BackgroundCircumcision is associated with significant reductions in HIV, HSV-2 and HPV infections among men and significant reductions in bacterial vaginosis among their female partners.Methodology/Principal FindingsWe assessed the penile (coronal sulci) microbiota in 12 HIV-negative Ugandan men before and after circumcision. Microbiota were characterized using sequence-tagged 16S rRNA gene pyrosequencing targeting the V3–V4 hypervariable regions. Taxonomic classification was performed using the RDP Naïve Bayesian Classifier. Among the 42 unique bacterial families identified, Pseudomonadaceae and Oxalobactericeae were the most abundant irrespective of circumcision status. Circumcision was associated with a significant change in the overall microbiota (PerMANOVA p = 0.007) and with a significant decrease in putative anaerobic bacterial families (Wilcoxon Signed-Rank test p = 0.014). Specifically, two families—Clostridiales Family XI (p = 0.006) and Prevotellaceae (p = 0.006)—were uniquely abundant before circumcision. Within these families we identified a number of anaerobic genera previously associated with bacterial vaginosis including: Anaerococcus spp., Finegoldia spp., Peptoniphilus spp., and Prevotella spp. Conclusions/SignificanceThe anoxic microenvironment of the subpreputial space may support pro-inflammatory anaerobes that can activate Langerhans cells to present HIV to CD4 cells in draining lymph nodes. Thus, the reduction in putative anaerobic bacteria after circumcision may play a role in protection from HIV and other sexually transmitted diseases.

24Indicator species (IS) are used to monitor environmental changes, assess the efficacy of 25 management, and provide warning signals for impending ecological shifts. Though widely 26 adopted in recent years by ecologists, conservation biologists, and environmental practitioners, 27 the use of IS has been criticized for several reasons, notably the lack of justification behind the 28 choice of any given indicator. In this review, we assess how ecologists have selected, used, and 36"bioindicator", and "biomonitor," but these and other terms often were not clearly defined.

Background: Although pelagic seabirds are broadly recognised as indicators of the health of marine systems, numerous gaps exist in knowledge of their at-sea distributions at the species level. These gaps have profound negative impacts on the robustness of marine conservation policies. Correlative modelling techniques have provided some information, but few studies have explored model development for non-breeding pelagic seabirds. Here, I present a first phase in developing robust niche models for highly mobile species as a baseline for further development. Methodology: Using observational data from a 12-year time period, 217 unique model parameterisations across three correlative modelling algorithms (boosted regression trees, Maxent and minimum volume ellipsoids) were tested in a time-averaged approach for their ability to recreate the at-sea distribution of non-breeding Wandering Albatrosses (Diomedea exulans) to provide a baseline for further development. Principle Findings/Results: Overall, minimum volume ellipsoids outperformed both boosted regression trees and Maxent. However, whilst the latter two algorithms generally overfit the data, minimum volume ellipsoids tended to underfit the data. Conclusions: The results of this exercise suggest a necessary evolution in how correlative modelling for highly mobile species such as pelagic seabirds should be approached. These insights are crucial for understanding seabird-environment interactions at macroscales, which can facilitate the ability to address population declines and inform effective marine conservation policy in the wake of rapid global change. ABSTRACT Boosted regression trees; digital accessible knowledge; distribution modelling; Maxent; minimum volume ellipsoids; pelagic seabird distribution; Diomedea exulans KEYWORDS

Aim Intercomparison of mechanistic and empirical models is an important step towards improving projections of potential species distribution and abundance. We aim to compare suitability and productivity estimates for a well‐understood crop species to evaluate the strengths and weaknesses of mechanistic versus empirical modelling. Location South Africa. Methods We compared four habitat suitability models for dryland maize based on climate and soil predictors. Two were created using maximum entropy (MAXENT), the first based on national crop distribution points and the second based only on locations with high productivity. The third approach used a generalized additive model (GAM) trained with continuous productivity data derived from the satellite normalized difference vegetation index (NDVI). The fourth model was a mechanistic crop growth model (DSSAT) made spatially explicit. We tested model accuracy by comparing the results with observed productivity derived from MODIS NDVI and with observed suitability based on the current spatial distribution of maize crop fields. Results The GAM and DSSAT results were linearly correlated to NDVI‐measured yield (R2 = 0.75 and 0.37, respectively). MAXENT suitability values were not linearly related to yield (R2 = 0.08); however, a MAXENT model based on occurrences of high‐productivity maize was linearly related to yield (R2 = 0.62). All models produced crop suitability maps of similarly good accuracy (Kappa = 0.73–75). Main conclusions These findings suggest that empirical models can achieve the same or better accuracy as mechanistic models for predicting both suitability (i.e. species range) and productivity (i.e. species abundance). While MAXENT could not predict productivity across the species range when trained on all occurrences, it could when trained with a high‐productivity subset, suggesting that ecological niche models can be adjusted to better correlate with species abundance.

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Summary1. Network analysis is a useful approach for investigating complex and relational data in many fields including ecology, molecular and evolutionary biology. 2. Here, we introduce enaR , an R package for Ecosystem Network Analysis (ENA). ENA is an analytical tool set rooted in ecosystem ecology with over 30 years of development that examines the structure and dynamics of matter and energy movement between discrete ecological compartments (e.g. a food web). 3. In addition to describing the primary functionality of the package, we highlight several features including a library of 100 empirical ecosystem models, the ability to analyse and compare multiple models simultaneously, and connections to other ecological network analysis tools in R.

Summary 1.Community genetics studies frequently focus on individual communities associated with individual plant genotypes, but little is known about the genetically based relationships among taxonomically and spatially disparate communities. We integrate studies of a wide range of communities living on the same plant genotypes to understand how the ecological and evolutionary dynamics of one community may be constrained or modulated by its underlying genetic connections to another community. 2. We use pre-existing data sets collected from Populus angustifolia (narrowleaf cottonwood) growing in a common garden to test the hypothesis that the composition of pairs of distinct communities (e.g. endophytes, pathogens, lichens, arthropods, soil microbes) covary across tree genotypes, such that individual plant genotypes that support a unique composition of one community are more likely to support a unique composition of another community. We then evaluate the hypotheses that physical proximity, taxonomic similarity, time between sampling (time attenuation), and interacting foundation species within communities explain the strength of correlations. 3. Three main results emerged. First, Mantel tests between communities revealed moderate to strong (q = 0.25-0.85) community-genetic correlations in almost half of the comparisons; correlations among phyllosphere endophyte, pathogen and arthropod communities were the most robust. Secondly, physical proximity determined the strength of community-genetic correlations, supporting a physical proximity hypothesis. Thirdly, consistent with the interacting foundation species hypothesis, the most abundant species drove many of the stronger correlations. Other hypotheses were not supported. 4. Synthesis. The field of community genetics demonstrates that the structure of communities varies among plant genotypes; our results add to this field by showing that disparate communities covary among plant genotypes. Eco-evolutionary dynamics between plants and their associated organisms may therefore be mediated by the shared connections of different communities to plant genotype, indicating that the organization of biodiversity in this system is genetically based and non-neutral.

Abstract. Network ecology provides a systems basis for approaching ecological questions, such as factors that influence biological diversity, the role of particular species or particular traits in structuring ecosystems, and long-term ecological dynamics (e.g., stability). Whereas the introduction of network theory has enabled ecologists to quantify not only the degree, but also the architecture of ecological complexity, these advances have come at the cost of introducing new challenges, including new theoretical concepts and metrics, and increased data complexity and computational intensity. Synthesizing recent developments in the network ecology literature, we point to several potential solutions to these issues: integrating network metrics and their terminology across sub-disciplines; benchmarking new network algorithms and models to increase mechanistic understanding; and improving tools for sharing ecological network research, in particular "model" data provenance, to increase the reproducibility of network models and analyses. We propose that applying these solutions will aid in synthesizing ecological sub-disciplines and allied fields by improving the accessibility of network methods and models.