The identification of population bottlenecks is critical in conservation because populations that have experienced significant reductions in abundance are subject to a variety of genetic and demographic processes that can hasten extinction. Genetic bottleneck tests constitute an appealing and popular approach for determining if a population decline has occurred because they only require sampling at a single point in time, yet reflect demographic history over multiple generations. However, a review of the published literature indicates that, as typically applied, microsatellite-based bottleneck tests often do not detect bottlenecks in vertebrate populations known to have experienced declines. This observation was supported by simulations that revealed that bottleneck tests can have limited statistical power to detect bottlenecks largely as a result of limited sample sizes typically used in published studies. Moreover, commonly assumed values for mutation model parameters do not appear to encompass variation in microsatellite evolution observed in vertebrates and, on average, the proportion of multi-step mutations is underestimated by a factor of approximately two. As a result, bottleneck tests can have a higher probability of 'detecting' bottlenecks in stable populations than expected based on the nominal significance level. We provide recommendations that could add rigor to inferences drawn from future bottleneck tests and highlight new directions for the characterization of demographic history.

Conserving genetic variation is critical for maintaining the evolutionary potential and viability of a species. Genetic studies seeking to delineate conservation units, however, typically focus on characterizing neutral genetic variation and may not identify populations harboring local adaptations. Here, variation at two major histocompatibility complex (MHC) class II B genes was characterized in four populations of marbled murrelets Brachyramphus marmoratus, a threatened species in which little neutral genetic population structure has been detected. High diversity, as well as evidence of balancing selection, was detected in exon 2 of these genes. Genetic population structure based on MHC markers was uncorrelated to genetic structure estimated with neutral markers, suggesting that selection played a more important role in shaping population structure at these markers than genetic drift. A high proportion of alleles and inferred peptides were unique to a single population, with the Aleutian Islands and southeast Alaska having the highest richness of both. Murrelets sampled in Oregon had low MHC exon 2 allele and inferred peptide richness, and were significantly differentiated from individuals sampled in the Aleutian Islands based on the frequency of exon 2 alleles. In addition, murrelets sampled in Oregon were differentiated from murrelets in both the Aleutian Islands and southeast Alaska based on inferred peptide frequencies, suggesting that the Oregon population could be prioritized for conservation measures. More broadly, combining information from neutral and adaptive genetic markers can improve the delineation of conservation units in threatened species.

In response to our review of the use of genetic bottleneck tests in the conservation literature (Peery et al. 2012, Molecular Ecology, 21, 3403–3418), Hoban et al. (2013, Molecular Ecology, in press) conducted population genetic simulations to show that the statistical power of genetic bottleneck tests can be increased substantially by sampling large numbers of microsatellite loci, as they suggest is now possible in the age of genomics. While we agree with Hoban and co‐workers in principle, sampling large numbers of microsatellite loci can dramatically increase the probability of committing type 1 errors (i.e. detecting a bottleneck in a stable population) when the mutation model is incorrectly assumed. Using conservative values for mutation model parameters can reduce the probability of committing type 1 errors, but doing so can result in significant losses in statistical power. Moreover, we believe that practical limitations associated with developing large numbers of high‐quality microsatellite loci continue to constrain sample sizes, a belief supported by a literature review of recent studies using next generation sequencing methods to develop microsatellite libraries. conclusion, we maintain that researchers employing genetic bottleneck tests should proceed with caution and carefully assess both statistical power and type 1 error rates associated with their study design.

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