DOI: 10.5061/dryad.mw6m9067x
title: Interactions between propagule pressure, native diversity, and local assembly processes in mediating invasion outcomes

description: Invasion of ecological communities by introduced species depends on the
 introduced species’ traits, biotic resistance, and propagule pressure.
 Each of these factors has been studied individually, but the interactions
 between them need further attention. For example, propagule pressure is
 considered an important predictor of invader success, but its effect is
 likely mediated by native composition and invader traits. Similarly,
 biotic resistance arises through the effects of local community assembly
 processes on native-exotic interactions, but is generally studied using
 native diversity alone. Here we examined interactions between propagule
 pressure and native community assembly processes in determining exotic
 species abundance, using spatially-explicit community assembly
 simulations. This dataset contains the code used for these simulations and
 the simulated data. 
description: # Interactions between propagule pressure, native diversity, and local
 assembly processes in mediating invasion outcomes Dataset DOI:
 [10.5061/dryad.mw6m9067x](10.5061/dryad.mw6m9067x) ## Description of the
 data and file structure This dataset contains code and simulated data from
 a study on the effect of propagule pressure and native community
 characteristics on the success of a virtual invasive species. Invasion of
 ecological communities by introduced species depends on the introduced
 species’ traits, biotic resistance, and propagule pressure. Each of these
 factors has been studied individually, but the interactions between them
 need further attention. For example, propagule pressure is considered an
 important predictor of invader success, but its effect is likely mediated
 by native composition and invader traits. Similarly, biotic resistance
 arises through the effects of local community assembly processes on
 native-exotic interactions, but is generally studied using native
 diversity alone. Therefore, here we examined interactions between
 propagule pressure and native community assembly processes in determining
 exotic species abundance, using spatially-explicit community assembly
 simulations. *All data in this dataset is simulated.* ### Files and
 variables ## File types **Simulation code files** - .R files with names
 beginning with a number, contain code used for simulations. **Data files**
 - .csv files, contain simulated data generated used the simulation code
 files. **Analysis code files** - .R files with names beginning with
 'Analysis', contain code used for plotting and analyzing
 simulated data. **Other files** - .rds files, used in the simulations. ##
 Common abbreviations in file names #### **Assembly scenario** * Nuet -
 Neutral scenario * LimSim1 - Limiting similarity sub-scenario with classic
 limiting similarity and character displacement * LimSim2 - Limiting
 similarity sub-scenario with modified limiting similarity and character
 displacement * LimSim3 - Limiting similarity sub-scenario with limiting
 similarity only * SelAss - Self-assembly scenario #### Independent
 variable * numvar - Number of propagules per introduction event was
 varied, while frequency of introduction events and initial native species
 richness kept constant (introduction frequency equal to once every time
 step during the introduction period, native richness equal to 10 unless
 otherwise mentioned). * richvar - Initial native species richness was
 varied. Number of propagules per introduction event also varied in a full
 factorial manner. Frequency of introduction events kept constant (equal to
 once every time step during the introduction period). * burnin - Native
 community dynamics during burn-in period. No propagule pressure. Initial
 native richness equal to 10 unless otherwise specified. ## Simulation code
 files File names begin with 3 digits in the format x.y.z, where: * x =
 assembly scenario * 1 - neutral (e.g. 1.0.1Neut_numvar.R,
 1.0.2Neut_richvar.R) * 2 - limiting similarity * 3 - self assembly (e.g.
 3.0.0SelAss_burnin.R, 3.0.1SelAss_numvar.R) * y = sub-scenario (in
 limiting similarity scenario only) * 1 - classic limiting similarity with
 character displacement (e.g. 2.1.0LimSim1_burnin.R, 2.1.1LimSim1_numvar.R,
 2.1.2LimSim1_richvar.R) * 2 - modified limiting similarity with character
 displacement (e.g. 2.2.0LimSim2_burnin.R, 2.2.1LimSim2_numvar.R,
 2.2.2LimSim2_richvar.R) * 3 - limiting similarity only (e.g.
 2.3.0LimSim3_burnin.R, 2.3.1LimSim3_numvar.R, 2.3.2LimSim3_richvar.R) * 0
 - no sub-scenario (in neutral and self-assembly scenarios) * z =
 independent variable varied * 1 - propagule pressure/number of propagules
 per introduction event (e.g. e.g. 1.0.1Neut_numvar.R) * 2 - initial native
 species richness (e.g. 1.0.2Neut_richvar.R) * 0 - only burn-in period
 simulated, no propagule pressure and no variation in native richness (e.g.
 2.1.0LimSim1_burnin.R) **Note: All simulation code files other than
 0.0.0Env_Grid_Neighbours.R are meant to be run on a high performance
 computing cluster. Running them on a personal computer may require
 excessive computational time**, unless the number of replicates and/or
 number of time steps is reduced.** **Each code file in its current form
 requires 96 cores for parallel processing. Users can adjust the number of
 cores using the 'ncores' variable. Note 2: All simulation code
 files other than 0.0.0Env_Grid_Neighbours.R will generate temporary .txt
 files while running, for keeping track of simulation progress. Users may
 wish to create folders for storing these .txt files, and will need to
 specify the appropriate file path inside the clusterEvalQ function. ####
 File: 0.0.0Env_Grid_Neighbours.R Code for generating the landscape grid, a
 list of neighbours of each grid cell (neighbours.rds), and the environment
 (env.rds). *Output from this file is needed for running all other
 simulation files.* #### All other simulation code files File names start
 with the three digit code explained above, which describes what each file
 does. There is no burn-in only code for the neutral scenario as there was
 no burn-in period in this scenario (i.e. there is no file with name
 beginning with 1.0.0). Additionally, the effect of varying initial native
 richness was not explored in the self-assembly scenario, hence there is no
 code file whose name begins with 3.0.2. ## Data files Data files
 containing community composition data are named according to the following
 format: CommunComp_*scenario**_****var*.csv, where * CommunComp -
 indicates that the file contains community composition data * *scenario* -
 abbreviated scenario name (see "Common abbreviations in file
 names" section) * *var* - abbreviation describing the independent
 variable that was varied (see "Common abbreviations in file
 names" section) In these files, each row contains data from a single
 simulated community at a single time step. The code files used for
 generating each of these data files can be identified using the scenario
 and independent variable abbreviations in the file names. Data files that
 do not contain community composition data: **env.csv** - environment
 variable used in the limiting similarity and self-assembly simulations.
 Each row corresponds to a grid cell in the simulated landscape. Generated
 using the code file 0.0.0Env_Grid_Neighbours.R **SpNiche_SelAss.csv** -
 environmental niche and demographic rates of each species in each species
 pool, used in the self-assembly scenario simulations. Each row corresponds
 to a single species in a given species pool. Generated using the code file
 3.0.0SelAss_burnin.R ### Description of variable names in each data file
 #### File: env.csv ##### Variables * x - x coordinates of the given grid
 cell * y - y coordinates of the given grid cell * env - value of the
 environmental variable in the given grid cell (unit-less) #### File:
 SpNiche**_SelAss**.csv ##### Variables * PoolNum: A unique number for each
 simulated species pool * Sp: A unique number for each simulated species in
 each species pool. Each pool contains 101 species (Sp101 corresponds to
 the introduced species, rest are native species). Note that species with
 the same ID number from different pools are unrelated to each other. *
 EnvOpt: Environmental niche optimum of the given species * EnvWidth:
 Environmental niche width of the given species * Fecund: Fecundity of the
 given species * SurvivalTradeoff: Maximum survival rate of adults of the
 given species in their optimal environment * Survival2Tradeoff: Maximum
 survival rate of propagules of the given species in their optimal
 environment #### Files with names ending in "numvar.csv" ######
 (i.e. CommunComp_Neut_numvar.csv, CommunComp_LimSim1_numvar.csv ,
 CommunComp_LimSim2_numvar.csv, CommunComp_LimSim3_numvar.csv,
 CommunComp_SelAss_numvar.csv) ##### Variables * Rep: A unique number for
 each simulated community * Time: Time step (may not start at zero because
 burn-in period is not saved) * SpRich: Species richness (number of species
 present in the landscape) at the given time step * PropNum: Propagule
 pressure (number of invader propagules introduced per introduction event,
 relative to the fecundity of a native individual) * Column names beginning
 with 'Sp' or 'sp' (sp1-sp11 or Sp1-Sp101): Abundance
 of each species at the given time step. Abundance refers to the number of
 landscape cells occupied by a given species. Column name with the highest
 number corresponds to the introduced species (sp11 in the neutral and
 limiting similarity scenarios, Sp101 in the self-assembly scenario). The
 file for the self-assembly scenario contains the additional column: *
 PoolNum: Species pool that the given community belongs to #### Files with
 names ending in "richvar.csv" ###### (i.e.
 CommunComp_Neut_richvar.csv, CommunComp_LimSim1_richvar.csv ,
 CommunComp_LimSim2_richvar.csv, CommunComp_LimSim3_richvar.csv) #####
 Variables * Rep: A unique number for each simulated community * Time: Time
 step (may not start at zero because burn-in period is not saved) * NumSp:
 Initial native species richness (including the introduced species) *
 PropNum: Propagule pressure (number of invader propagules introduced per
 introduction event, relative to the fecundity of a native individual) *
 NumSpRemain: Species richness at the given time step * MeanNAbund: Mean
 native species abundance at the given time step * MaxNAbund: Maximum
 native species abundance at the given time step (i.e. abundance of the
 most common native species) * SdNAbund: Standard deviation of native
 species abundance at the given time step * InvAbund: Abundance of
 introduced species at the given time step #### Files with names ending in
 "burnin.csv" ###### (i.e. CommunComp_LimSim1_burnin.csv ,
 CommunComp_LimSim2_burnin.csv, CommunComp_LimSim3_burnin.csv,
 CommunComp_SelAss_burnin.csv) ##### Variables Same as for files with names
 ending in 'numvar.csv', except that the Time variable starts at
 zero, and there is no introduced species (column 'sp11' or
 'Sp101' if present, is always zero). ## Analysis code files ####
 File: Analysis_burnin.R **Description:** For plotting and analyzing
 community composition trends during the burn-in period, in the
 self-assembly and limiting similarity scenarios. #### File:
 Analysis_richvar.R **Description:** For plotting and analyzing results of
 simulations where of varying native richness on introduced species
 abundance, and the effect of the introduced species on native diversity.
 #### File: Analysis_numvar.R **Description:** For plotting and analyzing
 the results of simulations where propagule pressure (number of propagules
 per introduction event) was varied. #### File:
 Analysis_SelAss_nativeniches.R **Description:** For plotting and analyzing
 the changes in native niche distributions during the burn-in period in the
 self-assembly scenario. #### File: Analysis_SelAss_randomforest.R
 **Description:** For the random forest analysis on the role of propagule
 pressure and native community characteristics in determining invader
 abundance, in the self-assembly scenario ## Other files **env.rds** - file
 containing the environment value associated with grid cell of the
 landscape. Generated using the code in 0.0.0Env_Grid_Neighbours.R and was
 used in the simulations. Contains the same information as the env.csv
 file. **neighbours.rds** - file containing the list of neighbours of each
 grid cell. Generated using the code in 0.0.0Env_Grid_Neighbours.R and in
 the simulations. ## Code/software All code was written in R version 4.2.0
 ## Access information Other publicly accessible locations of the data: *
 None Data was derived from the following sources: * None