Decomposing demographic contributions to the effective population size with moose as a case study

Area of Science:

• Ecology
• Population Genetics
• Conservation Biology

Background:

• Genetic drift, influenced by demographic factors, impacts population viability.
• Effective population size (N_e) quantifies genetic drift, but estimating its drivers has been challenging.
• Previous models for N_e estimation relied on simplifying assumptions unsuitable for complex populations.

Purpose of the Study:

• To demonstrate a stochastic demographic framework for estimating and decomposing N_e.
• To identify demographic factors contributing most to N_e/N in a harvested moose population.
• To assess which factors have the greatest potential to alter N_e/N in the future.

Main Methods:

• Utilized a stochastic demographic framework to model fluctuations in population size and age distribution.
• Estimated and decomposed the total demographic variance to determine the ratio of effective to total population size (N_e/N).
• Analyzed contributions from sex, age, survival, and reproduction to N_e/N.

Main Results:

• N_e/N in the moose population is most sensitive to the demographic variance of older males.
• Reproductive autocorrelations and covariance between survival and reproduction decrease N_e/N, increasing genetic drift.
• These factors, common in natural populations and influenced by hunting, reduce genetic variation.

Conclusions:

• The presented framework enhances understanding of demographic influences on genetic drift and population viability.
• Identified key demographic drivers impacting N_e/N, crucial for conservation.
• Provides a tool to inform management decisions for harvested populations facing genetic diversity loss.