DOI: 10.5061/dryad.mcvdnck5d
title: Effects of compositional heterogeneity and spatial autocorrelation on richness and diversity in simulated landscapes

description: Landscape structure plays a key role in mediating a variety of ecological
 processes affecting biodiversity patterns, however, its precise effects
 and the mechanisms underpinning them remain unclear. While the effects of
 landscape structure have been extensively investigated both empirically,
 and theoretically from a metapopulation perspective, the effects of
 spatial structure at the landscape scale remain poorly explored from a
 metacommunity perspective. Here, we attempt to address this gap using a
 spatially explicit, individual-based metacommunity model to explore the
 effects of landscape compositional heterogeneity and per se spatial
 configuration on diversity at the landscape and patch level via their
 influence on long-term community assembly processes. Our model simulates
 communities composed of lineages of annual, asexual organisms living,
 reproducing, dispersing, and competing within grid-based, fractal
 landscapes which vary in their magnitude of spatial environmental
 heterogeneity and their degree of spatial environmental autocorrelation.
 Communities are additionally subject to temporal environmental fluctuation
 and external immigration, allowing for turnover in community composition.
 We found that compositional heterogeneity and spatial autocorrelation had
 differing effects on richness and diversity and the landscape and patch
 scales. We also note a slight negative effect of compositional
 heterogeneity on the median total landscape population size.
 Landscape-level diversity was driven by community dissimilarity at the
 patch level and increased with greater heterogeneity, while landscape
 richness was largely the result of the short-term accumulation of
 immigrants and decreased with greater compositional heterogeneity. Both
 richness and diversity decreased in variance with greater compositional
 heterogeneity, indicating a reduction in community turnover over time.
 Patch-level richness and diversity patterns appeared to be driven by
 overall landscape richness and local mass effects, resulting in maximum
 patch-level richness and diversity at moderate levels of compositional
 heterogeneity and high spatial autocorrelation.
description: This dataset contains the results of a simulation experiment
 using an individual-based metacommunity simulation model. This dataset
 contains output from simulation scenarios covering different combinations
 of compositional heterogeneity and spatial autocorrelation parameters.
 Data files for each scenario contain combined data from 30 replicate runs
 of the simulation. Data files contain data over time for various landscape
 aggregated statistics (ending in _trend), data on individual organisms at
 the final timestep of the simulation, and patch aggregated data calculated
 from data on individual organisms for the final timestep of the simulation
 (ending in _patches). This archive additionally contains the parameter
 files used for the different scenarios, the shell scripts used to run the
 scenarios, and a PDF user guide describing the use of the simulation
 program and its output data as well as the R script used to analyse the
 data and produce figures.
description: # Effects of compositional heterogeneity and spatial autocorrelation on
 richness and diversity in simulated landscapes --- This dataset contains
 the results of a simulation experiment using an individual-based
 metacommunity simulation model. This dataset contains output from
 simulation scenarios covering 16 different combinations of compositional
 heterogeneity and spatial autocorrelation parameters. This archive
 additionally contains the parameter files used for the different
 scenarios, the shell scripts used to run the scenarios, a PDF user guide
 describing the use of the simulation program and its output data, and the
 R script used to create the figures in the journal article associated with
 this repository. ## Description of the data and file structure Data files
 consist of .txt files containing comma-separated values and can be opened
 directly via a text editor or imported into any data analysis software
 capable of importing text or CSV files. Data files for each scenario
 contain combined data from 30 replicate runs of the simulation. Data files
 contain landscape aggregated statistics data over time (ending in
 **_trend**), data on individual organisms at the final timestep of the
 simulation, and patch aggregated data calculated from data on individual
 organisms for the final timestep of the simulation (ending in
 **_patches**). This archive additionally contains the parameter files used
 for the different scenarios, the landscape files used for each of the
 replicates, and the shell scripts used to run the scenarios. Landscape
 files are .txt files containing semicolon-separated values representing
 environmental attributes for a patch. Each replicate run uses two
 landscape files. The precise files used are specified in the shell
 scripts. Parameter files consist of Julia code for a dictionary containing
 parameter values used for a simulation scenario. Parameter files are used
 as inputs for the simulation program. The parameter files used for a given
 scenario are specified in their respective shell scripts. ### Files and
 naming schemes #### Output Data Files Output data files are contained in
 the **/data** folder. Data files are subject to regular naming schemes.
 The **merged** prefix indicates that the file contains combined data from
 multiple replicates. All output data files in this repository contain
 combined data from 30 replicate simulation runs and are thus named as
 such. The **S1** prefix is a **deprecated prefix** originally intended to
 specify certain scenarios. The **G** prefix is followed by the value of
 the *G* simulation parameter. For example, G0.3 would indicate that this
 file contains data from the *G*=0.3 scenario. The **_T** prefix is
 followed by the number of time steps in a simulation run. This number is
 10,000 in all scenarios in this study. The **_AT** designation indicates
 the Hurst index of the landscapes used in the simulation set. For example,
 a file with AT1_AT1 uses landscape files with a Hurst index of 1. Files
 with the **patches** suffix contain data aggregated by patch. Files with
 **trend** contain landscape aggregated data for each time step of a
 simulation run. Output data files **with no suffix** contain a census of
 individual organisms at the final time step of the simulation run. ####
 Parameter Files Parameter files are contained in the **/parameter files**
 folder. Parameter files contain the parameters needed to run the
 simulation scenarios used in this study. These files are intended to be
 imported by the simulation program at runtime and can be specified in a
 shell script. The parameters are contained in the form of a Julia
 dictionary. The purpose of each parameter is specified in comments in the
 files themselves. This information, as well as a template parameter file,
 is available in the GitHub repository for the simulation program:
 [https://github.com/jtardanico/TardanicoHovestadt2023_Landscapes](https://github.com/jtardanico/TardanicoHovestadt2023_Landscapes) Parameter files are named **ParaDict_test** and are followed by the value of *G* for its respective scenario (e.g. **_g0.3**). The **lowAC** (low autocorrelation) indicates a Hurst index of 0. The **absence** of the lowAC designation indicates a Hurst index of 1. The **_trend** ending indicates a fluctuating mean *T*. As this is used in all scenarios, all parameter files have this designation. #### Shell Scripts Shell scripts are contained in the **/shell scripts** folder. Shell script files are used to run the simulation program. Shell script files contain commands that specify the Julia code files and any parameters and file inputs needed to run them. Shells script file names contain the *G* scenario which they run, e.g. **_G0.3 for *G*=0.3. Shell scripts run **both** Hurst index=0 and Hurst index=1 scenarios, running 30 replicates of each. #### Other files This repository additionally contains the R script **Tardanico_Hovestadt2023_figures.R** which contains code used to create the figures used in the journal article associated with this repository. This R script will set its working directory to the directory the R script file is located in and load data files from a relative subdirectory path. ### Data variables for data on individual organisms: | Variable | Explanation | | -------------------- | -------------------------------------------------------------- | | ID | Defunct lineage identifier, replaced with LineageID | | Replicate | Replicate number | | Timestep | The time step of data output | | H\_t | Hurst index for T attribute | | H\_h | Hurst index for H attribute | | alpha | Niche breadth trade-off parameter | | clim\_scen | Climate scenario (DEFUNCT) | | gradient | Strength of compositional heterogeneity (G) | | x,y | The x and y indices of the patch an organism is in | | T\_opt | T niche optimum | | H\_opt | H niche optimum | | T\_sd | T niche breadth | | H\_sd | H niche breadth | | disp\_l | Dispersal probability (Pdisp) | | disp\_g | Probability of dispersing via global dispersal (Pglobal) | | fert\_max | Intrinsic reproduction rate (R0), maximum expected offspring | | LineageID | Identifier for organism lineage/species | | Origin\_x, Origin\_y | Lineage patch of first appearance | | Origin\_time | Lineage time of first appearance | | temp\_t | Patch T attribute | | habitat | Patch H attribute | | trend | Fluctuation in T at a given timestep | | mean\_trend | Mean of T fluctuation,0 if no climate shift | | precip\_t | Defunct patch attribute from an early iteration of the program | ### Data variables for patch aggregated data (\_patches): | Variable | Explanation | | ----------- | ----------------------------------------------------------------------- | | x,y | The x and y indices of a patch | | Replicate | Replicate number | | Timestep | The time step of data output | | H\_t | Hurst index for T attribute | | H\_h | Hurst index for H attribute | | alpha | Niche breadth trade-off parameter | | clim\_scen | Climate scenario (DEFUNCT) | | gradient | Strength of compositional heterogeneity (G) | | pop | patch total population | | richness | Patch lineage/species richness | | shannon | Patch Shannon-Weiner diversity | | simpson | Patch Simpson diversity | | tdiff | Mean T\_opt-patch T difference at current timestep | | mt\_tdiff | Mean T\_opt-patch T difference on average over time | | tft | Mean fitness for patch T at current timestep[^1] | | mft | Mean fitness for patch average T over time[^1] | | mfh | Mean fitness for patch H[^2] | | time\_fit | mean overall fitness at current timestep[^3] | | mean\_fit | mean overall fitness under patch average conditions over time[^3] | | T\_opt | patch mean T niche optimum | | H\_opt | patch mean H niche optimum | | T\_sd | patch mean T niche breadth | | H\_sd | patch mean H niche breadth | | disp\_l | Patch mean dispersal probability (Pdisp) | | disp\_g | Probability of dispersing via global dispersal (Pglobal) | | fert | Patch mean intrinsic reproduction rate (R0), maximum expected offspring | | temperature | Patch T attribute | | habitat | Patch H attribute | | trend | Fluctuation in T at a given timestep | [^1]: Fitness calculated for the T component of an organism's niche. [^2]: Fitness calculated for the H component of an organism's niche. [^3]: Combined product of fitness for T and H niche components. ### Data variables for landscape time series (\_trend): | Variable | Explanation | | ------------- | ------------------------------------------------------------------------ | | Replicate | Replicate number | | Timestep | The time step of data output | | clim\_scen | Climate scenario (DEFUNCT) | | Pop | Landscape total population | | rich | Landscape total richness | | simp | Landscape Simpson diversity index | | shan | Landscape Shannon-Wiener diversity index | | T\_opt | Landscape mean T niche optimum | | H\_opt | Landscape mean H niche optimum | | T\_sd | Landscape mean T niche breadth (T\_tol) | | H\_sd | Landscape mean H niche breadth (H\_tol) | | disp | Landscape mean dispersal probability (P\_disp) | | disp\_g | Landscape mean global dispersal probability (P\_global) | | VT\_opt | Landscape T\_opt variance | | VH\_opt | Landscape H\_opt variance | | VT\_sd | Landscape T\_sd variance | | VH\_sd | Landscape H\_sd variance | | Vdisp | Landscape disp variance | | Vdisp\_g | Landscape disp\_g variance | | ft | Landscape mean fitness for patch T[^1] | | fh | Landscape mean fitness for patch H[^2] | | fit | Landscape mean fitness[^3] | | tdiff | Landscape mean difference between T\_opt and patch T at current timestep | | avgtdiff | Landscape mean difference between T\_opt and mean patch T | | varft | Landscape variance in fitness for patch T[^1] | | varfh | Landscape variance in fitness for patch H[^2] | | varfit | Landscape variance in fitness[^3] | | var\_tdiff | Landscape variance in tdiff | | var\_avgtdiff | Landscape variance in avgtdiff | | fert\_max | Intrinsic reproduction rate (R0), maximum expected offspring | | trend | Fluctuation in T at a given timestep | | mean\_trend | Mean of T fluctuation, 0 if no climate shift | | grad | Strength of compositional heterogeneity (G) | | H\_t Hurst | Hurst index for T attribute (Spatial autocorrelation) | | H\_h Hurst | Hurst index for H attribute (Spatial autocorrelation) | | alpha | Niche breadth trade-off parameter | [^1]: Fitness calculated for the T component of an organism's niche. [^2]: Fitness calculated for the H component of an organism's niche. [^3]: Combined product of fitness for T and H niche components. ## Code/Software The code for this simulation program used to generate the data in this archive is available from the following GitHub repository: [https://github.com/jtardanico/TardanicoHovestadt2023_Landscapes](https://github.com/jtardanico/TardanicoHovestadt2023_Landscapes) Information on the use and set-up of this code is available on the GitHub repository and in the user guide included in this archive.