Co-habiting amphibian species harbor unique skin bacterial communities in wild populations

Although all plant and animal species harbor microbial symbionts, we know surprisingly little about the specificity of microbial communities to their hosts. Few studies have compared the microbiomes of different species of animals, and fewer still have examined animals in the wild. We sampled four pond habitats in Colorado, USA, where multiple amphibian species were present. In total, 32 amphibian individuals were sampled from three different species including northern leopard frogs (Lithobates pipiens), western chorus frogs (Pseudacris triseriata) and tiger salamanders (Ambystoma tigrinum). We compared the diversity and composition of the bacterial communities on the skin of the collected individuals via barcoded pyrosequencing of the 16S rRNA gene. Dominant bacterial phyla included Acidobacteria, Actinobacteria, Bacteriodetes, Cyanobacteria, Firmicutes and Proteobacteria. In total, we found members of 18 bacterial phyla, comparable to the taxonomic diversity typically found on human skin. Levels of bacterial diversity varied strongly across species: L. pipiens had the highest diversity; A. tigrinum the lowest. Host species was a highly significant predictor of bacterial community similarity, and co-habitation within the same pond was not significant, highlighting that the skin-associated bacterial communities do not simply reflect those bacterial communities found in their surrounding environments. Innate species differences thus appear to regulate the structure of skin bacterial communities on amphibians. In light of recent discoveries that some bacteria on amphibian skin have antifungal activity, our finding suggests that host-specific bacteria may have a role in the species-specific resistance to fungal pathogens

Capturing expert knowledge for threatened species assessments: a case study using NatureServe conservation status ranks

Assessments for assigning the conservation status of threatened species that are based purely on subjective judgements become problematic because assessments can be influenced by hidden assumptions, personal biases and perceptions of risks, making the assessment process difficult to repeat. This can result in inconsistent assessments and misclassifications, which can lead to a lack of confidence in species assessments. It is almost impossible to Understand an expert's logic or visualise the underlying reasoning behind the many hidden assumptions used throughout the assessment process. In this paper, we formalise the decision making process of experts, by capturing their logical ordering of information, their assumptions and reasoning, and transferring them into a set of decisions rules. We illustrate this through the process used to evaluate the conservation status of species under the NatureServe system (Master, 1991). NatureServe status assessments have been used for over two decades to set conservation priorities for threatened species throughout North America. We develop a conditional point-scoring method, to reflect the current subjective process. In two test comparisons, 77% of species' assessments using the explicit NatureServe method matched the qualitative assessments done subjectively by NatureServe staff. Of those that differed, no rank varied by more than one rank level under the two methods. In general, the explicit NatureServe method tended to be more precautionary than the subjective assessments. The rank differences that emerged from the comparisons may be due, at least in part, to the flexibility of the qualitative system, which allows different factors to be weighted on a species-by-species basis according to expert judgement. The method outlined in this study is the first documented attempt to explicitly define a transparent process for weighting and combining factors under the NatureServe system. The process of eliciting expert knowledge identifies how information is combined and highlights any inconsistent logic that may not be obvious in Subjective decisions. The method provides a repeatable, transparent, and explicit benchmark for feedback, further development, and improvement. (C) 2004 Elsevier SAS. All rights reserved