Herein this repository were codes used to analyze species co-occurrence using available Passive Acoustic Monitoring (PAM) and Acoustic Telemetry (AT) data from the European Tracking Network (ETN). We created hourly presence-absence matrices for each species of interest and applied some of the most common analyses previously used to study species co-occurrence.
a. Raw data was extracted from https://lifewatch.be/etn. Data is under moratorium and access must be granted by data owners. See https://www.lifewatch.be/etn/assets/docs/ETN-DataPolicy.pdf
b. Cleaning, organizing and merging of data is found in organising_data.Rmd. Hourly presence-absence matrices were made for each species which were then merged. This code results to the final data set (DPH_final.csv) used for the subsequent analyses. A distinction was made between true absence (DPH=0) and uncertainty of presence/absence (DPH = NA) by creating a dataframe of all hours when CPODs/receivers were both active and fish had active acoustic transmitters.
Plotting of available data for each station, mapping out the stations and plotting of heat maps for species detected from the BPAN.
a. MonthlyOccupancy.R: Pairwise species monthly occupancy using the cooccur package (Griffith et al., 2016).
b. CScore.R: Quantification of association between species pairs based on the number of shared stations using the EcoSimR package (Gotelli et al., 2015).
c. DielOverlap.R: Graphing of hourly overlap of presence of species and calculation of overlap estimates using the overlap package (Meredith & Ridout, 2021).
d. sb_porp_glmm.R, cod_porp_glmm.R, dol_porp_glmm.R: Generalized Linear Mixed Effects/Generalized Linear Models for each species pair (Bates et al., 2022).
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01
Gotelli, ., Hart, ., & Ellison, . (2015). EcoSimR: Null Model Analysis for Ecological Data. Zenodo. https://doi.org/10.5281/zenodo.16636
Griffith, D. M., Veech, J. A., & Marsh, C. J. (2016). Cooccur: Probabilistic species co-occurrence analysis in R. Journal of Statistical Software, 69(1), 1–17. https://doi.org/10.18637/jss.v069.c02
Meredith, A. M., Ridout, M., & Meredith, M. M. (2021). Package ‘overlap.’ https://cran.r-project.org/web/packages/overlap/overlap.pdf