Disturbance duration governs mortality risk while cumulative energetic deficit shapes reproduction in Mediterranean mussels

Description

This dataset supports the manuscript "Disturbance duration governs mortality risk while cumulative energetic deficit shapes reproduction in Mediterranean mussels" (Marine Environmental Research). It contains the model code, the environmental forcing series, the full factorial simulation output and the analysis scripts needed to reproduce every result in the paper. The study uses a Dynamic Energy Budget model of two Mediterranean mussels, the native Mytilus galloprovincialis and the invasive Brachidontes pharaonis, exposed to regular trains of feeding interruptions, and decomposes the reproductive response into a survival component (the probability of reaching maturity) and a conditional component (reproductive investment given maturation). Contents: Forcing data (4 CSV files). Hourly sea surface temperature and food density over four years for Trieste, Palermo open, Palermo coastal and the Stagnone di Marsala lagoon. Columns: time, SST (degrees Celsius), food (dimensionless). Palermo open and Palermo coastal share the same temperature series and differ in food supply. Factorial output (1 XLSX file). End-state summary of the simulation ensemble, one row per design cell, with the design factors (species, site, scenario type, seasonal onset, feeding-stress intensity, maintenance-stress intensity, event frequency, event duration) and the responses (reproductive output, total eggs, reproductive events, final shell length, final survival and mortality, final maturity and reserve density). Model code and analysis scripts (9 Python files embedded in a zip file). model_core.py holds the parameter sets, the Arrhenius temperature correction and the hourly integrator. model_hurdle.py computes the decomposition used throughout the paper, together with the imposed and realised energetic deficits. model_energy.py returns the control assimilation trajectory. model_hazard.py implements four alternative starvation-hazard formulations for the sensitivity analysis. model_stage.py builds and restarts individuals at three ontogenetic stages. model_cont.py provides the control for the discrete spawning rule. grid_hurdle.py, stage_deconf.py and worker.py are the drivers that run the factorial grids. README.md, describing every file, the commands needed to reproduce the analyses, how the reported quantities are derived from the outputs and a summary of the model. Requirements: Python 3 with numpy and pandas.

Institutions

• University of Palermo

  Sicily, Palermo

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