Effects of space partitioning in a plant species diversity model

Understanding the mechanisms of species diversity maintenance within plant communities has become a fundamental issue in ecology over the past decades. While some models have tried to explore these mechanisms, few studies have integrated the dynamic interactions with neighbours in a spatially explicit way. The present model uses Voronoi polygons to dynamically partition a landscape patch into areas occupied by individual plants. It thus incorporates neighbourhood competition for space, unlike grid-based models with nearest-neighbour competition. In closed two-species communities, dynamic Voronoi partitioning promoted species coexistence, especially under local dispersal. This suggests that grid-based models overestimate species extinction rates. Likewise, multispecies communities without immigration had substantially greater species richness in the space partitioning model than in the grid-based model but only under distance-limited dispersal. In contrast, richness levels were similar in both models under global dispersal or with immigration from the metacommunity. Trait variation among species reduced species richness, but more so for traits associated with competition for space. This suggests that some traits are more important than others in governing species richness. Overall, our study demonstrates that combining species identity (traits) with partitioning of physical space can improve understanding of diversity regulation. (C) 2013 Elsevier B.V. All rights reserved

Use of Spatial Analysis to Test Hypotheses on Plant Recruitment in a Hyper-Arid Ecosystem

<div><p>Mounds originating from wind-blown sediment accumulation beneath vegetation (nebkhas) often indicate land degradation in dry areas. Thus far, most nebkha research has focused on individual plants. Here, we aimed to explore population-scale processes (up to scales of about 100 m) that might explain an observed nebkha landscape pattern. We mapped the <i>Rhazya stricta</i> Decne. population in a 3 ha study site in a hyper-arid region of Saudi Arabia. We compared the spatial patterns of five different cohorts (age classes) of observed nebkha host plants to those expected under several hypothesized drivers of recruitment and intraspecific interaction. We found that all <i>R. stricta</i> cohorts had a limited fractional vegetation cover and established in large-scale clusters. This clustering weakened with cohort age, possibly indicating merging of neighboring vegetation patches. Different cohort clusters did not spatially overlap in most cases, indicating that recruitment patterns changed position over time. Strong indications were found that the main drivers underlying <i>R. stricta</i> spatial configurations were allogenic (i.e. not driven by vegetation) and dynamic. Most likely these drivers were aeolian-driven sand movement or human disturbance which forced offspring recruitment in spatially dynamic clusters. Competition and facilitation were likely active on the field site too, but apparently had a limited effect on the overall landscape structure.</p></div

Hypotheses assessment summary.

<p>The hypotheses at the end of the framework's decision tree (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091184#pone-0091184-g001" target="_blank">Figure 1a</a>) are listed in the upper row. The bottom row states the decision for each hypothesis. Rejected hypotheses are indicated by “n”, supported ones by “y”. SL stands for seed limitation, HP for habitat patchiness, SL_DEN for density SL, SL_DIS for distance SL, HP_AL for allogenic HP, HP_{AL_SS} for small-scale allogenic HP, HP_{AL_LS} for large-scale allogenic HP, HP_{AL_LS_STAT} for static large-scale allogenic HP, HP_{AL_LS_DYN} for dynamic large-scale allogenic HP, HP_AU for autogenic HP, HP_{AU_CO} for competition, and HP_{AU_FA} signifies facilitation.</p

Study area with <i>R. stricta</i> patches displayed as discs.

<p>To make all unbranched individuals visible in this figure, they were enlarged to discs of 0.25(i.e. all branched individuals) are displayed proportional to their actual surface area. A grid with square plots with sides five times the diameter of the largest observed nebkha (5×4.2 m = 21 m) is overlain over the site. Only cells with white background completely fall inside the study site. Gray plots were omitted from the calculations. A second grid with cell sides ten times the diameter of the largest observed nebkha (10×4.2 m = 42 m) is also overlain. The sides of these grid cells (8 in total) are depicted in bold.</p

Five size classes of <i>R. stricta</i> patches (assumed as cohorts), separately depicted in the study site.

<p>Patterns correspond to (a) unbranched individuals (UI, N = 500), (b) branched juveniles (J_B, N = 125), (c) small adults (A_S, N = 178), (d) medium adults (A_M, N = 171), and (e) large adults (A_L, N = 82). To make all unbranched individuals visible, they were enlarged to discs of 0.25 m diameter, regardless of their actual size. Vegetation patches of the other cohorts are displayed proportional to their actual surface areas. Unbranched individuals (a) and branched juveniles (b) are depicted at a larger scale (together with a larger scale bar) to better visualize the cohorts with the smallest vegetation patches.</p

Histograms with 25 bins of <i>R. stricta</i> patch (a) surface areas and (b) log-transformed surface areas.

<p>Size classes (assumed cohorts) of unbranched individuals (UI), branched juveniles (J_B), small adults (A_S), medium adults (A_M), and large adults (A_L) are depicted inside the histograms, where possible. However, due to lack of space in the left histogram, size classes smaller than A_M are joined together (denoted asM).</p

Schematic overview of the proposed hypotheses on <i>R. Stricta</i> recruitment dynamics, organized in a framework (subfigure a), together with their expected vegetation pattern characteristics (subfigure b).

<p>SL stands for seed limitation, HP for habitat patchiness, SL_DEN for density SL, SL_DIS for distance SL, HP_AL for allogenic HP, HP_{AL_SS} for small-scale allogenic HP, HP_{AL_LS} for large-scale allogenic HP, HP_{AL_LS_STAT} for static large-scale allogenic HP, HP_{AL_LS_DYN} for dynamic large-scale allogenic HP, HP_AU for autogenic HP, HP_{AU_CO} for competition, and HP_{AU_FA} for facilitation. ISO(UI) = y signifies that unbranched individuals occur isolated, PCF(X)>0 denotes that the evaluated PCF lies significantly (<i>p</i><0.01) above the corresponding null model envelope (X representing the cohort under evaluation). PCCF(A_L,X)<0 and PCCF(A_L,X)>0 respectively indicate that the evaluated PCCF lies significantly (<i>p</i><0.01) under and above the corresponding null model envelope (X and A_L respectively standing for a cohort other than the large adults, and the large adult cohort, between which PCCFs are being calculated). Corr1 = s denotes whether adult FVC spatially correlates significantly (<i>p</i><0.05) with unbranched individual density. Corr2 = s or Corr2 = ns respectively mark whether the spatial correlations between densities of different cohorts are significant (<i>p</i><0.05) or not.</p

Correlation coefficients regarding <i>R. stricta</i> densities in 21 m square plots between different <i>R. stricta</i> size classes (assumed different cohorts).

<p>Included size classes are: unbranched individuals (UI; Ø<0.25 m), branched juveniles (J_B; 0.25 m<Ø<0.50 m), small adults (A_S; 0.50 m<Ø<1.00 m), medium adults (A_M; 1.00 m<Ø<2.00 m) and large adults (A_L; Ø>2.00 m). Significant correlations are flagged with 1, 2 or 3 asterisks, representing <i>p</i>-values between 5 10⁻² and 10⁻³, between 10⁻³ and 10⁻⁴, and below 10⁻⁴, respectively. As correlation matrices are symmetric, only the lower triangular part is shown.</p

Spatial second-order summary statistics of five size classes, alone (univariate), and related to the large adult <i>R. stricta</i> pattern (bivariate).

<p>The left panels display the PCFs of the five size classes, while the right panels list the PCCFs between the large adult size class and four other size classes [unbranched individuals (UI), branched juveniles (J_B), small adults (A_S), and medium adults (A_M)]. X represents the size class under analysis (either UI, J_B, A_S, A_M or A_L). Each PCF envelope is comprised of the minimal and maximal values in the PCF-set resulting from 499 simulated univariate null model patterns, while each PCCF envelope is comprised of the minimal and maximal values in the PCCF-set resulting from 499 simulated bivariate null model patterns (see Methods for null model descriptions). PCFs and PCCFs of observed patterns are subtracted from their associated null model envelopes. In this way only differences between observed PCFs and PCCFs and their respective envelopes are shown, as indicated by ΔPCF(<i>r</i>) and ΔPCCF(<i>r</i>), where <i>r</i> represents pair distances expressed in meters. Envelopes that fall completely above or beneath the null line, for a range of pair distances, therefore indicate deviations from randomness at those scales. Black and white intervals in bars above the graphs, respectively pinpoint significant positive deviations (indicating clustering for PCFs, and bivariate clustering for PCCFs) and significant negative diversions (indicating regularity for PCFs, and bivariate regularity for PCCFs) from envelopes, while gray intervals indicate scale intervals where the null model is not rejected according to GoF tests (<i>p</i> = 0.01).</p