SPM Journal Club: Individual life-history variation in population models

Differences between individuals can be large and have profound consequences for the dynamics of populations. Even if such differences have unknown causes and/or are unobservable, they can be incorporated into population models, allowing to assess their impacts on population-level patterns.

We discuss a recent paper presenting the inclusion of unobserved individual heterogeneity and individual stochasticity into a demographic analysis of Southern fulmars. The study outlines how unobserved states can be included in the estimation of demographic rates using mixture models, and showcases the assessment of their importance to outcomes of a resulting matrix population model:

"Interacting effects of unobserved heterogeneity and individual stochasticity in the life history of the southern fulmar"

(Jenouvrier et al. 2018, Journal of Animal Ecology)

http://onlinelibrary.wiley.com/doi/10.1111/1365-2656.12752/full

 

Abstract

  1. Individuals are heterogeneous in many ways. Some of these differences are incorporated as individual states (e.g. age, size, breeding status) in population models. However, substantial amounts of heterogeneity may remain unaccounted for, due to unmeasurable genetic, maternal or environmental factors.
  2. Such unobserved heterogeneity (UH) affects the behaviour of heterogeneous cohorts via intra-cohort selection and contributes to inter-individual variance in demographic outcomes such as longevity and lifetime reproduction. Variance is also produced by individual stochasticity, due to random events in the life cycle of wild organisms, yet no study thus far has attempted to decompose the variance in demographic outcomes into contributions from UH and individual stochasticity for an animal population in the wild.
  3. We developed a stage-classified matrix population model for the southern fulmar breeding on Ile des Pétrels, Antarctica. We applied multievent, multistate mark–recapture methods to estimate a finite mixture model accounting for UH in all vital rates and Markov chain methods to calculate demographic outcomes. Finally, we partitioned the variance in demographic outcomes into contributions from UH and individual stochasticity.
  4. We identify three UH groups, differing substantially in longevity, lifetime reproductive output, age at first reproduction and in the proportion of the life spent in each reproductive state.
    • 14% of individuals at fledging have a delayed but high probability of recruitment and extended reproductive life span.
    • 67% of individuals are less likely to reach adulthood, recruit late and skip breeding often but have the highest adult survival rate.
    • 19% of individuals recruit early and attempt to breed often. They are likely to raise their offspring successfully, but experience a relatively short life span.
    Unobserved heterogeneity only explains a small fraction of the variances in longevity (5.9%), age at first reproduction (3.7%) and lifetime reproduction (22%).
  5. UH can affect the entire life cycle, including survival, development and reproductive rates, with consequences over the lifetime of individuals and impacts on cohort dynamics. The respective role of UH vs. individual stochasticity varies greatly among demographic outcomes. We discuss the implication of our finding for the gradient of life-history strategies observed among species and argue that individual differences should be accounted for in demographic studies of wild populations.
Published Jan. 30, 2018 6:20 PM - Last modified Jan. 30, 2018 6:20 PM