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

Area of Science:

• Ecology
• Population Dynamics
• Theoretical Ecology

Background:

• Temporal variation in demographic processes significantly impacts population dynamics.
• Previous studies indicated fast life history species are more sensitive to environmental autocorrelation than slow life history species, but mechanisms remain unclear.

Purpose of the Study:

• To investigate the mechanistic pathways driving differential sensitivity to environmental autocorrelation in fast (Orchestia gammarellus) and slow (Manta alfredi) life history species.
• To examine how functional traits related to growth and reproduction influence population dynamics under varying food conditions and demographic stochasticity.

Main Methods:

• Utilized a mechanistic, functional trait approach to model population dynamics.
• Described growth and reproduction using functional traits adhering to energy conservation principles.
• Assessed demographic variation based on temporal autocorrelation in food availability.

Main Results:

• Orchestia gammarellus showed highest sensitivity to the frequency of good food conditions, driven by reproduction traits.
• Manta alfredi exhibited greatest sensitivity to temporal autocorrelation, influenced by growth parameters.
• Species-specific responses in body size and population growth rates (log(λs)) were observed under varying food conditions and demographic stochasticity.

Conclusions:

• Functional traits provide mechanistic insights into how environmental and demographic stochasticity affect population dynamics.
• Integrating functional traits into demographic models is crucial for understanding species' responses in stochastic environments.
• Differential sensitivity to functional traits explains observed variations in species' responses to environmental changes.