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