Code and data in Fiksen and Reglero 2021. Atlantic bluefin tuna spawn early to avoid metabolic meltdown in larvae.

The code is the basic model and the scripts needed to generate the figures in the paper.

OURNAL PUBLICATION CITATION: Øyvind Fiksen and Patricia Reglero. 2021. Atlantic bluefin tuna spawn early to avoid metabolic meltdown in larvae. Ecology. 

Python code for the analyses and figures in the paper. Field data of tuna larva and Cladocera abundances are embedded in the code, with environmental drivers read from the  .txt files (temperature, daylight) - see Appendix S1. Data from each realization of the model (sensitivity analyses) are included in separate files (.npy).  

 

Author 1: Øyvind Fiksen, Department of Biological Sciences, University of Bergen, 5020 Bergen, Norway; oyvind.fiksen@uib.no 

Author 2: Patricia Reglero, Centro Oceanográfico de Baleares, Instituto Español de Oceanografía (IEO, CSIC), 07015 Palma de Mallorca, Spain. patricia.reglero@ieo.es 

Main Python code with model analyses, and the output figures: 

 FiguresforPaper.py  - see code for explanatory text and details on the model, parameters and figures produced. 

 

Input temperature and daylength data: 

 AverageTempData_NOAA.txt – input average temperature data (fig 2 and 3, in paper) 

 HoursofLight.txt – modelled day-length (hours), fig S1 

Temperature data for different years (input data, for fig 3, main):  

  temp2003.npy 

 temp2004.npy 

 temp2006.npy 

 temp2011.npy 

 

Metafile used in the Github repository 

 README.md 

 

Sensitivity analysis. Egg fitness under combinations of fixed concentrations of nauplii (300-500) and Cladocera (10-100) (for fig S3):  

  flat_300_30.npy 

 flat_400_20.npy 

 flat_400_25.npy 

 flat_400_30.npy 

 flat_500_10.npy 

 flat_500_30.npy 

 flat_500_60.npy 

 flat_500_100.npy 

 

Sensitivity analysis. Egg fitness with combinations of fixed nauplii (300-600) and seasonal Cladocera abundance in 10, 15 and 20% (010-015-02) of nearshore concentrations (for fig S3): 

  surv_300_010.npy 

 surv_400_010.npy 

 surv_500_010.npy 

 surv_500_015.npy 

 surv_600_02.npy 

 

Sensitivity analysis – Growth output for combinations of temperature and prey combinations (for figs S4 and S5): 

  grT_n300C015.npy 

 gTr_n300C01.npy 

 gTr_n300C015.npy 

 gTr_n400C01.npy 

 gTr_n500C015.npy 

 gTr05_n300C01.npy 

 gTr05_n300C015.npy 

 gTr05_n400C01.npy 

 gTr05_n500_10.npy 

 gTr05_n500C015.npy 

 len_i_n300C01.npy 

 len_i_n400C01.npy 

 len_i_n500_01.npy 

 len_i_n500_10.npy 

 len_i_n500C015.npy 

 

Sensitivity analysis – Egg fitness under no food limitation, with different daylengths (testing only): 

  nofLim.npy 

 nofLim2h.npy 

 nofLim24h.npy 

  

 Sensitivity analysis – Egg fitness different years, warm and cold (and 2006): 

  SurvT2003.npy 

 SurvT2004.npy 

 survT2006.npy 

  

 

Figures produced from the code, for main paper (Main), Appendix S1 (Sx), and some extras – in both .svg and .png format: 

  fig_gut.png 

 Fig_Main_2AB.png 

 Fig_Main_3A.png 

 Fig_Main_3B.png 

 Figure_Main_2C.png 

 Figure_S3.png 

 Figure_S4.png 

 Figure_S5.png 

 Figure_S5_05.png 

 Figure_S4.svg 

 Figure_S5.svg 

 Figure_S5_05.svg 

 fig_gut.svg 

 Fig_Main_2AB.svg 

 Fig_Main_3A.svg 

 Fig_Main_3B.svg 

 fig_surv.svg 

 Figure_Main_2C.svg 

 Figure_S1.svg 

 Figure_S2.svg 

 Figure_S3.svg