The need to estimate power to link genetics and demography for conservation

Avise (1995) makes a compelling argument to combine genetics and demography to define population structure for conservation. If a population is significantly genetically subdivided and if these subdivisions are geographically stratified, the linkage between genetics, population dynamics, and management is clear, and the putative subpopulations should be managed as separate units. The converse does not hold true, however, that the putative subpopulations should be managed as a single unit when no evidence of geographically stratified genetic subdivisions is found. From a management perspective, there is a world of difference between the valid assertion that the null hypothesis (that samples were randomly drawn from a single population) could not be rejected, and the invalid assertion that the null hypothesis is somehow established and that scientific evidence therefore exists to manage the putative subpopulations collectively as a single unit. Although distressingly common in management arenas, it is nonetheless unacceptable that decisions to pool putative subpopulations be based solely on the failure to find genetic evidence of subpopulation structure (Dizon et al. 1995). Avise points out that a scientist may fail to find population structure because gene flow may be sufficiently high to homogenize populations genetically yet actually be quite low demographically. We fear, however, that this point will be overlooked by those using his forceful Fig. 2, which highlights differences between genetic markers for understanding gender-specific dispersal patterns and geneflow regimes. If not considered with the caveats in the text, reference by a hasty reader to the figure alone could contribute to this common misuse of genetic data. The figure taken by itself suggests that failing to reject the null hypothesis of no geographic structure in mtDNA means that no demographic autonomy exists. Here we supplement Avise's arguments about the connection of demography and genetics by showing the importance to conservation decisions of statistically estimating both the probability that genetic data came from a single population and the probability that the data came from subdivided populations (statistical power). True integration of management, population dynamics, and genetic theory must occur before genetic data can be fully used to define management units. Both conservation managers and geneticists bear some blame for this common misuse of genetic data. Geneticists think of biological populations in which gene flow is sufficiently low to produce such strong genetic differences that differences important to local adaptation or the adaptive responsiveness of the species may be present, which may occur on an evolutionary time scale. Managers often think of population units that should be managed separately if fewer than tens of individuals disperse each year, but they rarely clarify this objective for geneticists. What happens if the dispersal rate relevant to the manager is greater than the "resolution" of the experiment. That is, the study fails to reject the null hypothesis (Ho) because dispersal between the subpopulations has homogenized the alleles or haplotypes to the extent that no significant differences can be detected. Does this mean that the manager can pool the subpopulations? The answer is not obvious because the question cannot be framed on the basis of population genetics. The management unit must be framed first by the management objectives. For instance, the objective of the U.S. Endangered Species Act can be interpreted to mean the preservation of evolutionarily significant units. Here, the management unit is framed predominantly by population-genetic criteria (Waples 1991). The U.S. Marine Mammal Protection Act, in contrast, has more demographically-based objectives. To paraphrase the act, populations are to be kept as significant functioning elements of their ecosystem. The management unit is defined using these objectives combined with regional, human-caused mortality rates. Once management objectives are quantitatively defined, we can estimate statistical power: the probability Paper submitted July 31, 1995; revised manuscript accepted September 19, 1995.