Differential response of subterranean microbiome to exogenous organic matter input in a cave ecosystem

As a recurrent climatic phenomenon in the context of climate change, extreme rainstorms induce vertical translocation of organic matter and increase moisture content in terrestrial ecosystems. However, it remains unclear whether heavy rainstorms can impact microbial communities in the deep biosphere by modulating organic matter input. In this study, we present findings on the different responses of bacterial and fungal communities in a subsurface cave to rainstorms and moisture variations through field surveys and microcosm experiments. During periods of rainstorms, the influx of dissolved organic matter (DOM) from soil overlying the cave into cave sediments significantly enhanced the correlation between core bacteria and environmental factors, particularly fluorescence spectral indices. The resource utilization of core bacteria was diminished, while the functional diversity of core fungi remained relatively unaltered. We also performed simulated experiments with restricted external DOM inputs, in which DOM content was observed to decrease and microbial diversity increase in response to artificially increased moisture content (MC). The niche breadth of core bacteria decreased and became more closely associated with DOM as the MC increased, while the niche breadth of core fungi remained predominantly unchanged. Compared to fungi, cave bacteria exhibited higher sensitivity towards variations in DOM. The core microbiome can efficiently utilize the available organic matter and participate in nitrogen- and sulfur-related metabolic processes. The study systematically revealed distinct microbial responses to rainstorm events, thereby providing valuable insights for future investigations into energy utilization within deep biospheres.

Three-dimensional conservation planning of fish biodiversity metrics to achieve the deep-sea 30×30 conservation target

Accelerating rate of human impact and environmental change severely affects marine biodiversity and increases the urgency to implement the Convention on Biological Diversity (CBD) 30×30 plan for conserving 30% of sea areas by 2030. However, area-based conservation targets are complex to identify in a 3-dimensional (3D) ocean where deep-sea features such as seamounts have been seldom studied mostly due to challenging methodologies to implement at great depths. Yet, the use of emerging technologies, such as environmental DNA combined with modern modeling frameworks, could help address the problem. We collected environmental DNA, echosounder acoustic, and video data at 15 seamounts and deep island slopes across the Coral Sea. We modeled 7 fish community metrics and the abundances of 45 individual species and molecular operational taxonomic units (MOTUs) in benthic and pelagic waters (down to 600-m deep) with boosted regression trees and generalized joint attribute models to describe biodiversity on seamounts and deep slopes and identify 3D protection solutions for achieving the CBD area target in New Caledonia (1.4 million km2). We prioritized the identified conservation units in a 3D space, based on various biodiversity targets, to meet the goal of protecting at least 30% of the spatial domain, with a focus on areas with high biodiversity. The relationship between biodiversity protection targets and the spatial area protected by the solution was linear. The scenario protecting 30% of each biodiversity metric preserved almost 30% of the considered spatial domain and accounted for the 3D distribution of biodiversity. Our study paves the way for the use of combined data collection methodologies to improve biodiversity estimates in 3D structured marine environments for the selection of conservation areas and for the use of biodiversity targets to achieve area-based international targets.

Inferring ecological selection from multidimensional community trait distributions along environmental gradients

Understanding the drivers of community assembly is critical for predicting the future of biodiversity and ecosystem services. Ecological selection ubiquitously shapes communities by selecting for individuals with the most suitable trait combinations. Detecting selection types on key traits across environmental gradients and over time has the potential to reveal the underlying abiotic and biotic drivers of community dynamics. Here, we present a model-based predictive framework to quantify the multidimensional trait distributions of communities (community trait spaces), which we use to identify ecological selection types shaping communities along environmental gradients. We apply the framework to over 3600 boreal forest understory plant communities with results indicating that directional, stabilizing, and divergent selection all modify community trait distributions and that the selection type acting on individual traits may change over time. Our results provide novel and rare empirical evidence for divergent selection within a natural system. Our approach provides a framework for identifying key traits under selection and facilitates the detection of processes underlying community dynamics.

Remotely sensed crown nutrient concentrations modulate forest reproduction across the contiguous United States

Global forests are increasingly lost to climate change, disturbance, and human management. Evaluating forests' capacities to regenerate and colonize new habitats has to start with the seed production of individual trees and how it depends on nutrient access. Studies on the linkage between reproduction and foliar nutrients are limited to a few locations and few species, due to the large investment needed for field measurements on both variables. We synthesized tree fecundity estimates from the Masting Inference and Forecasting (MASTIF) network with foliar nutrient concentrations from hyperspectral remote sensing at the National Ecological Observatory Network (NEON) across the contiguous United States. We evaluated the relationships between seed production and foliar nutrients for 56,544 tree-years from 26 species at individual and community scales. We found a prevalent association between high foliar phosphorous (P) concentration and low individual seed production (ISP) across the continent. Within-species coefficients to nitrogen (N), potassium (K), calcium (Ca), and magnesium (Mg) are related to species differences in nutrient demand, with distinct biogeographic patterns. Community seed production (CSP) decreased four orders of magnitude from the lowest to the highest foliar P. This first continental-scale study sheds light on the relationship between seed production and foliar nutrients, highlighting the potential of using combined Light Detection And Ranging (LiDAR) and hyperspectral remote sensing to evaluate forest regeneration. The fact that both ISP and CSP decline in the presence of high foliar P levels has immediate application in improving forest demographic and regeneration models by providing more realistic nutrient effects at multiple scales.

Diverse bacterial consortia: key drivers of rhizosoil fertility modulating microbiome functions, plant physiology, nutrition, and soybean grain yield

Soybean cultivation in tropical regions relies on symbioses with nitrogen-fixing Bradyrhizobium and plant growth-promoting bacteria (PGPBs), reducing environmental impacts of N fertilizers and pesticides. We evaluate the effects of soybean inoculation with different bacterial consortia combined with PGPBs or microbial secondary metabolites (MSMs) on rhizosoil chemistry, plant physiology, plant nutrition, grain yield, and rhizosphere microbial functions under field conditions over three growing seasons with four treatments: standard inoculation of Bradyrhizobium japonicum and Bradyrhizobium diazoefficiens consortium (SI); SI plus foliar spraying with Bacillus subtilis (SI + Bs); SI plus foliar spraying with Azospirillum brasilense (SI + Az); and SI plus seed application of MSMs enriched in lipo-chitooligosaccharides extracted from B. diazoefficiens and Rhizobium tropici (SI + MSM). Rhizosphere microbial composition, diversity, and function was assessed by metagenomics. The relationships between rhizosoil chemistry, plant nutrition, grain yield, and the abundance of microbial taxa and functions were determined by generalized joint attribute modeling. The bacterial consortia had the most significant impact on rhizosphere soil fertility, which in turn affected the bacterial community, plant physiology, nutrient availability, and production. Cluster analysis identified microbial groups and functions correlated with shifts in rhizosoil chemistry and plant nutrition. Bacterial consortia positively modulated specific genera and functional pathways involved in biosynthesis of plant secondary metabolites, amino acids, lipopolysaccharides, photosynthesis, bacterial secretion systems, and sulfur metabolism. The effects of the bacterial consortia on the soybean holobiont, particularly the rhizomicrobiome and rhizosoil fertility, highlight the importance of selecting appropriate consortia for desired outcomes. These findings have implications for microbial-based agricultural practices that enhance crop productivity, quality, and sustainability.

Dissection of rhizosphere microbiome and exploiting strategies for sustainable agriculture

The rhizosphere microbiome plays critical roles in plant growth and provides promising solutions for sustainable agriculture. While the rhizosphere microbiome frequently fluctuates with the soil environment, recent studies have demonstrated that a small proportion of the microbiome is consistently assembled in the rhizosphere of a specific plant genotype regardless of the soil condition, which is determined by host genetics. Based on these breakthroughs, which involved exploiting the plant-beneficial function of the rhizosphere microbiome, we propose to divide the rhizosphere microbiome into environment-dominated and plant genetic-dominated components based on their different assembly mechanisms. Subsequently, two strategies to explore the different rhizosphere microbiome components for agricultural production are suggested, that is, the precise management of the environment-dominated rhizosphere microbiome by agronomic practices, and the elucidation of the plant genetic basis of the plant genetic-dominated rhizosphere microbiome for breeding microbiome-assisted crop varieties. We finally present the major challenges that need to be overcome to implement strategies for modulating these two components of the rhizosphere microbiome.

Disturbance sensitivity shapes patterns of tree species distribution in Afrotropical lowland rainforests more than climate or soil

Understanding how tropical forests respond to abiotic environmental changes is critical for preserving biodiversity, mitigating climate change, and maintaining ecosystem services in the coming century. To evaluate the relative roles of the abiotic environment and human disturbance on Central African tree community composition, we employ tree inventory data, remotely sensed climatic data, and soil nutrient data collected from 30 1-ha plots distributed across a large-scale observational experiment in forests that had been differently impacted by logging and hunting in northern Republic of Congo. We show that the composition of Afrotropical plant communities at this scale responds to human disturbance more than to climate, with particular sensitivities to hunting and distance to the nearest village (a proxy for other human activities, including tree-cutting and gathering). These findings contrast neotropical predictions, highlighting the unique ecological, evolutionary, and anthropogenic history of Afrotropical forests.

The soil microbiome modulates the sorghum root metabolome and cellular traits with a concomitant reduction of Striga infection

Sorghum bicolor is among the most important cereals globally and a staple crop for smallholder farmers in sub-Saharan Africa. Approximately 20% of sorghum yield is lost annually in Africa due to infestation with the root parasitic weed Striga hermonthica. Existing Striga management strategies are not singularly effective and integrated approaches are needed. Here, we demonstrate the functional potential of the soil microbiome to suppress Striga infection in sorghum. We associate this suppression with microbiome-mediated induction of root endodermal suberization and aerenchyma formation and with depletion of haustorium-inducing factors, compounds required for the initial stages of Striga infection. We further identify specific bacterial taxa that trigger the observed Striga-suppressive traits. Collectively, our study describes the importance of the soil microbiome in the early stages of root infection by Striga and pinpoints mechanisms of Striga suppression. These findings open avenues to broaden the effectiveness of integrated Striga management practices.

Rewilding soil and litter invertebrates and fungi increases decomposition rates and alters detritivore communities

Habitat degradation and associated reductions in ecosystem functions can be reversed by reintroducing or 'rewilding' keystone species. Rewilding projects have historically targeted restoration of processes such as grazing regimes or top-down predation effects. Few projects focus on restoring decomposition efficiency, despite the pivotal role decomposition plays in global carbon sequestration and nutrient cycling. Here, we tested whether rewilding entire communities of detritivorous invertebrates and fungi can improve litter decomposition efficiency and restore detritivore communities during ecological restoration. Rewilding was conducted by transplanting leaf litter and soil, including associated invertebrate and fungal communities from species-rich remnant sites into species-poor, and geographically isolated, revegetated farmland sites in a temperate woodland region of southeastern Australia. We compared communities in sites under the following treatments: remnant (conservation area and source of litter transplant), rewilded revegetation (revegetated farmland site with litter transplant) and control revegetation (revegetated site, no transplant). In one 'before' and three 'after' sampling periods, we measured litter decomposition and the abundance and diversity of detritivorous invertebrates and fungi. We quantified the effect of detritivores on the rate of litter decomposition using piecewise Structural Equation Modelling. Decomposition was significantly faster in rewilding sites than in both control and remnant areas and was largely driven by a greater abundance of invertebrate detritivores. Similarly, the abundance of invertebrate detritivores in rewilding revegetation sites exceeded the level of remnant communities, whereas there was little difference between control and remnant sites. In contrast, rewilding did not increase saprotrophic fungi relative abundance/diversity and there was no strong relationship between decomposition and fungal diversity. Our findings suggest the relatively simple act of transplanting leaf litter and soil can increase functional efficiency during restoration and alter community composition. Our methods may prove important across a range of contexts where other restoration methods have failed to restore ecosystem processes to pre-degradation levels.

Enhancing phosphate-solubilising microbial communities through artificial selection

Microbial communities, acting as key drivers of ecosystem processes, harbour immense potential for sustainable agriculture practices. Phosphate-solubilising microorganisms, for example, can partially replace conventional phosphate fertilisers, which rely on finite resources. However, understanding the mechanisms and engineering efficient communities poses a significant challenge. In this study, we employ two artificial selection methods, environmental perturbation, and propagation, to construct phosphate-solubilising microbial communities. To assess trait transferability, we investigate the community performance in different media and a hydroponic system with Chrysanthemum indicum. Our findings reveal a distinct subset of phosphate-solubilising bacteria primarily dominated by Klebsiella and Enterobacterales. The propagated communities consistently demonstrate elevated levels of phosphate solubilisation, surpassing the starting soil community by 24.2% in activity. The increased activity of propagated communities remains consistent upon introduction into the hydroponic system. This study shows the efficacy of community-level artificial selection, particularly through propagation, as a tool for successfully modifying microbial communities to enhance phosphate solubilisation.

Straw Soil Conditioner Modulates Key Soil Microbes and Nutrient Dynamics across Different Maize Developmental Stages

Soil amendments may enhance crop yield and quality by increasing soil nutrient levels and improving nutrient absorption efficiency, potentially through beneficial microbial interactions. In this work, the effects of amending soil with straw-based carbon substrate (SCS), a novel biochar material, on soil nutrients, soil microbial communities, and maize yield were compared with those of soil amendment with conventional straw. The diversity and abundance of soil bacterial and fungal communities were significantly influenced by both the maize growth period and the treatment used. Regression analysis of microbial community variation indicated that Rhizobiales, Saccharimonadales, and Eurotiales were the bacterial and fungal taxa that exhibited a positive response to SCS amendment during the growth stages of maize. Members of these taxa break down organic matter to release nutrients that promote plant growth and yield. In the seedling and vegetative stages of maize growth, the abundance of Rhizobiales is positively correlated with the total nitrogen (TN) content in the soil. During the tasseling and physiological maturity stages of corn, the abundance of Saccharimonadales and Eurotiales is positively correlated with the content of total carbon (TC), total phosphorus (TP), and available phosphorus (AP) in the soil. The results suggest that specific beneficial microorganisms are recruited at different stages of maize growth to supply the nutrients required at each stage. This targeted recruitment strategy optimizes the availability of nutrients to plants and ultimately leads to higher yields. The identification of these key beneficial microorganisms may provide a theoretical basis for the targeted improvement of crop yield and soil quality. This study demonstrates that SCS amendment enhances soil nutrient content and crop yield compared with conventional straw incorporation and sheds light on the response of soil microorganisms to SCS amendment, providing valuable insights for the future implementation of this material.

Evolution of masting in plants is linked to investment in low tissue mortality

Masting, a variable and synchronized variation in reproductive effort is a prevalent strategy among perennial plants, but the factors leading to interspecific differences in masting remain unclear. Here, we investigate interannual patterns of reproductive investment in 517 species of terrestrial perennial plants, including herbs, graminoids, shrubs, and trees. We place these patterns in the context of the plants' phylogeny, habitat, form and function. Our findings reveal that masting is widespread across the plant phylogeny. Nonetheless, reversion from masting to regular seed production is also common. While interannual variation in seed production is highest in temperate and boreal zones, our analysis controlling for environment and phylogeny indicates that masting is more frequent in species that invest in tissue longevity. Our modeling exposes masting-trait relationships that would otherwise remain hidden and provides large-scale evidence that the costs of delayed reproduction play a significant role in the evolution of variable reproduction in plants.

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.

Joint species distribution modeling reveals a changing prey landscape for North Pacific right whales on the Bering shelf

The eastern North Pacific right whale (NPRW) is the most endangered population of whale and has been observed north of its core feeding ground in recent years with low sea ice extent. Sea ice and water temperature are important drivers for zooplankton dynamics within the whale's core feeding ground in the southeastern Bering Sea, seasonally forming stable fronts along the shelf that give rise to distinct zooplankton communities. A northward shift in NPRW distribution driven by changing distribution of prey resources could put this species at increased risk of entanglement and vessel strikes. We modeled the abundance of NPRW prey, Calanus glacialis, Neocalanus, and Thysanoessa species, using a dynamic biophysical food web model of nine zooplankton guilds in the Bering shelf zooplankton community during a period of warming (2006-2016). This model is unique among prior zooplankton studies from the region in that it includes density dependence, thereby allowing us to ask whether species interactions influence zooplankton dynamics. Modeling confirmed the importance of sea ice and ocean temperature to zooplankton dynamics in the region. Density-independent growth drove community dynamics, while dependent factors were comparatively minimal. Overall, Calanus responded to environment terms, with the strength and direction of response driven by copepodite stage. Neocalanus and Thysanoessa responses were weaker, likely due to their primary occurrence on the outer shelf. We also modeled the steady-state (equilibrium) abundance of Calanus in conditions with and without wind gusts to test whether advection of outer shelf species might disrupt the steady-state dynamics of Calanus abundance; the results did not support disruption. Given the annual fall sampling design, we interpret our results as follows: low-ice-extent winters induced stronger spring winds and weakened fronts on the shelf, thereby advecting some outer shelf species into the study region; increased development rates in these warm conditions influenced the proportion of C. glacialis copepodite stages over the season. Residual correlation suggests missing drivers, possibly predators, and phytoplankton bloom composition. Given the continued loss of sea ice in the region and projected continued warming, our findings suggest that C. glacialis will move northward, and thus, whales may move northward to continue targeting them.

A Gulf Stream frontal eddy harbors a distinct microbiome compared to adjacent waters

Mesoscale oceanographic features, including eddies, have the potential to alter productivity and other biogeochemical rates in the ocean. Here, we examine the microbiome of a cyclonic, Gulf Stream frontal eddy, with a distinct origin and environmental parameters compared to surrounding waters, in order to better understand the processes dominating microbial community assembly in the dynamic coastal ocean. Our microbiome-based approach identified the eddy as distinct from the surround Gulf Stream waters. The eddy-associated microbial community occupied a larger area than identified by temperature and salinity alone, increasing the predicted extent of eddy-associated biogeochemical processes. While the eddy formed on the continental shelf, after two weeks both environmental parameters and microbiome composition of the eddy were most similar to the Gulf Stream, suggesting the effect of environmental filtering on community assembly or physical mixing with adjacent Gulf Stream waters. In spite of the potential for eddy-driven upwelling to introduce nutrients and stimulate primary production, eddy surface waters exhibit lower chlorophyll a along with a distinct and less even microbial community, compared to the Gulf Stream. At the population level, the eddy microbiome exhibited differences among the cyanobacteria (e.g. lower Trichodesmium and higher Prochlorococcus) and in the heterotrophic alpha Proteobacteria (e.g. lower relative abundances of specific SAR11 phylotypes) versus the Gulf Stream. However, better delineation of the relative roles of processes driving eddy community assembly will likely require following the eddy and surrounding waters since inception. Additionally, sampling throughout the water column could better clarify the contribution of these mesoscale features to primary production and carbon export in the oceans.

Constructing indicator species distribution models to study the potential invasion risk of invasive plants: A case of the invasion of Parthenium hysterophorus in China

Aim

As invasive plants are often in a non-equilibrium expansion state, traditional species distribution models (SDMs) are likely underestimating their suitable habitat. New methods are necessary to identify potential invasion risk areas.

Location

Tropical monsoon rainforest and subtropical evergreen broad-leaved forest regions in China.

Methods

We took Parthenium hysterophorus as a case study to predict its potential invasion risk using climate, terrain, and human activity variables. First, a generalized joint attribute model (GJAM) was constructed using the occurrence of P. hysterophorus and its 27 closely related species in Taiwan, given it is widely distributed in Taiwan. Based on the output correlation values, two positively correlated species (Cardiospermum halicacabum and Portulaca oleracea) and one negatively correlated species (Crassocephalum crepidioides) were selected as indicator species. Second, the distributions of P. hysterophorus and its indicator species in the study area were predicted separately using an ensemble model (EM). Third, when selecting indicator species to construct indicator SDMs, two treatments (indicator species with positive correlation only, or both positive and negative correlation) were considered. The indicator species' EM predictions were overlaid using a weighted average method, and a better indicator SDMs prediction result was selected by comparison. Finally, the EM prediction result of P. hysterophorus was used to optimize the indicator SDMs result by a maximum overlay.

Results

The optimized indicator SDMs prediction showed an expanded range beyond the current geographic range compared to EM and the thresholds for predicting key environmental variables were wider. It also reinforced the human activities' influence on the potential distribution of P. hysterophorus.

Main Conclusions

For invasive plants with expanding ranges, information about indicator species distribution can be borrowed as a barometer for areas not currently invaded. The optimized indicator SDMs allow for more efficient potential invasion risk prediction. On this basis, invasive plants can be prevented earlier.

Accelerating Bayesian inference of dependency between mixed-type biological traits

Inferring dependencies between mixed-type biological traits while accounting for evolutionary relationships between specimens is of great scientific interest yet remains infeasible when trait and specimen counts grow large. The state-of-the-art approach uses a phylogenetic multivariate probit model to accommodate binary and continuous traits via a latent variable framework, and utilizes an efficient bouncy particle sampler (BPS) to tackle the computational bottleneck-integrating many latent variables from a high-dimensional truncated normal distribution. This approach breaks down as the number of specimens grows and fails to reliably characterize conditional dependencies between traits. Here, we propose an inference pipeline for phylogenetic probit models that greatly outperforms BPS. The novelty lies in 1) a combination of the recent Zigzag Hamiltonian Monte Carlo (Zigzag-HMC) with linear-time gradient evaluations and 2) a joint sampling scheme for highly correlated latent variables and correlation matrix elements. In an application exploring HIV-1 evolution from 535 viruses, the inference requires joint sampling from an 11,235-dimensional truncated normal and a 24-dimensional covariance matrix. Our method yields a 5-fold speedup compared to BPS and makes it possible to learn partial correlations between candidate viral mutations and virulence. Computational speedup now enables us to tackle even larger problems: we study the evolution of influenza H1N1 glycosylations on around 900 viruses. For broader applicability, we extend the phylogenetic probit model to incorporate categorical traits, and demonstrate its use to study Aquilegia flower and pollinator co-evolution.

Masting is uncommon in trees that depend on mutualist dispersers in the context of global climate and fertility gradients

The benefits of masting (volatile, quasi-synchronous seed production at lagged intervals) include satiation of seed predators, but these benefits come with a cost to mutualist pollen and seed dispersers. If the evolution of masting represents a balance between these benefits and costs, we expect mast avoidance in species that are heavily reliant on mutualist dispersers. These effects play out in the context of variable climate and site fertility among species that vary widely in nutrient demand. Meta-analyses of published data have focused on variation at the population scale, thus omitting periodicity within trees and synchronicity between trees. From raw data on 12 million tree-years worldwide, we quantified three components of masting that have not previously been analysed together: (i) volatility, defined as the frequency-weighted year-to-year variation; (ii) periodicity, representing the lag between high-seed years; and (iii) synchronicity, indicating the tree-to-tree correlation. Results show that mast avoidance (low volatility and low synchronicity) by species dependent on mutualist dispersers explains more variation than any other effect. Nutrient-demanding species have low volatility, and species that are most common on nutrient-rich and warm/wet sites exhibit short periods. The prevalence of masting in cold/dry sites coincides with climatic conditions where dependence on vertebrate dispersers is less common than in the wet tropics. Mutualist dispersers neutralize the benefits of masting for predator satiation, further balancing the effects of climate, site fertility and nutrient demands.

Long-term impacts of an urban sanitation intervention on enteric pathogens in children in Maputo city, Mozambique: study protocol for a cross-sectional follow-up to the Maputo Sanitation (MapSan) trial 5 years postintervention

INTRODUCTION

We previously assessed the effect of an onsite sanitation intervention in informal neighbourhoods of urban Maputo, Mozambique on enteric pathogen detection in children after 2 years of follow-up (Maputo Sanitation (MapSan) trial, ClinicalTrials.gov: NCT02362932). We found significant reductions in Shigella and Trichuris prevalence but only among children born after the intervention was delivered. In this study, we assess the health impacts of the sanitation intervention after 5 years among children born into study households postintervention.

METHODS AND ANALYSIS

We are conducting a cross-sectional household study of enteric pathogen detection in child stool and the environment at compounds (household clusters sharing sanitation and outdoor living space) that received the pour-flush toilet and septic tank intervention at least 5 years prior or meet the original criteria for trial control sites. We are enrolling at least 400 children (ages 29 days to 60 months) in each treatment arm. Our primary outcome is the prevalence of 22 bacterial, protozoan, and soil transmitted helminth enteric pathogens in child stool using the pooled prevalence ratio across the outcome set to assess the overall intervention effect. Secondary outcomes include the individual pathogen detection prevalence and gene copy density of 27 enteric pathogens (including viruses); mean height-for-age, weight-for-age, and weight-for-height z-scores; prevalence of stunting, underweight, and wasting; and the 7-day period prevalence of caregiver-reported diarrhoea. All analyses are adjusted for prespecified covariates and examined for effect measure modification by age. Environmental samples from study households and the public domain are assessed for pathogens and faecal indicators to explore environmental exposures and monitor disease transmission.

ETHICS AND DISSEMINATION

Study protocols have been reviewed and approved by human subjects review boards at the Ministry of Health, Republic of Mozambique and the University of North Carolina at Chapel Hill. Deidentified study data will be deposited at https://osf.io/e7pvk/.

TRIAL REGISTRATION NUMBER

ISRCTN86084138.

Reproductive collapse in European beech results from declining pollination efficiency in large trees

Climate warming increases tree mortality which will require sufficient reproduction to ensure population viability. However, the response of tree reproduction to climate change remains poorly understood. Warming can reduce synchrony and interannual variability of seed production ("masting breakdown") which can increase seed predation and decrease pollination efficiency in trees. Here, using 40 years of observations of individual seed production in European beech (Fagus sylvatica), we showed that masting breakdown results in declining viable seed production over time, in contrast to the positive trend apparent in raw seed count data. Furthermore, tree size modulates the consequences of masting breakdown on viable seed production. While seed predation increased over time mainly in small trees, pollination efficiency disproportionately decreased in larger individuals. Consequently, fecundity declined over time across all size classes, but the overall effect was greatest in large trees. Our study showed that a fundamental biological relationship-correlation between tree size and viable seed production-has been reversed as the climate has warmed. That reversal has diverse consequences for forest dynamics; including for stand- and biogeographical-level dynamics of forest regeneration. The tree size effects suggest management options to increase forest resilience under changing climates.

Species traits and observer behaviors that bias data assimilation and how to accommodate them

Datasets that monitor biodiversity capture information differently depending on their design, which influences observer behavior and can lead to biases across observations and species. Combining different datasets can improve our ability to identify and understand threats to biodiversity, but this requires an understanding of the observation bias in each. Two datasets widely used to monitor bird populations exemplify these general concerns: eBird is a citizen science project with high spatiotemporal resolution but variation in distribution, effort, and observers, whereas the Breeding Bird Survey (BBS) is a structured survey of specific locations over time. Analyses using these two datasets can identify contradictory population trends. To understand these discrepancies and facilitate data fusion, we quantify species-level reporting differences across eBird and the BBS in three regions across the United States by jointly modeling bird abundances using data from both datasets. First, we fit a joint Species Distribution Model that accounts for environmental conditions and effort to identify reporting differences across the datasets. We then examine how these differences in reporting are related to species traits. Finally, we analyze species reported to one dataset but not the other and determine whether traits differ between reported and unreported species. We find that most species are reported more in the BBS than eBird. Specifically, we find that compared to eBird, BBS observers tend to report higher counts of common species and species that are usually detected by sound. We also find that species associated with water are reported less in the BBS. Species typically identified by sound are reported more at sunrise than later in the morning. Our results quantify reporting differences in eBird and the BBS to enhance our understanding of how each captures information and how they should be used. The reporting rates we identify can also be incorporated into observation models through detectability or effort to improve analyses across species and datasets. The method demonstrated here can be used to compare reporting rates across any two or more datasets to examine biases.

Microbiome resilience of Amazonian forests: Agroforest divergence to bacteria and secondary forest succession convergence to fungi

An alarming and increasing deforestation rate threatens Amazon tropical ecosystems and subsequent degradation due to frequent fires. Agroforestry systems (AFS) may offer a sustainable alternative, reportedly mimicking the plant-soil interactions of the natural mature forest (MF). However, the role of microbial community in tropical AFS remains largely unknown. This knowledge is crucial for evaluating the sustainability of AFS and practices given the key role of microbes in the aboveground-belowground interactions. The current study, by comparing different AFS and successions of secondary and MFs, showed that AFS fostered distinct groups of bacterial community, diverging from the MFs, likely a result of management practices while secondary forests converged to the same soil microbiome found in the MF, by favoring the same groups of fungi. Model simulations reveal that AFS would require profound changes in aboveground biomass and in soil factors to reach the same microbiome found in MFs. In summary, AFS practices did not result in ecosystems mimicking natural forest plant-soil interactions but rather reshaped the ecosystem to a completely different relation between aboveground biomass, soil abiotic properties, and the soil microbiome.

Whole-of-community invertebrate rewilding: Leaf litter transplants rapidly increase beetle diversity during restoration

Restoration of degraded areas is now a central tool in humanity's response to continued species-loss. However, restoration projects often report exceedingly slow or failed recolonization of fauna, especially dispersal-constrained groups such as invertebrates. Active interventions via reintroducing or "rewilding" invertebrates may assist recolonization and speed up restoration of communities toward a desired target. However, invertebrate rewilding is rarely implemented during ecological restoration. Here, we studied the efficacy of invertebrate rewilding as a means of reintroducing dispersal-constrained species and improving diversity and compositional similarities to remnant communities during restoration. Rewilding was conducted by transplanting leaf litter and soil, including associated communities of invertebrates from species rich remnant sites into species poor, and geographically isolated, revegetated farmland sites. We sampled pre- and post-rewilding invertebrate communities in remnant, rewilded revegetation, and control revegetation sites. We analyzed morphospecies richness, abundance, community composition, and modeled morphospecies traits (dispersal method/trophic guild) using a Hierarchical Modelling of Species Communities approach to determine which biological properties facilitated establishment. Beetle (Coleoptera) morphospecies richness increased rapidly in rewilded sites and was indistinguishable from remnant communities as early as 7 months post-rewilding. Beetle community similarity in the rewilding sites significantly deviated from the control sites 27 months post-rewilding, however remnant communities remained distinct over the study timeframe. Establishment success varied as other taxa did not respond as consistently as beetles within the study timeframe. Furthermore, there were no discernible shifts in dispersal traits in rewilded sites. However, predatory morphospecies were more likely to establish post-rewilding than other trophic groups. Our results demonstrate that the relatively simple act of transplanting leaf litter can result in comparatively large increases in morphospecies richness during restoration in a short timeframe. We advocate methodologies such as ours should be adopted more frequently to address failed community restoration as they are cost-effective and can be easily applied by practitioners in various restoration settings. However, further efficacy tests (e.g., varying the number of rewilding events) and longer study timeframes are needed to ensure effectiveness for a broader range of invertebrate taxa and ecosystems.

The community ecology perspective of omics data

The measurement of uncharacterized pools of biological molecules through techniques such as metabarcoding, metagenomics, metatranscriptomics, metabolomics, and metaproteomics produces large, multivariate datasets. Analyses of these datasets have successfully been borrowed from community ecology to characterize the molecular diversity of samples (ɑ-diversity) and to assess how these profiles change in response to experimental treatments or across gradients (β-diversity). However, sample preparation and data collection methods generate biases and noise which confound molecular diversity estimates and require special attention. Here, we examine how technical biases and noise that are introduced into multivariate molecular data affect the estimation of the components of diversity (i.e., total number of different molecular species, or entities; total number of molecules; and the abundance distribution of molecular entities). We then explore under which conditions these biases affect the measurement of ɑ- and β-diversity and highlight how novel methods commonly used in community ecology can be adopted to improve the interpretation and integration of multivariate molecular data. Video Abstract.

Inferring predator-prey interactions from camera traps: A Bayesian co-abundance modeling approach

Predator-prey dynamics are a fundamental part of ecology, but directly studying interactions has proven difficult. The proliferation of camera trapping has enabled the collection of large datasets on wildlife, but researchers face hurdles inferring interactions from observational data. Recent advances in hierarchical co-abundance models infer species interactions while accounting for two species' detection probabilities, shared responses to environmental covariates, and propagate uncertainty throughout the entire modeling process. However, current approaches remain unsuitable for interacting species whose natural densities differ by an order of magnitude and have contrasting detection probabilities, such as predator-prey interactions, which introduce zero inflation and overdispersion in count histories. Here, we developed a Bayesian hierarchical N-mixture co-abundance model that is suitable for inferring predator-prey interactions. We accounted for excessive zeros in count histories using an informed zero-inflated Poisson distribution in the abundance formula and accounted for overdispersion in count histories by including a random effect per sampling unit and sampling occasion in the detection probability formula. We demonstrate that models with these modifications outperform alternative approaches, improve model goodness-of-fit, and overcome parameter convergence failures. We highlight its utility using 20 camera trapping datasets from 10 tropical forest landscapes in Southeast Asia and estimate four predator-prey relationships between tigers, clouded leopards, and muntjac and sambar deer. Tigers had a negative effect on muntjac abundance, providing support for top-down regulation, while clouded leopards had a positive effect on muntjac and sambar deer, likely driven by shared responses to unmodelled covariates like hunting. This Bayesian co-abundance modeling approach to quantify predator-prey relationships is widely applicable across species, ecosystems, and sampling approaches and may be useful in forecasting cascading impacts following widespread predator declines. Taken together, this approach facilitates a nuanced and mechanistic understanding of food-web ecology.

Nitrogen, manganese, iron, and carbon resource acquisition are potential functions of the wild rice Oryza rufipogon core rhizomicrobiome

BACKGROUND

The assembly of the rhizomicrobiome, i.e., the microbiome in the soil adhering to the root, is influenced by soil conditions. Here, we investigated the core rhizomicrobiome of a wild plant species transplanted to an identical soil type with small differences in chemical factors and the impact of these soil chemistry differences on the core microbiome after long-term cultivation. We sampled three natural reserve populations of wild rice (i.e., in situ) and three populations of transplanted in situ wild rice grown ex situ for more than 40 years to determine the core wild rice rhizomicrobiome.

RESULTS

Generalized joint attribute modeling (GJAM) identified a total of 44 amplicon sequence variants (ASVs) composing the core wild rice rhizomicrobiome, including 35 bacterial ASVs belonging to the phyla Actinobacteria, Chloroflexi, Firmicutes, and Nitrospirae and 9 fungal ASVs belonging to the phyla Ascomycota, Basidiomycota, and Rozellomycota. Nine core bacterial ASVs belonging to the genera Haliangium, Anaeromyxobacter, Bradyrhizobium, and Bacillus were more abundant in the rhizosphere of ex situ wild rice than in the rhizosphere of in situ wild rice. The main ecological functions of the core microbiome were nitrogen fixation, manganese oxidation, aerobic chemoheterotrophy, chemoheterotrophy, and iron respiration, suggesting roles of the core rhizomicrobiome in improving nutrient resource acquisition for rice growth. The function of the core rhizosphere bacterial community was significantly (p < 0.05) shaped by electrical conductivity, total nitrogen, and available phosphorus present in the soil adhering to the roots.

CONCLUSION

We discovered that nitrogen, manganese, iron, and carbon resource acquisition are potential functions of the core rhizomicrobiome of the wild rice Oryza rufipogon. Our findings suggest that further potential utilization of the core rhizomicrobiome should consider the effects of soil properties on the abundances of different genera. Video Abstract.

Mesophotic depths hide high coral cover communities in French Polynesia

The rapid decline of shallow coral reefs has increased the interest in the long-understudied mesophotic coral ecosystems (MCEs). However, MCEs are usually characterised by rather low to moderate scleractinian coral cover, with only a few descriptions of high coral cover at depth. Here, we explored eight islands across French Polynesia over a wide depth range (6 to 120 m) to identify coral cover hotspots at mesophotic depths and the co-occurrent biotic groups and abiotic factors that influence such high scleractinian cover. Using Bayesian modelling, we found that 20 out of 64 of studied deep sites exhibited a coral cover higher than expected in the mesophotic range (e.g. as high as 81.8 % at 40 m, 74.5 % at 60 m, 53 % at 90 m and 42 % at 120 m vs the average expected values based on the model of 31.2 % at 40 m, 22.8 % at 60 m, 14.6 % at 90 m and 9.8 % at 120 m). Omitting the collinear factors light-irradiance and depth, these 'hotspots' of coral cover corresponded to mesophotic sites and depths characterised by hard substrate, a steep to moderate slope, and the dominance of laminar corals. Our work unveils the presence of unexpectedly and unique high coral cover communities at mesophotic depths in French Polynesia, highlighting the importance of expanding the research on deeper depths for the potential relevance in the conservation management of tropical coral reefs.

Eucalypt species drive rhizosphere bacterial and fungal community assembly but soil phosphorus availability rearranges the microbiome

Soil phosphorus (P) availability may limit plant growth and alter root-soil interactions and rhizosphere microbial community composition. The composition of the rhizosphere microbial community can also be shaped by plant genotype. In this study, we examined the rhizosphere microbial communities of young plants of 24 species of eucalypts (22 Eucalyptus and two Corymbia species) under low or sufficient soil P availability. The taxonomic diversity of the rhizosphere bacterial and fungal communities was assessed by 16S and 18S rRNA gene amplicon sequencing. The taxonomic modifications in response to low P availability were evaluated by principal component analysis, and co-inertia analysis was performed to identify associations between bacterial and fungal community structures and parameters related to plant growth and nutritional status under low and sufficient soil P availability. The sequencing results showed that while both soil P availability and eucalypt species influenced the microbial community assembly, eucalypt species was the stronger determinant. However, when the plants are subjected to low P-availability, the rhizosphere selection became strongest. In response to low P, the bacterial and fungal communities in the rhizosphere of some species showed significant changes, whereas in others remained relatively constant under low and sufficient P. Co-inertia analyses revealed a significant co-dependence between plant nutrient contents and bacterial and fungal community composition only under sufficient P. By contrast, under low P, bacterial community composition was related to plant biomass production. In conclusion, our study shows that eucalypt species identity was the main factor modulating rhizosphere microbial community composition; significant shifts due to P availability were observed only for some eucalypt species.

Global change re-structures alpine plant communities through interacting abiotic and biotic effects

Global change is altering patterns of community assembly, with net outcomes dependent on species' responses to the abiotic environment, both directly and mediated through biotic interactions. Here, we assess alpine plant community responses in a 15-year factorial nitrogen addition, warming and snow manipulation experiment. We used a dynamic competition model to estimate the density-dependent and -independent processes underlying changes in species-group abundances over time. Density-dependent shifts in competitive interactions drove long-term changes in abundance of species-groups under global change while counteracting environmental drivers limited the growth response of the dominant species through density-independent mechanisms. Furthermore, competitive interactions shifted with the environment, primarily with nitrogen and drove non-linear abundance responses across environmental gradients. Our results highlight that global change can either reshuffle species hierarchies or further favour already-dominant species; predicting which outcome will occur requires incorporating both density-dependent and -independent mechanisms and how they interact across multiple global change factors.

Rapid changes in coastal ocean microbiomes uncoupled with shifts in environmental variables

Disturbances, here defined as events that directly alter microbial community composition, are commonly studied in host-associated and engineered systems. In spite of global change both altering environmental averages and increasing extreme events, there has been relatively little research into the causes, persistence and population-level impacts of disturbance in the dynamic coastal ocean. Here, we utilize 3 years of observations from a coastal time series to identify disturbances based on the largest week-over-week changes in the microbiome (i.e. identifying disturbance as events that alter the community composition). In general, these microbiome disturbances were not clearly linked to specific environmental factors and responsive taxa largely differed, aside from SAR11, which generally declined. However, several disturbance metagenomes identified increased phage-associated genes, suggesting that unexplained community shifts might be caused by increased mortality. Furthermore, a category 1 hurricane, the only event that would likely be classified a priori as an environmental disturbance, was not an outlier in microbiome composition, but did enhance a bloom in seasonally abundant phytoplankton. Thus, as extreme environmental changes intensify, assumptions of what constitutes a disturbance should be re-examined in the context of ecological history and microbiome responses.

Rhizosphere microbiome response to host genetic variability: a trade-off between bacterial and fungal community assembly

Rhizosphere microbial community composition is strongly influenced by plant species and cultivar. However, our understanding of the impact of plant cultivar genetic variability on microbial assembly composition remains limited. Here, we took advantage of vegetatively propagated chrysanthemum (Chrysanthemum indicum L.) as a plant model and induced roots in five commercial cultivars: Barolo, Chic, Chic 45, Chic Cream, and Haydar. We observed strong rhizosphere selection for the bacterial community but weaker selection for the fungal community. The genetic distance between cultivars explained 42.83% of the total dissimilarity between the bacteria selected by the different cultivars. By contrast, rhizosphere fungal selection was not significantly linked to plant genetic dissimilarity. Each chrysanthemum cultivar selected unique bacterial and fungal genera in the rhizosphere. We also observed a trade-off in the rhizosphere selection of bacteria and fungi in which the cultivar with the strongest selection of fungal communities showed the weakest bacterial selection. Finally, bacterial and fungal family taxonomic groups consistently selected by all cultivars were identified (bacteria Chitinophagaceae, Beijerinckiaceae, and Acidobacteriaceae and fungi Pseudeurotiaceae and Chrysozymaceae). Taken together, our findings suggest that chrysanthemum cultivars select distinct rhizosphere microbiomes and share a common core of microbes partially explained by the genetic dissimilarity between cultivars.

Age-Related Changes in the Nasopharyngeal Microbiome Are Associated With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection and Symptoms Among Children, Adolescents, and Young Adults

BACKGROUND

Children are less susceptible to SARS-CoV-2 infection and typically have milder illness courses than adults, but the factors underlying these age-associated differences are not well understood. The upper respiratory microbiome undergoes substantial shifts during childhood and is increasingly recognized to influence host defense against respiratory pathogens. Thus, we sought to identify upper respiratory microbiome features associated with SARS-CoV-2 infection susceptibility and illness severity.

METHODS

We collected clinical data and nasopharyngeal swabs from 285 children, adolescents, and young adults (<21 years) with documented SARS-CoV-2 exposure. We used 16S ribosomal RNA gene sequencing to characterize the nasopharyngeal microbiome and evaluated for age-adjusted associations between microbiome characteristics and SARS-CoV-2 infection status and respiratory symptoms.

RESULTS

Nasopharyngeal microbiome composition varied with age (PERMANOVA, P < .001; R2 = 0.06) and between SARS-CoV-2-infected individuals with and without respiratory symptoms (PERMANOVA, P  = .002; R2 = 0.009). SARS-CoV-2-infected participants with Corynebacterium/Dolosigranulum-dominant microbiome profiles were less likely to have respiratory symptoms than infected participants with other nasopharyngeal microbiome profiles (OR: .38; 95% CI: .18-.81). Using generalized joint attributed modeling, we identified 9 bacterial taxa associated with SARS-CoV-2 infection and 6 taxa differentially abundant among SARS-CoV-2-infected participants with respiratory symptoms; the magnitude of these associations was strongly influenced by age.

CONCLUSIONS

We identified interactive relationships between age and specific nasopharyngeal microbiome features that are associated with SARS-CoV-2 infection susceptibility and symptoms in children, adolescents, and young adults. Our data suggest that the upper respiratory microbiome may be a mechanism by which age influences SARS-CoV-2 susceptibility and illness severity.

Distinct Community-Wide Responses to Forecasted Climate Change in Afrotropical Forests

More refined knowledge of how tropical forests respond to changes in the abiotic environment is necessary to mitigate climate change, maintain biodiversity, and preserve ecosystem services. To evaluate the unique response of diverse Afrotropical forest communities to disturbances in the abiotic environment, we employ country-wide tree species inventories, remotely sensed climate data, and future climate predictions collected from 104 1-ha plots in the central African country of Gabon. We predict a 3–8% decrease in Afrotropical forest species richness by the end of the century, in contrast to the 30–50% loss of plant diversity predicted to occur with equivalent warming in the Neotropics. This work reveals that forecasts of community species composition are not generalizable across regions, and more representative studies are needed in understudied diverse biomes. This study serves as an important counterpoint to work done in the Neotropics by providing contrasting predictions for Afrotropical forests with substantially different ecological, evolutionary, and anthropogenic histories.

North American tree migration paced by climate in the West, lagging in the East

Suitable habitats for forest trees may be shifting fast with recent climate change. Studies tracking the shift in suitable habitat for forests have been inconclusive, in part because responses in tree fecundity and seedling establishment can diverge. Analysis of both components at a continental scale reveals a poleward migration of northern species that is in progress now. Recruitment and fecundity both contribute to poleward spread in the West, while fecundity limits spread in the East, despite a fecundity hotspot in the Southeast. Fecundity limitation on population spread can confront conservation and management efforts with persistent disequilibrium between forest diversity and rapid climate change.

Tree fecundity and recruitment have not yet been quantified at scales needed to anticipate biogeographic shifts in response to climate change. By separating their responses, this study shows coherence across species and communities, offering the strongest support to date that migration is in progress with regional limitations on rates. The southeastern continent emerges as a fecundity hotspot, but it is situated south of population centers where high seed production could contribute to poleward population spread. By contrast, seedling success is highest in the West and North, serving to partially offset limited seed production near poleward frontiers. The evidence of fecundity and recruitment control on tree migration can inform conservation planning for the expected long-term disequilibrium between climate and forest distribution.

Jointly modeling marine species to inform the effects of environmental change on an ecological community in the Northwest Atlantic

Single species distribution models (SSDMs) are typically used to understand and predict the distribution and abundance of marine fish by fitting distribution models for each species independently to a combination of abiotic environmental variables. However, species abundances and distributions are influenced by abiotic environmental preferences as well as biotic dependencies such as interspecific competition and predation. When species interact, a joint species distribution model (JSDM) will allow for valid inference of environmental effects. We built a joint species distribution model of marine fish and invertebrates of the Northeast US Continental Shelf, providing evidence on species relationships with the environment as well as the likelihood of species to covary. Predictive performance is similar to SSDMs but the Bayesian joint modeling approach provides two main advantages over single species modeling: (1) the JSDM directly estimates the significance of environmental effects; and (2) predicted species richness accounts for species dependencies. An additional value of JSDMs is that the conditional prediction of species distributions can use not only the environmental associations of species, but also the presence and abundance of other species when forecasting future climatic associations.

Niche Shifts From Trees to Fecundity to Recruitment That Determine Species Response to Climate Change

Anticipating the next generation of forests requires understanding of recruitment responses to habitat change. Tree distribution and abundance depend not only on climate, but also on habitat variables, such as soils and drainage, and on competition beneath a shaded canopy. Recent analyses show that North American tree species are migrating in response to climate change, which is exposing each population to novel climate-habitat interactions (CHI). Because CHI have not been estimated for either adult trees or regeneration (recruits per year per adult basal area), we cannot evaluate migration potential into the future. Using the Masting Inference and Forecasting (MASTIF) network of tree fecundity and new continent-wide observations of tree recruitment, we quantify impacts for redistribution across life stages from adults to fecundity to recruitment. We jointly modeled response of adult abundance and recruitment rate to climate/habitat conditions, combined with fecundity sensitivity, to evaluate if shifting CHI explain community reorganization. To compare climate effects with tree fecundity, which is estimated from trees and thus is "conditional" on tree presence, we demonstrate how to quantify this conditional status for regeneration. We found that fecundity was regulated by temperature to a greater degree than other stages, yet exhibited limited responses to moisture deficit. Recruitment rate expressed strong sensitivities to CHI, more like adults than fecundity, but still with substantial differences. Communities reorganized from adults to fecundity, but there was a re-coalescence of groups as seedling recruitment partially reverted to community structure similar to that of adults. Results provide the first estimates of continent-wide community sensitivity and their implications for reorganization across three life-history stages under climate change.

Patchy Distributions and Distinct Niche Partitioning of Mycoplankton Populations across a Nearshore to Open Ocean Gradient

Evidence increasingly suggests planktonic fungi (or mycoplankton) play an important role in marine food webs and biogeochemical cycles. In order to better understand their ecological role and how oceanographic gradients from the coastal to open ocean shape the mycoplankton community, molecular approaches were used to study fungal dynamics along a repeatedly sampled, five-station transect beginning at the mouth of an estuary and continuing 87 km across the continental shelf to the oligotrophic waters at the boundary of the Sargasso Sea. Similar to patterns in chlorophyll a, fungal 18S rRNA gene abundance showed a sharp decrease from nearshore to offshore stations. While Shannon's diversity was not statistically different across the transect, nonmetric multidimensional scaling (NMDS) ordination revealed that fungal communities at the nearshore station were significantly different from those at other stations. Even though spatial gradients were consistently strong, the shelf mycoplankton were more similar to those of the offshore communities when temperature was high (>20°C) and while they shifted toward the nearshore communities when temperature was low (<19°C), suggesting a role for additional seasonal factors (such as temperature) in shaping mycoplankton distributions. However, overall phylotype distributions were patchy with few taxa observed at all stations and the majority observed at a single station with the nearshore station exhibiting the largest number of exclusive phylotypes. Overall, our findings revealed the patchy spatial distributions and distinct niche partitioning of mycoplankton populations across a nearshore to open ocean gradient, which improved our understanding of fungal ecology in coastal waters. IMPORTANCE Fungi are an important, but understudied, group of heterotrophic microbes in marine environments. Traditionally, fungi in the coastal ocean were largely assumed to be derived from terrestrial inputs. Yet here we find many fungal taxa are endemic to the open ocean environment but are rare or absent in nearshore waters, suggesting they are not washed into the ocean from the land. As observed for the bacterioplankton, coastal oceanographic gradients can function as habitat barriers to partition fungal communities. Compared to the bacterioplankton, however, the mycoplankton exhibit a much patchier distribution pattern, suggesting differential drivers and the potential for spatially/temporally limited habitats or strong density-dependent selection. Therefore, our results show that mycoplankton in the coastal ocean may play a significant but complementary role to that of the bacterioplankton.

Snapshot Wisconsin: networking community scientists and remote sensing to improve ecological monitoring and management

Biological data collection is entering a new era. Community science, satellite remote sensing (SRS), and local forms of remote sensing (e.g., camera traps and acoustic recordings) have enabled biological data to be collected at unprecedented spatial and temporal scales and resolution. There is growing interest in developing observation networks to collect and synthesize data to improve broad-scale ecological monitoring, but no examples of such networks have emerged to inform decision-making by agencies. Here, we present the implementation of one such jurisdictional observation network (JON), Snapshot Wisconsin, which links synoptic environmental data derived from SRS to biodiversity observations collected continuously from a trail camera network to support management decision-making. We use several examples to illustrate that Snapshot Wisconsin improves the spatial, temporal, and biological resolution and extent of information available to support management, filling gaps associated with traditional monitoring and enabling consideration of new management strategies. JONs like Snapshot Wisconsin further strengthen monitoring inference by contributing novel lines of evidence useful for corroboration or integration. SRS provides environmental context that facilitates inference, prediction, and forecasting, and ultimately helps managers formulate, test, and refine conceptual models for the monitored systems. Although these approaches pose challenges, Snapshot Wisconsin demonstrates that expansive observation networks can be tractably managed by agencies to support decision making, providing a powerful new tool for agencies to better achieve their missions and reshape the nature of environmental decision-making.

Coral reef fishes reveal strong divergence in the prevalence of traits along the global diversity gradient

Coral reefs are experiencing declines due to climate change and local human impacts. While at a local scale these impacts induce biodiversity loss and shifts in community structure, previous biogeographical analyses recorded consistent taxonomic structure of fish communities across global coral reefs. This suggests that regional communities represent a random subset of the global species and traits pool, whatever their species richness. Using distributional data on 3586 fish species and latest advances in species distribution models, we show marked gradients in the prevalence of size classes and diet categories across the biodiversity gradient. This divergence in trait structure is best explained by reef isolation during past unfavourable climatic conditions, with large and piscivore fishes better represented in isolated areas. These results suggest the risk of a global community re-organization if the ongoing climate-induced reef fragmentation is not halted.

Extending coverage and thematic resolution of compositional land cover maps in a hierarchical Bayesian framework

Ecological models are constrained by the availability of high-quality data at biologically appropriate resolutions and extents. Modeling a species' affinity or aversion with a particular land cover class requires data detailing that class across the full study area. Data sets with detailed legends (i.e., high thematic resolution) and/or high accuracy often sacrifice geographic extent, while large-area data sets often compromise on the number of classes and local accuracy. Consequently, ecologists must often restrict their study extent to match that of the more precise data set, or ignore potentially key land cover associations to study a larger area. We introduce a hierarchical Bayesian model to capitalize on the thematic resolution and accuracy of a regional land cover data set, and on the geographic breadth of a large area land cover data set. For the full extent (i.e., beyond the regional data set), the model predicts systematic discrepancies of the large-area data set with the regional data set, and divides an aggregated class into two more specific classes detailed by the regional data set. We illustrate the application of our model for mapping eastern white pine (Pinus strobus) forests, an important timber species that also provides habitat for an invasive shrub in the northeastern United States. We use the National Land Cover Database (NLCD), which covers the full study area but includes only generalized forest classes, and the NH GRANIT land cover data set, which maps White Pine Forest and has high accuracy, but only exists within New Hampshire. We evaluate the model at coarse (20 km² ) and fine (2 km² ) resolutions, with and without spatial random effects. The hierarchical model produced improved maps of compositional land cover for the full extent, reducing inaccuracy relative to NLCD while partitioning a White Pine Forest class out of the Evergreen Forest class. Accuracy was higher with spatial random effects and at the coarse resolution. All models improved upon simply partitioning Evergreen Forest in NLCD based on the predicted distribution of white pine. This flexible statistical method helps ecologists leverage localized mapping efforts to expand models of species distributions, population dynamics, and management strategies beyond the political boundaries that frequently delineate land cover data sets.

Clustering Species With Residual Covariance Matrix in Joint Species Distribution Models

Modeling species distributions over space and time is one of the major research topics in both ecology and conservation biology. Joint Species Distribution models (JSDMs) have recently been introduced as a tool to better model community data, by inferring a residual covariance matrix between species, after accounting for species' response to the environment. However, these models are computationally demanding, even when latent factors, a common tool for dimension reduction, are used. To address this issue, Taylor-Rodriguez et al. (2017) proposed to use a Dirichlet process, a Bayesian nonparametric prior, to further reduce model dimension by clustering species in the residual covariance matrix. Here, we built on this approach to include a prior knowledge on the potential number of clusters, and instead used a Pitman–Yor process to address some critical limitations of the Dirichlet process. We therefore propose a framework that includes prior knowledge in the residual covariance matrix, providing a tool to analyze clusters of species that share the same residual associations with respect to other species. We applied our methodology to a case study of plant communities in a protected area of the French Alps (the Bauges Regional Park), and demonstrated that our extensions improve dimension reduction and reveal additional information from the residual covariance matrix, notably showing how the estimated clusters are compatible with plant traits, endorsing their importance in shaping communities.

On the Interpretations of Joint Modeling in Community Ecology

Explaining and modeling species communities is more than ever a central goal of ecology. Recently, joint species distribution models (JSDMs), which extend species distribution models (SDMs) by considering correlations among species, have been proposed to improve species community analyses and rare species predictions while potentially inferring species interactions. Here, we illustrate the mathematical links between SDMs and JSDMs and their ecological implications and demonstrate that JSDMs, just like SDMs, cannot separate environmental effects from biotic interactions. We provide a guide to the conditions under which JSDMs are (or are not) preferable to SDMs for species community modeling. More generally, we call for a better uptake and clarification of novel statistical developments in the field of biodiversity modeling.

Extracting the GEMs: Genotype, Environment, and Microbiome Interactions Shaping Host Phenotypes

One of the fundamental tenets of biology is that the phenotype of an organism (Y) is determined by its genotype (G), the environment (E), and their interaction (GE). Quantitative phenotypes can then be modeled as Y = G + E + GE + e, where e is the biological variance. This simple and tractable model has long served as the basis for studies investigating the heritability of traits and decomposing the variability in fitness. The importance and contribution of microbe interactions to a given host phenotype is largely unclear, nor how this relates to the traditional GE model. Here we address this fundamental question and propose an expansion of the original model, referred to as GEM, which explicitly incorporates the contribution of the microbiome (M) to the host phenotype, while maintaining the simplicity and tractability of the original GE model. We show that by keeping host, environment, and microbiome as separate but interacting variables, the GEM model can capture the nuanced ecological interactions between these variables. Finally, we demonstrate with an in vitro experiment how the GEM model can be used to statistically disentangle the relative contributions of each component on specific host phenotypes.

Joint Microbial and Metabolomic Network Estimation with the Censored Gaussian Graphical Model

Joint analysis of microbiome and metabolomic data represents an imperative objective as the field moves beyond basic microbiome association studies and turns towards mechanistic and translational investigations. We present a censored Gaussian graphical model framework, where the metabolomic data are treated as continuous and the microbiome data as censored at zero, to identify direct interactions (defined as conditional dependence relationships) between microbial species and metabolites. Simulated examples show that our method metaMint performs favorably compared to the existing ones. metaMint also provides interpretable microbe-metabolite interactions when applied to a bacterial vaginosis data set. R implementation of metaMint is available on GitHub.