Author(s): Galina M. Jönsson
This repository contains all the code for:
Jönsson, G. M. 2023. Chapter 2. Development and diagnosis of occupancy models for integrating historical specimens and contemporary data. PhD Thesis.
I present three different occupancy model formulations (Models A-C) for integrating historical collection specimens data and contemporary observational records for producing long-term occupancy trends for British butterflies between 1900 and 2016. I select a balanced sample of twelve species for comparing the performance of Models A-C and use three separate methods to address questions of model selection (DIC) and goodness-of-fit for the data at hand (PPCs and residual-based assessment). JAGS code for the three model formulations is given below, but see my sparta fork repository for details on the model code.
Model A specifies that list length should be considered as a categorical variable. This model formulation is equivalent to our previous paper:
for(j in 1:nvisit) {
y[j] ~ dbern(Py[j])
Py[j]<- z[Site[j],Year[j]]*p[j]
logit(p[j]) <- alpha.p[Year[j]] + dtype2.p * DATATYPE2[j] + dtype3.p * DATATYPE3[j]
} }
Model B defines list length as both continuous and categorical variable:
for(j in 1:nvisit) {
y[j] ~ dbern(Py[j])
Py[j]<- z[Site[j],Year[j]] * p[j]
logit(p[j]) <- alpha.p[Year[j]] + LL.p * logL[j] + dtype2.p * DATATYPE2[j] + dtype3.p*DATATYPE3[j]
}
Model C specifies list length as both continuous and categorical variables with varying year effects between list lengths:
for(j in 1:nvisit) {
y[j] ~ dbern(Py[j])
Py[j]<- z[Site[j],Year[j]] * p[j]
logit(p[j]) <- alpha.p[Year[j]] * (1-DATATYPE3[j]) + LL.p * logL[j] + dtype2.p * DATATYPE2[j] + dtype3.p * DATATYPE3[j]
} }
I used three sources of data ('datatypes'): 1) Butterfly Conservation’s (BC) general recording scheme, Butterflies for the New Millennium (BNM), 2) the UK Butterfly Monitoring Scheme (UKBMS), and 3) digitised specimen records from four NHCs. The BNM and UKBMS data is available upon request from:
Biological Records Centre
UK Centre for Ecology & Hydrology Wallingford Oxfordshire OX10 8BB UK
Telephone: +44 (0) 1491 692357
Email: [email protected]
The georeferenced NHM dataset is available via the NHM data portal. If you use them, please cite appropriately.
Note that in this repository, I have uploaded two small auxiliary datasets in the folder /data/auxiliaryData. Both are grid cell look-ups for the BNM and UKBMS data.
For reproducibility purposes, download these data sets into the data/rawdata/ folder to rerun these analyses from scratch. For reproducibility purposes, format the raw data according to the code in the first two Rmd.files (details below) and place into the folder /output/formattedData. Place model outputs into the /output/modelOutput folder to rerun these analyses.
The analyses code is divided into .Rmd files that run the analyses for each section of the manuscript, more detailed scripts for some functions used in analyses and called by the .Rmd files, and scripts for the figures found in the manuscript.
Code for figures is given in .R files with names beginning with 'fig-' and the latter part matching the figure numbers of the manuscript. Code for functions is given in 'function-'.R-files.
Note that throughout I've commented out write.csv and saveRDS commands in order to not clog up your machine. For code chunks that run the models, I've set eval to FALSE, again, to not clog up your macine as the analyses are computationally expensive and were run on high performance machines.
- 01-data-cleaning.Rdm cleans and standardises the raw occurrence records.
- 02-format-data.Rdm formats the standardised records for modelling.
- 03-models.Rdm fits three models to each of 54 butterfly species.
- 04-Occupancy-Trends.Rmd briefly inspects and summarises the outputs.
- 05-Model-Selection.Rmd computes DIC-values and PPCs.
- 06-Residuals.Rmd computes residuals according to the methodology of Wright et al., (2019).
- 07-SiteYearHandover.Rmd
/figscontains the figures with file names matching those of the manuscript/outputcontains the empty subfolders/output/formattedDataand/output/modelOutputs, as well as the subfolder/output/summaryTablesthat contains tables summarising both the data used in models and model outputs.
For reproducibility purposes, here is the output of devtools::session_info() used to perform the analyses in the publication.
─ Session info ─────────────────────────────────────────────────────────────────────────────────────────
setting value
version R version 4.0.2 (2020-06-22)
os OS X 13.3.1
system x86_64, darwin17.0
ui RStudio
language (EN)
collate en_GB.UTF-8
ctype en_GB.UTF-8
tz Europe/London
date 2023-05-20
rstudio 2022.07.2+576 Spotted Wakerobin (desktop)
pandoc 2.19.2 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown)
─ Packages ─────────────────────────────────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
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assertthat 0.2.1 2019-03-21 [2] CRAN (R 4.0.2)
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broom 1.0.3 2023-01-25 [2] CRAN (R 4.0.2)
cachem 1.0.6 2021-08-19 [2] CRAN (R 4.0.2)
callr 3.7.0 2021-04-20 [2] CRAN (R 4.0.2)
car 3.1-1 2022-10-19 [2] CRAN (R 4.0.2)
carData 3.0-5 2022-01-06 [2] CRAN (R 4.0.5)
cli 3.6.0 2023-01-09 [2] CRAN (R 4.0.2)
colorspace 2.0-3 2022-02-21 [2] CRAN (R 4.0.5)
crayon 1.5.2 2022-09-29 [2] CRAN (R 4.0.2)
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devtools 2.4.5 2022-10-11 [2] CRAN (R 4.0.2)
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fansi 1.0.4 2023-01-22 [2] CRAN (R 4.0.2)
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[1] /Users/galinajonsson/Library/R/4.0/library
[2] /Library/Frameworks/R.framework/Versions/4.0/Resources/library
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