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Multiple-batch spawning: a risk spreading strategy disarmed by highly intensive size-selective fishing

Cite this dataset

Hočevar, Sara (2022). Multiple-batch spawning: a risk spreading strategy disarmed by highly intensive size-selective fishing [Dataset]. Dryad.


Here we upload the files that support our research on the role of risk-spreading strategies in the light of fisheries-induced evolution. The code is stored in Zenodo.

Abstract from the paper:

Can the advantage of risk-managing life-history strategies become a disadvantage under human-induced evolution? Organisms have adapted to the variability and the uncertainty of environmental conditions with a vast diversity of life-history strategies. One of such evolved strategies is multiple-batch spawning, a spawning strategy common to long-lived fishes that ‘hedge their bets’, by distributing the risk to their offspring on a temporal and spatial scale. The fitness benefits of this spawning strategy increase with female body size, the very trait that size-selective fishing targets. By applying an empirically and theoretically motivated eco-evolutionary mechanistic model that was parameterized for Atlantic cod (Gadus morhua), we explored how fishing intensity may alter the life-history traits and fitness of fishes that are multiple-batch spawners. Our main findings are twofold; first, the risk-spreading strategy of multiple-batch spawning is not effective against fisheries selection, because the fisheries selection favours smaller fish with lower risk-spreading effect, and second, the ecological recovery in population size does not secure evolutionary recovery in the population size structure. The beneficial risk-spreading mechanism of the batch spawning strategy highlights the importance of recovery in the size structure of overfished stocks, from which a full recovery in the population size can follow.


We used the empirically-motivated individual-based eco-evolutionary model to simulate the dataset.

Usage notes

Simulations and data analyses were conducted in the open-source statistical programming language R (R Core Team, 2021).