Between-species contagion drives HPAI transmission and mass mortality in seabirds
Data files
Jun 04, 2026 version files 34.09 MB
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AI_Spread_16.qmd
90.47 KB
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AISpread14_3.rda
33.94 MB
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Gannet_carcass_zeros3.csv
6.12 KB
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Gannet_colonies_overview.csv
4.39 KB
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Gannet_colony_accessibility.csv
110 B
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Gannet_colony_distances.csv
30.85 KB
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Gannet_posterior_colony_sizes.csv
500 B
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README.md
2.85 KB
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Real_data_14_3_(New_SynGrav_-_Bjornoya).R
10 KB
Abstract
The unprecedented avian panzootic caused by High‐Pathogenicity Avian Influenza (HPAI) caused widespread mortality in seabird populations, but the exact scales and patterns of transmission, basic epidemiological parameters and mortality outcomes are unknown. Mass mortalities combined with potentially detrimental sub-lethal effects of exposure will have currently unpredictable consequences for population viability of affected wild bird species, many of which are already subjected to acute anthropogenic pressures. In the associated article to this data set, we focus on one of the most severely affected seabird species, the Northern gannet (Morus bassanus). Using a metapopulation epidemiological framework, fitted to counts of dead gannets in or near gannet breeding colonies across the NorthEast Atlantic, we estimate key epidemiological parameters, quantify spatial patterns of mortality and isolate external transmission routes. Here, we provide the data, JAGS code and Quarto files for the associated article.
Dataset DOI: 10.5061/dryad.gxd25482f
Overall description of the data and file structure
This repository contains all necessary files for running the model described in the paper and setting out the analysis in manuscript form. The following two routes are possible, independently of eachother.
Route 1: Rerun the epidemiological model and create posterior samples for further analysis. To do this place all files prefaced by the word "Gannet..." in a folder named Data, in your working directory. Then you can run the R code in the file prefaced "Real data...". You will need to have a working installation of JAGS for this. Thr model periodically saves a file of necessary posterior sample draws for relevant parameters and states. It places this in a data file prefaced "AISpread...", which requires a folder named "Output" in the working directory. The "AISpread..." file used for this paper is already provided in the repository.
Route 2: Produce the text and graphical output of the paper. Using the "AISpread..." file provided here, that you will need to place into a folder named "Output", as well as the "Gannet..." data files provided here, that you will need to place into a folder named "Data", you can run the Quarto file prefaced "AI Spread...".
You can either follow the complete workflow by running Routes 1 & 2 one after the other, or simply work on each of them separately. For instance, you may want the output of the epidemiological model for your own analyses (so you just want to follow route 1) or you may want to experiment with the paper's graphical output, given the existing epidemiological output (so you may want to work on route 2 only).
Specific information for individual document and data files:
- Gannet_carcass_zeros3.csv: Carcass counts with initial zeroes in early period and NAs for missing data
- Gannet_colonies_overview.csv: Summary data for all colonies
- Gannet_colony_accessibility.csv: Accessibility of colonies
- Gannet_colony_distances.csv: Intercolony distances (circumventing land)
- Gannet_posterior_colony_sizes.csv: Colony sizes estimated at point of outbreak by Jeglinski et al 2023.
- AI_Spread_16.qmd: The Quarto file containing the paper, and code for generating posterior analyses with CODA file from MCMC Jags Model.
- Real_data_14_3_(New_SynGrav_-_Bjornoya).R: The JAGS model file. It takes in the csv data files and produces the Posterior MCMC draws file.
- AISpread14_3.rda: Posterior MCMC draws generated by fitting the JAGS Model to the data
Code/software
JAGS: https://mcmc-jags.sourceforge.io/
Quarto: https://quarto.org/