DOI: 10.5061/dryad.3tx95x6nf
title: Plant interaction networks reveal the limits of our understanding of diversity maintenance

description: Species interactions are key drivers of biodiversity and ecosystem
 stability. Current theoretical frameworks for understanding the role of
 interactions make many assumptions which, unfortunately, do not always
 hold in natural, diverse communities. This mismatch extends to annual
 plants, a common model system for studying coexistence, where interactions
 are typically averaged across environmental conditions and transitive
 competitive hierarchies are assumed to dominate. We quantify interaction
 networks for a community of annual wildflowers in Western Australia across
 a natural shade gradient at local scales. Whilst competition dominated,
 intraspecific and interspecific facilitation were widespread in all shade
 categories. Interaction strengths and directions varied substantially
 despite close spatial proximity and similar levels of local species
 richness, with most species interacting in different ways under different
 environmental conditions. Contrary to expectations, all networks were
 predominantly intransitive. These findings encourage us to rethink how we
 conceive of and categorise the mechanisms driving biodiversity in plant
 systems.
description: This dataset was collected from a winter annual wildflower
 community at Perenjori Reserve in Western Australia. This is the same
 dataset as made available by Bimler et al. (2023a) at https://doi.org/10.5061/dryad.h44j0zpq3 but processed slightly differently. Data collection is described extensively in Bimler et al. (2023b) and in the Methods and Supplementary Information S1.1 of the manuscript associated with this dataset, "<em>Plant interaction networks reveal the limits of our understanding of diversity maintenance</em>". The dataset concerns a diverse and well-studied community of annual plants which grow, set seed and die within approximately 4 months every year. Individual fecundity data were collected in 2016 when 100 50 x 50 cm plots established in the understory of West Perenjori Reserve were monitored over the length of the full field season.  <strong>References:</strong> Bimler, Malyon; Mayfield, Margaret; Martyn, Trace; Stouffer, Daniel (2023a). Estimating interaction strengths for diverse horizontal systems using performance data [Dataset]. Dryad. https://doi.org/10.5061/dryad.h44j0zpq3 Bimler, M. D., Mayfield, M. M., Martyn, T. E., & Stouffer, D. B. (2023b). Estimating interaction strengths for diverse horizontal systems using performance data. <em>Methods in Ecology and Evolution</em>, 14, 968–980. https://doi.org/10.1111/2041-210X.14068
description: # Plant interaction networks reveal the limits of our understanding of
 diversity maintenance Data used for the Bimler 2023 paper 'Plant
 interaction networks reveal the limits of our understanding of diversity
 maintenance', see methods in the main text and Supplementary Methods
 S1.1 for details on the data collection and experimental design. Data
 primarily consists of individual observations of plant fecundity and the
 identity and abundance of surrounding herbaceous plant neighbours,
 collected from a natural, annual wildflower community in Western
 Australia. These data were binned into three environmental categories
 based on the percentage of overhead canopy cover: 'open' (0 to
 7.9% canopy cover), 'intermediate' (8 to 17.9% canopy cover),
 and 'shady' (18 to 40% canopy cover), each with their own
 separate data files. These categories are referred to in the names of the
 data files below as 1, 2, and 3, respectively. ## File structure ###
 Community data split into environmental categories: The files below exist
 for each environmental category 1, 2, and 3, as given at the end of the
 file names. We describe each type of file only once to avoid repetition.
 *fecundities*.csv:* rows are observations of individual seed production
 and neighbour abundances. The first four columns refer to: * plot = plot
 number (identifier) from 1 to 100 * seeds = observation of indivdual seed
 production * focal = four-letter focal species code (identifier) * focalID
 = individual plant identifier * Each column following those four is named
 after a species code and contains the abundance of each neighbouring
 species for that individual observation. Neighbour columns are organised
 alphabetically, with focal species first then non-focal neighbour species.
 This should match the size and order of the associated
 key_neighbourID*.csv file. *key_speciesID*.csv:* single-column list of
 four-letter species codes identifying focal species in that environmental
 category, in alphabetical order. *key_neighbourID*.csv:* single-column
 list of four-letter species codes identifying all neighbouring species in
 that environmental category. Focal species comes first in alphabetical
 order, followed by non-focal neighbours also in alphabetical order. ###
 Plot and demographic data: **plot_data.csv:** Environmental, diversity and
 density data associated with each plot. The column names refer to: * Plot
 = plot number (identifier) from 0 to 100 * Density = whether the plot was
 left at full density (100) or thinned to 60% (60) or 30% (30) density
 without targeting any particular species. * Per.Litt = percentage litter
 cover * Per.Bare = percentage bare ground * Per.Woody = percentage woody
 debris (logs, fallen branches) * Soil.P = soil phosphorus as measured by
 Colwell extraction (mg/kg) * Soil.Water = soil moisture content *
 Canopy.Cover = percentage canopy cover * Class.Canopy = environmental
 category whereby 1='open', 2='intermediate' and
 3='shady' * evenness = plot-level Shannon evenness index *
 diversity = total plot-level species richness * total.density = total
 plot-level abundance of all species **plot_species_abundances.csv:** rows
 are abundance of each species present in each plot. The columns refer to:
 * plot = plot number (identifier) from 0 to 100 * species = four-letter
 species code * Num_indivs = number of individuals of that species in that
 plot * Num_indiv_seed = number of individuals for which seeds were
 collected * Class.Canopy = environmental category whereby
 1='open', 2='intermediate' and 3='shady'
 **seed_rates.csv:** estimates of focal species seed germination and seed
 survival rates. The community mean is substituted for those species which
 did not have reliable estimates (STPA, PEDU, WAAC, EROD, GITE survival
 rate). The first column is intended to be used as row names when read into
 R. * name = focal species name * code = four-letter species code * germ =
 seed germination rate from 0 to 1 * surv = seed survival rate from 0 to 1
 **species_list.csv:** list of species present in the dataset. Columns
 give, in order: four-letter species code, species name, family, whether
 they are considered focal or not, and size of the interaction
 neighbourhood