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Alluvial aquifers are key components of river floodplains and biodiversity worldwide, but they contain extreme environmental conditions and have limited sources of carbon for sustaining food webs. Despite this, they support abundant populations of aquifer stoneflies that have large proportions of their biomass carbon derived from methane. Methane is typically produced in freshwater ecosystems in anoxic conditions, while stoneflies (Order: Plecoptera) are thought to require highly oxygenated water. The potential importance of methane‐derived food resources raises the possibility that stonefly consumers have evolved anoxia‐resistant behaviors and physiologies. Here we tested the anoxic and hypoxic responses of 2,445 stonefly individuals in three aquifer species and nine benthic species. We conducted experimental trials in which we reduced oxygen levels, documented locomotor activity, and measured survival rates. Compared to surface‐dwelling benthic relatives, stoneflies from the alluvial aquifer on the Flathead River (Montana) performed better in hypoxic and anoxic conditions. Aquifer species sustained the ability to walk after 4–76 h of anoxia vs. 1 h for benthic species and survived on average three times longer than their benthic counterparts. Aquifer stoneflies also sustained aerobic respiration down to much lower levels of ambient oxygen. We show that aquifer taxa have gene sequences for hemocyanin, an oxygen transport respiratory protein, representing a possible mechanism for surviving low oxygen. This remarkable ability to perform well in low‐oxygen conditions is unique within the entire order of stoneflies (Plecoptera) and uncommon in other freshwater invertebrates. These results show that aquifer stoneflies can exploit rich carbon resources available in anoxic zones, which may explain their extraordinarily high abundance in gravel‐bed floodplain aquifers. These stoneflies are part of a novel food web contributing biodiversity to river floodplains.

 

New computational methods and next‐generation sequencing (NGS) approaches have enabled the use of thousands or hundreds of thousands of genetic markers to address previously intractable questions. The methods and massive marker sets present both new data analysis challenges and opportunities to visualize, understand, and apply population and conservation genomic data in novel ways. The large scale and complexity of NGS data also increases the expertise and effort required to thoroughly and thoughtfully analyze and interpret data. To aid in this endeavor, a recent workshop entitled “Population Genomic Data Analysis,” also known as “ConGen 2017,” was held at the University of Montana. The ConGen workshop brought 15 instructors together with knowledge in a wide range of topics including NGS data filtering, genome assembly, genomic monitoring of effective population size, migration modeling, detecting adaptive genomic variation, genomewide association analysis, inbreeding depression, and landscape genomics. Here, we summarize the major themes of the workshop and the important take‐home points that were offered to students throughout. We emphasize increasing participation by women in population and conservation genomics as a vital step for the advancement of science. Some important themes that emerged during the workshop included the need for data visualization and its importance in finding problematic data, the effects of data filtering choices on downstream population genomic analyses, the increasing availability of whole‐genome sequencing, and the new challenges it presents. Our goal here is to help motivate and educate a worldwide audience to improve population genomic data analysis and interpretation, and thereby advance the contribution of genomics to molecular ecology, evolutionary biology, and especially to the conservation of biodiversity.

 

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 Recent molecular analyses of transcriptome data from 94 species across 92 genera of North American Plecoptera identified the genus Kathroperla Banks, 1920 as sister group to Chloroperlidae + Perlodidae. Given that the genus Kathroperla has historically been included as a member of the family Chloroperlidae, this discovery indicated further investigation of the genus and the subfamily Paraperlinae was needed. Both transcriptome and genome sequencing datasets were generated from 32 species of the infraorder Systellognatha, including all described species of the Paraperlinae, to test the phylogenetic placement of these taxa. From these datasets, a large phylogenomic data matrix of 800 orthologous genes was produced, and multiple analyses were conducted, including both concatenated and coalescent analyses. Morphological comparisons were made among all Paraperlinae using light microscopy. All molecular results support a monophyletic Kathroperla, which is supported as sister taxon to the remaining Perloidea by five of six molecular analyses. Postocular head length is determined to be a distinct morphological character of this genus. Combined molecular and morphological evidence support the designation of Kathroperlidae, fam. n., as the seventeenth family of extant Plecoptera. 

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. 

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.