Assessing variation in life-history tactics within a population using mixture regression models: a practical guide...

Area of Science:

• Ecology and Evolutionary Biology
• Quantitative Ecology
• Life-History Evolution

Background:

• Mixed models are standard in ecology for analyzing individual variation but assume normal distributions for random effects.
• Violations of this normality assumption, often due to clustering, can lead to inaccurate ecological and evolutionary analyses.
• Mixture regression models offer an alternative for handling multi-modal distributions and identifying distinct subgroups within populations.

Purpose of the Study:

• To demonstrate the utility of mixture regression models for analyzing variation in individual life-history tactics within ecological populations.
• To compare the performance of mixture models against traditional mixed models in ecological contexts, especially concerning clustered data.
• To encourage the adoption of mixture models by ecologists and evolutionary biologists for a more nuanced understanding of population dynamics.

Main Methods:

• Conducted simulations to assess the ability of mixture models to detect latent clusters and compare estimation accuracy with mixed models.
• Applied mixture models to empirical life-history data from long-term studies of large mammals.
• Evaluated model performance using information criteria (AIC, BIC) and bootstrap methods for cluster selection.

Main Results:

• Mixture models effectively identified latent clusters in simulated and empirical data, outperforming mixed models when random effects were non-normal.
• While mixed models provided unbiased population-level fixed effects, mixture models yielded more precise estimates for cluster-specific fixed and random effects.
• Model selection criteria reliably identified the correct number of clusters across various ecological scenarios, with exceptions for Bernoulli distributions and small sample sizes.

Conclusions:

• Mixture regression models are a powerful tool for uncovering and quantifying diverse life-history tactics within populations, addressing limitations of standard mixed models.
• These models provide reliable estimates for each identified cluster, enhancing the study of ecological and evolutionary processes.
• Mixture models represent a significant statistical advancement for evolutionary ecologists, particularly when investigating within-population heterogeneity.