Published January 19, 2022 | Version Version 1
Dataset Open

Data and code for 'Age structure of amphibian populations with endemic chytridiomycosis, across climatic regions with markedly different infection risk'

  • 1. Charles Sturt University
  • 2. The University of Queensland
  • 3. CHarles Sturt University
  • 1. Charles Sturt University
  • 2. The University of Queensland

Description

This repository provides all data and R code from the analysis presented in the following paper:

Turner, A., Heard, G., Hall, A., Wassens, S. (in review). Age structure of amphibian populations with endemic chytridiomycosis, across climatic regions with markedly different infection risk.

The data are provided as a series of .csv files, R script and two zip folders of R packages (Surv_mod and VB_mod)

1. Skeleto_dat_ready_Jan2021.csv Data from frog surveys conducted by Anna Turner

2. Geoffs_data.csv Data from frog surveys conducted by Geoff Heard

3. Environmental_variables_skeleto.csv Environmental data collected during surveys 

4. sk.dat_July21.csv Collated data from Anna and Geoff - created by 'Data_collation_for_analysis_2.R' ready for analysis

5. Variables_that_are_highly_correlated_with_each_other_season_wide.csv Testing for correlation

6. Model_structure_skeleto_2.csv creates model structure for analysis

7. Model_selection_statistics_June_21.csv Output from model

R code is provided seperately for each of the following components:

1. Data_collation_for_analysis_2.R Collating data from Anna and Geoffs datasets

2. Skeleto_analysis_5.R - First uses regression modelling to explore factors correlated with variation in age

                                         - Following Scheele et al. (2016) regression models with a poisson distribution

                                         - Use bayesian non-linear regression to fit the Von Bertalanffy growth model to size-at-age data

                                         - Plots male and female growth curves

                                         - Uses catch curve approach to estimate survival from best fitting regression model following Scroggie                                                   (2012) but with bayesian implementation

Files

Environmental_variables_skeleto.csv

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