Spatial pattern of trees in tropical lowland rain forest in Bawangling of Hainan Island, China

doi:10.3724/SP.J.1258.2012.00269

Abstract

Abstract:

Aims Understanding processes underlying spatial distribution of tree species is fundamental to the study of species coexistence and diversity. Our objective was to determine the spatial structure and identify the processes that may generate spatial patterns of trees in a tropical lowland rain forest community on Hainan Island of South China.
Methods Based on four models of point pattern analysis (homogenous Poisson process, inhomogenous Poisson process, homogenous Thomas process and inhomogenous Thomas process), we evaluated the potential contribution of habitat heterogeneity and dispersal limitation to the formation of spatial patterns of tree species in two 1-hm ² stem-mapped forest dynamic plots. The relative importance of each process was assessed at six different spatial scales (< 2 m, 2-5 m, 5-10 m, 10-15 m, 15-20 m and 20-25 m).
Important findings All stems combined revealed a strong aggregation at short distance (≤2 m), and the degree of aggregation decreased with increasing distance. Among the four models simulating tree distribution and patterning, the homogeneous Thomas process was the best-fit model. This result suggested that spatial patterns of tree species in tropical lowland rain forest might be formed by dispersal limitation. The homogeneous Poisson process that models the effect of spatial complete randomness was the second-best model. The inhomogeneous Thomas process and inhomogeneous Poisson process were equally important to forming spatial patterns of trees; they simulated the joint effects of habitat associations and dispersal limitation and modeled heterogeneity, respectively. The proportion of best-fit models differed across different scales. The dispersal limitation was a most important mechanism in spatial patterning of tree species at most scales, while complete randomness process was second in importance. The joint effects of habitat associations and dispersal limitation mainly influenced tree distribution at small scales (0-5 m). However, habitat heterogeneity only affected the distribution at larger scales (15-25 m).

Key words: dispersal limitation, habitat association, Hainan Island, spatial pattern, species coexistence, tropical lowland rain forest

HUANG Yun-Feng,DING Yi,ZANG Run-Guo,LI Xiao-Cheng,ZOU Zheng-Chong,HAN Wen-Tao. Spatial pattern of trees in tropical lowland rain forest in Bawangling of Hainan Island, China[J]. Chin J Plant Ecol, 2012, 36(4): 269-280.

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URL: https://www.plant-ecology.com/EN/10.3724/SP.J.1258.2012.00269

https://www.plant-ecology.com/EN/Y2012/V36/I4/269

Figures/Tables 7

Fig. 1 The species-abundance accumulation curve, species rank-abundance distribution curve (log-abundances), and distribution structure of DBH class, and height class of trees (DBH ≥ 1 cm) in LOG1 and LOG2 plots. DBH, diameter at breast height. LOG1 and LOG2 are forest dynamic plots with 1-hm2 area.

Fig. 2 Plot-level patterns of stems and their corresponding L-functions and pair correlation functions (PCFs). Confidence envelopes (gray lines) estimated at the α = 0.01 level using a homogeneous Poisson model. In the maps of two plots, smallest dots represent trees < 10 cm in diameter at breast height (DBH), and the circle sizes are proportional to the DBH classes. X, west-east direction; Y, south-north direction; LOG1 and LOG2 are forest dynamic plots with 1-hm2 area. L(r) represent the value of Ripley’s K function; g(r) represent the value of pair correlation function.

Table 1 Proportion of the best-fit models for tree species in each plot

模型 Model	LOG1	LOG2
均质Poisson过程 Homogenous Poisson process	26% (18)	33% (20)
异质Poisson过程 Inhomogenous Poisson process	13% (9)	10% (6)
均质Thomas过程 Homogenous Thomas process	38% (26)	43% (26)
异质Thomas过程 Inhomogenous Thomas process	23% (16)	15% (9)

Fig. 3 Box-and-whisker plot of species abundance classified by the best-fit model of each species in each plot. HP, homogenous Poisson process; IP, inhomogenous Poisson process; ITH, inhomogenous Thomas process; TH, homogenous Thomas process. Dashed lines represent the smallest observation and the largest observation, the hinges of box represent lower quartile and upper quartile, black dots represent median, and circles represent outliers. LOG1 and LOG2 are forest dynamic plots with 1-hm2 area.

Fig. 4 Distribution map of plant species in LOG1 and LOG2 plots. Black dots represent Machilus suaveolens (LOG1) and Koilodepas hainanense (LOG2), respectively. X, west-east direction; Y, south-north direction; LOG1 and LOG2 are forest dynamic plots with 1-hm2 area.

Fig. 5 Typical example of spatial distribution of species fit by each of the four spatial pattern models. LOG1 and LOG2 are forest dynamic plots with 1-hm2 area. The Thomas process provides the best fit to Machilus suaveolens at LOG1, and the inhomogeneous Thomas process provides the best fit to Koilodepas hainanense at LOG2. The observed L(r) values are expressed as solid lines and the dashed lines represent the upper and lower limits of the 95% confidence intervals of L(r) generated by Monte Carlo simulation, where r represents the distance. HP, homogenous Poisson process; IP, inhomogenous Poisson process; ITH, inhomogenous Thomas process; TH, homogenous Thomas process.

Fig. 6 Proportion of the best-fit models for tree species in each plot at varying spatial scales. LOG1 and LOG2 are forest dynamic plots with 1-hm2 area. White bars represent homogenous Poisson process; grey bars represent inhomogenous Poisson process; dark grey represent homogenous Thomas process; black bars represent inhomogenous Thomas process. Roman letters represent different scales, I: <2 m; II: 2-5 m; III: 5-10 m; IV: 10-15 m; V: 15-20 m; VI: 20-25 m.

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