Chromosome-scale assemblies of Acanthamoeba castellanii genomes provide insights into Legionella pneumophila infection-related chromatin reorganization

The unicellular amoeba Acanthamoeba castellanii is ubiquitous in aquatic environments, where it preys on bacteria. The organism also hosts bacterial endosymbionts, some of which are parasitic, including human pathogens such as Chlamydia and Legionella spp. Here we report complete, high-quality genome sequences for two extensively studied A. castellanii strains, Neff and C3. Combining long- and short-read data with Hi-C, we generated near chromosome-level assemblies for both strains with 90% of the genome contained in 29 scaffolds for the Neff strain and 31 for the C3 strain. Comparative genomics revealed strain-specific functional enrichment, most notably genes related to signal transduction in the C3 strain and to viral replication in Neff. Furthermore, we characterized the spatial organization of the A. castellanii genome and showed that it is reorganized during infection by Legionella pneumophila Infection-dependent chromatin loops were found to be enriched in genes for signal transduction and phosphorylation processes. In genomic regions where chromatin organization changed during Legionella infection, we found functional enrichment for genes associated with metabolism, organelle assembly, and cytoskeleton organization. Given Legionella infection is known to alter its host's cell cycle, to exploit the host's organelles, and to modulate the host's metabolism in its favor, these changes in chromatin organization may partly be related to mechanisms of host control during Legionella infection.

A Micromachined Picocalorimeter Sensor for Liquid Samples with Application to Chemical Reactions and Biochemistry

Calorimetry has long been used to probe the physical state of a system by measuring the heat exchanged with the environment as a result of chemical reactions or phase transitions. Application of calorimetry to microscale biological samples, however, is hampered by insufficient sensitivity and the difficulty of handling liquid samples at this scale. Here, a micromachined calorimeter sensor that is capable of resolving picowatt levels of power is described. The sensor consists of low-noise thermopiles on a thin silicon nitride membrane that allow direct differential temperature measurements between a sample and four coplanar references, which significantly reduces thermal drift. The partial pressure of water in the ambient around the sample is maintained at saturation level using a small hydrogel-lined enclosure. The materials used in the sensor and its geometry are optimized to minimize the noise equivalent power generated by the sensor in response to the temperature field that develops around a typical sample. The experimental response of the sensor is characterized as a function of thermopile dimensions and sample volume, and its capability is demonstrated by measuring the heat dissipated during an enzymatically catalyzed biochemical reaction in a microliter-sized liquid droplet. The sensor offers particular promise for quantitative measurements on biological systems.

Virus-induced spore formation as a defense mechanism in marine diatoms

Algal viruses are important contributors to carbon cycling, recycling nutrients and organic material through host lysis. Although viral infection has been described as a primary mechanism of phytoplankton mortality, little is known about host defense responses. We show that viral infection of the bloom-forming, planktonic diatom Chaetoceros socialis induces the mass formation of resting spores, a heavily silicified life cycle stage associated with carbon export due to rapid sinking. Although viral RNA was detected within spores, mature virions were not observed. 'Infected' spores were capable of germinating, but did not propagate or transmit infectious viruses. These results demonstrate that diatom spore formation is an effective defense strategy against viral-mediated mortality. They provide a possible mechanistic link between viral infection, bloom termination, and mass carbon export events and highlight an unappreciated role of viruses in regulating diatom life cycle transitions and ecological success.

Environmental DNA surveys detect distinct metazoan communities across abyssal plains and seamounts in the western Clarion Clipperton Zone

The deep seafloor serves as a reservoir of biodiversity in the global ocean, with >80% of invertebrates at abyssal depths still undescribed. These diverse and remote deep-sea communities are critically under-sampled and increasingly threatened by anthropogenic impacts, including future polymetallic nodule mining. Using a multigene environmental DNA (eDNA) metabarcoding approach, we characterized metazoan communities sampled from sediments, polymetallic nodules and seawater in the western Clarion Clipperton Zone (CCZ) to test the hypotheses that deep seamounts (a) are species richness hotspots in the abyss, (b) have structurally distinct communities in comparison to other deep-sea habitats, and (c) that seafloor particulate organic carbon (POC) flux and polymetallic nodule density are positively correlated with metazoan diversity. eDNA metabarcoding was effective at characterizing distinct biotas known to occur in association with different abyssal substrate types (e.g., nodule- and sediment-specific fauna), with distinct community composition and few taxa shared across substrates. Seamount faunas had higher overall taxonomic richness, and different community composition and biogeography than adjacent abyssal plains, with seamount communities displaying less connectivity between regions than comparable assemblages on the abyssal plains. Across an estimated gradient of low to moderate POC flux, we find lowest taxon richness at the lowest POC flux, as well as an effect of nodule size on community composition. Our results suggest that while abyssal seamounts are important reservoirs of metazoan diversity in the CCZ, given limited taxonomic overlap between seamount and plains fauna, conservation of seamount assemblages will be insufficient to protect biodiversity and ecosystem function in regions targeted for mining.

Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales

Fire emissions of gases and aerosols alter atmospheric composition and have substantial impacts on climate, ecosystem function, and human health. Warming climate and human expansion in fire-prone landscapes exacerbate fire impacts and call for more effective management tools. Here we developed a global fire forecasting system that predicts monthly emissions using past fire data and climate variables for lead times of 1 to 6 months. Using monthly fire emissions from the Global Fire Emissions Database (GFED) as the prediction target, we fit a statistical time series model, the Autoregressive Integrated Moving Average model with eXogenous variables (ARIMAX), in over 1,300 different fire regions. Optimized parameters were then used to forecast future emissions. The forecast system took into account information about region-specific seasonality, long-term trends, recent fire observations, and climate drivers representing both large-scale climate variability and local fire weather. We cross-validated the forecast skill of the system with different combinations of predictors and forecast lead times. The reference model, which combined endogenous and exogenous predictors with a 1 month forecast lead time, explained 52% of the variability in the global fire emissions anomaly, considerably exceeding the performance of a reference model that assumed persistent emissions during the forecast period. The system also successfully resolved detailed spatial patterns of fire emissions anomalies in regions with significant fire activity. This study bridges the gap between the efforts of near-real-time fire forecasts and seasonal fire outlooks and represents a step toward establishing an operational global fire, smoke, and carbon cycle forecasting system.

Uptake and Retention of Nanoplastics in Quagga Mussels

Here, a set of experiments to assess the feasibility of using an invasive and widespread freshwater mussel (Dreissena rostrformis bugensis) as a sentinel species for nanoplastic detection is reported. Under laboratory experimental conditions, mussels ingest and retain fluorescent polystyrene (PS) beads with carboxylic acid (-COOH) termination over a size range of 200-2000 nm. The number of beads the mussels ingested is quantified using fluorescence spectroscopy and the location of the beads in the mussels is imaged using fluorescence microscopy. PS beads of similar size (1000-2000 nm) to mussels' preferred food are trafficked in the ciliated food grooves of the gills. Beads of all sizes are observed in the mussels' digestive tracts, indicating that the mussels do not efficiently reject the beads as unwanted foreign material, regardless of size. Fluorescence microscopy shows all sizes of beads are concentrated in the siphons and are retained there for longer than one month postexposure. Combined atomic force microscopy-infrared spectroscopy and photothermal infrared spectroscopy are used to locate, image, and chemically identify the beads in the mussel siphons. In sum, these experiments demonstrate the potential for using mussels, specifically their siphons, to monitor environmental accumulation of aquatic nanoplastics.

When parks work: Effect of anthropogenic disturbance on occupancy of tropical forest mammals

Protected areas (PAs) in the tropics are vulnerable to human encroachment, and, despite formal protection, they do not fully mitigate anthropogenic threats to habitats and biodiversity. However, attempts to quantify the effectiveness of PAs and to understand the status and changes of wildlife populations in relation to protection efficiency remain limited. Here, we used camera-trapping data collected over 8 consecutive years (2009-2016) to investigate the yearly occurrences of medium-to-large mammals within the Udzungwa Mountains National Park (Tanzania), an area of outstanding importance for biological endemism and conservation. Specifically, we evaluated the effects of habitat and proxies of human disturbance, namely illegal hunting with snares and firewood collection (a practice that was banned in 2011 in the park), on species' occurrence probabilities. Our results showed variability in species' responses to disturbance: The only species that showed a negative effect of the number of snares found on occurrence probability was the Harvey's duiker, a relatively widespread forest antelope. Similarly, we found a moderate positive effect of the firewood collection ban on only the suni, another common antelope, and a negative effect on a large opportunistic rodent, the giant-pouched rat. Importantly, we found evidence of temporal stability in occurrence probability for all species over the 8-year study period. Our findings suggest that well-managed PAs can sustain mammal populations in tropical forests. However, variability among species in their responses to anthropogenic disturbance necessitates consideration in the design of conservation action plans for multiple taxa.

Seasons of Syn

Synechococcus is a widespread and important marine primary producer. Time series provide critical information for identifying and understanding the factors that determine abundance patterns. Here, we present the results of analysis of a 16-yr hourly time series of Synechococcus at the Martha's Vineyard Coastal Observatory, obtained with an automated, in situ flow cytometer. We focus on understanding seasonal abundance patterns by examining relationships between cell division rate, loss rate, cellular properties (e.g., cell volume, phycoerythrin fluorescence), and environmental variables (e.g., temperature, light). We find that the drivers of cell division vary with season; cells are temperature-limited in winter and spring, but light-limited in the fall. Losses to the population also vary with season. Our results lead to testable hypotheses about Synechococcus ecophysiology and a working framework for understanding the seasonal controls of Synechococcus cell abundance in a temperate coastal system.

One size does not fit all: Customizing MCMC methods for hierarchical models using NIMBLE

Improved efficiency of Markov chain Monte Carlo facilitates all aspects of statistical analysis with Bayesian hierarchical models. Identifying strategies to improve MCMC performance is becoming increasingly crucial as the complexity of models, and the run times to fit them, increases. We evaluate different strategies for improving MCMC efficiency using the open-source software NIMBLE (R package nimble) using common ecological models of species occurrence and abundance as examples. We ask how MCMC efficiency depends on model formulation, model size, data, and sampling strategy. For multiseason and/or multispecies occupancy models and for N-mixture models, we compare the efficiency of sampling discrete latent states vs. integrating over them, including more vs. fewer hierarchical model components, and univariate vs. block-sampling methods. We include the common MCMC tool JAGS in comparisons. For simple models, there is little practical difference between computational approaches. As model complexity increases, there are strong interactions between model formulation and sampling strategy on MCMC efficiency. There is no one-size-fits-all best strategy, but rather problem-specific best strategies related to model structure and type. In all but the simplest cases, NIMBLE's default or customized performance achieves much higher efficiency than JAGS. In the two most complex examples, NIMBLE was 10-12 times more efficient than JAGS. We find NIMBLE is a valuable tool for many ecologists utilizing Bayesian inference, particularly for complex models where JAGS is prohibitively slow. Our results highlight the need for more guidelines and customizable approaches to fit hierarchical models to ensure practitioners can make the most of occupancy and other hierarchical models. By implementing model-generic MCMC procedures in open-source software, including the NIMBLE extensions for integrating over latent states (implemented in the R package nimbleEcology), we have made progress toward this aim.

Contrasting Mixotrophic Lifestyles Reveal Different Ecological Niches in Two Closely Related Marine Protists

Many marine microbial eukaryotes combine photosynthetic with phagotrophic nutrition, but incomplete understanding of such mixotrophic protists, their functional diversity, and underlying physiological mechanisms limits the assessment and modeling of their roles in present and future ocean ecosystems. We developed an experimental system to study responses of mixotrophic protists to availability of living prey and light, and used it to characterize contrasting physiological strategies in two stramenopiles in the genus Ochromonas. We show that oceanic isolate CCMP1393 is an obligate mixotroph, requiring both light and prey as complementary resources. Interdependence of photosynthesis and heterotrophy in CCMP1393 comprises a significant role of mitochondrial respiration in photosynthetic electron transport. In contrast, coastal isolate CCMP2951 is a facultative mixotroph that can substitute photosynthesis by phagotrophy and hence grow purely heterotrophically in darkness. In contrast to CCMP1393, CCMP2951 also exhibits a marked photoprotection response that integrates non-photochemical quenching and mitochondrial respiration as electron sink for photosynthetically produced reducing equivalents. Facultative mixotrophs similar to CCMP2951 might be well adapted to variable environments, while obligate mixotrophs similar to CCMP1393 appear capable of resource efficient growth in oligotrophic ocean environments. Thus, the responses of these phylogenetically close protists to the availability of different resources reveals niche differentiation that influences impacts in food webs and leads to opposing carbon cycle roles.

Understanding the roles of three academic communities in a prospective learning health ecosystem for diagnostic excellence

Inaccurate, untimely, and miscommunicated medical diagnoses represent a wicked problem requiring comprehensive and coordinated approaches, such as those demonstrated in the characteristics of learning health systems (LHSs). To appreciate a vision for how LHS methods can optimize processes and outcomes in medical diagnosis (diagnostic excellence), we interviewed 32 individuals with relevant expertise: 18 who have studied diagnostic processes using traditional behavioral science and health services research methods, six focused on machine learning (ML) and artificial intelligence (AI) approaches, and eight multidisciplinary researchers experienced in advocating for and incorporating LHS methods, ie, scalable continuous learning in health care. We report on barriers and facilitators, identified by these subjects, to applying their methods toward optimizing medical diagnosis. We then employ their insights to envision the emergence of a learning ecosystem that leverages the tools of each of the three research groups to advance diagnostic excellence. We found that these communities represent a natural fit forward, in which together, they can better measure diagnostic processes and close the loop of putting insights into practice. Members of the three academic communities will need to network and bring in additional stakeholders before they can design and implement the necessary infrastructure that would support ongoing learning of diagnostic processes at an economy of scale and scope.

Avian ecological succession in the Amazon: A long-term case study following experimental deforestation

Approximately 20% of the Brazilian Amazon has now been deforested, and the Amazon is currently experiencing the highest rates of deforestation in a decade, leading to large-scale land-use changes. Roads have consistently been implicated as drivers of ongoing Amazon deforestation and may act as corridors to facilitate species invasions. Long-term data, however, are necessary to determine how ecological succession alters avian communities following deforestation and whether established roads lead to a constant influx of new species.We used data across nearly 40 years from a large-scale deforestation experiment in the central Amazon to examine the avian colonization process in a spatial and temporal framework, considering the role that roads may play in facilitating colonization.Since 1979, 139 species that are not part of the original forest avifauna have been recorded, including more secondary forest species than expected based on the regional species pool. Among the 35 species considered to have colonized and become established, a disproportionate number were secondary forest birds (63%), almost all of which first appeared during the 1980s. These new residents comprise about 13% of the current community of permanent residents.Widespread generalists associated with secondary forest colonized quickly following deforestation, with few new species added after the first decade, despite a stable road connection. Few species associated with riverine forest or specialized habitats colonized, despite road connection to their preferred source habitat. Colonizing species remained restricted to anthropogenic habitats and did not infiltrate old-growth forests nor displace forest birds.Deforestation and expansion of road networks into terra firme rainforest will continue to create degraded anthropogenic habitat. Even so, the initial pulse of colonization by nonprimary forest bird species was not the beginning of a protracted series of invasions in this study, and the process appears to be reversible by forest succession.

Estimating aboveground net biomass change for tropical and subtropical forests: Refinement of IPCC default rates using forest plot data

As countries advance in greenhouse gas (GHG) accounting for climate change mitigation, consistent estimates of aboveground net biomass change (∆AGB) are needed. Countries with limited forest monitoring capabilities in the tropics and subtropics rely on IPCC 2006 default ∆AGB rates, which are values per ecological zone, per continent. Similarly, research into forest biomass change at a large scale also makes use of these rates. IPCC 2006 default rates come from a handful of studies, provide no uncertainty indications and do not distinguish between older secondary forests and old-growth forests. As part of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, we incorporate ∆AGB data available from 2006 onwards, comprising 176 chronosequences in secondary forests and 536 permanent plots in old-growth and managed/logged forests located in 42 countries in Africa, North and South America and Asia. We generated ∆AGB rate estimates for younger secondary forests (≤20 years), older secondary forests (>20 years and up to 100 years) and old-growth forests, and accounted for uncertainties in our estimates. In tropical rainforests, for which data availability was the highest, our ∆AGB rate estimates ranged from 3.4 (Asia) to 7.6 (Africa) Mg ha^-1  year^-1 in younger secondary forests, from 2.3 (North and South America) to 3.5 (Africa) Mg ha^-1  year^-1 in older secondary forests, and 0.7 (Asia) to 1.3 (Africa) Mg ha^-1  year^-1 in old-growth forests. We provide a rigorous and traceable refinement of the IPCC 2006 default rates in tropical and subtropical ecological zones, and identify which areas require more research on ∆AGB. In this respect, this study should be considered as an important step towards quantifying the role of tropical and subtropical forests as carbon sinks with higher accuracy; our new rates can be used for large-scale GHG accounting by governmental bodies, nongovernmental organizations and in scientific research.

Expansion of Coccidioidomycosis Endemic Regions in the United States in Response to Climate Change

Coccidioidomycosis (Valley fever) is a fungal disease endemic to the southwestern United States. Across this region, temperature and precipitation influence the extent of the endemic region and number of Valley fever cases. Climate projections for the western United States indicate that temperatures will increase and precipitation patterns will shift, which may alter disease dynamics. We estimated the area potentially endemic to Valley fever using a climate niche model derived from contemporary climate and disease incidence data. We then used our model with projections of climate from Earth system models to assess how endemic areas will change during the 21st century. By 2100 in a high warming scenario, our model predicts that the area of climate-limited endemicity will more than double, the number of affected states will increase from 12 to 17, and the number of Valley fever cases will increase by 50%. The Valley fever endemic region will expand north into dry western states, including Idaho, Wyoming, Montana, Nebraska, South Dakota, and North Dakota. Precipitation will limit the disease from spreading into states farther east and along the central and northern Pacific coast. This is the first quantitative estimate of how climate change may influence Valley fever in the United States. Our predictive model of Valley fever endemicity may provide guidance to public health officials to establish disease surveillance programs and design mitigation efforts to limit the impacts of this disease.

Categorization of species as native or nonnative using DNA sequence signatures without a complete reference library

New genetic diagnostic approaches have greatly aided efforts to document global biodiversity and improve biosecurity. This is especially true for organismal groups in which species diversity has been underestimated historically due to difficulties associated with sampling, the lack of clear morphological characteristics, and/or limited availability of taxonomic expertise. Among these methods, DNA sequence barcoding (also known as "DNA barcoding") and by extension, meta-barcoding for biological communities, has emerged as one of the most frequently utilized methods for DNA-based species identifications. Unfortunately, the use of DNA barcoding is limited by the availability of complete reference libraries (i.e., a collection of DNA sequences from morphologically identified species), and by the fact that the vast majority of species do not have sequences present in reference databases. Such conditions are critical especially in tropical locations that are simultaneously biodiversity rich and suffer from a lack of exploration and DNA characterization by trained taxonomic specialists. To facilitate efforts to document biodiversity in regions lacking complete reference libraries, we developed a novel statistical approach that categorizes unidentified species as being either likely native or likely nonnative based solely on measures of nucleotide diversity. We demonstrate the utility of this approach by categorizing a large sample of specimens of terrestrial insects and spiders (collected as part of the Moorea BioCode project) using a generalized linear mixed model (GLMM). Using a training data set of known endemic (n = 45) and known introduced species (n = 102), we then estimated the likely native/nonnative status for 4,663 specimens representing an estimated 1,288 species (412 identified species), including both those specimens that were either unidentified or whose endemic/introduced status was uncertain. Using this approach, we were able to increase the number of categorized specimens by a factor of 4.4 (from 794 to 3,497), and the number of categorized species by a factor of 4.8 from (147 to 707) at a rate much greater than chance (77.6% accuracy). The study identifies phylogenetic signatures of both native and nonnative species and suggests several practical applications for this approach including monitoring biodiversity and facilitating biosecurity.

Fires, Smoke Exposure, and Public Health: An Integrative Framework to Maximize Health Benefits From Peatland Restoration

Emissions of particulate matter from fires associated with land management practices in Indonesia contribute to regional air pollution and mortality. We assess the public health benefits in Indonesia, Malaysia, and Singapore from policies to reduce fires by integrating information on fire emissions, atmospheric transport patterns, and population exposure to fine particulate matter (PM2.5). We use adjoint sensitivities to relate fire emissions to PM2.5 for a range of meteorological conditions and find that a Business-As-Usual scenario of land use change leads, on average, to 36,000 excess deaths per year into the foreseeable future (the next several decades) across the region. These deaths are largely preventable with fire reduction strategies, such as blocking fires in peatlands, industrial concessions, or protected areas, which reduce the health burden by 66, 45, and 14%, respectively. The effectiveness of these different strategies in mitigating human health impacts depends on the location of fires relative to the population distribution. For example, protecting peatlands through eliminating all fires on such lands would prevent on average 24,000 excess deaths per year into the foreseeable future across the region because, in addition to storing large amounts of fuel, many peatlands are located directly upwind of densely populated areas. We also demonstrate how this framework can be used to prioritize restoration locations for the Indonesian Peatland Restoration Agency based on their ability to reduce pollution exposure and health burden. This scientific framework is publicly available through an online decision support tool that allows stakeholders to readily determine the public health benefits of different land management strategies.

Closely related viruses of the marine picoeukaryotic alga Ostreococcus lucimarinus exhibit different ecological strategies

In marine ecosystems, viruses are major disrupters of the direct flow of carbon and nutrients to higher trophic levels. Although the genetic diversity of several eukaryotic phytoplankton virus groups has been characterized, their infection dynamics are less understood, such that the physiological and ecological implications of their diversity remain unclear. We compared genomes and infection phenotypes of the two most closely related cultured phycodnaviruses infecting the widespread picoprasinophyte Ostreococcus lucimarinus under standard- (1.3 divisions per day) and limited-light (0.41 divisions per day) nutrient replete conditions. OlV7 infection caused early arrest of the host cell cycle, coinciding with a significantly higher proportion of infected cells than OlV1-amended treatments, regardless of host growth rate. OlV7 treatments showed a near-50-fold increase of progeny virions at the higher host growth rate, contrasting with OlV1's 16-fold increase. However, production of OlV7 virions was more sensitive than OlV1 production to reduced host growth rate, suggesting fitness trade-offs between infection efficiency and resilience to host physiology. Moreover, although organic matter released from OlV1- and OlV7-infected hosts had broadly similar chemical composition, some distinct molecular signatures were observed. Collectively, these results suggest that current views on viral relatedness through marker and core gene analyses underplay operational divergence and consequences for host ecology.

Coccidioidomycosis Dynamics in Relation to Climate in the Southwestern United States

Valley fever is endemic to the southwestern United States. Humans contract this fungal disease by inhaling spores of Coccidioides spp. Changes in the environment can influence the abundance and dispersal of Coccidioides spp., causing fluctuations in valley fever incidence. We combined county-level case records from state health agencies to create a regional valley fever database for the southwestern United States, including Arizona, California, Nevada, New Mexico, and Utah. We used this data set to explore how environmental factors influenced the spatial pattern and temporal dynamics of valley fever incidence during 2000-2015. We compiled climate and environmental geospatial data sets from multiple sources to compare with valley fever incidence. These variables included air temperature, precipitation, soil moisture, surface dust concentration, normalized difference vegetation index, and cropland area. We found that valley fever incidence was greater in areas with warmer air temperatures and drier soils. The mean annual cycle of incidence varied throughout the southwestern United States and peaked following periods of low precipitation and soil moisture. From year-to-year, however, autumn incidence was higher following cooler, wetter, and productive springs in the San Joaquin Valley of California. In southcentral Arizona, incidence increased significantly through time. By 2015, incidence in this region was more than double the rate in the San Joaquin Valley. Our analysis provides a framework for interpreting the influence of climate change on valley fever incidence dynamics. Our results may allow the U.S. Centers for Disease Control and Prevention to improve their estimates of the spatial pattern and intensity of valley fever endemicity.

Threats to intact tropical peatlands and opportunities for their conservation

Large, intact areas of tropical peatland are highly threatened at a global scale by the expansion of commercial agriculture and other forms of economic development. Conserving peatlands on a landscape scale, with their hydrology intact, is of international conservation importance to preserve their distinctive biodiversity and ecosystem services and maintain their resilience to future environmental change. We explored threats to and opportunities for conserving remaining intact tropical peatlands; thus, we excluded peatlands of Indonesia and Malaysia, where extensive deforestation, drainage, and conversion to plantations means conservation in this region can protect only small fragments of the original ecosystem. We focused on a case study, the Pastaza-Marañón Foreland Basin (PMFB) in Peru, which is among the largest known intact tropical peatland landscapes in the world and is representative of peatland vulnerability. Maintenance of the hydrological conditions critical for carbon storage and ecosystem function of peatlands is, in the PMFB, primarily threatened by expansion of commercial agriculture linked to new transport infrastructure that is facilitating access to remote areas. There remain opportunities in the PMFB and elsewhere to develop alternative, more sustainable land-use practices. Although some of the peatlands in the PMFB fall within existing legally protected areas, this protection does not include the most carbon-dense (domed pole forest) areas. New carbon-based conservation instruments (e.g., REDD+, Green Climate Fund), developing markets for sustainable peatland products, transferring land title to local communities, and expanding protected areas offer pathways to increased protection for intact tropical peatlands in Amazonia and elsewhere, such as those in New Guinea and Central Africa which remain, for the moment, broadly beyond the frontier of commercial development.

Ribonucleoprotein purification and characterization using RNA Mango

The characterization of RNA-protein complexes (RNPs) is a difficult but increasingly important problem in modern biology. By combining the compact RNA Mango aptamer with a fluorogenic thiazole orange desthiobiotin (TO1-Dtb or TO3-Dtb) ligand, we have created an RNA tagging system that simplifies the purification and subsequent characterization of endogenous RNPs. Mango-tagged RNP complexes can be immobilized on a streptavidin solid support and recovered in their native state by the addition of free biotin. Furthermore, Mango-based RNP purification can be adapted to different scales of RNP isolation ranging from pull-down assays to the isolation of large amounts of biochemically defined cellular RNPs. We have incorporated the Mango aptamer into the S. cerevisiae U1 small nuclear RNA (snRNA), shown that the Mango-snRNA is functional in cells, and used the aptamer to pull down a U1 snRNA-associated protein. To demonstrate large-scale isolation of RNPs, we purified and characterized bacterial RNA polymerase holoenzyme (HE) in complex with a Mango-containing 6S RNA. We were able to use the combination of a red-shifted TO3-Dtb ligand and eGFP-tagged HE to follow the binding and release of the 6S RNA by two-color native gel analysis as well as by single-molecule fluorescence cross-correlation spectroscopy. Together these experiments demonstrate how the Mango aptamer in conjunction with simple derivatives of its flurophore ligands enables the purification and characterization of endogenous cellular RNPs in vitro.

Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models

Understanding the processes that determine above-ground biomass (AGB) in Amazonian forests is important for predicting the sensitivity of these ecosystems to environmental change and for designing and evaluating dynamic global vegetation models (DGVMs). AGB is determined by inputs from woody productivity [woody net primary productivity (NPP)] and the rate at which carbon is lost through tree mortality. Here, we test whether two direct metrics of tree mortality (the absolute rate of woody biomass loss and the rate of stem mortality) and/or woody NPP, control variation in AGB among 167 plots in intact forest across Amazonia. We then compare these relationships and the observed variation in AGB and woody NPP with the predictions of four DGVMs. The observations show that stem mortality rates, rather than absolute rates of woody biomass loss, are the most important predictor of AGB, which is consistent with the importance of stand size structure for determining spatial variation in AGB. The relationship between stem mortality rates and AGB varies among different regions of Amazonia, indicating that variation in wood density and height/diameter relationships also influences AGB. In contrast to previous findings, we find that woody NPP is not correlated with stem mortality rates and is weakly positively correlated with AGB. Across the four models, basin-wide average AGB is similar to the mean of the observations. However, the models consistently overestimate woody NPP and poorly represent the spatial patterns of both AGB and woody NPP estimated using plot data. In marked contrast to the observations, DGVMs typically show strong positive relationships between woody NPP and AGB. Resolving these differences will require incorporating forest size structure, mechanistic models of stem mortality and variation in functional composition in DGVMs.