Neutral Markers, Quantitative Genetics and the Use of Statistics to Inform Conservation

Conservation genetics is a booming field focused on assessing the genetic structure and diversity among subpopulations of different species. However, the utilization of genetic analyses in management plans remains unclear because it is not known how often they are considered during creation of plans. Chapter one considers the question of how closely correlated the results from a genetic assessment on population structure are with the recommendations given at the conclusion of a study. Since conservation tends to have limited financial resources, it is imperative that the money spent on genetic studies is providing beneficial information for conservation. This analysis shows that genetic divergence is correlated with the recommendations, but different genetic markers (i.e. microsatellites) and divergence metrics (i.e. Fst) show different relationships between the recommendations and genetic divergence, possibly due to differences in the sample size associated with different markers.Small populations, such as those of conservation concern, that inbreed may lose alleles over time. This can be problematic if the alleles lost are beneficial, but making genetic assessments based on neutral genetic markers gives no information on the fitness of populations. Ideally, conservation programs should use quantitative genetics to assess population fitness. Chapter two looks at the change in additive genetic variance in populations following a dramatic decrease in population size (bottleneck). On average, populations experiencing a bottleneck showed an increase in additive genetic variance for populations that had levels of inbreeding equal to or less than that of sibling mating (0.25); above that a decrease in genetic variation was observed.Since genetic assessments using neutral markers can be costly, and the use of quantitative genetics may be impractical for informing conservation recommendations, chapter three looks at the use of statistical models to assess International Union for the Conservation of Nature Red list status using ecological characteristics. Here characteristics shared among species that have been assigned a threat status and those that have not been assigned a status, or those needing an update, are used in logistic regressions, regression tree analyses and discriminant function analyses to predict threat status and identify those species in most immediate need of attention. We found that logistic regression and discriminant function analysis are very good at predicting threat status. When resources are limited, using data on previously assessed species may be a good alternative to inform conservation recommendations