DOI: 10.5061/dryad.ttdz08m4s
title: Distance-dependence seed set of Vasconcellea chilensis

description: Plant reproductive failure is a critical concern for conserving rare and
 endangered species that typically have low-density and sparse populations.
 One important factor contributing to reproductive failure is the spatial
 arrangement of plants within a population, which can lead to isolation and
 negatively affect seed output, particularly in obligate outcrosses.
 Additionally, this effect can be compounded by plant size. Here, we
 investigate how plants' spatial distribution and size influence the
 reproductive success of Vasconcellea chilensis, a threatened
 papaya species from northern Chile. Using flower exclusion
 experiments, we first examined whether V. chilensis can produce seeds via
 apomixis. We then used Spatial Point Pattern Analysis (SPPA) in three
 populations to examine the spatial arrangement of plants in three
 populations, and, finally, we assessed whether plant size and mate
 distance influence the reproductive success of this plant species. V.
 chilensis is a dioecious shrub unable to produce fruits through apomixis.
 The SPPA revealed significant clustering of female and male plants at
 different spatial scales, indicating a non-random distribution. Moreover,
 a significant attraction between the sexes suggested a preference for
 proximity. In two populations, closer proximity to male plants was linked
 to higher seed production. Our study revealed that the absence of
 apomixis in V. chilensis makes it prone to experiencing distance-dependent
 reproductive failure. In particular, the seed set was compromised in
 female plants isolated from male neighbors. This link between isolation
 and seed production was especially significant in the driest site, and we
 discussed how environmental factors can exacerbate this effect.
description: <em>Vasconcellea chilensis</em> (Planch. Ex A. Dc.)
 Solms 1889 (Caricaceae). Dioecious
 shrub with pistillate flowers (female) and staminate flowers
 (male). <strong>Population</strong>
 <strong>Surveyed</strong>: Conchillas (CCH), Conchalí (CNL), and Puntilla
 Las Vacas (PLV). <strong>Apomixis</strong>: We excluded pistillate flowers with mesh bags and marked untreated flowers as controls. After five to six months, we collected fruits and counted their seeds.  <strong>Seed</strong> <strong>set</strong>: We collected five fruits from all female plants located in each population. For each plant, we calculate the seed set by dividing the mean number of seeds (in five fruits) by the mean number of ovules (in five flowers).  <strong>Spatial</strong> <strong>Analyses</strong>: Individual plants were geo-referenced in the three populations. In each population, we run Spatial Point Pattern Analyses (SPPA) using Programita software. First, we calculate a univariate pair-correlation function (g(r)) separately for each sex, and second, we calculate a bivariate pair-correlation function (g12(r)) among both sexes (see details below).
description: # Distance-dependence seed set of *Vasconcellea chilensis*
 [https://doi.org/10.5061/dryad.ttdz08m4s](https://doi.org/10.5061/dryad.ttdz08m4s) We present the results of apomixis experiments, Spatial Point Pattern Analyses (SPPA), isolation, and Seed set of *Vasconcellea chilensis.* ## Description of the data and file structure This repository contains data and code for the following manuscript: García-Guzmán, P., Loayza, A.P., Carvajal, D.E., and G. Carozzi-Figueroa. Lonesome plants: How isolation affects seed set of a threatened dioecious shrub. *Ecology and Evolution*. The following folders and files are included: ### 1. Isolation (folder): * Iso_Conchali.txt / Iso_Conchillas.txt / Iso_PLasVacas.txt / Isolation_All.txt = Datasets contain information for each female plant of plant size (Volume in m^3); brood size (Bsize), Seed set (Sset), Nearest male neighbor distance (NND) and the sum of all distances to male plants (SMD). * Isolation.R = Rcode for lineal model fitting * Sset.R = R code used for Seed set ANOVA * Apomixis.xlsx = Dataset shows the fruit formation incidences resulting from flower bagging experiments conducted in the three populations. ### 2. SPPA\_Analyses (folder): #### SPPA\_Analyses/input files/ (CCH, CNL, and PLV indicate populations) * CCH_M.dat / CCH_F.dat / CNL_M.dat / CNL_F.dat / PLV_M.dat / PLV_F.dat = Datasets to run univariate analyses on Programita software. For male (M) and female plants (F). * CCH_MF.dat / CNL_MF.dat / PLV_MF.dat = Datasets to run bivariate analyses on Programita software. For both sexes (MF). * CNL_Win.irr / PLV_Win.irr = Datasets contain the window frame (irregular polygon) used univariate and bivariate analyses (in these two populations). #### SPPA\_Analyses/output files/ (CCH, CNL, and PLV indicate populations) 1. CCH_Bivariate.txt / CNL_Bivariate.txt PLV_Bivariate.txt = Output files from bivariate SPPAs with Programita software. Files contain, for each distance (Dist), information of the observed *g*<sub>12</sub>(*r*) statistics (gr12), the expected value assuming a random distribution of plants (exp12), the minimum boundary of the global envelope (Glmin), the maximum boundary of the global envelope (Glmax). 2. CCH_UniMF.txt / CNL_UniMF.txt / PLV_UniMF.txt = Output files from univariate SPPA's with Programita software. Files contain, for each distance (Dist), information of the observed g(*r*) statistics (gr), the expected value assuming a random distribution of plants (exp), the minimum boundary of the global envelope (glmin), the maximum boundary of the global envelope (glmax) and the corresponding sex (sex). 3. Plots_ggplot_g12.R / Plots_ggplot_gr.R = R code used for graph the SPPA's output. ## Code/Software ### SPPA Analyses: Programita software, November 2018 version. #### Parameters used: * Data were loaded from .dat files, and the irregular window frames from .irr files (only for CNL and PLV populations). * **Analysis type:** Standard Analysis; no grid; Calculate simulation envelopes. * **Observation window:** Rectangle (for CCH population) and Irregularly shaped study region (For CNL and PLV populations). * **Estimator method (Default parameters):** Wiegand & Maloney (2013) method (WM); No edge correction used for Dk(r); No mean distance to Kth NN; Adapted intensity estimators. * **Ring width (*dr*):** We used the suggested value, calculated from *dr* = 0.2/λ<sup>0.5</sup> (λ= data density)**.** * **Bin size (cell size):** Bind with= 1; Scale point size= 1 * **Summary statistic:** *g*(*r*) * **Null Model selected:** Pattern 1 and 2 Complete Spatial Randomness (CSR); Goodness of fit (GoF); Lowest/higher confidence limits = 5% * **GoF options (for Univariate and bivariate analyses):** Minimum *r* distance =1; maximum *r* distance= 50; Transformation of residual = Global envelope.