b1 x þ b2 x2 þ b3 x2 y Topographical data (elevation and slope) were the only synthetic environmental variables available; they are related to and indicators of several abiotic factors, such as drainage condition, nutrient flow, etc. All variables were measured at the scale of 10 m 10 m quadrats in the 25-ha plot. The same elimination procedure as for question 1 above was applied to the environmental data and their combination (relative elevation z1 and slope z2), resulted in the following equations for species richness (S ), abundance (A), and Shannon diversity (D), respectively: S ¼ c1 z1 þ c2 z22 þ c3 z1 z2 A ¼ c1 z1 þ c2 z31 D ¼ c1 z1 þ c2 z2 Partial regression analysis was applied to measure the amount of variation in each of the three vectors of diversity data in turn that could be explained by the environmental factor, spatial variable or their interactions. The total variation of a variable is decomposed into four fractions (Borcard et al., 1992; He et al., 1996), as described below: (a) Pure spatial contribution. This is the pure spatial effect that cannot be described by the environmental variables, that is, is independent of any environmental variables. (b) Spatial þ environmental contribution. This is the proportion of variation explained by the environmental and the spatial variables together. Two types of situation may be responsible for this fraction of variation: firstly, diversity may vary spatially as a function of the environmental factors in the model or, secondly, there may exist other processes, unidentified in the regression model under study, which control both the species diversity and the environmental factors in the model. (c) Pure environmental contribution. This is the proportion of the diversity variation independent of any spatial structure. (d) Undetermined contribution. This fraction, which measures the unexplained fraction of variation, does not possess large-scale spatial structure which would have come out in fractions (b) or (c). It may be the consequence of stochastic fluctuations or sampling error, or it may reflect some spatially structured variation which exists at a scale smaller than the sampling scale. 3. Results 3.1. Species diversity In the Changbai plot, the species-area relationship described the tendency for species richness to increase with sampling area; a relationship whose slope declines (but remains positive) as sampling area increased. When the sampling area increased to 5 ha, there were about 42 species in all sampling designs, approximately 80% of the total number of species in the Changbai plot, then the curve slowed becoming asymptotic (Fig. 2). Among the three models, the logistic model best described the species-area curves (Fig. 3), with the lowest sums of residuals (Table 1). Furthermore, the different sampling designs (Fig. 1b) did not significantly affect the parameters of the model. For example, the limits of the 95% confidence intervals for parameter a in the logistic model for sample designs a and b are (60.95, 71.51) and (50.72, 63.68), respectively (Table 1), which shows no significant difference. However, the expected species-area curve was significantly different from the observed species-area curves (Fig. 2), showing that species in the Changbai temperate forest plot are not randomly distributed. The relations between abundance and sampling area are extremely well fitted by linear models (Fig. 4). The confidence intervals of the parameters of the linear models indicate that these models are not significantly different among sampling designs. For example, the limits of the confidence intervals of slope for designs a and b are (2360.23, 2391.19) and (2377.22, 2402.12), respectively. The predicted values of abundance would not vary significantly for different sampling designs. The density (individuals/unit area) -area curves (Fig. 5) show that the density in different sampling designs varies greatly, especially within sampling areas less than 5–10 ha. This indicates that with smaller sample sizes, the variance of the estimates would be very large. 60 50 Species richness surface equation whose contribution to each of the three vectors of species diversity data was not significant (P < 0.01). The following terms were retained for the three trend surface equations of species richness (S ), abundance (A), and Shannon diversity (D), respectively: 40 30 a b 20 c d e 10 f 0 0 5 10 15 20 25 Area (ha) Fig. 2 – Species richness-area curves. a–e indicate different sampling designs of Fig. 1b. f is the expected richness-area curve under the assumption that all species are randomly distributed over the study area. 349 acta oecologica 33 (2008) 345–354 parameters for the same model show that for different sampling designs, the model parameters may not be significantly different. 60 Species richness 50 3.2. 40 30 True 20 Logistic model Exponential model 10 Power model 0 0 5 10 15 20 25 Area (ha) Fig. 3 – Logistic, Exponential and Power models fitted to the species richness-area relations for sampling design a. The relationship between Shannon diversity and area also shows that Shannon diversity varies greatly within small sample areas in different sampling designs in the Changbai temperate forest plot (Fig. 6). The Shannon diversity-area curves are best fitted neither by the power nor by the exponential model, but by a parabolic model (Fig. 7 and Table 2). In addition, the confidence intervals of corresponding Spatial structure of species diversity The variograms of richness (Fig. 8, 1a, 2a) show some evidence of anisotropy in that the semi-variances increase relatively quickly with increased distances for 90 and 135 , whereas there are only slight increases for the other two directions. However, although the semi-variances for species richness are not equal in the four directions, the difference is not obvious, probably because of relatively low species diversity in Changbai temperate forest. In addition, the relative nugget effect for the four directions is similar, about 54% (Fig. 8, 1a and 2a). The spatial structure of abundance shows that the rapid increase in semi-variances in the small distance classes indicates that random variation characterizes the distributions of abundance (Fig. 8, 1b and 2b). In addition, the nugget effects for the four directions are high, more than 85%, although the nugget effects of the 0 and 90 variograms seem lower than for the 45 and 135 directions. In addition, in the 135 direction, the semi-variance of abundance decreases greatly after the distance exceeds about 200 m, which is obviously different to the other three directions. The spatial structure of Shannon diversity shows pure nugget effect in the 0 and 45 direction, which exhibits no spatial auto-correlation at the study scale. However, in the other directions, the semi-variances increase with distance, Table 1 – Comparisons of three species (S)-area (A) models: logistic, exponential and power. a–e indicate the different sampling designs of Fig. 1. f is the expected species-area curve, and g is the large-tree group. ‘Residual’ is the sum of squared residuals after fitting the given model, and ‘conf. interval’ is the half-width of the 95% confidence intervals of the parameter values. The logistic model is the best one to fit species-area curves, whereas the power model is the worst Models sampling a b c d e f g Logistic model Exponential model Power model S ¼ a=b þ expðglnðAÞÞ S ¼ a þ blnðAÞ S ¼ aAb Parameters conf. interval Residual a ¼ 66.23 5.28 b ¼ 1.07 0.14 g ¼ 0.48 0.05 a ¼ 57.2 6.48 b ¼ 0.81 0.18 g ¼ 0.39 0.06 a ¼ 58.81 7.31 b ¼ 0.91 0.21 g ¼ 0.42 0.07 a ¼ 44.29 6.13 b ¼ 0.57 1.96 g ¼ 0.36 0.07 a ¼ 53.46 4.94 b ¼ 0.84 0.14 g ¼ 0.29 0.06 a ¼ 103.9 1.35 b ¼ 1.81 0.03 g ¼ 0.5 0.01 a ¼ 31.38 2.8 b ¼ 1.03 0.14 g ¼ 0.6 0.07 122.87 Parameters conf. interval Residual Parameters conf. interval Residual a ¼ 32.54 0.7 b ¼ 6.28 0.32 192.68 a ¼ 31.17 1.19 b ¼ 0.18 0.02 395.3 93.49 a ¼ 32.33 0.49 b ¼ 6.17 0.22 95.42 a ¼ 30.84 0.91 b ¼ 0.17 0.01 231.73 118.26 a ¼ 31.24 0. 51 b ¼ 6.08 0.24 104.21 a ¼ 28.82 0.99 b ¼ 0.18 0.01 269.6 127.72 a ¼ 29.43 0. 61 b ¼ 6.12 0.27 143.35 a ¼ 27.67 0.84 b ¼ 0.19 0.01 193.06 99.99 a ¼ 30.22 0. 67 b ¼ 6.7 0.3 176.58 a ¼ 28.47 1.09 b ¼ 0.2 0.02 322.8 1.07 a ¼ 35.94 0.45 b ¼ 5.59 0.2 80.3 a ¼ 35.13 1.09 b ¼ 0.14 0.01 345.18 31.03 a ¼ 15.76 0.35 b ¼ 3.7 0.16 47.8 a ¼ 15 0.76 b ¼ 0.2 0.02 157.18 350 acta oecologica 33 (2008) 345–354 3 Shannon diversity index Species abundance (N) 40000 30000 20000 a b 10000 c 2.5 2 1.5 a b c d e 1 d e 0 0.5 0 5 10 15 20 25 0 5 10 Area (ha) Fig. 4 – Abundance-area curves for the different sampling designs of Fig. 1b. especially in the 135 direction (Fig. 8, 1c and 2c). In the study area of the Changbai temperate forest, the spatial structure of Shannon diversity is closer to richness than abundance at the scales observed. 3.3. Species spatial patterns 3.3.1. Richness 15 20 25 Area (ha) The total variance of richness is 3.07 (Fig. 8, 1a and 2a) and the coefficient of variation (CV) is 28.5%. The explained portion of variation (a þ b þ c) is only 7.7% of the total variation in the richness data. However, the undetermined proportion (d) is very high (Fig. 9), indicating that the contributions a, b and c to the spatial patterns of diversity are very low. The spatially Fig. 6 – Shannon diversity-area curves. a–e indicate different sampling designs in Fig. 1b. structured environmental contribution (b) is higher than for the abundance data. 3.3.2. Abundance The total variance of the abundance data is 110.74 (Fig. 8, 1b, 2b) and the coefficient of variation (CV) is 44.5%. The results show that the explained portion (a þ b þ c) accounts for 2.7% of the total variation of the abundance data (Fig. 9). The topographic and spatial contribution (a, b, and c) is, similarly, very low, indicating that the relationship of the abundance data to topographic and spatial factors is weak. The undetermined proportion (d) for abundance is also very high (97.3%). 2.5 Shannon diversity index 4000 Density (N/ha) 3000 2000 a 1000 2 1.5 True 1 Parabolic model b Exponential model c Power model d 0.5 -5 e 0 0 5 10 15 20 Area (ha) Fig. 5 – Density-area curves. a–e indicates different sampling designs in Fig. 1b. 25 -4 -3 -2 -1 0 1 2 3 4 ln(area) Fig. 7 – Parabolic model, exponential model and power models fitted to the Shannon diversity-ln(area) relation for sampling design a. 351 acta oecologica 33 (2008) 345–354 Table 2 – Comparison of three Shannon diversity (D)-area (A) models: parabolic, exponential and power. a to e indicate the different sampling designs of Fig. 1. ‘Residual’ is the sum of squared residuals after fitting the given model, and ‘conf. interval’ is the half-width of the 95% confidence intervals of the parameter values. The parabolic model is the best one to fit Shannon diversity-area curves, whereas the power model is the worst Models sampling Parabolic model 2 D ¼ a þ blnðAÞ þ glnðAÞ a b c d e 3.3.3. Exponential model Power model D ¼ a þ blnðAÞ D ¼ aAb Parameters conf. interval Residual Parameters conf. interval Residual a ¼ 2.17 0.04 b ¼ 0.1 0.014 g ¼ 0.02 0.006 a ¼ 2.56 0.04 b ¼ 0.04 0.012 g ¼ 0.05 0.006 a ¼ 2.01 0.047 b ¼ 0.09 0.016 g ¼ 0.01 0.007 a ¼ 2.05 0.043 b ¼ 0.13 0.015 g ¼ 0.03 0.006 a ¼ 2.01 0.018 b ¼ 0.1 0.006 g ¼ 0.01 0.003 0.34 a ¼ 2.08 0.04 b ¼ 0.09 0.02 0.72 a ¼ 2.07 0.05 b ¼ 0.04 0.01 0.79 0.26 a ¼ 2.35 0.07 b ¼ 0.03 0.03 1.96 a ¼ 2.35 0.07 b ¼ 0.01 0.014 1.97 0.46 a ¼ 1.97 0. 04 b ¼ 0.08 0.01 0.50 a ¼ 1.97 0.04 b ¼ 0.04 0.01 0.53 0.40 a ¼ 1.92 0. 05 b ¼ 0.12 0.02 1.06 a ¼ 1.92 0.06 b ¼ 0.056 0.014 1.22 0.07 a ¼ 1.98 0. 02 b ¼ 0.1 0.01 0.10 a ¼ 1.98 0.02 b ¼ 0.047 0.004 0.13 Diversity The total variance for Shannon diversity is 0.12 (Fig. 8, 1c, 2c); its coefficient of variation (CV) is only 25.6%. However, the results show that the explained portion (a þ b þ c) accounts for only 6.7% of the total variation of the Shannon diversity data (Fig. 9) of which topographic and spatial factors make low contributions. The spatially structured environmental contribution (b) is higher than for the abundance data but similar to the richness data. As before, the undetermined proportion (d) is high. 4. Discussion and conclusion 4.1. Species diversity The three diversity variables change differently with increasing sampling area, because they represent two categories of variables that have very different spatial properties. Abundance is additive when aggregated across sampling areas, whereas richness and Shannon diversity are non-additive (He and Legendre, 1996; Legendre and Legendre, 1998; He et al., 2002). For example, assume n1 and n2 are the abundances of species in two adjacent subplots, and s1 and s2 are their corresponding species richness values. The total abundance in the two combined subplots n ¼ n1 þ n2, whereas the total number of species s s1 þ s2 (the equal sign holds only when the two subplots have totally different species composition). Shannon diversity is a combination of species richness and abundance, which is also non-additive. As a result, the Shannon diversity in the combined plot also does not equal the sum of that in the two subplots. The species-area relationship is well fitted by the logistical model but not the power model. The power model assumes a dynamic equilibrium (Preston, 1960; MacArthur and Wilson, Parameters conf. interval Residual 1967). Our results suggest that the temperate forest under study would not be in a state of equilibrium. If the forest were in equilibrium, species abundance would be estimated in an unbiased way by any sample size as trees would be randomly distributed throughout the plot. However, in the Changbai plot, the density in the different designs varied greatly, especially within small sampling areas, indicating that the trees were not randomly distributed. The diversity (richness, abundance and Shannon diversity)area curves may be influenced by the spatial patterns of species distributions (Hubbell and Foster, 1983). However, in the Changbai temperate forest, the sampling location does not appear to significantly influence these species diversity-area curves. With different sampling designs (Fig. 1b), the same theoretical models display no significant differences (Table 1). However, because the samples are not independent of one another, the confidence intervals of the parameters are likely to be narrower than they should be for the normal a ¼ 5% level (Legendre, 1993). Therefore, only well-separated confidence intervals should lead to the conclusion that parameters differ significantly. As a result, sampling design d (Table 1) should not be considered significantly different from designs a and b, despite the difference between the limits of the 95% confidence intervals for parameter a in the logistic model for sampling design d (38.16, 50.42) and the confidence limits for sampling designs a (60.95, 71.51) and b (50.72, 63.68). However, He et al. (1996) found similar species diversity-area curves changed significantly with different sampling locations in tropical rain forests. Given the relative simplicity and low species diversity compared to tropical rain forests, the results observed in Changbai temperate forests are not surprising. 4.2. The spatial structure of species diversity Variograms of richness, abundance and Shannon diversity in the Changbai temperate forests showed differences 352 acta oecologica 33 (2008) 345–354 5 5 1a 2a 4 4 135° 90° 3 3 0° 45° 2 2 Richness 1 0 100 Richness 200 300 400 1 0 200 300 400 500 120 120 2b 1b 115 Semi-variance 100 115 0° 45° 110 110 135° 90° 105 105 Abundance 100 0 100 Abundance 200 300 400 100 0 100 300 400 500 400 500 0.2 0.2 1c 2c 90° 0.15 135° 0.15 0° 0.1 45° 0.1 0.05 0.05 Shannon diversity Shannon diversity 0 200 0 100 200 300 400 0 0 100 200 300 Distance (m) Fig. 8 – Variograms of richness (1a–2a), abundance (1b–2b) and Shannon diversity (1c–2c) in the four geographic directions: 08 is east (E)-west (W), 908 is south (S)-north (N), 458 is SE-NW and 1358 is NE-SW. The horizontal lines indicate the overall variance of the variables in the plot. between the four directions, but are not clearly anisotropic, unlike that found in tropical forests. For example, He et al. (1996) studied the spatial pattern of diversity in a rainforest in Malaysia, and found that the spatial structure of diversity was clearly anisotropic. Furthermore, in this Changbai study, all variograms of the three diversity indices showed relatively high nugget effects, probably because of smallscale processes that may operate in the temperate forest. Some interesting spatial features may be detected at finer scales than the smallest scale used here (¼10 m). These small-scale processes may include competition, predation, dispersal, microbial interactions, etc., which could result in the observed spatial heterogeneity of diversity in the Changbai temperate forests. For example, some small-scale disturbances, such as windthrow, fire and insects, are known to promote the regeneration of a diverse array of species. These small-scale disturbances create open places favorable for some pioneering plants, such as white birch and aspen, which might result in different species diversity compared with that in later successional stages (Hao et al., 1994, 2002; Wu, 1998). Also, the intensity of the disturbance could lead to different species diversities. The Intermediate Disturbance Hypothesis states that higher species diversity occurs at intermediate levels of disturbance because species coexistence is maintained at a non-equilibrium state and no strong competitor can dominate completely (Connell, 1978). However, due to its cold climate and relatively gentle terrain, species richness in the Changbai plot is low, which might cause the spatial structure of diversity to be weakly anisotropic in these temperate forests. acta oecologica 33 (2008) 345–354 Richness 353 but this is certainly not the case here since the survey has been exhaustive. Finally, niche differentiation, species specificity and the lack of dominant controlling forces (many processes controlling the structure of temperate communities, each one playing but a small role) may be invoked. Acknowledgements Abundance Shannon diversity a: pure space c: pure environment b: space + environment d: undetermined This paper is sponsored by the Knowledge Innovation Program of the Chinese Academy of Sciences (KZCX2-YW-430), National Natural Science Foundation of China (30700093 and 30570306), and National Key Technologies R&D Program of China (2006BAD03A09). The authors thank all those who provided helpful suggestions and critical comments on this manuscript, including Fangliang He, He Hong S, Michael Papaik, and Bill Loneragan. Fig. 9 – Relative percentage of variation partitioning of species richness, abundance and Shannon diversity. references 4.3. Spatial patterns and controlling processes Partitioning the variation in species diversity helps us to understand the community structure in Changbai temperate forest, and the processes that may have contributed to its formation. In this study there are several common features to the partitioning of variation in the richness, abundance and Shannon diversity data, which indicate similar underlying controlling processes in the Changbai forest, with fairly large pure spatial components (a) and very small pure environmental components (c), as well as extremely high undetermined proportions (d). Relatively high (a) may result either from spatially structured environmental factors, from spatially structured historical processes, or from environment independent processes, such as growth, reproduction, predation and competition with neighbours acting to shape the community. Low environmental explanation (c) may be attributed, on the one hand, to the relatively flat topography of the study area which is typical of Changbai temperate forests; on the other hand, it may result from the absence of dominant environmental controlling factors in this study area. Whatever the cause, clearly the observed diversity does not vary as a function of topography. According to our hypothesis, if the community is under equilibrium, then the predictable proportion (a, b and c) should be high and the undetermined component (d) low. In other words, if the diversity varies as a function of the environmental variables, the amount of explained variation in fraction (a þ b) is expected to be high and significant; and if the diversity varies as a function of the spatial variables, then fraction (b þ c) is expected to be significantly high. The high unexplained components (d) in this study seem to be indicating the temperate forest in Changbai Nature Reserve is not in equilibrium. There are some possible explanations for the high unexplained proportions (d). One is that there is only a small amount of variability to be explained in this temperate forest, and this could not be captured by the trend-surface equations at the small-scales studied. Another possible explanation is high sampling error, Ashton, P.S., 1969. Speciation among tropical forest trees: some deductions in the light of recent evidence. In: LoweMcConnell, R.H. (Ed.), Speciation in Tropical Environment. Academic Press, London, pp. 155–196. Borcard, D., Legendre, P., 1994. Environmental control and spatial structure in ecological communities: an example using Oribatid mites (Acari, Oribatei). Environmental and Ecological Statistics 1, 37–53. Borcard, D., Legendre, P., Drapeau, P., 1992. Partialling out the spatial component of ecological variation. Ecology 73, 1045–1055. Busing, R.T., White, P.S., 1997. Species diversity and small-scale disturbance in an old-growth temperate forest: a consideration of gap partitioning concepts. Oikos 78, 562–568. Chen, J., Bradshaw, G.A., 1999. Forest structure in space: a case study of an old growth spruce-fir forest in Changbaishan Natural Reserve, PR China. Forest Ecology and Management 120, 219–233. Coleman, B.D., Mares, M.A., Willig, M.R., Hsieh, Y.H., 1982. Randomness, area, and species richness. Ecology 63, 1121–1133. Condit, R., 1998. Tropical Forest Census Plots: Methods and Results from Barro Colorado Island, Panama and a Comparison with Other Plots. Springer, Berlin. Condit, R., Pitman, N., Leigh, E.G., Chave, J., Terborgh, J., Foster, R. B., Nunez, P., Aguilar, S., Valencia, R., Villa, G., MullerLandau, H.C., Losos, E., Hubbell, S.P., 2002. Beta-diversity in tropical forest trees. Science 295, 549–562. Condit, R., Ashton, P., Bunyavejchewin, S., Dattaraja, H.S., Davies, S., Esufali, S., Ewango, C., Foster, R., Gunatilleke, I.A.U. N., Gunatilleke, C.V.S., Hall, P., Harms, K.E., Hart, T., Hernandez, C., Hubbell, S., Itoh, A., Kiratiprayoon, S., LaFrankie, J., Lao, S., Makana, J.-R., Noor, Supardi, Md, N., Rahman Kassim, A., Russo, S., Sukumar, R., Samper, C., Suresh, H.S., Tan, S., Thomas, S., Valencia, R., Vallejo, M., Villa, G., Zillio, T., 2006. The importance of demographic niches to tree diversity. Science 313, 98–101. Connell, J.H., 1978. Diversity in tropical rain forest and coral reefs. Science 199, 1304–1310. Denslow, J.S., 1987. Tropical rain forest gaps and tree species diversity. Annual review of ecology and Systematics 18, 431–451. Getzin, S., Dean, C., He, F., Trofymow, J.A., Wiegand, K., Wiegand, T., 2006. Spatial patterns and competition of tree species in a Douglas-fir chronosequence on Vancouver Island. Ecography 29 (5), 671–682. 354 acta oecologica 33 (2008) 345–354 Hao, Z., Zhao, S., Tao, D., 1994. Species diversity and its seasonal dynamics of herbs in a broad-leaved Korean pine forest on the northern slope of Changbai Mountain. Biodiversity Science 2 (3), 125–132 (in Chinese). Hao, Z., Guo, S., Cao, T., 2002. Species diversity and distribution patterns in Changbai Mountain. Liaoning Science and Technology Publishing House, Shenyang (in Chinese). Hao, Z., Li, B., Zhang, J., Wang, X., Ye, J., Yao, X., 2008. Broad-leaved Korean pine (Pinus koraiensis) mixed forest plot in Changbaishan (CBS) of China: Community composition and structure. Acta Phytoecologica Sinica 32 (2), 238–250 (in Chinese). He, F., Legendre, P., 1996. On species-area relations. American Naturalist 148, 719–737. He, F., Legendre, P., 2002. Species diversity patterns derived from species-area models. Ecology 83, 1185–1198. He, F., Legendre, P., LaFrankie, J.V., 1996. Spatial pattern of diversity in a tropical rain forest in Malaysia. Journal of Biogeography 23, 57–74. He, F., LaFrankie, J.V., Song, B., 2002. Scale dependence of tree abundance and richness in a tropical rain forest, Malaysia. Landscape Ecology 17, 559–568. Hubbell, S.P., Foster, R.B., 1983. Diversity of canopy trees in Neotropical forest and implications for conservation. In: Sutton, S.L., Whitmore, T., Chadwick, A.C. (Eds.), Tropical Rain Forest: Ecology and Management. Blackwell Science, Oxford, pp. 25–41. Hubbell, S.P., Foster, R.B., O’Brien, S.T., Harms, K.E., Condit, R., Wechsler, B., Wright, S.J., Loo de Lao, S., 1999. Light-gap disturbance, recruitment limitation, and tree diversity in a Neotropical forest. Science 283, 68–71. Hubbell, S.P., Ahumada, J.A., Condit, R., Foster, R.B., 2001. Local neighborhood effects on long-term survival of individual trees in a neotropical forest. Ecological Research 16, 859–875. Latham, R.E., Ricklefs, R.E., 1993. Continental comparisons of temperate-zone tree species diversity. In: Ricklefs, R.E., Schluter, D. (Eds.), Species Diversity in Ecological Communities. University of Chicago Press, Chicago, pp. 294–314. Legendre, P., 1993. Spatial autocorrelation: trouble or new paradigm? Ecology 74, 1659–1673. Legendre, P., Fortin, M.J., 1989. Spatial pattern and ecological analysis. Vegetatio 80, 107–138. Legendre, P., Legendre, L., 1998. Numerical Ecology, second ed. Elsevier Science, Amsterdam, The Netherlands. Liu, Q., Wang, Z., Wang, S., 1992. Recent volcano eruptions and vegetation history of alpine and sub-alpine of Changbai Mountain. Forest Ecosystem Research 6, 57–62 (in Chinese). Lundholm, J.T., Larson, D.W., 2003. Relationships between spatial environmental heterogeneity and plant species diversity on a limestone pavement. Ecography 26, 715–722. MacArthur, R.H., 1969. Patterns of communities in the tropics. Biological Journal of the Linnean Society 1, 19–30. MacArthur, R.H., Wilson, E.O., 1967. The Theory of Island Biogeography. Princeton Univ. Press, Princeton, N.J. Margalef, R., 1958. Information theory in ecology. General Systems 3, 36–71. de Oliveira, A.A., Mori, S.A., 1999. A central Amazonian terra firme forest. I. High tree species richness on poor soils. Biodiversity and Conservation 8, 1219–1244. Preston, F.W., 1960. Time and space and the variation of species. Ecology 41, 611–627. Rice, B., Westoby, M., 1982. Plant species richness at the 0.1 hectare scale in Australian vegetation compared to other continents. Vegetatio 52, 129–140. Shao, G., 1996. Potential impacts of climate change on a mixed broadleaved-Korean pine forest stand: a gap model approach. Climatic Change 34, 263–268. Sokal, R.R., Rohlf, F.J., 1981. Biometry. Freeman, San Francisco. Valencia, R., Foster, R.B., Villa, G., Condit, R., Svenning, J.C., Hernandez, C., Romoleroux, K., Losos, E., Magard, E., Balslev, H., 2004. Tree species distributions and local habitat variation in Amazon: large forest plot in eastern Ecuador. Journal of Ecology 92, 214–229. Wang, X., He, H.S., Li, X., Hu, Y., 2006a. Assessing the cumulative effects of post-fire management on forest landscape dynamics in northeastern China. Canadian Journal of Forest research 36, 1992–2002. Wang, X., He, H.S., Li, X., Chang, Y., Hu, Y., Xu, C., Bu, R., Xie, F., 2006b. Simulating the effects of reforestation on a large catastrophic fire landscape in Northeastern China. Forest Ecology and Management 225, 82–96. Wang, X., Hao, Z., Ye, J., Zhang, J., Li, B., Yao, X., in press. Spatial variation of species diversity across scales in an old-growth temperate forest of China. Ecological Research, in press. doi: 10.1007/s11284-007-0430-8. Wright, S.J., 2002. Plant diversity in tropical forests: a review of mechanisms of species coexistence. Oecologia 130, 1–14. Wu, G., 1998. Regeneration dynamics of tree species in gaps of Korean pine broad-leaved mixed forest in Changbai Mountains. Chinese Journal of Applied Ecology 9 (5), 449–452 (in Chinese). Zhao, D., 1981. Preliminary study of the effects of volcano on vegetation development and succession. Forest Ecosystem Research 2, 81–87 (in Chinese).
© Copyright 2018