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Knit directory: Energy-Budget-Model/

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From traits to travel: A mechanistic approach to quantifying the energetics of animal dispersal

Paper link (XXXX)

Authors

Caitlin Wilkinson (caitlin.wilkinson@idiv.de)

Ulrich Brose

Alexander Dyer

Myriam R. Hirt

Remo Ryser

Overview

This repository contains all underlying data and code needed to reproduce the analyses in the paper mentioned above.

Accessing code and data

See the code directory on GitHub for the script that generates the figures and analyses in the manuscript.

The raw data needed for analyses is provided in data.

The model outputs and transformed maximum dispersal distance data is provided inoutput.

Structure of the analysis

0-disp-workflowr-script provides the code to create a workflowr project and organised subdirectories in GitHub (Blischak et al. 2023).
1-fish-energy-reserve-and-trait-data provides the code to:
1. Extract body mass data from Fishbase and SeaLifeBase (Boettiger et al. 2023)
1. Refit the energy storage data provided in (Martin et al. 2017) to convert length (mm) to mass (g). This was to obtain the intercept and slope for the fish energy storage allometry used in our model
2-disp-raw-data-transformation provides the code to transform the raw maximum dispersal distance data to:
1. Extract and harmonise names using rgbif
1. Add in missing body mass data
1. Add in missing movement mode
1. Filtering data to include flying birds, running mammals, swimming fish only
1. Harmonising all units for distance (m) and body mass (g)
3-parameter-conversions provides the code to convert the units from the original parameter allometries to the ones used in the energy budget model.
4-energy-function provides the energy-budget model function needed to obtain the energetic costs of dispersal in J.
5-energy-data-visualisation provides the code needed to produce figures 3a-c and 4 in the paper mentioned above.
6-disp-function provides the energy-budget model function needed to obtain the maximum dispersal distance of animals in m.
7-disp-data-visualisation provides the code needed to:
1. Make model predictions for maximum dispersal distance for each movement mode and related taxonomic group to compare to the empirical data
1. Calculate the percentage of data which lies above model predictions
1. Produce figures 2a-d and 5a-c in the paper mentioned above.
8-supplementary-visualisation provides the code needed to conduct:
1. Sensitivity analyses showing the effect of changing the residual energy needed upon arrival (𝝀) on maximum dispersal distance predictions
1. Sensitivity analyses showing the effect of adding resting time or stop overs (ꞵ) on maximum dispersal distance predictions

R-packages used

rgbif (Chamberlain et al. 2024)
rstudioapi (Ushey et al. 2024)
tidyverse (Wickham et al. 2019)
viridis (Garnier et al. 2024)
scales (Wickham et al. 2023)
igraph (Csardi et al. 2024)
ggraph (Pedersen. 2024)
rfishbase (Boettiger et al. 2023)
rsealifebase (Boettiger et al. 2023)

References

Blischak, J., Carbonetto, P., Stephens, M., GitLab), L.Z. (Instructions for hosting with, Server), P.F. (Support for hosting with S., code), T.T. (Instructions for sharing common, et al. (2023). workflowr: A Framework for Reproducible and Collaborative Data Science.

Boettiger, C., Chamberlain, S., Lang, D.T., Wainwright, P. & Cazelles, K. (2023). rfishbase: R Interface to “FishBase.” Chamberlain, S., Oldoni, D., Barve, V., Desmet, P., Geffert, L., Mcglinn, D., et al. (2024). rgbif: Interface to the Global Biodiversity Information Facility API.

Csárdi, G., Nepusz, T., Traag, V., Horvát, S., Zanini, F., Noom, D., et al. (2024). igraph: Network Analysis and Visualization. Garnier, S., Ross, N., Rudis, B., Sciaini, M., Camargo, A.P. & Scherer, C. (2024). viridis: Colorblind-Friendly Color Maps for R.

Martin, B.T., Heintz, R., Danner, E.M. & Nisbet, R.M. (2017). Integrating lipid storage into general representations of fish energetics. Journal of Animal Ecology, 86, 812–825.

Pedersen, T.L. & RStudio. (2024). ggraph: An Implementation of Grammar of Graphics for Graphs and Networks.

R Core Team. (2021). R: A Language and Environment for Statistical Computing.

Ushey, K., Allaire, J.J., Wickham, H., Ritchie, G. & RStudio. (2024). rstudioapi: Safely Access the RStudio API.