Vocal convergence and social proximity shape the calls of the most basal Passeriformes, New Zealand Wrens.

Despite extensive research on avian vocal learning, we still lack a general understanding of how and when this ability evolved in birds. As the closest living relatives of the earliest Passeriformes, the New Zealand wrens (Acanthisitti) hold a key phylogenetic position for furthering our understanding of the evolution of vocal learning because they share a common ancestor with two vocal learners: oscines and parrots. However, the vocal learning abilities of New Zealand wrens remain unexplored. Here, we test for the presence of prerequisite behaviors for vocal learning in one of the two extant species of New Zealand wrens, the rifleman (Acanthisitta chloris). We detect the presence of unique individual vocal signatures and show how these signatures are shaped by social proximity, as demonstrated by group vocal signatures and strong acoustic similarities among distantly related individuals in close social proximity. Further, we reveal that rifleman calls share similar phenotypic variance ratios to those previously reported in the learned vocalizations of the zebra finch, Taeniopygia guttata. Together these findings provide strong evidence that riflemen vocally converge, and though the mechanism still remains to be determined, they may also suggest that this vocal convergence is the result of rudimentary vocal learning abilities.

Microbiology of Big Soda Lake, a multi-extreme meromictic volcanic crater lake in the Nevada desert.

Big Soda Lake, Nevada, is a multi-extreme meromictic lake, whose hypersaline hyperalkaline bottom waters feature permanent anoxia and high concentrations of arsenic, sulphide and ammonia. These properties make Big Soda Lake-and the adjacent Little Soda Lake-a fascinating system for exploring life's boundaries, discovering novel microbial taxa and identifying biotechnologically useful strains. To date, the taxonomic diversity and metabolic capabilities of microorganisms in this system remain largely unknown. Here, we fill this gap using microbiome surveys across the Big and Little Soda Lake water columns, including 16S rRNA sequencing, fungal ITS2 sequencing and gene- and genome-resolved metagenomics. We accompany these surveys with measurements of salinity, pH, temperature, oxygen, ammonium and ammonia concentrations. Our analyses reveal rich bacterial communities, taxonomically and functionally differentiated along Big Soda Lake's oxycline and, to lesser extent, between lakes. Fungal communities were dominated by a small number of families, while nearly no archaea were detected. Pathways related to perchlorate reduction, anoxygenic phototrophy, fermentation, dissimilatory metabolism of arsenite/arsenate, sulphur compounds, nitrogen compounds and hydrogen, were particularly prevalent. A total of 129 high-quality bacterial and archaeal metagenome-assembled genomes (completeness ≥ 80%, contamination ≤ 5%) were recovered, yielding insight into the taxonomic distribution of microbial metabolic pathways.

Trait biases in microbial reference genomes.

Common culturing techniques and priorities bias our discovery towards specific traits that may not be representative of microbial diversity in nature. So far, these biases have not been systematically examined. To address this gap, here we use 116,884 publicly available metagenome-assembled genomes (MAGs, completeness ≥80%) from 203 surveys worldwide as a culture-independent sample of bacterial and archaeal diversity, and compare these MAGs to the popular RefSeq genome database, which heavily relies on cultures. We compare the distribution of 12,454 KEGG gene orthologs (used as trait proxies) in the MAGs and RefSeq genomes, while controlling for environment type (ocean, soil, lake, bioreactor, human, and other animals). Using statistical modeling, we then determine the conditional probabilities that a species is represented in RefSeq depending on its genetic repertoire. We find that the majority of examined genes are significantly biased for or against in RefSeq. Our systematic estimates of gene prevalences across bacteria and archaea in nature and gene-specific biases in reference genomes constitutes a resource for addressing these issues in the future.

Transport-limited reactions in microbial systems.

Predicting microbial metabolic rates and emergent biogeochemical fluxes remains challenging due to the many unknown population dynamical, physiological and reaction-kinetic parameters and uncertainties in species composition. Here, we show that the need for these parameters can be eliminated when population dynamics and reaction kinetics operate at much shorter time scales than physical mixing processes. Such scenarios are widespread in poorly mixed water columns and sediments. In this 'fast-reaction-transport' (FRT) limit, all that is required for predictions are chemical boundary conditions, the physical mixing processes and reaction stoichiometries, while no knowledge of species composition, physiology or population/reaction kinetic parameters is needed. Using time-series data spanning years 2001-2014 and depths 180-900 m across the permanently anoxic Cariaco Basin, we demonstrate that the FRT approach can accurately predict the dynamics of major electron donors and acceptors (Pearson r ≥ 0.9 in all cases). Hence, many microbial processes in this system are largely transport limited and thus predictable regardless of species composition, population dynamics and kinetics. Our approach enables predictions for many systems in which microbial community dynamics and kinetics are unknown. Our findings also reveal a mechanism for the frequently observed decoupling between function and taxonomy in microbial systems.

The scaling of diversification rates with age is likely explained by sampling bias.

Numerous phylogenetic studies reported the existence of a pervasive scaling relationship between the ages of extant eukaryotic clades and their estimated diversification rates. The causes of this age-rate-scaling (ARS), whether biological and/or artifactual, remain unresolved. Here we fit diversification models to thousands of eukaryotic time-calibrated phylogenies to explore multiple potential causes of the ARS including parameter non-identifiability, model inadequacy, biases in taxonomic practice, and an important and ubiquitous form of sampling bias-preferentially analyzing larger extant clades. We distinguish between two mechanism by which such sampling biases can cause an ARS: First, by favoring clades that happen to be unusually large merely by chance (i.e., due to the stochastic nature of the cladogenic process), thus leading to rate overestimation, and second, by favoring clades that have truly higher diversification rates. We find that, of the proposed explanations, only sampling biases are likely to contribute to the observed ARS. We develop methods for fully correcting for sampling bias mechanism 1, and find that despite these corrections a substantial ARS remains. We then confirm using simulations that preferring trees with truly higher rates (mechanism 2) likely explains this residual ARS. Since we do not have a completely unbiased sample of clades, including extinct ones, for phylogenetic analyses, it is difficult to demonstrate unambiguously that sampling biases are the sole cause of the ARS. Sampling biases are, however, a parsimonious and plausible explanation for this widely observed macroevolutionary pattern, and this has implications for how we interpret the distribution of diversification rate estimates in extant clades.

Solutions in microbiome engineering: prioritizing barriers to organism establishment.

Microbiome engineering is increasingly being employed as a solution to challenges in health, agriculture, and climate. Often manipulation involves inoculation of new microbes designed to improve function into a preexisting microbial community. Despite, increased efforts in microbiome engineering inoculants frequently fail to establish and/or confer long-lasting modifications on ecosystem function. We posit that one underlying cause of these shortfalls is the failure to consider barriers to organism establishment. This is a key challenge and focus of macroecology research, specifically invasion biology and restoration ecology. We adopt a framework from invasion biology that summarizes establishment barriers in three categories: (1) propagule pressure, (2) environmental filtering, and (3) biotic interactions factors. We suggest that biotic interactions is the most neglected factor in microbiome engineering research, and we recommend a number of actions to accelerate engineering solutions.

The rates of global bacterial and archaeal dispersal.

The phylogenetic resolution at which microorganisms display geographic endemism, the rates at which they disperse at global scales, and the role of humans on global microbial dispersal are largely unknown. Answering these questions is necessary for interpreting microbial biogeography, ecology, and macroevolution and for predicting the spread of emerging pathogenic strains. To resolve these questions, I analyzed the geographic and evolutionary relationships between 36,795 bacterial and archaeal ("prokaryotic") genomes from ∼7000 locations around the world. I find clear signs of continental-scale endemism, including strong correlations between phylogenetic divergence and geographic distance. However, the phylogenetic scale at which endemism generally occurs is extremely small, and most "species" (defined by an average nucleotide identity ≥ 95%) and even closely related strains (average nucleotide identity ≥ 99.9%) are globally distributed. Human-associated lineages display faster dispersal rates than other terrestrial lineages; the average net distance between any two human-associated cell lineages diverging 50 years ago is roughly 580 km. These results suggest that many previously reported global-scale microbial biogeographical patterns are likely the result of recent or current environmental filtering rather than geographic endemism. For human-associated lineages, estimated transition rates between Europe and North America are particularly high, and much higher than for non-human associated terrestrial lineages, highlighting the role that human movement plays in global microbial dispersal. Dispersal was slowest for hot spring- and terrestrial subsurface-associated lineages, indicating that these environments may act as "isolated islands" of microbial evolution.

SARS-CoV-2 infections in 165 countries over time.

BACKGROUND: Understanding the dynamics of the COVID-19 pandemic and evaluating the efficacy of control measures requires knowledge of the number of infections over time. This number, however, often differs from the number of confirmed cases because of a large fraction of asymptomatic infections and different testing strategies. METHODS: This study uses death count statistics, age-dependent infection fatality risks, and stochastic modeling to estimate the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections among adults (aged 20 years or older) in 165 countries over time, from early 2020 until June 25, 2021. The accuracy of the approach is confirmed through comparison with previous nationwide seroprevalence surveys. RESULTS: The estimates presented reveal that the fraction of infections that are detected vary widely over time and between countries, and hence confirmed cases alone often yield a false picture of the pandemic. As of June 25, 2021, the nationwide cumulative fraction of SARS-CoV-2 infections (cumulative infections relative to population size) was estimated as 98% (95% confidence interval [CI] 93-100%) for Peru, 83% (95% CI 61-94%) for Brazil, and 36% (95% CI 23-61%) for the United States. CONCLUSIONS: The time-resolved estimates presented expand the possibilities to study the factors that influenced and still influence the pandemic's progression in 165 countries.

Response to "Vast (but avoidable) underestimation of global biodiversity".

This Formal Comment provides clarifications on the authors' recent estimates of global bacterial diversity and the current status of the field, and responds to a Formal Comment from John Wiens regarding their prior work.

Unifying Phylogenetic Birth-Death Models in Epidemiology and Macroevolution.

Birth-death stochastic processes are the foundations of many phylogenetic models and are widely used to make inferences about epidemiological and macroevolutionary dynamics. There are a large number of birth-death model variants that have been developed; these impose different assumptions about the temporal dynamics of the parameters and about the sampling process. As each of these variants was individually derived, it has been difficult to understand the relationships between them as well as their precise biological and mathematical assumptions. Without a common mathematical foundation, deriving new models is nontrivial. Here, we unify these models into a single framework, prove that many previously developed epidemiological and macroevolutionary models are all special cases of a more general model, and illustrate the connections between these variants. This unification includes both models where the process is the same for all lineages and those in which it varies across types. We also outline a straightforward procedure for deriving likelihood functions for arbitrarily complex birth-death(-sampling) models that will hopefully allow researchers to explore a wider array of scenarios than was previously possible. By rederiving existing single-type birth-death sampling models, we clarify and synthesize the range of explicit and implicit assumptions made by these models. [Birth-death processes; epidemiology; macroevolution; phylogenetics; statistical inference.].

Why extinction estimates from extant phylogenies are so often zero.

Time-calibrated phylogenies of extant species ("extant timetrees") are widely used to estimate historical speciation and extinction rates by fitting stochastic birth-death models.1 These approaches have long been controversial, as many phylogenetic studies report zero extinction in many taxa, contradicting the high extinction rates seen in the fossil record and the fact that the majority of species ever to have existed are now extinct.2-9 To date, the causes of this discrepancy remain unresolved. Here, we provide a novel and simple explanation for these "zero-inflated" extinction estimates, based on the recent discovery that there exist many alternative "congruent" diversification scenarios that cannot be distinguished based solely on extant timetrees.10 Due to such congruencies, estimation methods tend to converge to some scenario congruent to (i.e., statistically indistinguishable from) the true diversification scenario, but not necessarily to the true diversification scenario itself. This congruent scenario may exhibit negative extinction rates, a biologically meaningless but mathematically feasible situation, in which case estimators will tend to stick to the boundary of zero extinction. Based on this explanation, we make multiple testable predictions, which we confirm using analyses of simulated trees and 121 empirical trees. In contrast to other proposed mechanisms for erroneous extinction rate estimates,5,11-14 our proposed mechanism specifically explains the zero inflation of previous extinction rate estimates in the absence of detectable model violations, even for large trees. Not only do our results likely resolve a long-standing mystery in phylogenetics, they demonstrate that model congruencies can have severe consequences in practice.

Fundamental Identifiability Limits in Molecular Epidemiology.

Viral phylogenies provide crucial information on the spread of infectious diseases, and many studies fit mathematical models to phylogenetic data to estimate epidemiological parameters such as the effective reproduction ratio (Re) over time. Such phylodynamic inferences often complement or even substitute for conventional surveillance data, particularly when sampling is poor or delayed. It remains generally unknown, however, how robust phylodynamic epidemiological inferences are, especially when there is uncertainty regarding pathogen prevalence and sampling intensity. Here, we use recently developed mathematical techniques to fully characterize the information that can possibly be extracted from serially collected viral phylogenetic data, in the context of the commonly used birth-death-sampling model. We show that for any candidate epidemiological scenario, there exists a myriad of alternative, markedly different, and yet plausible "congruent" scenarios that cannot be distinguished using phylogenetic data alone, no matter how large the data set. In the absence of strong constraints or rate priors across the entire study period, neither maximum-likelihood fitting nor Bayesian inference can reliably reconstruct the true epidemiological dynamics from phylogenetic data alone; rather, estimators can only converge to the "congruence class" of the true dynamics. We propose concrete and feasible strategies for making more robust epidemiological inferences from viral phylogenetic data.

Phylogeographic Estimation and Simulation of Global Diffusive Dispersal.

The analysis of time-resolved phylogenies (timetrees) and geographic location data allows estimation of dispersal rates, for example, for invasive species and infectious diseases. Many estimation methods are based on the Brownian Motion model for diffusive dispersal on a 2D plane; however, the accuracy of these methods deteriorates substantially when dispersal occurs at global scales because spherical Brownian motion (SBM) differs from planar Brownian motion. No statistical method exists for estimating SBM diffusion coefficients from a given timetree and tip coordinates, and no method exists for simulating SBM along a given timetree. Here, I present new methods for simulating SBM along a given timetree, and for estimating SBM diffusivity from a given timetree and tip coordinates using a modification of Felsenstein's independent contrasts and maximum likelihood. My simulation and fitting methods can accommodate arbitrary time-dependent diffusivities and scale efficiently to trees with millions of tips, thus enabling new analyses even in cases where planar BM would be a sufficient approximation. I demonstrate these methods using a timetree of marine and terrestrial Cyanobacterial genomes, as well as timetrees of two globally circulating Influenza B clades. My methods are implemented in the R package "castor." [Independent contrasts; phylogenetic; random walk; simulation; spherical Brownian motion.].

Effects of forced taxonomic transitions on metabolic composition and function in microbial microcosms.

Surveys of microbial systems indicate that in many situations taxonomy and function may constitute largely independent ('decoupled') axes of variation. However, this decoupling is rarely explicitly tested experimentally, partly because it is hard to directly induce taxonomic variation without affecting functional composition. Here we experimentally evaluate this paradigm using microcosms resembling lake sediments and subjected to two different levels of salinity (0 and 19) and otherwise similar environmental conditions. We used DNA sequencing for taxonomic and functional profiling of bacteria and archaea and physicochemical measurements to monitor metabolic function, over 13 months. We found that the taxonomic composition of the saline systems gradually but strongly diverged from the fresh systems. In contrast, the metabolic composition (in terms of proportions of various genes) remained nearly identical across treatments and over time. Oxygen consumption rates and methane concentrations were substantially lower in the saline treatment, however, their similarity either increased (for oxygen) or did not change significantly (for methane) between the first and last sampling time, indicating that the lower metabolic activity in the saline treatments was directly and immediately caused by salinity rather than the gradual taxonomic divergence. Our experiment demonstrates that strong taxonomic shifts need not directly affect metabolic rates.

Extant timetrees are consistent with a myriad of diversification histories.

Time-calibrated phylogenies of extant species (referred to here as 'extant timetrees') are widely used for estimating diversification dynamics1. However, there has been considerable debate surrounding the reliability of these inferences2-5 and, to date, this critical question remains unresolved. Here we clarify the precise information that can be extracted from extant timetrees under the generalized birth-death model, which underlies most existing methods of estimation. We prove that, for any diversification scenario, there exists an infinite number of alternative diversification scenarios that are equally likely to have generated any given extant timetree. These 'congruent' scenarios cannot possibly be distinguished using extant timetrees alone, even in the presence of infinite data. Importantly, congruent diversification scenarios can exhibit markedly different and yet similarly plausible dynamics, which suggests that many previous studies may have over-interpreted phylogenetic evidence. We introduce identifiable and easily interpretable variables that contain all available information about past diversification dynamics, and demonstrate that these can be estimated from extant timetrees. We suggest that measuring and modelling these identifiable variables offers a more robust way to study historical diversification dynamics. Our findings also make it clear that palaeontological data will continue to be crucial for answering some macroevolutionary questions.

Simulating trees with millions of species.

MOTIVATION: The birth-death (BD) model constitutes the theoretical backbone of most phylogenetic tools for reconstructing speciation/extinction dynamics over time. Performing simulations of reconstructed trees (linking extant taxa) under the BD model in backward time, conditioned on the number of species sampled at present day and, in some cases, a specific time interval since the most recent common ancestor (MRCA), is needed for assessing the performance of reconstruction tools, for parametric bootstrapping and for detecting data outliers. The few simulation tools that exist scale poorly to large modern phylogenies, which can comprise thousands or even millions of tips (and rising). RESULTS: Here I present efficient software for simulating reconstructed phylogenies under time-dependent BD models in backward time, conditioned on the number of sampled species and (optionally) on the time since the MRCA. On large trees, my software is 1000-10 000 times faster than existing tools. AVAILABILITY AND IMPLEMENTATION: The presented software is incorporated into the R package 'castor', which is available on The Comprehensive R Archive Network (CRAN). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A General and Efficient Algorithm for the Likelihood of Diversification and Discrete-Trait Evolutionary Models.

As the size of phylogenetic trees and comparative data continue to grow and more complex models are developed to investigate the processes that gave rise to them, macroevolutionary analyses are becoming increasingly limited by computational requirements. Here, we introduce a novel algorithm, based on the "flow" of the differential equations that describe likelihoods along tree edges in backward time, to reduce redundancy in calculations and efficiently compute the likelihood of various macroevolutionary models. Our algorithm applies to several diversification models, including birth-death models and models that account for state- or time-dependent rates, as well as many commonly used models of discrete-trait evolution, and provides an alternative way to describe macroevolutionary model likelihoods. As a demonstration of our algorithm's utility, we implemented it for a popular class of state-dependent diversification models-BiSSE, MuSSE, and their extensions to hidden-states. Our implementation is available through the R package $\texttt{castor}$. We show that, for these models, our algorithm is one or more orders of magnitude faster than existing implementations when applied to large phylogenies. Our algorithm thus enables the fitting of state-dependent diversification models to modern massive phylogenies with millions of tips and may lead to potentially similar computational improvements for many other macroevolutionary models.

Microbial metabolite fluxes in a model marine anoxic ecosystem.

Permanently anoxic regions in the ocean are widespread and exhibit unique microbial metabolic activity exerting substantial influence on global elemental cycles and climate. Reconstructing microbial metabolic activity rates in these regions has been challenging, due to the technical difficulty of direct rate measurements. In Cariaco Basin, which is the largest permanently anoxic marine basin and an important model system for geobiology, long-term monitoring has yielded time series for the concentrations of biologically important compounds; however, the underlying metabolite fluxes remain poorly quantified. Here, we present a computational approach for reconstructing vertical fluxes and in situ net production/consumption rates from chemical concentration data, based on a 1-dimensional time-dependent diffusive transport model that includes adaptive penalization of overfitting. We use this approach to estimate spatiotemporally resolved fluxes of oxygen, nitrate, hydrogen sulfide, ammonium, methane, and phosphate within the sub-euphotic Cariaco Basin water column (depths 150-900 m, years 2001-2014) and to identify hotspots of microbial chemolithotrophic activity. Predictions of the fitted models are in excellent agreement with the data and substantially expand our knowledge of the geobiology in Cariaco Basin. In particular, we find that the diffusivity, and consequently fluxes of major reductants such as hydrogen sulfide, and methane, is about two orders of magnitude greater than previously estimated, thus resolving a long-standing apparent conundrum between electron donor fluxes and measured dark carbon assimilation rates.

Circumventing kinetics in biogeochemical modeling.

Microbial metabolism drives biogeochemical fluxes in virtually every ecosystem. Modeling these fluxes is challenged by the incredible diversity of microorganisms, whose kinetic parameters are largely unknown. In poorly mixed systems, such as stagnant water columns or sediments, however, long-term bulk microbial metabolism may become limited by physical transport rates of substrates across space. Here we mathematically show that under these conditions, biogeochemical fluxes are largely predictable based on the system's transport properties, chemical boundary conditions, and the stoichiometry of metabolic pathways, regardless of the precise kinetics of the resident microorganisms. We formalize these considerations into a predictive modeling framework and demonstrate its use for the Cariaco Basin subeuphotic zone, one of the largest anoxic marine basins worldwide. Using chemical concentration data solely from the upper boundary (depth 180 m) and lower boundary (depth 900 m), but without a priori knowledge of metabolite fluxes, chemical depth profiles, kinetic parameters, or microbial species composition, we predict the concentrations and vertical fluxes of biologically important substances, including oxygen, nitrate, hydrogen sulfide, and ammonium, across the entire considered depth range (180-900 m). Our predictions largely agree with concentration measurements over a period of 14 years ([Formula: see text] = 0.78-0.92) and become particularly accurate during a period where the system was near biogeochemical steady state (years 2007-2009, [Formula: see text] = 0.86-0.95). Our work enables geobiological predictions for a large class of ecosystems without knowledge of kinetic parameters or geochemical depth profiles. Conceptually, our work provides a possible explanation for the decoupling between microbial species composition and bulk metabolic function, observed in various ecosystems.

A census-based estimate of Earth's bacterial and archaeal diversity.

The global diversity of Bacteria and Archaea, the most ancient and most widespread forms of life on Earth, is a subject of intense controversy. This controversy stems largely from the fact that existing estimates are entirely based on theoretical models or extrapolations from small and biased data sets. Here, in an attempt to census the bulk of Earth's bacterial and archaeal ("prokaryotic") clades and to estimate their overall global richness, we analyzed over 1.7 billion 16S ribosomal RNA amplicon sequences in the V4 hypervariable region obtained from 492 studies worldwide, covering a multitude of environments and using multiple alternative primers. From this data set, we recovered 739,880 prokaryotic operational taxonomic units (OTUs, 16S-V4 gene clusters at 97% similarity), a commonly used measure of microbial richness. Using several statistical approaches, we estimate that there exist globally about 0.8-1.6 million prokaryotic OTUs, of which we recovered somewhere between 47%-96%, representing >99.98% of prokaryotic cells. Consistent with this conclusion, our data set independently "recaptured" 91%-93% of 16S sequences from multiple previous global surveys, including PCR-independent metagenomic surveys. The distribution of relative OTU abundances is consistent with a log-normal model commonly observed in larger organisms; the total number of OTUs predicted by this model is also consistent with our global richness estimates. By combining our estimates with the ratio of full-length versus partial-length (V4) sequence diversity in the SILVA sequence database, we further estimate that there exist about 2.2-4.3 million full-length OTUs worldwide. When restricting our analysis to the Americas, while controlling for the number of studies, we obtain similar richness estimates as for the global data set, suggesting that most OTUs are globally distributed. Qualitatively similar results are also obtained for other 16S similarity thresholds (90%, 95%, and 99%). Our estimates constrain the extent of a poorly quantified rare microbial biosphere and refute recent predictions that there exist trillions of prokaryotic OTUs.