General overview The following datasets are described by this metadata record, and are available for download from the provided URL.

• Raw log files, physical parameters raw log files
• Raw excel files, respiration/PAM chamber raw excel spreadsheets
• Processed and cleaned excel files, respiration chamber biomass data
• Raw rapid light curve excel files (this is duplicated from Raw log files), combined dataset pH, temperature, oxygen, salinity, velocity for experiment
• Associated R script file for pump cycles of respirations chambers

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Physical parameters raw log files

Raw log files 1) DATE= 2) Time= UTC+11 3) PROG=Automated program to control sensors and collect data 4) BAT=Amount of battery remaining 5) STEP=check aquation manual 6) SPIES=check aquation manual 7) PAR=Photoactive radiation 8) Levels=check aquation manual 9) Pumps= program for pumps 10) WQM=check aquation manual

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Respiration/PAM chamber raw excel spreadsheets

Abbreviations in headers of datasets Note: Two data sets are provided in different formats. Raw and cleaned (adj). These are the same data with the PAR column moved over to PAR.all for analysis. All headers are the same. The cleaned (adj) dataframe will work with the R syntax below, alternative add code to do cleaning in R.

Date: ISO 1986 - Check Time:UTC+11 unless otherwise stated DATETIME: UTC+11 unless otherwise stated ID (of instrument in respiration chambers) ID43=Pulse amplitude fluoresence measurement of control ID44=Pulse amplitude fluoresence measurement of acidified chamber ID=1 Dissolved oxygen ID=2 Dissolved oxygen ID3= PAR ID4= PAR PAR=Photo active radiation umols F0=minimal florescence from PAM Fm=Maximum fluorescence from PAM Yield=(F0 – Fm)/Fm rChl=an estimate of chlorophyll (Note this is uncalibrated and is an estimate only) Temp=Temperature degrees C PAR=Photo active radiation PAR2= Photo active radiation2 DO=Dissolved oxygen %Sat= Saturation of dissolved oxygen Notes=This is the program of the underwater submersible logger with the following abreviations: Notes-1) PAM= Notes-2) PAM=Gain level set (see aquation manual for more detail) Notes-3) Acclimatisation= Program of slowly introducing treatment water into chamber Notes-4) Shutter start up 2 sensors+sample…= Shutter PAMs automatic set up procedure (see aquation manual) Notes-5) Yield step 2=PAM yield measurement and calculation of control Notes-6) Yield step 5= PAM yield measurement and calculation of acidified Notes-7) Abatus respiration DO and PAR step 1= Program to measure dissolved oxygen and PAR (see aquation manual). Steps 1-4 are different stages of this program including pump cycles, DO and PAR measurements.

8) Rapid light curve data Pre LC: A yield measurement prior to the following measurement After 10.0 sec at 0.5% to 8%: Level of each of the 8 steps of the rapid light curve Odessey PAR (only in some deployments): An extra measure of PAR (umols) using an Odessey data logger Dataflow PAR: An extra measure of PAR (umols) using a Dataflow sensor. PAM PAR: This is copied from the PAR or PAR2 column PAR all: This is the complete PAR file and should be used Deployment: Identifying which deployment the data came from

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Respiration chamber biomass data

The data is chlorophyll a biomass from cores from the respiration chambers. The headers are: Depth (mm) Treat (Acidified or control) Chl a (pigment and indicator of biomass) Core (5 cores were collected from each chamber, three were analysed for chl a), these are psudoreplicates/subsamples from the chambers and should not be treated as replicates.

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Associated R script file for pump cycles of respirations chambers

Associated respiration chamber data to determine the times when respiration chamber pumps delivered treatment water to chambers. Determined from Aquation log files (see associated files). Use the chamber cut times to determine net production rates. Note: Users need to avoid the times when the respiration chambers are delivering water as this will give incorrect results. The headers that get used in the attached/associated R file are start regression and end regression. The remaining headers are not used unless called for in the associated R script. The last columns of these datasets (intercept, ElapsedTimeMincoef) are determined from the linear regressions described below.

To determine the rate of change of net production, coefficients of the regression of oxygen consumption in discrete 180 minute data blocks were determined. R squared values for fitted regressions of these coefficients were consistently high (greater than 0.9). We make two assumptions with calculation of net production rates: the first is that heterotrophic community members do not change their metabolism under OA; and the second is that the heterotrophic communities are similar between treatments.

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Combined dataset pH, temperature, oxygen, salinity, velocity for experiment

This data is rapid light curve data generated from a Shutter PAM fluorimeter. There are eight steps in each rapid light curve. Note: The software component of the Shutter PAM fluorimeter for sensor 44 appeared to be damaged and would not cycle through the PAR cycles. Therefore the rapid light curves and recovery curves should only be used for the control chambers (sensor ID43).

The headers are PAR: Photoactive radiation relETR: F0/Fm x PAR Notes: Stage/step of light curve Treatment: Acidified or control

The associated light treatments in each stage. Each actinic light intensity is held for 10 seconds, then a saturating pulse is taken (see PAM methods).

After 10.0 sec at 0.5% = 1 umols PAR After 10.0 sec at 0.7% = 1 umols PAR After 10.0 sec at 1.1% = 0.96 umols PAR After 10.0 sec at 1.6% = 4.32 umols PAR After 10.0 sec at 2.4% = 4.32 umols PAR After 10.0 sec at 3.6% = 8.31 umols PAR After 10.0 sec at 5.3% =15.78 umols PAR After 10.0 sec at 8.0% = 25.75 umols PAR

This dataset appears to be missing data, note D5 rows potentially not useable information

See the word document in the download file for more information.

UPDATED on October 15 2020 After some mistakes in some of the data were found, we updated this data set. The changes to the data are detailed on Zenodo (http://doi.org/10.5281/zenodo.4061807), and an Erratum has been submitted. This data set under CC-BY license contains time series of total abundance and/or biomass of assemblages of insect, arachnid and Entognatha assemblages (grouped at the family level or higher taxonomic resolution), monitored by standardized means for ten or more years. The data were derived from 165 data sources, representing a total of 1668 sites from 41 countries. The time series for abundance and biomass represent the aggregated number of all individuals of all taxa monitored at each site. The data set consists of four linked tables, representing information on the study level, the plot level, about sampling, and the measured assemblage sizes. all references to the original data sources can be found in the pdf with references, and a Google Earth file (kml) file presents the locations (including metadata) of all datasets. When using (parts of) this data set, please respect the original open access licenses. This data set underlies all analyses performed in the paper 'Meta-analysis reveals declines in terrestrial, but increases in freshwater insect abundances', a meta-analysis of changes in insect assemblage sizes, and is accompanied by a data paper entitled 'InsectChange – a global database of temporal changes in insect and arachnid assemblages'. Consulting the data paper before use is recommended. Tables that can be used to calculate trends of specific taxa and for species richness will be added as they become available. The data set consists of four tables that are linked by the columns 'DataSource_ID'. and 'Plot_ID', and a table with references to original research. In the table 'DataSources', descriptive data is provided at the dataset level: Links are provided to online repositories where the original data can be found, it describes whether the dataset provides data on biomass, abundance or both, the invertebrate group under study, the realm, and describes the location of sampling at different geographic scales (continent to state). This table also contains a reference column. The full reference to the original data is found in the file 'References_to_original_data_sources.pdf'. In the table 'PlotData' more details on each site within each dataset are provided: there is data on the exact location of each plot, whether the plots were experimentally manipulated, and if there was any spatial grouping of sites (column 'Location'). Additionally, this table contains all explanatory variables used for analysis, e.g. climate change variables, land-use variables, protection status. The table 'SampleData' describes the exact source of the data (table X, figure X, etc), the extraction methods, as well as the sampling methods (derived from the original publications). This includes the sampling method, sampling area, sample size, and how the aggregation of samples was done, if reported. Also, any calculations we did on the original data (e.g. reverse log transformations) are detailed here, but more details are provided in the data paper. This table links to the table 'DataSources' by the column 'DataSource_ID'. Note that each datasource may contain multiple entries in the 'SampleData' table if the data were presented in different figures or tables, or if there was any other necessity to split information on sampling details. The table 'InsectAbundanceBiomassData' provides the insect abundance or biomass numbers as analysed in the paper. It contains columns matching to the tables 'DataSources' and 'PlotData', as well as year of sampling, a descriptor of the period within the year of sampling (this was used as a random effect), the unit in which the number is reported (abundance or biomass), and the estimated abundance or biomass. In the column for Number, missing data are included (NA). The years with missing data were added because this was essential for the analysis performed, and retained here because they are easier to remove than to add. Linking the table 'InsectAbundanceBiomassData.csv' with 'PlotData.csv' by column 'Plot_ID', and with 'DataSources.csv' by column 'DataSource_ID' will provide the full dataframe used for all analyses. Detailed explanations of all column headers and terms are available in the ReadMe file, and more details will be available in the forthcoming data paper. WARNING: Because of the disparate sampling methods and various spatial and temporal scales used to collect the original data, this dataset should never be used to test for differences in insect abundance/biomass among locations (i.e. differences in intercept). The data can only be used to study temporal trends, by testing for differences in slopes. The data are standardized within plots to allow the temporal comparison, but not necessarily among plots (even within one dataset).

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

The difference between NSW Office of Water GW licences - CLM v2 and v3 is that an additional column has been added, 'Asset Class' that aggregates the purpose of the licence into the set classes for the Asset Database. Also the 'Completed_Depth' has been added, which is the total depth of the groundwater bore. These columns were added for the purpose of the Asset Register.

The aim of this dataset was to be able to map each groundwater works with the volumetric entitlement without double counting the volume and to aggregate/ disaggregate the data depending on the final use.

This has not been clipped to the CLM PAE, therefore the number of economic assets/ relevant licences will drastically reduce once this occurs.

The Clarence Moreton groundwater licences includes an extract of all licences that fell within the data management acquisition area as provided by BA to NSW Office of Water.

Aim: To get a one to one ratio of licences numbers to bore IDs.

Important notes about data:

Data has not been clipped to the PAE.

No decision have been made in regards to what purpose of groundwater should be protected. Therefore the purpose currently includes groundwater bores that have been drilled for non-extractive purposes including experimental research, test, monitoring bore, teaching, mineral explore and groundwater explore

No volume has been included for domestic & stock as it is a basic right. Therefore an arbitrary volume could be applied to account for D&S use.

Licence Number - Each sheet in the Original Data has a licence number, this is assumed to be the actual licence number. Some are old because they have not been updated to the new WA. Some are new (From_Spreadsheet_WALs). This is the reason for the different codes.

WA/CA - This number is the 'works' number. It is assumed that the number indicates the bore permit or works approval. This is why there can be multiple works to licence and licences to works number. (For complete glossary see here http://registers.water.nsw.gov.au/wma/Glossary.jsp). Originally, the aim was to make sure that the when there was more than more than one licence to works number or mulitple works to licenes that the mulitple instances were compelte.

Clarence Moreton worksheet links the individual licence to a works and a volumetric entitlement. For most sites, this can be linked to a bore which can be found in the NGIS through the HydroID. (\wron\Project\BA\BA_all\Hydrogeology_National_Groundwater_Information_System_v1.1_Sept2013). This will allow analysis of depths, lithology and hydrostratigraphy where the data exists.

We can aggregate the data based on water source and water management zone as can be seen in the other worksheets.

Data available:

Original Data: Any data that was bought in from NSW Offcie of Water, includes

Spatial locations provided by NoW- This is a exported data from the submitted shape files. Includes the licence (LICENCE) numbers and the bore ID (WORK_NUO). (Refer to lineage NSW Office of Water Groundwater Entitlements Spatial Locations).

Spreadsheet_WAL - The spread sheet from the submitted data, WLS-EXTRACT_WALs_volume. (Refer to Lineage NSW Office of Water Groundwater Licence Extract CLM- Oct 2013)

WLS_extracts - The combined spread sheets from the submitted data, WLS-EXTRACT . (Refer to Lineage NSW Office of Water Groundwater Licence Extract CLM- Oct 2013)

Aggregated share component to water sharing plan, water source and water management zone

Dataset History

The difference between NSW Office of Water GW licences - CLM v2 and v3 is that an additional column has been added, 'Asset Class' that aggregates the purpose of the licence into the set classes for the Asset Database.

Where purpose = domestic; or domestic & stock; or stock then it was classed as 'basic water right'. Where it is listed as both a domestic/stock and a licensed use such as irrigation, it was classed as a 'water access right.' All other take and use were classed as a 'water access right'. Where purpose = drainage, waste disposal, groundwater remediation, experimental research, null, conveyancing, test bore - these were not given an asset class. Monitoring bores were classed as 'Water supply and monitoring infrastructure'

Depth has also been included which is the completed depth of the bore.

Instructions

Procedure: refer to Bioregional assessment data conversion script.docx

1) Original spread sheets have mulitple licence instances if there are more than one WA/CA number. This means that there are more than one works or permit to the licence. The aim is to only have one licence instance.

2) The individual licence numbers were combined into one column

3) Using the new column of licence numbers, several vlookups were created to bring in other data. Where the columns are identical in the original spreadsheets, they are combined. The only ones that don't are the Share/Entitlement/allocation, these mean different things.

4) A hydro ID column was created, this is a code that links this NSW to the NGIS, which is basically a ".1.1" at the end of the bore code.

5) All 'cancelled' licences were removed

6) A count of the number of works per licence and number of bores were included in the spreadsheet.

7) Where the ShareComponent = NA, the Entitlement = 0, Allocation = 0 and there was more than one instance of the same bore, this means that the original licence assigned to the bore has been replaced by a new licence with a share component. Where these criteria were met, the instances were removed

8) a volume per works ensures that the volume of the licence is not repeated for each works, but is divided by the number of works

Bioregional assessment data conversion script

Aim: The following document is the R-Studio script for the conversion and merging of the bioregional assessment data.

Requirements: The user will need R-Studio. It would be recommended that there is some basic knowledge of R. If there isn't, the only thing that would really need to be changed is the file location and name. The way that R reads files is different to windows and also the locations that R-Studio read is dependent on where R-Studio is originally installed to point. This would need to be completed properly before the script can be run.

Procedure: The information below the dashed line is the script. This can be copied and pasted directly into R-Studio. Any text with '#' will not be read as a script, so that can be added in and read as an instruction.

###########

# 18/2/2014

# Code by Brendan Dimech

#

# Script to merge extract files from submitted NSW bioregional

# assessment and convert data into required format. Also use a 'vlookup'

# process to get Bore and Location information from NGIS.

#

# There are 3 scripts, one for each of the individual regions.

#

############

# CLARENCE MORTON

# Opening of files. Location can be changed if needed.

# arc.file is the exported *.csv from the NGIS data which has bore data and Lat/long information.

# Lat/long weren't in the file natively so were added to the table using Arc Toolbox tools.

arc.folder = '/data/cdc_cwd_wra/awra/wra_share_01/GW_licencing_and_use_data/Rstudio/Data/Vlookup/Data'

arc.file = "Moreton.csv"

# Files from NSW came through in two types. WALS files, this included 'newer' licences that had a share component.

# The 'OTH' files were older licences that had just an allocation. Some data was similar and this was combined,

# and other information that wasn't similar from the datasets was removed.

# This section is locating and importing the WALS and OTH files.

WALS.folder = '/data/cdc_cwd_wra/awra/wra_share_01/GW_licencing_and_use_data/Rstudio/Data/Vlookup/Data'

WALS.file = "GW_Clarence_Moreton_WLS-EXTRACT_4_WALs_volume.xls"

OTH.file.1 = "GW_Clarence_Moreton_WLS-EXTRACT_1.xls"

OTH.file.2 = "GW_Clarence_Moreton_WLS-EXTRACT_2.xls"

OTH.file.3 = "GW_Clarence_Moreton_WLS-EXTRACT_3.xls"

OTH.file.4 = "GW_Clarence_Moreton_WLS-EXTRACT_4.xls"

newWALS.folder = '/data/cdc_cwd_wra/awra/wra_share_01/GW_licencing_and_use_data/Rstudio/Data/Vlookup/Products'

newWALS.file = "Clarence_Moreton.csv"

arc <- read.csv(paste(arc.folder, arc.file, sep="/" ), header =TRUE, sep = ",")

WALS <- read.table(paste(WALS.folder, WALS.file, sep="/" ), header =TRUE, sep = "\t")

# Merge any individual WALS and OTH files into a single WALS or OTH file if there were more than one.

OTH1 <- read.table(paste(WALS.folder, OTH.file.1, sep="/" ), header =TRUE, sep = "\t")

OTH2 <- read.table(paste(WALS.folder, OTH.file.2, sep="/" ), header =TRUE, sep = "\t")

OTH3 <- read.table(paste(WALS.folder, OTH.file.3, sep="/" ), header =TRUE, sep = "\t")

OTH4 <- read.table(paste(WALS.folder, OTH.file.4, sep="/" ), header =TRUE, sep = "\t")

OTH <- merge(OTH1,OTH2, all.y = TRUE, all.x = TRUE)

OTH <- merge(OTH,OTH3, all.y = TRUE, all.x = TRUE)

OTH <- merge(OTH,OTH4, all.y = TRUE, all.x = TRUE)

# Add new columns to OTH for the BORE, LAT and LONG. Then use 'merge' as a vlookup to add the corresponding

# bore and location from the arc file. The WALS and OTH files are slightly different because the arc file has

# a different licence number added in.

OTH <- data.frame(OTH, BORE = "", LAT = "", LONG = "")

OTH$BORE <- arc$WORK_NO[match(OTH$LICENSE.APPROVAL, arc$LICENSE)]

OTH$LAT <- arc$POINT_X[match(OTH$LICENSE.APPROVAL, arc$LICENSE)]

OTH$LONG <-

# Archived outputs from models of alternative reproductive tactics

This repository contains outputs from models of the evolution of alternative reproductive tactics, with accompanying code available on github (https://github.com/spflanagan/ARTs).
The repository supports a manuscript submitted to Proceedings of the Royal Society B, which is investigating the effects of explicit genetic architecture on evolutionary dynamics of alternative reproductive tactics.

The analysis used two separate programs: a baseline analytical model (written in R) and a simulation-based model (written in C++). Outputs from both of these models are archived here.

## Baseline model

- `morph_results_Ns.RDS`: An R data file containing a data.frame with the results of the baseline analytical model. It contains 11 columns:
- initial_CP = frequency of the courter/parent morph in the initial generation of the model
- initial_CN = frequency of the courter/non-parent morph in the initial generation of the model
- initial_NP = frequency of the non-courter/parent morph in the initial generation of the model
- initial_NN = frequency of the non-courter/non-parent morph in the initial generation of the model
- CP = frequency of the courter/parent morph in the final generation of the model
- CN = frequency of the courter/non-parent morph in the final generation of the model
- NP = frequency of the non-courter/parent morph in the final generation of the model
- NN = frequency of the non-courter/non-parent morph in the final generation of the model
- r = the relative reproductive investment parameter
- c = the sperm competition coefficient
- num_sneak = the number of males allowed to sneak fertilisations within a single clutch
- `morph_results_10000_equalStart.RDS`: An R data file containing a data.frame with the results of the baseline analytical model after it had been run for 10,000 generations. It contains the same 11 columns as `morph_results_Ns.RDS`.

## Simulation model

The simulation model was run with a variety of parameter combinations, which have been summarised in various files and the archived files are provided.

### Raw outputs

These results were generated by running the scripts `scripts/101_model-informed-single-locus.sh`, `101_model-informed-single-locus-tradeoffs.sh`, and `102_model-informed-genetics.sh`. Each parameter combination was run multiple times and generated the same sets of files:

- `*_parameters.txt`: Outputs the parameter settings for that run in a text file with each parameter on its own line
- `*_log.txt`: A text file with the log outputs from the model - will note whether any errors occurred in that run.
- `*_traits.txt`: A tab-delimited file containing the trait data for every individual in generation 0 and generation 12000. The columns are:
- Gen: generation
- Pop: population ID within simulations that started with identical starting conditions
- Individual: A numerical index for the individual whose information is output.
- Sex: whether the individual is MALE or FEMALE
- Courter: A boolean value for whether the individual has the courter trait (1) or the non-courter trait (0). Note that females can carry the courter trait (i.e., have a value of 1 in this column) but do not express the courter trait.
- CourtTrait: The actual trait value for the individual, which is the sum of allelic effects at courter QTLs.
- Parent: A boolean value for whether the individual has the parent trait (1) or the non-parent trait (0). Note that females can carry the parent trait (i.e., have a value of 1 in this column) but do not express the parent trait.
- ParentTrait: The actual trait value for the individual, which is the sum of allelic effects at parent QTLs.
- Preference: A boolean value for whether the individual prefers the courting male (1) or non-courting males (0). For the simulation runs here, all individuals have a preference for courters.
- PrefTrait: The trait value for the preference trait if the trait has a genetic basis. In all iterations of the model shared here, the preference trait was not genetically inherited.
- MateFound: A count of how many mates the individual was able to obtain.
- PotRS: The potential reproductive success of the individual, based on their fecundity (which is set by the parameter settings in the model).
- LifetimeRS: The realised reproductive success of the individual based on the number of matings and the number of offpsring produced.
- Alive: A boolean value tracking whether the individual died or survived to mate.
- `*_summary.txt`: A tab-delimited file summarising the final frequencies of various morphs and other demographic parameters for each generation of the model, and for each population (when populations were initiated with identical starting parameters). It contains the following columns:
- Generation: Generation number (an integer)
- Pop: Numerical population ID. All populations were initiatlised with identical conditions within a single file.
- PopSize: The population size (i.e., number of adults)
- NumMal: Number of adult males in the population
- NumFem: Number of adult females in the population
- NumProgeny: The number of progeny produced
- ParentThresh: The population-level threshold for the parent trait to switch from parent to non-parent (this is the mean allelic effects in Gen 0).
- ParentFreq: Frequency of the parent trait in the population
- ParentAEmean: Mean allelic effects of the Parent QTLs
- ParentAEsd: Standard deviation in allelic effects of the Parent QTLs
- ParentW: Relative fitness of parent males
- NonParentW: Relative fitness of non-parent males
- CourterThresh: The population-level threshold for the courter trait to switch from courter to non-courter (this is the mean allelic effects in Gen 0).
- CourterFreq: Frequency of the courter trait in the population
- CourterAEmean: Mean allelic effects of courter QTLs
- CourterAEsd: Standard deviation of allelic effects of courter QTLs
- CourterW: Relative fitness of courting males
- NonCourterW: Relative fitness of non-courting males
- FreqNcNp: Frequency of non-courting/non-parent (NN) morph
- FreqCNp: Frequency of courting/non-parent (CN) morph
- FreqNcP: Frequency of non-courting/parent (NP) morph
- Freq CP: Frequency of courting/parent (CP) morph
- PrefThresh: The population-level threshold for the preference trait to switch from preferring the courting to non-courting males (not relevant to these simulations)
- PrefFreq: Frequency of the preference for the courting male in th epopulation (not relevant to these simulations)
- NumRandMate: Number of females that randomly mated (i.e., did not find a partner with the preferred trait)

The runs with explicit genetic architectures also have the following files:

- `*_qtlinfo.txt`: A tab-delimited file summarising the location of each type of QTL. Each column is a different QTL and each row is a different population. If they are initialised to be identical, the QTL information will be the same for each population. The format of the QTL location information is a the chromosome number as an integer (starting at 0), followed by a decimal, and the following numbers are the location among the marker loci. So, 0.850 refers to a QTL on chromosome 0 at marker location 850 (out of 1000).
- `*_allelic-effects.txt`: A tab-delimited file containing the allelic effects for each QTL. These are the additive contributions each QTL makes towards the trait, and these mutate if a mutation occurs at the location of the QTL (which is recorded in the corresponding `*_qtlinfo.txt` file).
- `*_markers.txt`: A tab-delimited file summarising the allele frequency at each marker locus.
- `*vcf`: A variant call format file for the population in the final generation of the simulations. See standard formats for this type of file online (e.g., https://samtools.github.io/hts-specs/VCFv4.2.pdf)
- `*Tajima.D`: The vcftools output format containing Tajima's D statistics, which was generated using the vcf file. See the vcftools manual for more details (https://vcftools.github.io/man_latest.html)
- `*LD.geno.ld`: The vcftools output format containing pairwise linkage disequilibrium statistics, which was generated using the vcf file. See the vcftools manual for more details (https://vcftools.github.io/man_latest.html)
- `*_gt.csv`: A comma-separated file summarising the genotype information for each individual at all marker loci. It is similar to vcf file format, with the following columns:
- Marker: marker ID
- Chrom: Chromosome ID or number (starting at 0)
- Position: Locaiton on the chromosome (starting at 0)
- REF: Reference allele
- ALT: Alternative allele
- The remaining columns are each individual's genotype
- `*_pheno.csv`: A comma-separated file summarising the phenotypes for a population with the following columns:
- ID: individual ID
- CourtTrait: Whether the individual is a courter (2) or a non-courter (1)
- ParentTrait: Whether the individual is a parent (2) or a non-parent (1)
- Sex: Whether the individual is a male (MAL) or a female (FEM)
- Morph: The morph of the individual (CP = courter/parent, C = courter, P = non-courter/parent, N = non-courter/non-parent; females can have preferences as well but this is not relevant to these datasets)

These above outputs, run with various parameter settings, are found in the following `tar.gz` files:

- stochasticity.tar.gz: Contains the