Fecal microbial biomarkers represent a less invasive alternative for acquiring information on wildlife populations than many traditional sampling methodologies. Our goal was to evaluate linkages between fecal microbiome communities in Rocky Mountain elk (Cervus canadensis) and four host factors including sex, age, population, and physical condition (body-fat). We paired a feature-selection algorithm with an LDAclassifier trained on elk differential bacterial abundance (16S-rRNA amplicon survey) to predict host health factors from 104 elk microbiomes across four elk populations.We validated the accuracy of the various classifier predictions with leave-one-out cross-validation using known measurements. We demonstrate that the elk fecal microbiome can predict the four host factors tested. Our results show that elk microbiomes respond to both the strong extrinsic factor of biogeography and simultaneously occurring, but more subtle, intrinsic forces of individual body-fat, sex, and age-class.

The increasing availability and complexity of next-generation sequencing (NGS) data sets make ongoing training an essential component of conservation and population genetics research. A workshop entitled “ConGen 2018” was recently held to train researchers in conceptual and practical aspects of NGS data production and analysis for conservation and ecological applications. Sixteen instructors provided helpful lectures, discussions, and hands-on exercises regarding how to plan, produce, and analyze data for many important research questions. Lecture topics ranged from understanding probabilistic (e.g., Bayesian) genotype calling to the detection of local adaptation signatures from genomic, transcriptomic, and epigenomic data. We report on progress in addressing central questions of conservation genomics, advances in NGS data analysis, the potential for genomic tools to assess adaptive capacity, and strategies for training the next generation of conservation genomicists.

Rocky Mountain elk (Cervus elaphus nelsoni) seasonal migration, body-condition and sex ratios are important parameters for characterizing elk populations but have thus far been outside the scope of non-invasive methods. Fecal microbiomes can be surveyed non-invasively from scat samples and are associated with changes in diet, stress, age, disease and physical condition of the host, as well as differences between sexes. With this in mind, we surveyed the fecal microbiome of Montana elk that varied geographically (i.e. populations), by body condition, age and by sex. Our goal was to explore an approach for evaluating linkages between the host animal and its microbiome composition, and to develop bioinformatic techniques useful for characterizing host categories and population parameters based on microbiome analysis. We built a supervised-machine learning classifier based on bacterial taxa with cross validation to predict each fecal microbiome's affiliation to known host categories. The microbiome classifier predicted host population, sex, age and body-condition with promising cross validation results. Monitoring wildlife microbiomes represents a breakthrough for non-invasive conservation biology, and we provide proof of concept for obtaining low cost, fine scale, management-relevant information from scat samples.

Understanding how different taxa respond to abiotic characteristics of the environment is of key interest for understanding the assembly of communities. Yet, whether eDNA data will suffice to accurately capture environmental imprints has been the topic of some debate. In this study, we characterised patterns of species occurrences and co-occurrences in Zackenberg in northeast Greenland using environmental DNA. To explore the potential for extracting ecological signals from eDNA data alone, we compared two approaches (visual vegetation surveys and soil eDNA metabarcoding) to describing plant communities and their responses to abiotic conditions. We then examined plant associations with microbes using a joint species distribution model. We found that most (68%) of plant genera were detectable by both vegetation surveys and eDNA signatures. Species-specific occurrence data revealed how plants, bacteria and fungi responded to their abiotic environment – with plants, bacteria and fungi all responding similarly to soil moisture. Nonetheless, a large proportion of fungi decreased in occurrences with increasing soil temperature. Regarding biotic associations, the nature and proportion of the plant-microbe associations detected were consistent between plant data identified via vegetation surveys and eDNA. Of pairs of plants and microbe genera showing statistically supported associations (while accounting for joint responses to the environment), plants and bacteria mainly showed negative associations, whereas plants and fungi mainly showed positive associations. Ample ecological signals detected by both vegetation surveys and by eDNA-based methods and a general correspondence in biotic associations inferred by both methods, suggested that purely eDNA-based approaches constitute a promising and easily applicable tool for studying plant-soil microbial associations in the Arctic and elsewhere.