A functional trait approach to identifying life history patterns in stochastic environments

Temporal variation in demographic processes can greatly impact population dynamics. Perturbations of statistical coefficients that describe demographic rates within matrix models have, for example, revealed that stochastic population growth rates (log(λ_s)) of fast life histories are more sensitive to temporal autocorrelation of environmental conditions than those of slow life histories. Yet, we know little about the mechanisms that drive such patterns. Here, we used a mechanistic, functional trait approach to examine the functional pathways by which a typical fast life history species, the macrodetrivore Orchestia gammarellus, and a typical slow life history species, the reef manta ray Manta alfredi, differ in their sensitivity to environmental autocorrelation if (a) growth and reproduction are described mechanistically by functional traits that adhere to the principle of energy conservation, and if (b) demographic variation is determined by temporal autocorrelation in food conditions. Opposite to previous findings, we found that O. gammarellus log(λ_s) was most sensitive to the frequency of good food conditions, likely because reproduction traits, which directly impact population growth, were most influential to log(λ_s). Manta alfredi log(λ_s) was instead most sensitive to temporal autocorrelation, likely because growth parameters, which impact population growth indirectly, were most influential to log(λ_s). This differential sensitivity to functional traits likely also explains why we found that O. gammarellus mean body size decreased (due to increased reproduction) but M. alfredi mean body size increased (due to increased individual growth) as food conditions became more favorable. Increasing demographic stochasticity under constant food conditions decreased O. gammarellus mean body size and increased log(λ_s) due to increased reproduction, whereas M. alfredi mean body and log(λ_s) decreased, likely due to decreased individual growth. Our findings signify the importance of integrating functional traits into demographic models as this provides mechanistic understanding of how environmental and demographic stochasticity affects population dynamics in stochastic environments.