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Strong neutral spatial effects shape tree species distributions across life stages at multiple scales.

Hu YH, Lan GY, Sha LQ, Cao M, Tang Y, Li YD, Xu DP - PLoS ONE (2012)

Bottom Line: The explained variations of species distribution data did not differ significantly between the two types of data at either the individual species level or the community level, indicating that the two types of data can be used nearly identically to model species distributions.Neutral spatial effects represented by spatial autoregressive parameters and the PCNM eigenfunctions drove species distributions on multiple scales, different life stages and individual species and community levels in this plot.We concluded that strong neutral spatial effects are the principal mechanisms underlying the species distributions and thus shape biodiversity spatial patterns.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, Yunnan, China.

ABSTRACT
Traditionally, ecologists use lattice (regional summary) count data to simulate tree species distributions to explore species coexistence. However, no previous study has explicitly compared the difference between using lattice count and basal area data and analyzed species distributions at both individual species and community levels while simultaneously considering the combined scenarios of life stage and scale. In this study, we hypothesized that basal area data are more closely related to environmental variables than are count data because of strong environmental filtering effects. We also address the contribution of niche and the neutral (i.e., solely dependent on distance) factors to species distributions. Specifically, we separately modeled count data and basal area data while considering life stage and scale effects at the two levels with simultaneous autoregressive models and variation partitioning. A principal coordinates of neighbor matrix (PCNM) was used to model neutral spatial effects at the community level. The explained variations of species distribution data did not differ significantly between the two types of data at either the individual species level or the community level, indicating that the two types of data can be used nearly identically to model species distributions. Neutral spatial effects represented by spatial autoregressive parameters and the PCNM eigenfunctions drove species distributions on multiple scales, different life stages and individual species and community levels in this plot. We concluded that strong neutral spatial effects are the principal mechanisms underlying the species distributions and thus shape biodiversity spatial patterns.

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Cell connectivity at each of the four scales of cell size for Sloanea tomentosa in DBH class 4.
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pone-0038247-g006: Cell connectivity at each of the four scales of cell size for Sloanea tomentosa in DBH class 4.

Mentions: The increase in cell connectivity with cell size observed in this study may explain both the increases in the R-squared values and the total explained variation in the results of the SAR models and variation partitioning, repectively. As an example, cell connectivity clearly increased with increasing cell size for trees of Sloanea tomentosa in class 4 (Fig. 6). This results in decreasing p-values of λ with increasing cell size, except when basal area data were used at the 10-m scale. The R-squared values of the fitted models for S. tomentosa tend to increase with increasing cell size, except when count data are used at the 10-m scale (Fig. S13), consistent with previous work demonstrating that the variation explained by auto-Poisson regressive models when count data were used was much smaller at the 10-m scale than at the 20-m and 25-m scales in a 20-ha subtropical forest plot in southern China [4]. By contrast, in a study of the beta diversity of tree species in a 24-ha subtropical forest plot, Legendre et al. [5] found that the total explained variations in species richness and community composition varied little across sampling scales. Here, we found that the R-squared values decreased with increasing total abundance of species (Figs. 2, Figs. S3, S4, S5), in contrast to the finding of Wang et al. [4]. Because we simultaneously considered spatial scale and life stage, our analyses generated more replicates than in previous studies [4], [5], and our results may therefore more broadly reflect patterns at the individual species and community levels.


Strong neutral spatial effects shape tree species distributions across life stages at multiple scales.

Hu YH, Lan GY, Sha LQ, Cao M, Tang Y, Li YD, Xu DP - PLoS ONE (2012)

Cell connectivity at each of the four scales of cell size for Sloanea tomentosa in DBH class 4.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3362550&req=5

pone-0038247-g006: Cell connectivity at each of the four scales of cell size for Sloanea tomentosa in DBH class 4.
Mentions: The increase in cell connectivity with cell size observed in this study may explain both the increases in the R-squared values and the total explained variation in the results of the SAR models and variation partitioning, repectively. As an example, cell connectivity clearly increased with increasing cell size for trees of Sloanea tomentosa in class 4 (Fig. 6). This results in decreasing p-values of λ with increasing cell size, except when basal area data were used at the 10-m scale. The R-squared values of the fitted models for S. tomentosa tend to increase with increasing cell size, except when count data are used at the 10-m scale (Fig. S13), consistent with previous work demonstrating that the variation explained by auto-Poisson regressive models when count data were used was much smaller at the 10-m scale than at the 20-m and 25-m scales in a 20-ha subtropical forest plot in southern China [4]. By contrast, in a study of the beta diversity of tree species in a 24-ha subtropical forest plot, Legendre et al. [5] found that the total explained variations in species richness and community composition varied little across sampling scales. Here, we found that the R-squared values decreased with increasing total abundance of species (Figs. 2, Figs. S3, S4, S5), in contrast to the finding of Wang et al. [4]. Because we simultaneously considered spatial scale and life stage, our analyses generated more replicates than in previous studies [4], [5], and our results may therefore more broadly reflect patterns at the individual species and community levels.

Bottom Line: The explained variations of species distribution data did not differ significantly between the two types of data at either the individual species level or the community level, indicating that the two types of data can be used nearly identically to model species distributions.Neutral spatial effects represented by spatial autoregressive parameters and the PCNM eigenfunctions drove species distributions on multiple scales, different life stages and individual species and community levels in this plot.We concluded that strong neutral spatial effects are the principal mechanisms underlying the species distributions and thus shape biodiversity spatial patterns.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, Yunnan, China.

ABSTRACT
Traditionally, ecologists use lattice (regional summary) count data to simulate tree species distributions to explore species coexistence. However, no previous study has explicitly compared the difference between using lattice count and basal area data and analyzed species distributions at both individual species and community levels while simultaneously considering the combined scenarios of life stage and scale. In this study, we hypothesized that basal area data are more closely related to environmental variables than are count data because of strong environmental filtering effects. We also address the contribution of niche and the neutral (i.e., solely dependent on distance) factors to species distributions. Specifically, we separately modeled count data and basal area data while considering life stage and scale effects at the two levels with simultaneous autoregressive models and variation partitioning. A principal coordinates of neighbor matrix (PCNM) was used to model neutral spatial effects at the community level. The explained variations of species distribution data did not differ significantly between the two types of data at either the individual species level or the community level, indicating that the two types of data can be used nearly identically to model species distributions. Neutral spatial effects represented by spatial autoregressive parameters and the PCNM eigenfunctions drove species distributions on multiple scales, different life stages and individual species and community levels in this plot. We concluded that strong neutral spatial effects are the principal mechanisms underlying the species distributions and thus shape biodiversity spatial patterns.

Show MeSH