Evaluating drivers of spatiotemporal variability in individual condition of a bottom-associated marine fish, Atlantic cod (Gadus morhua)

We use Spatiotemporal linear mixed-effects models and a predictive-modeling framework within the R package sdmTMB to understand spatiotemporal variation in, and the effects of covariates on body condition of Atlantic cod in the Baltic Sea. This repo contains R code and data to reproduce data processing and analysis.

ICES Journal of Marine Science: Lindmark, M., Anderson, S. C., Gogina, M., and Casini, M. Evaluating drivers of spatiotemporal variability in individual condition of a bottom-associated marine fish, Atlantic cod (Gadus morhua). ICES Journal of Marine Science 80, 1539–1550 (2023).

Preprint: Lindmark, M., Anderson, S. C., Gogina, M., and Casini, M. 2022, October 21. Evaluating drivers of spatiotemporal individual condition of a bottom-associated marine fish. bioRxiv. https://www.biorxiv.org/content/10.1101/2022.04.19.488709v3.

How to replicate our analyses and navigate this repo

data Contains data from the following sources:

• Cod, flounder and condition data are downloaded from ICES databases DATRAS
• Herring and sprat abundance estimates are from the ICES WGBIFS database for the BIAS survey.
• Modelled oxygen and temperature data stem from the NEMO-Nordic-SCOBI model (Almroth-Rosell et al., 2011; Eilola et al., 2009; Kuznetsov et al., 2016), downloaded from EU Copernicus
• Biomass density estimates of Saduria are from Gogina et al., (2020)

R Contains code for analysis and data processing

figures Contains figures of results

output Contains .rds objects of model outputs due to long computation times

For reproducing the main results, run density_model.Rmd and condition_model_cf.Rmd . For collating data from scratch, run scripts in this order:

1. R/clean_data/collate_cpue_data_exchange.Rmd (clean cpue data)
2. R/clean_data/cod_fle_density_models_as_covars.Rmd (fit cpue models without covariates for prediction onto data and grid)
3. R/clean_data/make_pred_grid_utm.Rmd (to get large scale predictor variables [ices rectangle and sub-division])
4. R/clean_data/collate_cond_data_exchange.Rmd (to get condition data, haul-level and large scale covariates from the cpue model and the pred_grid)

References

Almroth-Rosell, E., Eilola, K., Hordoir, R., Meier, H.E.M., Hall, P.O.J., 2011. Transport of fresh and resuspended particulate organic material in the Baltic Sea --- a model study. J.Mar.Sys. 87, 1-12. Doi: 10.1016/j.jmarsys.2011.02.005

Eilola, K., Meier, M.H.E., Almroth, E., 2009. On the dynamics of oxygen, phosphorus and cyanobacteria in the Baltic Sea; A model study. J.Mar.Sys. 75, 163-184. Doi: doi:10.1016/j.jmarsys.2008.08.009.

Gogina M, Zettler ML, Wåhlström I, Andersson H, Radtke H, Kuznetsov I, MacKenzie BR. 2020. A combination of species distribution and ocean-biogeochemical models suggests that climate change overrides eutrophication as the driver of future distributions of a key benthic crustacean in the estuarine ecosystem of the Baltic Sea. ICES J Mar Sci 77:2089--2105. doi:10.1093/icesjms/fsaa107

Kuznetsov, I., Eilola, K., Dieterich, C., Hordoir, R., Axell, L., Höglund, A. and Schimanke, S., 2016. Model study on the variability of ecosystem parameters in the Skagerrak-Kattegat area, effect of load reduction in the North Sea and possible effect of BSAP on Skagerrak-Kattegat area. SMHI Report Oceanografi 119