Multiple‐batch spawning as a bet‐hedging strategy in highly stochastic environments: An exploratory analysis of Atlantic cod

• Published in: Evolutionary Applications
• Main Authors: Hočevar, Sara, Hutchings, Jeffrey A., Kuparinen, Anna
• Other Authors: Academy of Finland, Natural Sciences and Engineering Research Council of Canada, H2020 European Research Council
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2021
• Subjects: atlantic cod; Gadus morhua
• Online Access: http://dx.doi.org/10.1111/eva.13251; https://onlinelibrary.wiley.com/doi/pdf/10.1111/eva.13251; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/eva.13251

Abstract Stochastic environments shape life‐history traits and can promote selection for risk‐spreading strategies, such as bet‐hedging. Although the strategy has often been hypothesized to exist for various species, empirical tests providing firm evidence have been rare, mainly due to the challenge in tracking fitness across generations. Here, we take a ‘proof of principle’ approach to explore whether the reproductive strategy of multiple‐batch spawning constitutes a bet‐hedging. We used Atlantic cod ( Gadus morhua ) as the study species and parameterized an eco‐evolutionary model, using empirical data on size‐related reproductive and survival traits. To evaluate the fitness benefits of multiple‐batch spawning (within a single breeding period), the mechanistic model separately simulated multiple‐batch and single‐batch spawning populations under temporally varying environments. We followed the arithmetic and geometric mean fitness associated with both strategies and quantified the mean changes in fitness under several environmental stochasticity levels. We found that, by spreading the environmental risk among batches, multiple‐batch spawning increases fitness under fluctuating environmental conditions. The multiple‐batch spawning trait is, thus, advantageous and acts as a bet‐hedging strategy when the environment is exceptionally unpredictable. Our research identifies an analytically flexible, stochastic, life‐history modelling approach to explore the fitness consequences of a risk‐spreading strategy and elucidates the importance of evolutionary applications to life‐history diversity.