DOI: 10.5061/dryad.98sf7m0h7
title: Evolving thermal thresholds explain the distribution of temperature sex reversal in an Australian dragon lizard

description: Aim: Species with temperature-dependent sex determination (TSD)
 are particularly vulnerable to climate change because a resultant skew in
 population sex ratio can have severe demographic consequences and increase
 vulnerability to local extinction. The Australian central bearded dragon
 (Pogona vitticeps) has a thermosensitive ZZ male/ZW female system of
 genetic sex determination (GSD). High incubation temperatures cause
 reversal of the ZZ genotype to a viable female phenotype. Nest
 temperatures in the wild are predicted to vary on a scale likely to
 produce heterogeneity in the occurrence of sex reversal, and so we predict
 that sex reversal will correlate positively with inferred incubation
 conditions. Location: Mainland Australia
 Methods: Wild-caught specimens of P. vitticeps vouchered in
 museum collections and collected during targeted field trips were
 genotypically and phenotypically sexed to determine the distribution of
 sex reversal across the species range. To determine whether environmental
 conditions or genetic structure can explain this distribution, we infer
 the incubation conditions experienced by each individual and apply a
 multi-model inference approach to determine which conditions associate
 with sex reversal. Further, we conduct reduced representation sequencing
 on a subset of specimens to characterise the population structure of this
 broadly distributed species. Results: Here we show that sex
 reversal in this widespread Australian dragon lizard is spatially
 restricted to the eastern part of the species range. Neither climatic
 variables during the inferred incubation period nor geographic population
 genetic structure explain this disjunct distribution of sex reversal. The
 main source of genetic variation arose from isolation by distance across
 the species range. Main Conclusions: We propose that local genetic
 adaptation in the temperature threshold for sex reversal can counteract
 the sex-reversing influence of high incubation temperatures in P.
 vitticeps. Our study demonstrates that complex evolutionary processes need
 to be incorporated into modelling biological responses to future climate
 scenarios.
description: README for genetic and incubation condition analysis in Diversity
 and Distributions manuscript "Evolving thermal thresholds explain the
 distribution of temperature sex reversal in an Australian dragon
 lizard:" SPECIMEN COLLECTION INFORMATION AND
 ANALYSIS Field_Pogona_Data.xlsx<br> Contains
 collection information and collated environmental data (from the
 Scientific Information for Landowners dataset
 https://www.longpaddock.qld.gov.au/silo/) for the inferred incubation
 period for each individual. The first sheet in the excel workbook
 describes the data contained within each column, and this data is used in
 the "Inferred_Incubation_Conditions.R" script.
 pogona_dart_genlight<br> RDS object containing the raw
 DArT-seq data for each of the specimens sequenced for population genetic
 analyses. This object is read into the R script
 "pogona_dart_analysis_Aug2020.R" for all subsequent analyses,
 mainly using the R package "dartR".
 pogona_dart_analysis_Aug2020.R<br> R script containing the
 analyses used to investigate the population genetics of Pogona vitticeps.
 Based mostly on the functions within the R package dartR, uses the
 "pogona_dart_genlight" object.
 Inferred_Incubation_Conditions.R<br> R script containing
 analyses used to investigate the association between sex reversal and
 inferred incubation temperatures. Uses the data in
 "Field_Pogona_Data.xlsx"
 silo_data_extraction.py<br> Python code for the extraction
 of climatic data (SILO climate dataset) from relevant dates for each
 collected specimen. Uses data from "Field_Pogona_Data.xlsx" and
 relevant SILO climate netCDF files CONSTANT TEMPERATURE
 EQUIVALENT ANALYSIS Please note: detailed instructions
 on how to run each piece of code can be found within each piece of
 code's comments (primarily at the top and in the input parameters
 sections). This is an overview of the pipeline rather than a replication
 of those instructions. To go from climate data to
 CTE:<br> 1: Climate data: Download elevation and SILO climate data
 (variables listed in the Castelli_MicroclimateAndCTE_RandomLocations.R
 file). This has been completed and climate data are provided in csv files
 with the prefix "randomlocations_". This includes temperature,
 rainfall, relative humidity, radiation and vapour pressure.
 2: Wind data: Download the near speed wind data and format it as
 detailed in Castelli_NearSurfaceWindData_extraction.R. Run that file to
 extract the wind speed data for the locations and times of
 interest. 3: SLGA grids: Download the soil grids
 (.tiff) as documented in
 Castelli_MicroclimateAndCTE_RandomLocations.R 4: To go
 from climate data to soil temperature data (calculating the microclimate
 using NicheMapR): Open Castelli_MicroclimateAndCTE_RandomLocations.R.
 Follow the instructions within and change the input parameters to match
 the file paths on your computer. Run or source the file.
 5: To go from soil temperature data to CTE: Open
 Castelli_SomaCTE_RandomLocations.R. Follow the instructions to update the
 input parameters to match your computer. Run or source this file to
 calculate the mean CTE per year (and the daily CTE if required). This file
 can be run sequentially (very slow), or in parallel (calculating multiple
 sites at once, much faster). It took about 5 days to process 1000 sites
 using 20 cores. 6:
 Figure1_SupplementaryData.xlsx<br> Contains the constant temperature
 equivalent (CTE) estimates for each randomly selected location within the
 range of Pogona vitticeps (displayed in Figure 1 of the manuscript). The
 first sheet in the excel workbook explains the data contained within each
 column. This file contains the summarised output of the steps outlined
 above.