Initial abundance and stochasticity influence competitive outcome in communities

Area of Science:

• Ecology
• Population Dynamics
• Theoretical Ecology

Background:

• Deterministic models are often used to predict community outcomes, but natural systems exhibit inherent stochasticity.
• Stochasticity, such as environmental and demographic variations, can lead to weaker competitors excluding stronger ones, a phenomenon known as competitive indeterminacy.
• The role of multiple stochasticity forms in natural communities remains largely unknown and untested.

Purpose of the Study:

• To investigate the influence of various forms of stochasticity on competitive outcomes in ecological communities.
• To test the effects of multiple stochasticity types on theoretical and experimental community dynamics.
• To assess the impact of stochasticity on transient coexistence and competitive indeterminacy.

Main Methods:

• Developed two-species stochastic Ricker models incorporating four types of stochasticity: environmental, demographic, demographic heterogeneity, and stochastic sex determination.
• Fitted these models to experimental data from flour beetle (Tribolium sp.) communities.
• Simulated the fitted models to analyze long-term community behavior, including coexistence duration and competitive indeterminacy.

Main Results:

• The duration of transient coexistence was sensitive to the specific forms of stochasticity included in the models.
• The degree of competitive indeterminacy was also significantly affected by the types of stochasticity considered.
• Model simulations revealed that different combinations of stochasticity influence community dynamics in distinct ways.

Conclusions:

• Estimates of extinction risk, coexistence probabilities, and competitive exclusion times may be inaccurate if models neglect relevant forms of stochasticity.
• Stochasticity plays a crucial role in shaping the dynamics of ecological communities, influencing competitive interactions.
• Future ecological modeling should incorporate multiple forms of stochasticity to provide more realistic predictions of community behavior.