A new multiplex qPCR assay to detect and differentiate big cat species in the illegal wildlife trade

All species of big cats, including tigers, cheetahs, leopards, lions, snow leopards, and jaguars, are protected under the Convention on the International Trade in Endangered Species (CITES). This is due in large part to population declines resulting from anthropogenic factors, especially poaching and the unregulated and illegal trade in pelts, bones, teeth and other products that are derived from these iconic species. To enhance and scale up monitoring for big cat products in this trade, we created a rapid multiplex qPCR test that can identify and differentiate DNA from tiger (Panthera tigris), cheetah (Acinonyx jubatus), leopard (Panthera pardus), lion (Panthera leo), snow leopard (Panthera uncia), and jaguar (Panthera onca) in wildlife products using melt curve analysis to identify each species by its unique melt peak temperature. Our results showed high PCR efficiency (> 90%), sensitivity (detection limit of 5 copies of DNA per PCR reaction) and specificity (no cross amplification between each of the 6 big cat species). When paired with a rapid (< 1 h) DNA extraction protocol that amplifies DNA from bone, teeth, and preserved skin, total test time is less than three hours. This test can be used as a screening method to improve our understanding of the scale and scope of the illegal trade in big cats and aid in the enforcement of international regulations that govern the trade in wildlife and wildlife products, both ultimately benefiting the conservation of these species worldwide.

Effects of coancestry on accuracy of individual assignments to population of origin: examples using Great Lakes lake trout (Salvelinus namaycush)

Methods for assigning individuals to population of origin are widely used in ecological genetics, resources management, and forensics. Characteristics of genetic data obtained from putative source populations that enhance accuracy of assignment are well established. How non-independence within and among unknown individuals to be classified [i.e., gene correlations within individual (inbreeding) and gene correlations among individuals within group (coancestry)] affect assignment accuracy is poorly understood. We used empirical data for six microsatellite loci and offspring from full-sib crosses of hatchery strains of lake trout (Salvelinus namaycush; Salmonidae) representing known levels of coancestry (mean theta = 0.006 and 0.06) within families to investigate how gene correlations can affect assignment. Additional simulations were conducted to further investigating the influence of allelic diversity (2, 6 or 10 alleles per locus) and inbreeding (F = 0.00, 0.05, and 0.15) on assignment accuracy for cases of low and high inter-population variance in allele frequency (mean F (st) = 0.01 and 0.1, respectively). Inbreeding had no effect on accuracy of assignments. In contrast, variance in assignment accuracy across replicated simulations, and for each empirical case study increased with increasing coancestry, reflecting non-independence of probabilities of correct assignment among members of kin groups. Empirical estimates of assignment error rates should be interpreted with caution if appreciable levels of coancestry are suspected. Additional emphasis should be placed on sampling designs (spatially and temporally) that define or minimize the potential for sampling related individuals.

Genetic variation over space and time: analyses of extinct and remnant lake trout populations in the Upper Great Lakes

Lake trout (Salvelinus namaycush) in the upper Laurentian Great Lakes of North America experienced striking reductions in abundance and distribution during the mid-twentieth century. Complete collapse of populations was documented for Lake Michigan, and a few remnant populations remained only in lakes Huron and Superior. Using DNA obtained from historical scale collections, we analysed patterns of genetic diversity at five microsatellite loci from archived historical samples representing 15 populations (range 1940-1959) and from three contemporary remnant populations across lakes Huron and Superior (total n = 893). Demographic declines in abundance and the extirpation of native lake trout populations during the past 40 years have resulted in the loss of genetic diversity between lakes owing to extirpation of Lake Michigan populations and a temporal trend for reduction in allelic richness in the populations of lakes Superior and Huron. Naturally reproducing populations in Lake Superior, which had been considered to be remnants of historical populations, and which were believed to be responsible for the resurgence of lake trout numbers and distribution, have probably been affected by hatchery supplementation.

Comparisons of likelihood and machine learning methods of individual classification

Classification methods used in machine learning (e.g., artificial neural networks, decision trees, and k-nearest neighbor clustering) are rarely used with population genetic data. We compare different nonparametric machine learning techniques with parametric likelihood estimations commonly employed in population genetics for purposes of assigning individuals to their population of origin ("assignment tests"). Classifier accuracy was compared across simulated data sets representing different levels of population differentiation (low and high F(ST)), number of loci surveyed (5 and 10), and allelic diversity (average of three or eight alleles per locus). Empirical data for the lake trout (Salvelinus namaycush) exhibiting levels of population differentiation comparable to those used in simulations were examined to further evaluate and compare classification methods. Classification error rates associated with artificial neural networks and likelihood estimators were lower for simulated data sets compared to k-nearest neighbor and decision tree classifiers over the entire range of parameters considered. Artificial neural networks only marginally outperformed the likelihood method for simulated data (0-2.8% lower error rates). The relative performance of each machine learning classifier improved relative likelihood estimators for empirical data sets, suggesting an ability to "learn" and utilize properties of empirical genotypic arrays intrinsic to each population. Likelihood-based estimation methods provide a more accessible option for reliable assignment of individuals to the population of origin due to the intricacies in development and evaluation of artificial neural networks.