Cyclical disturbances in the environment, such as seasons or tides, are fundamental to many systems in nature. Mounting evidence suggests that species’ life history traits are tightly correlated with environmental cycles and that perturbations to cycles due to climate change can drive ‘phenological shifts’ such as earlier reproductive maturity. Yet, ecologists currently lack a general quantitative framework for understanding how environmental cycles shape life histories. To build such a framework we considered how fitness is maximized when energy allocation and trade-offs vary as a direct function of cycle frequency. Then we analytically solved for optimal life history strategies across frequencies. We tested our theoretical predictions in natural populations of the marine intertidal copepod Tigriopus californicus which occur in upper tide pools on rocky shores of the Pacific coast. Populations get flushed by waves at the highest of high tides, causing periodic mortality events. Depending on height on the shore, pools experience wave disturbance at periods of 1 day to 3 weeks. We sampled 24 isolated pool populations of T. californicus across varying heights on rocky shores of northern Washington, USA, capturing an array of disturbance frequencies. We measured life history traits across populations after rearing them in common garden settings.
Results/Conclusions
Our analytical models reveal that the direction and strength of life history evolution depend on the frequency of environmental disturbance. Classical life history evolution theory considers environmental perturbations in press or pulse disturbance scenarios. Here we extend this framework by showing that when disturbances happen cyclically, such as in seasonal or tidal systems, the very trade-off and optimization dynamics are governed by the frequency with which the disturbances occur. Key life history traits of T. californicus, such as fecundity and age at sexual maturity, vary with tidal disturbance frequencies across natural populations as predicted by our models. Our scalable theoretical framework may serve two broadly important purposes: 1) by explicitly considering the various oscillation patterns across systems, we may gain a novel perspective on the age-old question of why life history strategies are so diverse in nature; and 2) we can improve our predictive power of how life histories will evolve in systems in which the underlying environmental cycles such as seasons are disrupted.