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sanivult

Evaluating the impacts of the outbreak of a Bovine Spongiform Encephalopathy epidemic on the demographic and population dynamics of one of the world's largest colonies of the Eurasian Griffon vulture (Gyps fulvus).

https://github.com/palmaraz/sanivult

Science Score: 67.0%

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reproducible-research
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Evaluating the impacts of the outbreak of a Bovine Spongiform Encephalopathy epidemic on the demographic and population dynamics of one of the world's largest colonies of the Eurasian Griffon vulture (Gyps fulvus).

Basic Info
  • Host: GitHub
  • Owner: palmaraz
  • License: mit
  • Language: TeX
  • Default Branch: master
  • Homepage:
  • Size: 22.8 MB
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Topics
reproducible-research
Created over 4 years ago · Last pushed 9 months ago
Metadata Files
Readme License Citation

README.html 














README






























































License: MIT DOI







The SaniVult project






Table of Contents


    

  1. 
About




    2. 

  2. 
Reproducible workflow


    3. 

  3. 
Roadmap

    4. 

  4. 
License

    5. 

  5. 
Contact

    6. 

  6. 
R packages used in this
project

    7. 









About


This is the GitHub hosting of the project SaniVult. The paper
associated to the project is published in the journal Ecological
Applications. See the CITATION file for a BibTex entry
to the article. This folder contains the files needed to reproduce all
the results of the project, and compile the manuscript of the associated
paper.




CRediT authorship


This project was conducted by:


 Pablo
Almaraz (see contact below), which
participated in study conception, designed and conducted the analyses,
and led manuscript writing.


 Guillermo
Blanco, which led and conceived the study, conducted the field
surveys and participated in manuscript writing.


 Flix Martnez, which
conducted the field surveys.


 Zebensui
Morales-Reyes, which contributed ideas and participated in
manuscript writing.


 Jos
A. Snchez Zapata, which contributed ideas and participated in
manuscript writing.


The major goal of the project is to evaluate the impacts of the
outbreak of a Bovine
Spongiform Encephalopathy epidemic in Europe on the demographic and
population dynamics of one of the worlds largest colonies of the Eurasian
Griffon vulture (Gyps fulvus). The Eurasian Griffon vulture
is a keystone scavenger providing fundamental ecosystem services
worldwide. For further details, see the abstract
below and the file ms/main_text.pdf. Read the published
version of the paper.



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Abstract


Scavenging is a key ecological process controlling energy flow in
ecosystems and providing valuable ecosystem services worldwide. As
long-lived species, the demographic dynamics of vultures can be
disrupted by spatio-temporal fluctuations in food availability, with
dramatic impacts on their population viability and the ecosystem
services provided. In Europe, the outbreak of Bovine Spongiform
Encephalopathy (BSE) in 2001 prompted a restrictive sanitary legislation
banning the presence of livestock carcasses in the wild at a continental
scale. In long-lived vertebrate species the buffering hypothesis
predicts that the demographic traits with the largest contribution to
population growth rate should be less temporally variable. The BSE
outbreak provides a unique opportunity to test for the impact of
demographic buffering in a keystone scavenger suffering abrupt but
transient food shortages. We study the 42-year dynamics (1978-2020) of
one of the worlds largest breeding colonies of Eurasian griffon
vultures (Gyps fulvus). We fitted an inverse Bayesian
state-space model with density-dependent demographic rates to the
time-series of stage-structured abundances to investigate shifts in
vital rates and population dynamics before, during and after the
implementation of a restrictive sanitary regulation. Prior to the BSE
outbreak the dynamics was mainly driven by adult survival: 83% of
temporal variance in abundance was explained by variability in this
rate. Moreover, during this period the regulation of population size
operated through density-dependent fecundity and sub-adult survival.
However, after the onset of the European ban, a one-month delay in
average laying date, a drop in fecundity and a reduction in the number
of fledglings induced a transient increase in the impact of fledgling
and sub-adult recruitment on dynamics. Although adult survival rate
remained constantly high, as predicted by the buffering hypothesis, its
relative impact on the temporal variance in abundance dropped to 71%
during the sanitary legislation and to 54% after the ban was lifted. A
significant increase in the relative impact of environmental
stochasticity on dynamics was modeled after the BSE outbreak. These
results provide empirical evidence on how abrupt environmental
deterioration may induce dramatic demographic and dynamic changes in the
populations of keystone scavengers, with far-reaching impacts on
ecosystem functioning worldwide.



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Built With


This is a workflowr project
bootstraped by a suite of open-source tools.



A suite of R packages were
used in this project. I am grateful to all the
people involved in the development of these open-source packages. Run
the following R command from
within the project for producing a reference list of the packages
used:


grateful::cite_packages(out.format = "rmd", out.dir = file.path(getwd(), "analysis"))


A list of these packages is placed at the end of this document.



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Reproducible workflow


This section shows how to reproduce the results of the accompanying
paper. The folder ./code has the following structure:


.
 ./code
  ./code/S4D3M_JAGS_Fitting.R
  ./code/S4D3M_JAGS_model.jags
  ./code/utilities.R


In this folder, the file ./code/utilities.R contains all
the functions and utilities necessary to conduct the analyses. The file
./code/S4D3M_JAGS_model.jags contains the state-space
stage-structured demographic density-dependent model (S4D3M)
developed in the accompanying
paper written in the JAGS
language.


The data
folder has the following structure:


 ./data
  ./data/Breeding_output.csv
  ./data/BSE_cases.csv
  ./data/data.csv


The manuscript
folder has the following structure:


 ms
  appendix.pdf
  appendix.tex
  arxiv.sty
  biblio.bib
  DataS1.zip
  figs
   Fig1.pdf
   Fig2.pdf
   Fig3.pdf
   Fig4.pdf
   FigS1.pdf
   FigS3.pdf
   FigS4.pdf
  main_text.pdf
  main_text.tex
  MetadataS1.docx
  MetadataS1.pdf



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Prerequisites


Prior to reproducing the results, make sure to have installed all the
necessary software. In particular, you need JAGS and R. The R libraries needed to reproduce
the results (see below)
will be automatically installed by the package pacman.






Workflow


Note that the S4D3M
is fitted through Bayesian MCMC methods using Gibbs sampling, and runs
in JAGS: even
though JAGS is
written in the C++ language, the code
can take several hours to run depending on the architecture used. Note
that there are relatively easy ways of parallelizing this code.


You can reproduce the results of the accompanying paper with three
methods:


    

  1. The first, easiest way to reproduce all the analyses in the
project is to use the Makefile. With simple GNU Make syntax, you can
reproduce all the project, from statistical analyses to manuscript
production. For example, in GNU/Linux
based systems, you can point with the command shell to the project
folder and run the following command:
    

    make all
    

    This command will first conduct all the statistical analyses in the
project, and produce all the figures. It then will assemble and compile
the manuscript and associated supplementary materials with the necessary
figures. Finally, it will open the files. Alternatively, note that you
can run this command within RStudio from the Terminal
tab.
    2. 

  2. From within R, simply source the file
./code/S4D3M_JAGS_Fitting.R. This will perform all the
analyses of the paper in the required order.
    3. 

  3. The final method is to open the R Markdown file
./analysis/index.Rmd to interactively execute the
workflow.
    

    

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    4. 








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License


Distributed under the MIT License. See LICENSE for more
information.



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R packages used in this project


    

  • base (R Core Team 2021)
    • 

  • workflowr (Blischak, Carbonetto, and Stephens 2019)
    • 

  • rmarkdown (Xie, Dervieux, and Riederer 2020)
    • 

  • checkpoint (Ooi, de Vries, and Microsoft 2021)
    • 

  • coda (Plummer et al.2006)
    • 

  • data.table (Dowle and Srinivasan 2021)
    • 

  • ggmcmc (Fernndez-i-Marn 2016)
    • 

  • ggsci (Xiao 2018)
    • 

  • grateful (Rodrguez-Snchez and Hutchins 2020)
    • 

  • mvtnorm (Genz and Bretz 2009)
    • 

  • pacman (Rinker and Kurkiewicz 2018)
    • 

  • patchwork (Pedersen 2020)
    • 

  • runjags (Denwood 2016)
    • 

  • tidyverse (Wickham et al.2019)
    • 

  • truncnorm (Mersmann et al.2018)
    • 

  • viridis (Garnier et al.2021)
    • 

  • xtable (Dahl et al.2019)
    • 

  • dplyr (Wickham et al.2021)
    • 

  • ggpubr (Kassambara 2020)
    • 

  • plyr (Wickham 2011)
    • 

  • readr (Wickham and Hester 2021)
    • 

  • reshape2 (Wickham 2007)
    • 

  • tibble (Mller and Wickham 2021)
    • 





References


Blischak, John D, Peter Carbonetto, and Matthew Stephens. 2019.
Creating and Sharing Reproducible Research Code the Workflowr Way
F1000Research 8 (1749). https://doi.org/10.12688/f1000research.20843.1.


Dahl, David B., David Scott, Charles Roosen, Arni Magnusson, and
Jonathan Swinton. 2019. Xtable: Export Tables to LaTeX or HTML.
https://CRAN.R-project.org/package=xtable.


Denwood, Matthew J. 2016. runjags: An R
Package Providing Interface Utilities, Model Templates, Parallel
Computing Methods and Additional Distributions for MCMC Models in JAGS.
Journal of Statistical Software 71 (9): 125. https://doi.org/10.18637/jss.v071.i09.


Dowle, Matt, and Arun Srinivasan. 2021. Data.table: Extension of
Data.frame. https://CRAN.R-project.org/package=data.table.


Fernndez-i-Marn, Xavier. 2016. ggmcmc:
Analysis of MCMC Samples and Bayesian Inference. Journal of
Statistical Software 70 (9): 120. https://doi.org/10.18637/jss.v070.i09.


Garnier, Simon, Ross, Noam, Rudis, Robert, Camargo, et al.2021.
viridis - Colorblind-Friendly Color Maps for
r. https://doi.org/10.5281/zenodo.4679424.


Genz, Alan, and Frank Bretz. 2009. Computation of Multivariate
Normal and t Probabilities. Lecture Notes in Statistics.
Heidelberg: Springer-Verlag.


Kassambara, Alboukadel. 2020. Ggpubr: Ggplot2 Based Publication
Ready Plots. https://CRAN.R-project.org/package=ggpubr.


Mersmann, Olaf, Heike Trautmann, Detlef Steuer, and Bjrn Bornkamp.
Truncnorm: Truncated Normal Distribution. https://CRAN.R-project.org/package=truncnorm.


Mller, Kirill, and Hadley Wickham. 2021. Tibble: Simple Data
Frames. https://CRAN.R-project.org/package=tibble.


Ooi, Hong, Andrie de Vries, and Microsoft. 2021. Checkpoint:
Install Packages from Snapshots on the Checkpoint Server for
Reproducibility. https://CRAN.R-project.org/package=checkpoint.


Pedersen, Thomas Lin. 2020. Patchwork: The Composer of
Plots. https://CRAN.R-project.org/package=patchwork.


Plummer, Martyn, Nicky Best, Kate Cowles, and Karen Vines. 2006.
CODA: Convergence Diagnosis and Output Analysis for MCMC. R
News 6 (1): 711. https://journal.r-project.org/archive/.


R Core Team. 2021. R: A Language and Environment for Statistical
Computing. Vienna, Austria: R Foundation for Statistical Computing.
https://www.R-project.org/.


Rinker, Tyler W., and Dason Kurkiewicz. 2018. pacman: Package Management for R. Buffalo, New
York. http://github.com/trinker/pacman.


Rodrguez-Snchez, Francisco, and Shaurita D. Hutchins. 2020.
Grateful: Facilitate Citation of r Packages. https://github.com/Pakillo/grateful.


Wickham, Hadley. 2007. Reshaping Data with the reshape Package. Journal of Statistical
Software 21 (12): 120. http://www.jstatsoft.org/v21/i12/.


. 2011. The Split-Apply-Combine Strategy for Data Analysis.
Journal of Statistical Software 40 (1): 129. http://www.jstatsoft.org/v40/i01/.


Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy
DAgostino McGowan, Romain Franois, Garrett Grolemund, et al.2019.
Welcome to the tidyverse. Journal of
Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.


Wickham, Hadley, Romain Franois, Lionel Henry, and Kirill Mller.
2021. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.


Wickham, Hadley, and Jim Hester. 2021. Readr: Read Rectangular
Text Data. https://CRAN.R-project.org/package=readr.


Xiao, Nan. 2018. Ggsci: Scientific Journal and Sci-Fi Themed
Color Palettes for Ggplot2. https://CRAN.R-project.org/package=ggsci.


Xie, Yihui, Christophe Dervieux, and Emily Riederer. 2020. R
Markdown Cookbook. Boca Raton, Florida: Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown-cookbook.



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Owner

  • Name: Pablo Almaraz
  • Login: palmaraz
  • Kind: user
  • Location: Spain
  • Company: CSIC, US

A quantitative ecologist working in the mathematical and statistical analysis of biological data from real world applications.

Citation (CITATION) 

@article{Almaraz2021,
author = {Almaraz, Pablo and Mart{\'{i}}nez, F{\'{e}}lix and Morales-reyes, Zebensui and S{\'{a}}nchez-Zapata, Jos{\'{e}} A.},
journal = {Ecological Applications},
title = {{Long-term demographic dynamics of a keystone scavenger disrupted by human-induced shifts in food availability}},
year = {2021}
}

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