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Table 1. Basic information for six dominant species in the Baishanzu evergreen-broad leaved forest. The important value was the sum of relative abundance (i.e. abundance of a species divided by totalabundance) and relative basal area (basal area of a species divided by total basal area).Importantvalue (%) Aridity-tolerance Shade-toleranceDispersal mode (dispersalunit: mm) Growth formNo. ofindividuals(and adults)Species code FamilyLithocarpus brevicaudatus Lt Fagaceae 4930 (1093) gravity or rodent (nut: 13.7) canopy 23.2 aridity-tolerant shade-tolerantCyclobalanopsis multinervis Mo Fagaceae 3500 (891) gravity or rodent (nut: 14.7) canopy 16.6 mid-tolerant shade-tolerantSchima superba Gt Theaceae 1988 (759) gravity or bird (seed: 4.5) canopy 14.5 aridity-tolerant mid-tolerantCleyera pachyphylla Tc Theaceae 2986 (428) gravity or bird (Berry: 5.0) midstory 11.5 aridity-tolerant shade-tolerantSycopsis sinensis Cf Hamamelidaceae 2935 (127) gravity or explosion (seed: 2) midstory 8.2 water-demanding shade-tolerantCyclobalanopsis stewardiana Bo Fagaceae 1098 (312) gravity or rodent (nut: 14.4) canopy 7.8 mid-tolerant shade-tolerantare used (He and Duncan 2000, Getzin et al. 2008, Zhuet al. 2010). We used the pair-correlation function g(r) toanalyze spatial patterns based on point-to-point distances(Stoyan and Stoyan 1994, Wiegand and Moloney 2004).The univariate pair-correlation function g(r) is relatedto the widely used Ripley’s K-function (Ripley 1976),where the g(r) function is the probability density functionof K-function. Due to its non-cumulative property, g(r) isrecommended for exploratory data analysis to identify specificscales of deviation from the null model (Wiegand andMoloney 2004). Similar to the K-function, g(r) can also beextended to describe point patterns with two types of points:the bivariate pair-correlation function g 12 (r) is the expecteddensity of points of patterns 2 at distance r from an arbitrarypoint of pattern 1, divided by intensity l 2 of pattern 2. Incase of inhomogeneous g(r) function, the intensity l is notconstant but varies with the location (x, y). In this study,we applied the Monte Carlo simulation to test significanceof departure from an underlying null model. To control forboth the effects of habitat preference and random mortality,we selected heterogeneous Poisson process and random-labelingas null model in the point pattern analyses (discussedin the following parts). Approximate 95% confidence intervalswere estimated from 5th highest and 5th lowest g(r)values obtained from 199 simulations. We used the packagespatstat in R statistical software (R Development Core Team2009) to perform spatial point pattern analysis.In this analysis, we divided individuals of a species alivein 2003 into four basic size classes: 1) ‘seedlings’ with dbh1 cm, 2) ‘saplings’ with dbh ranging from 1 to 5 cm, 3)‘poles’ with dbh ranging from 5 to 10 cm, and 4) ‘adults’with dbh 10 cm.Biological hypotheses and null modelsPoint pattern analysis 1: strength of clustering of differentsize classesIt is usually expected that clustering of trees declines withincreasing size class due to density-dependent self-thinning(Hubbell 1979, Moeur 1997). Therefore, we comparedthe spatial patterns for four different size classes mentionedabove using g(r) functions. To factor out the confoundingeffect of environmental heterogeneity, we used inhomogeneousg(r) functions based on the intensity function l(x, y)of the locations of individuals belonging to each size-class.We constructed a statistic g dif (r) g l (r) g s (r) to describe thedifference between pattern l and s, where g l (r) and g s (r) areg(r) functions for pattern l and s respectively. g dif (r) 0 indicatesthat pattern 1 is more aggregated than pattern s, whileg dif (r) 0 indicates the opposite. We implemented a nullmodel of heterogeneous Poisson process for each size class tocreate simulation g dif (r) values and the 95% envelope interval.It is the simplest null model that accounts for first-order effectssuch as habitat preference. In this null model, the intensity l isnot approximately constant but varies with the location (x, y).We used a nonparametric method to estimate intensity l(x, y)of the spatial distribution of a species which combines a movingwindow estimator with an Epanechnikov kernel (Stoyanand Stoyan 1994, Zhu et al. 2010). The fixed bandwidth of themoving window was 20 m which is little larger than scales atwhich local plant–plant interactions are likely to be present.2861241