Journals must expand access to peer review data.

Empirical studies on peer review bias are primarily conducted by people from privileged groups and with affiliations with the journals studied. Data access is one major barrier to conducting peer review research. Accordingly, we propose pathways to broaden access to peer review data to people from more diverse backgrounds.

Integrated community models: A framework combining multispecies data sources to estimate the status, trends and dynamics of biodiversity.

Data deficiencies among rare or cryptic species preclude assessment of community-level processes using many existing approaches, limiting our understanding of the trends and stressors for large numbers of species. Yet evaluating the dynamics of whole communities, not just common or charismatic species, is critical to understanding and the responses of biodiversity to ongoing environmental pressures. A recent surge in both public science and government-funded data collection efforts has led to a wealth of biodiversity data. However, these data collection programmes use a wide range of sampling protocols (from unstructured, opportunistic observations of wildlife to well-structured, design-based programmes) and record information at a variety of spatiotemporal scales. As a result, available biodiversity data vary substantially in quantity and information content, which must be carefully reconciled for meaningful ecological analysis. Hierarchical modelling, including single-species integrated models and hierarchical community models, has improved our ability to assess and predict biodiversity trends and processes. Here, we highlight the emerging 'integrated community modelling' framework that combines both data integration and community modelling to improve inferences on species- and community-level dynamics. We illustrate the framework with a series of worked examples. Our three case studies demonstrate how integrated community models can be used to extend the geographic scope when evaluating species distributions and community-level richness patterns; discern population and community trends over time; and estimate demographic rates and population growth for communities of sympatric species. We implemented these worked examples using multiple software methods through the R platform via packages with formula-based interfaces and through development of custom code in JAGS, NIMBLE and Stan. Integrated community models provide an exciting approach to model biological and observational processes for multiple species using multiple data types and sources simultaneously, thus accounting for uncertainty and sampling error within a unified framework. By leveraging the combined benefits of both data integration and community modelling, integrated community models can produce valuable information about both common and rare species as well as community-level dynamics, allowing for holistic evaluation of the effects of global change on biodiversity.

Deep learning with citizen science data enables estimation of species diversity and composition at continental extents.

Effective solutions to conserve biodiversity require accurate community- and species-level information at relevant, actionable scales and across entire species' distributions. However, data and methodological constraints have limited our ability to provide such information in robust ways. Herein we employ a Deep-Reasoning Network implementation of the Deep Multivariate Probit Model (DMVP-DRNets), an end-to-end deep neural network framework, to exploit large observational and environmental data sets together and estimate landscape-scale species diversity and composition at continental extents. We present results from a novel year-round analysis of North American avifauna using data from over nine million eBird checklists and 72 environmental covariates. We highlight the utility of our information by identifying critical areas of high species diversity for a single group of conservation concern, the North American wood warblers, while capturing spatiotemporal variation in species' environmental associations and interspecific interactions. In so doing, we demonstrate the type of accurate, high-resolution information on biodiversity that deep learning approaches such as DMVP-DRNets can provide and that is needed to inform ecological research and conservation decision-making at multiple scales.

Peer review perpetuates barriers for historically excluded groups.

Peer review is central to the scientific process and scientists' career advancement, but bias at various stages of the review process disadvantages some authors. Here we use peer review data from 312,740 biological sciences manuscripts across 31 studies to (1) examine evidence for differential peer review outcomes based on author demographics, (2) evaluate the efficacy of solutions to reduce bias and (3) describe the current landscape of peer review policies for 541 ecology and evolution journals. We found notably worse review outcomes (for example, lower overall acceptance rates) for authors whose institutional affiliations were in Asia, for authors whose country's primary language is not English and in countries with relatively low Human Development Indices. We found few data evaluating efficacy of interventions outside of reducing gender bias through double-blind review or diversifying reviewer/editorial boards. Despite evidence for review outcome gaps based on author demographics, few journals currently implement policies intended to mitigate bias (for example, 15.9% of journals practised double-blind review and 2.03% had reviewer guidelines that mentioned social justice issues). The lack of demographic equity signals an urgent need to better understand and implement evidence-based bias mitigation strategies.

Challenges and opportunities for using natural history collections to estimate insect population trends.

Natural history collections (NHC) provide a wealth of information that can be used to understand the impacts of global change on biodiversity. As such, there is growing interest in using NHC data to estimate changes in species' distributions and abundance trends over historic time horizons when contemporary survey data are limited or unavailable. However, museum specimens were not collected with the purpose of estimating population trends and thus can exhibit spatiotemporal and collector-specific biases that can impose severe limitations to using NHC data for evaluating population trajectories. Here we review the challenges associated with using museum records to track long-term insect population trends, including spatiotemporal biases in sampling effort and sparse temporal coverage within and across years. We highlight recent methodological advancements that aim to overcome these challenges and discuss emerging research opportunities. Specifically, we examine the potential of integrating museum records and other contemporary data sources (e.g. collected via structured, designed surveys and opportunistic citizen science programs) in a unified analytical framework that accounts for the sampling biases associated with each data source. The emerging field of integrated modelling provides a promising framework for leveraging the wealth of collections data to accurately estimate long-term trends of insect populations and identify cases where that is not possible using existing data sources.

Disentangling direct and indirect effects of extreme events on coastal wetland communities.

One of the primary ways in which climate change will impact coastal freshwater wetlands is through changes in the frequency, intensity, timing and distribution of extreme weather events. Disentangling the direct and indirect mechanisms of population- and community-level responses to extreme events is vital to predicting how species composition of coastal wetlands will change under future conditions. We extended static structural equation modelling approaches to incorporate system dynamics in a multi-year multispecies occupancy model to quantify the effects of extreme weather events on a coastal freshwater wetland system. We used data from an 8-year study (2009-2016) on St. Marks National Wildlife Refuge in Florida, USA, to quantify species-specific and community-level changes in amphibian and fish occupancy associated with two flooding events in 2012 and 2013. We examine how physical changes to the landscape, including potential changes in salinity and increased wetland connectivity, may have contributed to or exacerbated the effects of these extreme weather events on the biota of isolated coastal wetlands. We provide evidence that the primary effects of flooding on the amphibian community were through indirect mechanisms via changes in the composition of the sympatric fish community that may have had lethal (i.e. through direct predation) or non-lethal (i.e. through direct or indirect competitive interactions) effects. In addition, we have shown that amphibian species differed in their sensitivity to direct flooding effects and indirect changes in the fish community and wetland-specific conductance, which led to variable responses across the community. These effects led to the overall decline in amphibian species richness from 2009 to 2016, suggesting that wetland-breeding amphibian communities on St. Marks National Wildlife Refuge may not be resilient to predicted changes in coastal disturbance regimes because of climate change. Understanding both direct and indirect effects, as well as species interactions, is important for predicting the effects of a changing climate on individual species, communities and ecosystems.

COVID-19 virtual patient cohort suggests immune mechanisms driving disease outcomes.

To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results suggest that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8+ T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings suggest biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation.

COVID-19 virtual patient cohort reveals immune mechanisms driving disease outcomes.

To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results indicate that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8 + T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation that was mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings identify biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation. AUTHOR SUMMARY: Understanding of how the immune system responds to SARS-CoV-2 infections is critical for improving diagnostic and treatment approaches. Identifying which immune mechanisms lead to divergent outcomes can be clinically difficult, and experimental models and longitudinal data are only beginning to emerge. In response, we developed a mechanistic, mathematical and computational model of the immunopathology of COVID-19 calibrated to and validated against a broad set of experimental and clinical immunological data. To study the drivers of severe COVID-19, we used our model to expand a cohort of virtual patients, each with realistic disease dynamics. Our results identify key processes that regulate the immune response to SARS-CoV-2 infection in virtual patients and suggest viable therapeutic targets, underlining the importance of a rational approach to studying novel pathogens using intra-host models.

Iterative community-driven development of a SARS-CoV-2 tissue simulator.

The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving regular refinements. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health. More broadly, this effort is creating a reusable, modular framework for studying viral replication and immune response in tissues, which can also potentially be adapted to related problems in immunology and immunotherapy.

Leveraging Computational Modeling to Understand Infectious Diseases.

PURPOSE OF REVIEW: Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation. RECENT FINDINGS: Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps. SUMMARY: Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.

Quantifying climate sensitivity and climate-driven change in North American amphibian communities.

Changing climate will impact species' ranges only when environmental variability directly impacts the demography of local populations. However, measurement of demographic responses to climate change has largely been limited to single species and locations. Here we show that amphibian communities are responsive to climatic variability, using >500,000 time-series observations for 81 species across 86 North American study areas. The effect of climate on local colonization and persistence probabilities varies among eco-regions and depends on local climate, species life-histories, and taxonomic classification. We found that local species richness is most sensitive to changes in water availability during breeding and changes in winter conditions. Based on the relationships we measure, recent changes in climate cannot explain why local species richness of North American amphibians has rapidly declined. However, changing climate does explain why some populations are declining faster than others. Our results provide important insights into how amphibians respond to climate and a general framework for measuring climate impacts on species richness.

Ecological correlates of the spatial co-occurrence of sympatric mammalian carnivores worldwide.

The composition of local mammalian carnivore communities has far-reaching effects on terrestrial ecosystems worldwide. To better understand how carnivore communities are structured, we analysed camera trap data for 108 087 trap days across 12 countries spanning five continents. We estimate local probabilities of co-occurrence among 768 species pairs from the order Carnivora and evaluate how shared ecological traits correlate with probabilities of co-occurrence. Within individual study areas, species pairs co-occurred more frequently than expected at random. Co-occurrence probabilities were greatest for species pairs that shared ecological traits including similar body size, temporal activity pattern and diet. However, co-occurrence decreased as compared to other species pairs when the pair included a large-bodied carnivore. Our results suggest that a combination of shared traits and top-down regulation by large carnivores shape local carnivore communities globally.

Use of field-portable ultrasonography reveals differences in developmental phenology and maternal egg provisioning in two sympatric viviparous snakes.

A thorough understanding of the life cycles underlying the demography of wild species is limited by the difficulty of observing hidden life-history traits, such as embryonic development. Major aspects of embryonic development, such as the rate and timing of development, and maternal-fetal interactions can be critical features of early-life fitness and may impact population trends via effects on individual survival. While information on development in wild snakes and lizards is particularly limited, the repeated evolution of viviparity and diversity of reproductive mode in this clade make it a valuable subject of study. We used field-portable ultrasonography to investigate embryonic development in two sympatric garter snake species, Thamnophis sirtalis and Thamnophis elegans in the Sierra Nevada mountains of California. This approach allowed us to examine previously hidden reproductive traits including the timing and annual variation in development and differences in parental investment in young. Both species are viviparous, occupy similar ecological niches, and experience the same annual environmental conditions. We found that T. sirtalis embryos were more developmentally advanced than T. elegans embryos during June of three consecutive years. We also found that eggs increased in volume more substantially across developmental stages in T. elegans than in T. sirtalis, indicating differences in maternal provisioning of embryos via placental transfer of water. These findings shed light on interspecific differences in parental investment and timing of development within the same environmental context and demonstrate the value of field ultrasonography for pursuing questions relating to the evolution of reproductive modes, and the ecology of development.

Correction: A clinically parameterized mathematical model of Shigella immunity to inform vaccine design.

[This corrects the article DOI: 10.1371/journal.pone.0189571.].

A clinically parameterized mathematical model of Shigella immunity to inform vaccine design.

We refine and clinically parameterize a mathematical model of the humoral immune response against Shigella, a diarrheal bacteria that infects 80-165 million people and kills an estimated 600,000 people worldwide each year. Using Latin hypercube sampling and Monte Carlo simulations for parameter estimation, we fit our model to human immune data from two Shigella EcSf2a-2 vaccine trials and a rechallenge study in which antibody and B-cell responses against Shigella's lipopolysaccharide (LPS) and O-membrane proteins (OMP) were recorded. The clinically grounded model is used to mathematically investigate which key immune mechanisms and bacterial targets confer immunity against Shigella and to predict which humoral immune components should be elicited to create a protective vaccine against Shigella. The model offers insight into why the EcSf2a-2 vaccine had low efficacy and demonstrates that at a group level a humoral immune response induced by EcSf2a-2 vaccine or wild-type challenge against Shigella's LPS or OMP does not appear sufficient for protection. That is, the model predicts an uncontrolled infection of gut epithelial cells that is present across all best-fit model parameterizations when fit to EcSf2a-2 vaccine or wild-type challenge data. Using sensitivity analysis, we explore which model parameter values must be altered to prevent the destructive epithelial invasion by Shigella bacteria and identify four key parameter groups as potential vaccine targets or immune correlates: 1) the rate that Shigella migrates into the lamina propria or epithelium, 2) the rate that memory B cells (BM) differentiate into antibody-secreting cells (ASC), 3) the rate at which antibodies are produced by activated ASC, and 4) the Shigella-specific BM carrying capacity. This paper underscores the need for a multifaceted approach in ongoing efforts to design an effective Shigella vaccine.

Species interactions and the effects of climate variability on a wetland amphibian metacommunity.

Disentangling the role that multiple interacting factors have on species responses to shifting climate poses a significant challenge. However, our ability to do so is of utmost importance to predict the effects of climate change on species distributions. We examined how populations of three species of wetland-breeding amphibians, which varied in life history requirements, responded to a six-year period of extremely variable precipitation. This interval was punctuated by both extensive drought and heavy precipitation and flooding, providing a natural experiment to measure community responses to environmental perturbations. We estimated occurrence dynamics using a discrete hidden Markov modeling approach that incorporated information regarding habitat state and predator-prey interactions. This approach allowed us to measure how metapopulation dynamics of each amphibian species was affected by interactions among weather, wetland hydroperiod, and co-occurrence with fish predators. The pig frog, a generalist, proved most resistant to perturbations, with both colonization and persistence being unaffected by seasonal variation in precipitation or co-occurrence with fishes. The ornate chorus frog, an ephemeral wetland specialist, responded positively to periods of drought owing to increased persistence and colonization rates during periods of low-rainfall. Low probabilities of occurrence of the ornate chorus frog in long-duration wetlands were driven by interactions with predators due to low colonization rates when fishes were present. The mole salamander was most sensitive to shifts in water availability. In our study area, this species never occurred in short-duration wetlands and persistence probabilities decreased during periods of drought. At the same time, negative effects occurred with extreme precipitation because flooding facilitated colonization of fishes to isolated wetlands and mole salamanders did not colonize wetlands once fishes were present. We demonstrate that the effects of changes in water availability depend on interactions with predators and wetland type and are influenced by the life history of each of our species. The dynamic species occurrence modeling approach we used offers promise for other systems when the goal is to disentangle the complex interactions that determine species responses to environmental variability.

Life history plasticity does not confer resilience to environmental change in the mole salamander (Ambystoma talpoideum).

Plasticity in life history strategies can be advantageous for species that occupy spatially or temporally variable environments. We examined how phenotypic plasticity influences responses of the mole salamander, Ambystoma talpoideum, to disturbance events at the St. Marks National Wildlife Refuge (SMNWR), FL, USA from 2009 to 2014. We observed periods of extensive drought early in the study, in contrast to high rainfall and expansive flooding events in later years. Flooding facilitated colonization of predatory fishes to isolated wetlands across the refuge. We employed multistate occupancy models to determine how this natural experiment influenced the occurrence of aquatic larvae and paedomorphic adults and what implications this may have for the population. We found that, in terms of occurrence, responses to environmental variation differed between larvae and paedomorphs, but plasticity (i.e. the ability to metamorphose rather than remain in aquatic environment) was not sufficient to buffer populations from declining as a result of environmental perturbations. Drought and fish presence negatively influenced occurrence dynamics of larval and paedomorphic mole salamanders and, consequently, contributed to observed short-term declines of this species. Overall occurrence of larval salamanders decreased from 0.611 in 2009 to 0.075 in 2014 and paedomorph occurrence decreased from 0.311 in 2009 to 0.121 in 2014. Although variation in selection pressures has likely maintained this polyphenism previously, our results suggest that continued changes in environmental variability and the persistence of fish in isolated wetlands could lead to a loss of paedomorphosis in the SMNWR population and, ultimately, impact regional persistence in the future.

A discrete stage-structured model of California newt population dynamics during a period of drought.

We introduce a mathematical model for studying the population dynamics under drought of the California newt (Taricha torosa), a species of special concern in the state of California. Since 2012, California has experienced a record-setting drought, and multiple studies predict drought conditions currently underway will persist and even increase in severity. Recent declines and local extinctions of California newt populations in Santa Monica Mountain streams motivate our study of the impact of drought on newt population sizes. Although newts are terrestrial salamanders, they migrate to streams each spring to breed and lay eggs. Since egg and larval stages occur in water, a precipitation deficit due to drought conditions reduces the space for newt egg-laying and the necessary habitat for larval development. To mathematically forecast newt population dynamics, we develop a nonlinear system of discrete equations that includes demographic parameters such as survival rates for newt life stages and egg production, which depend on habitat availability and rainfall. We estimate these demographic parameters using 15 years of stream survey data collected from Cold Creek in Los Angeles County, California, and our model captures the observed decline of the parameterized Cold Creek newt population. Based upon data analysis, we predict how the number of available newt egg-laying sites varies with annual precipitation. Our model allows us to make predictions about how the length and severity of drought can affect the likelihood of persistence and the time to critical endangerment of a local newt population. We predict that sustained severe drought will critically endanger the newt population but that the newt population can rebound if a drought is sufficiently short.

Extracellular Matrix Stiffening Induces a Malignant Phenotypic Transition in Breast Epithelial Cells.

Tumors are much stiffer than healthy tissue, and progressively stiffen as the cancer develops. Tumor stiffening is largely the result of extracellular matrix (ECM) remodeling, for example, deposition and crosslinking of collagen I. Well established in vitro models have demonstrated the influence of the microenvironment in regulating tissue homeostasis, with matrix stiffness being a particularly influential mediator. Non-malignant MCF10A mammary epithelial cells (MECs) lose their epithelial characteristics and become invasive when cultured in stiff microenvironments, leading to the hypothesis that tumor stiffening could contribute directly to disease progression. However, previous studies demonstrating MCF10A invasion have been performed in gels with constant mechanical properties, unlike the dynamically stiffening tumor microenvironment. Here, we employ a temporally stiffening hydrogel platform to demonstrate that matrix stiffening induces invasion from and proliferation in MCF10A mammary acini. After allowing MCF10A acini to form in soft hydrogels for 14 days, the gels were stiffened to the level of a malignant tumor, giving rise to a proliferative and invasive phenotype. Cells were observed to collectively migrate away from mammary acini while maintaining cell-cell contacts. Small molecule inhibition of PI3K and Rac1 pathways was sufficient to significantly reduce the number and size of invasive acini after stiffening. Our results demonstrate that temporal matrix stiffening can induce invasion from mammary acini and supports the notion that tumor stiffening could be implicated in disease progression and metastasis.