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Pathogen interactions arising during coinfection can exacerbate disease severity, for example when the immune response mounted against one pathogen negatively affects defense of another. It is also possible that host immune responses to a pathogen, shaped by historical evolutionary interactions between host and pathogen, may modify host immune defenses in ways that have repercussions for other pathogens. In this case, negative interactions between two pathogens could emerge even in the absence of concurrent infection. Parasitic worms and tuberculosis (TB) are involved in one of the most geographically extensive of pathogen interactions, and during coinfection worms can exacerbate TB disease outcomes. Here, we show that in a wild mammal natural resistance to worms affects bovine tuberculosis (BTB) severity independently of active worm infection. We found that worm-resistant individuals were more likely to die of BTB than were nonresistant individuals, and their disease progressed more quickly. Anthelmintic treatment moderated, but did not eliminate, the resistance effect, and the effects of resistance and treatment were opposite and additive, with untreated, resistant individuals experiencing the highest mortality. Furthermore, resistance and anthelmintic treatment had nonoverlapping effects on BTB pathology. The effects of resistance manifested in the lungs (the primary site of BTB infection), while the effects of treatment manifested almost entirely in the lymph nodes (the site of disseminated disease), suggesting that resistance and active worm infection affect BTB progression via distinct mechanisms. Our findings reveal that interactions between pathogens can occur as a consequence of processes arising on very different timescales.

 

ABSTRACT Aquatic insects cope with hypoxia and anoxia using a variety of behavioral and physiological responses. Most stoneflies (Plecoptera) occur in highly oxygenated surface waters, but some species live underground in alluvial aquifers containing heterogeneous oxygen concentrations. Aquifer stoneflies appear to be supported by methane-derived food resources, which they may exploit using anoxia-resistant behaviors. We documented dissolved oxygen dynamics and collected stoneflies over 5 years in floodplain wells of the Flathead River, Montana. Hypoxia regularly occurred in two wells, and nymphs of Paraperla frontalis were collected during hypoxic periods. We measured mass-specific metabolic rates (MSMRs) at different oxygen concentrations (12, 8, 6, 4, 2, 0.5 mg l −1 , and during recovery) for 111 stonefly nymphs to determine whether aquifer and benthic taxa differed in hypoxia tolerance. Metabolic rates of aquifer taxa were similar across oxygen concentrations spanning 2 to 12 mg l −1 ( P >0.437), but the MSMRs of benthic taxa dropped significantly with declining oxygen ( P <0.0001; 2.9-times lower at 2 vs. 12 mg l −1 ). Aquifer taxa tolerated short-term repeated exposure to extreme hypoxia surprisingly well (100% survival), but repeated longer-term (>12 h) exposures resulted in lower survival (38–91%) and lower MSMRs during recovery. Our work suggests that aquifer stoneflies have evolved a remarkable set of behavioral and physiological adaptations that allow them to exploit the unique food resources available in hypoxic zones. These adaptations help to explain how large-bodied consumers might thrive in the underground aquifers of diverse and productive river floodplains. 

Abstract Biodiversity projections with uncertainty estimates under different climate, land-use, and policy scenarios are essential to setting and achieving international targets to mitigate biodiversity loss. Evaluating and improving biodiversity predictions to better inform policy decisions remains a central conservation goal and challenge. A comprehensive strategy to evaluate and reduce uncertainty of model outputs against observed measurements and multiple models would help to produce more robust biodiversity predictions. We propose an approach that integrates biodiversity models and emerging remote sensing and in-situ data streams to evaluate and reduce uncertainty with the goal of improving policy-relevant biodiversity predictions. In this article, we describe a multivariate approach to directly and indirectly evaluate and constrain model uncertainty, demonstrate a proof of concept of this approach, embed the concept within the broader context of model evaluation and scenario analysis for conservation policy, and highlight lessons from other modeling communities. 

Abstract Estimation of the effective number of breeders per reproductive event (Nb) using single sample DNA-marker-based methods has rapidly grown in recent years. However, estimating Nb is difficult in age-structured populations because the performance of estimators is influenced by the Nb / Ne ratio, which varies among species with different life histories. We provide a computer program, AgeStrucNb, to simulate age-structured populations (including life history) and also estimate Nb. The AgeStrucNb program is composed of 4 major components to simulate, subsample, estimate, and then visualize Nb time series data. AgeStrucNb allows users to also quantify the precision and accuracy of any set of loci or sample size to estimate Nb for many species and populations. AgeStrucNb allows users to conduct power analysis to evaluate sensitivity to detect changes in Nb or the power to detect a correlation between trends in Nb and environmental variables (e.g., temperature, habitat quality, predator or pathogen abundance) that could be driving changes in Nb. The software provides Nb estimates for empirical data sets using the LDNe (linkage disequilibrium) method, includes publication-quality output graphs, and outputs genotype files in Genepop format for use in other programs. AgeStrucNb will help advance the application of genetic markers for monitoring Nb, which will help biologists to detect population declines and growth, which is crucial for research and conservation of natural and managed populations. 

Abstract A current challenge in the fields of evolutionary, ecological, and conservation genomics is balancing production of large-scale datasets with additional training often required to handle such datasets. Thus, there is an increasing need for conservation geneticists to continually learn and train to stay up-to-date through avenues such as symposia, meetings, and workshops. The ConGen meeting is a near-annual workshop that strives to guide participants in understanding population genetics principles, study design, data processing, analysis, interpretation, and applications to real-world conservation issues. Each year of ConGen gathers a diverse set of instructors, students, and resulting lectures, hands-on sessions, and discussions. Here, we summarize key lessons learned from the 2019 meeting and more recent updates to the field with a focus on big data in conservation genomics. First, we highlight classical and contemporary issues in study design that are especially relevant to working with big datasets, including the intricacies of data filtering. We next emphasize the importance of building analytical skills and simulating data, and how these skills have applications within and outside of conservation genetics careers. We also highlight recent technological advances and novel applications to conservation of wild populations. Finally, we provide data and recommendations to support ongoing efforts by ConGen organizers and instructors—and beyond—to increase participation of underrepresented minorities in conservation and eco-evolutionary sciences. The future success of conservation genetics requires both continual training in handling big data and a diverse group of people and approaches to tackle key issues, including the global biodiversity-loss crisis. 

Environmental change is intensifying the biodiversity crisis and threatening species across the tree of life. Conservation genomics can help inform conservation actions and slow biodiversity loss. However, more training, appropriate use of novel genomic methods and communication with managers are needed. Here, we review practical guidance to improve applied conservation genomics. We share insights aimed at ensuring effectiveness of conservation actions around three themes: (1) improving pedagogy and training in conservation genomics including for online global audiences, (2) conducting rigorous population genomic analyses properly considering theory, marker types and data interpretation and (3) facilitating communication and collaboration between managers and researchers. We aim to update students and professionals and expand their conservation toolkit with genomic principles and recent approaches for conserving and managing biodiversity. The biodiversity crisis is a global problem and, as such, requires international involvement, training, collaboration and frequent reviews of the literature and workshops as we do here.