Spatial-temporal stratifications in natural populations and how they affect understanding and estimation of effective population size

Authors

Robin Waples, NOAAFollow

Date of this Version

2010

Citation

Molecular Ecology Resources (2010) 10, 785–796; doi: 10.1111/j.1755-0998.2010.02876.x

Abstract

The concept of effective population size (N_e) is based on an elegantly simple idea which, however, rapidly becomes very complex when applied to most real-world situations. In natural populations, spatial and temporal stratifications create different classes of individuals with different vital rates, and this in turn affects (generally reduces) N_e in complex ways. I consider how these natural stratifications influence our understanding of effective size and how to estimate it, and what the consequences are for conservation and management of natural populations. Important points that emerge include the following:

1. The relative influences of local vs. metapopulation N_e depend on a variety of factors, including the time frame of interest.
2. Levels of diversity in local populations are strongly influenced by even low levels of migration, so these measures are not reliable indicators of local N_e.
3. For long-term effective size, obtaining a reliable estimate of mutation rate is the most important consideration; unless this is accomplished, estimates can be biased by orders of magnitude.
4. At least some estimators of contemporary N_e appear to be robust to relatively high (approximately 10%) equilibrium levels of migration, so under many realistic scenarios they might yield reliable estimates of local N_e.
5. Age structure probably has little effect on long-term estimators of N_e but can strongly influence contemporary estimates.
6. More research is needed in several key areas: (i) to disentangle effects of selection and drift in metapopulations connected by intermediate levels of migration; (ii) to elucidate the relationship between N_b (effective number of breeders per year) and N_e per generation in age-structured populations; (iii) to perform rigorous sensitivity analyses of new likelihood and coalescent-based methods for estimating demographic and evolutionary histories.