The Environment, Not Space, Dominantly Structures the Landscape Patterns of the Richness and Composition of the Tropical Understory Vegetation

<div><p>The mechanisms driving the spatial patterns of species richness and composition are essential to the understanding of biodiversity. Numerous studies separately identify the contributions of the environment (niche process) and space (neutral process) to the species richness or composition at different scales, but few studies have investigated the contributions of both types of processes in the two types of data at the landscape scale. In this study, we partitioned the spatial variations in all, exotic and native understory plant species richness and composition constrained by environmental variables and space in 134 plots that were spread across 10 counties in Hainan Island in southern China. The 134 plots included 70 rubber (<i>Hevea brasiliensis</i>) plantation plots, 50 eucalyptus (<i>Eucalyptus urophylla</i>) plantation plots, and 14 secondary forest plots. RDA based variation partitioning was run to assess the contribution of environment and space to species richness and composition. The results showed that the environmental variables alone explained a large proportion of the variations in both the species richness and composition of all, native, and exotic species. The RDA results indicated that overstory composition (forest type here) plays a leading role in determining species richness and composition patterns. The alpha and beta diversities of the secondary forest plots were markedly higher than that of the two plantations. In conclusion, niche differentiation processes are the principal mechanisms that shape the alpha and beta diversities of understory plant species in Hainan Island.</p> </div

Venn diagram showing the species composition across the three types of forests.

<p>Black circle represents eucalyptus plantation, Green circle represents rubber plantation; and Red circle represents secondary forest. Abbreviations: #sp (number of species), #ge (number of genera), #fa (number of families), #ex (numer of exotic species), and #na (number of native species).</p

RDA tri-plots of the species composition data constrained by the selected environmental variables and PCNMs, scaling 2.

<p>Abbreviations: AL (altitude), AR (annual rainfall), BA (basal area of trees per square meter), LC (litter coverage), SL (slope), SM (soil moisture). The bottom and left-hand scales are for the objects and the response variables, the top and right-hand scales are for the explanatory variables.</p

Boxplots of the alpha and beta diversity values for the three types of forests.

<p>Boxplots of the alpha and beta diversity values for the three types of forests.</p

Clustering with the soil moisture constraint using MRT.

<p>Group 1 represents the sites with soil moisture greater than 11.55%. Group 2 represents those sites with soil moisture less than 11.55%.</p

Variation partitioning results of all species.

<p>The two figure panels show Venn diagrams that represent the partitioning of the variations of the species richness and composition constrained by the selected environmental variables (environment) and PCNMs (space). Each box represents 100% of the variation in the corresponding response variable. The fractions shown are the adjusted R-square statistics.</p

Distribution map of the 134 plots.

<p>Abbreviations: rubber (rubber plantation), eucalyptus (eucalyptus plantation), and secondary (secondary forest).</p

Selected PCNMs from the 79 PCNMs that exhibit a positive spatial correlation for the richness and composition of all species.

<p>The PCNMs are represented by square dots. </p

Results of the partitioning variation between environmental variables and spatial effects for each of the 20 combinations of DBH and cell size using basal area data.

<p>Note: Adjusted R-squared statistics are shown. Fractions [a] – [d] are as follows: [a]  =  variation explained by the environmental variables and not spatially structured; [b]  =  variation explained by the environmental variables and spatially structured; [c]  =  spatially structured variation not explained by the environmental variables; [d]  =  residual variation. Fraction [b] is the intersection of the variation explained by linear models of the two groups of explanatory factors. Topographic and edaphic variables were used to compute fractions [a] and [b]. Principal coordinates of neighbor matrix eigenfunctions were used as explanatory variables to compute fractions [b] and [c]. class 0 (DBH ≥1 cm), class 1 (1 cm ≤ DBH <5 cm), class 2 (5 cm ≤ DBH <10 cm), class 3 (10 cm ≤ DBH <25 cm), class 4 (DBH ≥25 cm).</p

Relationships between the R-squared values of the fitted SAR models and total species abundance for each of the 5 DBH classes at the 20-m scale.

<p>Circles and triangles represent count data and basal area data, respectively. Classes 0 to 4 are defined as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038247#pone-0038247-g001" target="_blank">Figure 1</a>.</p