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Environmental fluctuations are becoming increasingly volatile in many ecosystems, highlighting the need to better understand how stochastic and deterministic processes shape patterns of commonness and rarity, particularly in high-diversity systems like coral reefs. Here, we analyze reef fish time-series across the Great Barrier Reef to show that approximately 75% of the variance in relative species abundance is attributable to deterministic, intrinsic species differences. Nevertheless, the relative importance of stochastic factors is markedly higher on reefs that have experienced stronger coral cover volatility. By contrast, α-diversity and species composition are independent of coral cover volatility but depend on environmental gradients. Our findings imply that increased environmental volatility on coral reefs erodes assemblage’s niche structure, an erosion that is not detectable from static measures of biodiversity.One-Sentence SummaryCoral cover volatility modulates how stochastic and deterministic processes shape commonness and rarity in coral reef fishes.

Genomic data reveal single-nucleotide polymorphisms (SNPs) that may carry information about the evolutionary history of bacteria. However, it remains unclear what inferences about selection can be made from genomic SNP data. Bacterial species are often sampled during epidemic outbreaks or within hosts during the course of chronic infections. SNPs obtained from genomic analysis of these data are not necessarily fixed. Treating them as fixed during analysis by using measures such as the ratio of nonsynonymous to synonymous evolutionary changes (dN/dS) may lead to incorrect inferences about the strength and direction of selection. In this study, we consider data from a range of whole-genome sequencing studies of bacterial pathogens and explore patterns of nonsynonymous variation to assess whether evidence of selection can be identified by investigating SNP counts alone across multiple WGS studies. We visualize these SNP data in ways that highlight their relationship to neutral baseline expectations. These neutral expectations are based on a simple model of mutation, from which we simulate SNP accumulation to investigate how SNP counts are distributed under alternative assumptions about positive and negative selection. We compare these patterns with empirical SNP data and illustrate the general difficulty of detecting positive selection from SNP data. Finally, we consider whether SNP counts observed at the between-host population level differ from those observed at the within-host level and find some evidence that suggests that dynamics across these two scales are driven by different underlying processes. IMPORTANCE Identifying selection from SNP data obtained from whole-genome sequencing studies is challenging. Some current measures used to identify and quantify selection acting on genomes rely on fixed differences; thus, these are inappropriate for SNP data where variants are not fixed. With the increase in whole-genome sequencing studies, it is important to consider SNP data in the context of evolutionary processes. How SNPs are counted and analyzed can help in understanding mutation accumulation and trajectories of strains. We developed a tool for identifying possible evidence of selection and for comparative analysis with other SNP data. We propose a model that provides a rule-of-thumb guideline and two new visualization techniques that can be used to interpret and compare SNP data. We quantify the expected proportion of nonsynonymous SNPs in coding regions under neutrality and demonstrate its use in identifying evidence of positive and negative selection from simulations and empirical data.

Background Resident soil microbiota play key roles in sustaining the core ecosystem processes of terrestrial Antarctica, often involving unique taxa with novel functional traits. However, the full scope of biodiversity and the niche-neutral processes underlying these communities remain unclear. In this study, we combine multivariate analyses, co-occurrence networks and fitted species abundance distributions on an extensive set of bacterial, micro-eukaryote and archaeal amplicon sequencing data to unravel soil microbiome patterns of nine sites across two east Antarctic regions, the Vestfold Hills and Windmill Islands. To our knowledge, this is the first microbial biodiversity report on the hyperarid Vestfold Hills soil environment. Results Our findings reveal distinct regional differences in phylogenetic composition, abundance and richness amongst microbial taxa. Actinobacteria dominated soils in both regions, yet Bacteroidetes were more abundant in the Vestfold Hills compared to the Windmill Islands, which contained a high abundance of novel phyla. However, intra-region comparisons demonstrate greater homogeneity of soil microbial communities and measured environmental parameters between sites at the Vestfold Hills. Community richness is largely driven by a variable suite of parameters but robust associations between co-existing members highlight potential interactions and sharing of niche space by diverse taxa from all three microbial domains of life examined. Overall, non-neutral processes appear to structure the polar soil microbiomes studied here, with niche partitioning being particularly strong for bacterial communities at the Windmill Islands. Eukaryotic and archaeal communities reveal weaker niche-driven signatures accompanied by multimodality, suggesting the emergence of neutrality. Conclusion We provide new information on assemblage patterns, environmental drivers and non-random occurrences for Antarctic soil microbiomes, particularly the Vestfold Hills, where basic diversity, ecology and life history strategies of resident microbiota are largely unknown. Greater understanding of these basic ecological concepts is a pivotal step towards effective conservation management.

Terrestrial Antarctica, a predominantly microbial realm, encompasses some of the most unique environments on Earth where resident soil microbiota play key roles in the sustainability and evolution of the ecosystem. Yet the fundamental ecological processes that govern the assemblage of these natural communities remain unclear. Here, we combined multivariate analyses, co-occurrence networks and fitted species abundance distributions of amplicon sequencing data to disentangle community assemblage patterns of polar soil microbiomes across two ice-free deserts (Windmill Islands and Vestfold Hills) situated along the coastline of eastern Antarctica. Our findings report that communities were predominantly structured by non-neutral processes, with niche partitioning being particularly strong for bacterial communities at the Windmill Islands. In contrast, both eukaryotic and archaeal communities exhibited multimodal distributions, indicating the potential emergence of neutrality. Between the three microbial domains, polar soil bacterial communities consistently demonstrated the greatest taxonomic diversity, estimated richness, network connectivity and linear response to contemporary environmental soil parameters. We propose that reduced niche overlap promotes greater phylogenetic diversity enabling more bacterial species to co-exist and essentially thrive under adversity. However, irrespective of overall relative abundance, consistent and robust associations between co-existing community members from all three domains of life highlights the key roles that diverse taxa play in ecosystem dynamics.SignificanceIn the face of a warming Antarctica, contemporary dynamics between polar soil microbial communities will inevitably change due to the climate-induced expansion of new ice-free areas. Increasing concern about disturbance and rapid biodiversity loss has intensified the need to better understand microbial community structure and function in high-latitude soils. We have taken an integrated approach to elucidate domain-level assemblage patterns across east Antarctic soil microbiomes. These assemblage patterns will be available to feed into policy management and conservation planning frameworks to potentially mitigate future biodiversity loss.

The extent to which populations in nature are regulated by density-dependent processes is unresolved. While experiments increasingly find evidence of strong density dependence, unmanipulated population time series yield much more ambiguous evidence of regulation, especially when accounting for effects of observation error. Here, we reexamine the evidence for density dependence in time series of population sizes in nature, by conducting an aggregate analysis of the populations in the Global Population Dynamics Database (GPDD). First, following the conventional approach, we fit a density-dependent and a density-independent variant of the Gompertz state-space model to each time series. Then, we conduct an aggregate analysis of the entire database by considering two random-effects density-dependent models that leverage information across data sets. When individual time series are tested independently, we find very little evidence for density dependence. However, in the aggregate, we find very strong evidence for density dependence, even though, paradoxically, estimated strengths of density dependence for individual time series tend to be weaker than when each individual time series is analyzed independently. Furthermore, a hierarchical model that accounts for taxonomic variation in the strength of density dependence reveals that density dependence is consistently stronger in insects and fish than in birds and mammals. Our findings resolve apparent inconsistencies between observational and experimental studies of density dependence by revealing that the observational record does indeed contain strong support for the hypothesis that density dependence is widespread in nature.

Using as example the area covered in West Africa by the countries of the Sub Regional Fisheries Commission and their EEZ, the authors describe the main characteristics of existing fisheries Information Systems (IS) and the difficulties encountered with these. The two objectives of the conservation of marine ecosystems and of good management of activities related to fishing seems to be driving the development of IS in opposite directions, respectively, toward large scales, and toward national and local scales. It appears that this divergence can be dangerous for both sides and cooperation between the two movements of developing IS must be the subject of increased attention. Solutions of a technical nature and possibilities of good institutional practices do exist and could allow this cooperation between IS to finally see the light of day.

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)-common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of "model-free bootstrap", adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods.

Biodiversity may provide insurance against ecosystem collapse by stabilizing assemblages that perform particular ecological functions (the "portfolio effect"). However, the extent to which this occurs in nature and the importance of different mechanisms that generate portfolio effects remain controversial. On coral reefs, herbivory helps maintain coral dominated states, so volatility in levels of herbivory has important implications for reef ecosystems. Here, we used an extensive time series of abundances on 35 reefs of the Great Barrier Reef of Australia to quantify the strength of the portfolio effect for herbivorous fishes. Then, we disentangled the contributions of two mechanisms that underlie it (compensatory interactions and differential responses to environmental fluctuations ["response diversity"]) by fitting a community-dynamic model that explicitly includes terms for both mechanisms. We found that portfolio effects operate strongly in herbivorous fishes, as shown by nearly independent fluctuations in abundances over time. Moreover, we found strong evidence for high response diversity, with nearly independent responses to environmental fluctuations. In contrast, we found little evidence that the portfolio effect in this system was enhanced by compensatory ecological interactions. Our results show that portfolio effects are driven principally by response diversity for herbivorous fishes on coral reefs. We conclude that portfolio effects can be very strong in nature and that, for coral reefs in particular, response diversity may help maintain herbivory above the threshold levels that trigger regime shifts.

A major ecosystem effect of biodiversity is to stabilise assemblages that perform particular functions. However, diversity–stability relationships (DSRs) are analysed using a variety of different population and community properties, most of which are adopted from theory that makes several restrictive assumptions that are unlikely to be reflected in nature. Here, we construct a simple synthesis and generalisation of previous theory for the DSR. We show that community stability is a product of two quantities: the synchrony of population fluctuations, and an average species-level population stability that is weighted by relative abundance. Weighted average population stability can be decomposed to consider effects of the mean-variance scaling of abundance, changes in mean abundance with diversity and differences in species' mean abundance in monoculture. Our framework makes explicit how unevenness in the abundances of species in real communities influences the DSR, which occurs both through effects on community synchrony, and effects on weighted average population variability. This theory provides a more robust framework for analysing the results of empirical studies of the DSR, and facilitates the integration of findings from real and model communities.

Positive density dependence (i.e., the Allee effect; AE) often has important implications for the dynamics and conservation of populations. Here, we show that density-dependent sex ratio adjustment in response to sexual selection may be a common AE mechanism. Specifically, using an analytical model we show that an AE is expected whenever one sex is more fecund than the other and sex ratio bias toward the less fecund sex increases with density. We illustrate the robustness of this pattern, using Monte Carlo simulations, against a range of body size-fecundity relationships and sex-allocation strategies. Finally, we test the model using the sex-changing polygynous reef fish Parapercis cylindrica; positive density dependence in the strength of sexual selection for male size is evidenced as the causal mechanism driving local sex ratio adjustment, hence the AE. Model application may extend to invertebrates, reptiles, birds, and mammals, in addition to over 70 reef fishes. We suggest that protected areas may often outperform harvest quotas as a conservation tool since the latter promotes population fragmentation, reduced polygyny, a balancing of the sex ratio, and hence up to a 50% decline in per capita fecundity, while the former maximizes polygyny and source-sink potential.