Interannual, seasonal, and diel variation in soil respiration relative to ecosystem respiration at a wetland to upland slope at Harvard Forest

Soil carbon dioxide efflux (soil respiration, SR) was measured with eight autochambers at two locations along a wetland to upland slope at Harvard Forest over a 4 year period, 2003–2007. SR was consistently higher in the upland plots than at the wetland margin during the late summer/early fall. Seasonal and diel hystereses with respect to soil temperatures were of sufficient magnitude to prevent quantification of the influence of soil moisture, although apparent short‐term responses of SR to precipitation occurred. Calculations of annual cumulative SR illustrated a decreasing trend in SR over the 5 year period, which were correlated with decreasing springtime mean soil temperatures. Spring soil temperatures decreased despite rising air temperatures over the same period, possibly as an effect of earlier leaf expansion and shading. The synchronous decrease in spring soil temperatures and SR during regional warming of air temperatures may represent a negative feedback on a warming climate by reducing CO2 production from soils. SR reached a maximum later in the year than total ecosystem respiration (ER) measured at a nearby eddy covariance flux tower, and the seasonality of their temperature response patterns were roughly opposite. SR, particularly in the upland, exceeded ER in the late summer/early fall in each year, suggesting that areas of lower efflux such as the wetland may be significant in the flux tower footprint or that long‐term bias in either estimate may create a mismatch. Annual estimates of ER decreased over the same period and were highly correlated with SR

A new model of Holocene peatland net primary production, decomposition, water balance, and peat accumulation

Peatland carbon and water cycling are tightly coupled, so dynamic modeling of peat accumulation over decades to millennia should account for carbon-water feedbacks. We present initial results from a new simulation model of long-term peat accumulation, evaluated at a wellstudied temperate bog in Ontario, Canada. The Holocene Peat Model (HPM) determines vegetation community composition dynamics and annual net primary productivity based on peat depth (as a proxy for nutrients and acidity) and water table depth. Annual peat (carbon) accumulation is the net balance above- and below-ground productivity and litter/peat decomposition – a function of peat hydrology (controlling depth to and degree of anoxia). Peat bulk density is simulated as a function of degree of humification, and affects the water balance through its influence on both the growth rate of the peat column and on peat hydraulic conductivity and the capacity to shed water. HPM output includes both time series of annual carbon and water fluxes, peat height, and water table depth, as well as a final peat profile that can be “cored” and compared to field observations of peat age and macrofossil composition. A stochastic 8500-yr, annual precipitation time series was constrained by a published Holocene climate reconstruction for southern Quebec. HPM simulated 5.4 m of ´ peat accumulation (310 kg C m−2 ) over 8500 years, 6.5% of total NPP over the period. Vascular plant functional types accounted for 65% of total NPP over 8500 years but only 35% of the final (contemporary) peat mass. Simulated age-depth and carbon accumulation profiles were compared to a radiocarbon dated 5.8 m, c.9000-yr core. The simulated core was younger than observations at most depths, but had a similar overall trajectory; carbon accumulation rates were generally higher in the simulation and were somewhat more variable than observations. HPM results were sensitive to centuryscale anomalies in precipitation, with extended drier periods (precipitation reduced ∼10%) causing the peat profile to lose carbon (and height), despite relatively small changes in NP

QTL and systems genetics analysis of mouse grooming and behavioral responses to novelty in an open field

International audienceThe open field is a classic test used to assess exploratory behavior, anxiety, and locomotor activity in rodents. Here we mapped quantitative trait loci (QTLs) underlying behaviors displayed in an open field, using a panel of 53 BXD recombinant inbred mouse strains with deep replication (10 per strain and sex). The use of these strains permits the integration and comparison of data obtained in different laboratories, and also offers the possibility to study trait covariance by exploiting powerful bioinformatics tools and resources. We quantified behavioral traits during 20 min test sessions including (1) percent time spent and distance travelled near the wall (thigmotaxis), (2) leaning against the wall, (3) rearing, (4) jumping, (5) grooming duration, (6) grooming frequency, (7) locomotion, and (8) defecation. All traits exhibit moderate heritability making them amenable to genetic analysis. We identified a significant QTL on chromosome M.m. 4 at ~104 Mb that modulates grooming duration in both males and females (LRS values of ~18, explaining 25% and 14% of the variance, respectively) and a suggestive QTL modulating locomotion that maps to the same locus. Bioinformatic analysis indicates Disabled 1 (Dab1, a key protein in the reelin signaling pathway) as a particularly strong candidate gene modulating these behaviors. We also found two highly suggestive QTLs for a sex by strain interaction for grooming duration on chromosomes 13 and 17. In addition, we identified a pairwise epistatic interaction between loci on chromosomes 12 at 36-37 Mb and 14 at 34-36 Mb that influences rearing frequency in males

Integration of heterogeneous functional genomics data in gerontology research to find genes and pathway underlying aging across species.

Understanding the biological mechanisms behind aging, lifespan and healthspan is becoming increasingly important as the proportion of the world\u27s population over the age of 65 grows, along with the cost and complexity of their care. BigData oriented approaches and analysis methods enable current and future bio-gerontologists to synthesize, distill and interpret vast, heterogeneous data from functional genomics studies of aging. GeneWeaver is an analysis system for integration of data that allows investigators to store, search, and analyze immense amounts of data including user-submitted experimental data, data from primary publications, and data in other databases. Aging related genome-wide gene sets from primary publications were curated into this system in concert with data from other model-organism and aging-specific databases, and applied to several questions in genrontology using. For example, we identified Cd63 as a frequently represented gene among aging-related genome-wide results. To evaluate the role of Cd63 in aging, we performed RNAi knockdown of the C. elegans ortholog, tsp-7, demonstrating that this manipulation is capable of extending lifespan. The tools in GeneWeaver enable aging researchers to make new discoveries into the associations between the genes, normal biological processes, and diseases that affect aging, healthspan, and lifespan

Relationship between ecosystem productivity and photosynthetically-active radiation for northern peatlands

We analyzed the relationship between net ecosystem exchange of carbon dioxide (NEE) and irradiance (as photosynthetic photon flux density or PPFD), using published and unpublished data that have been collected during midgrowing season for carbon balance studies at seven peatlands in North America and Europe. NEE measurements included both eddy-correlation tower and clear, static chamber methods, which gave very similar results. Data were analyzed by site, as aggregated data sets by peatland type (bog, poor fen, rich fen, and all fens) and as a single aggregated data set for all peatlands. In all cases, a fit with a rectangular hyperbola (NEE = α PPFD Pmax/(α PPFD + Pmax) + R) better described the NEE-PPFD relationship than did a linear fit (NEE = β PPFD + R). Poor and rich fens generally had similar NEE-PPFD relationships, while bogs had lower respiration rates (R = −2.0μmol m−2s−1 for bogs and −2.7 μmol m−2s−1 for fens) and lower NEE at moderate and high light levels (Pmax = 5.2 μmol m−2s−1 for bogs and 10.8 μmol m−2s−1 for fens). As a single class, northern peatlands had much smaller ecosystem respiration (R = −2.4 μmol m−2s−1) and NEE rates (α = 0.020 and Pmax = 9.2μmol m−2s−1) than the upland ecosystems (closed canopy forest, grassland, and cropland) summarized by Ruimy et al. [1995]. Despite this low productivity, northern peatland soil carbon pools are generally 5–50 times larger than upland ecosystems because of slow rates of decomposition caused by litter quality and anaerobic, cold soils

Influence of Different Plant Species on Methane Emissions from Soil in a Restored Swiss Wetland

Plants are a major factor influencing methane emissions from wetlands, along with environmental parameters such as water table, temperature, pH, nutrients and soil carbon substrate. We conducted a field experiment to study how different plant species influence methane emissions from a wetland in Switzerland. The top 0.5 m of soil at this site had been removed five years earlier, leaving a substrate with very low methanogenic activity. We found a sixfold difference among plant species in their effect on methane emission rates: Molinia caerulea and Lysimachia vulgaris caused low emission rates, whereas Senecio paludosus, Carex flava, Juncus effusus and Typha latifolia caused relatively high rates. Centaurea jacea, Iris sibirica, and Carex davalliana caused intermediate rates. However, we found no effect of either plant biomass or plant functional groups – based on life form or productivity of the habitat – upon methane emission. Emissions were much lower than those usually reported in temperate wetlands, which we attribute to reduced concentrations of labile carbon following topsoil removal. Thus, unlike most wetland sites, methane production in this site was probably fuelled chiefly by root exudation from living plants and from root decay. We conclude that in most wetlands, where concentrations of labile carbon are much higher, these sources account for only a small proportion of the methane emitted. Our study confirms that plant species composition does influence methane emission from wetlands, and should be considered when developing measures to mitigate the greenhouse gas emissions

Cross-Species Analyses Identify Dlgap2 as a Regulator of Age-Related Cognitive Decline and Alzheimer\u27s Dementia.

Genetic mechanisms underlying age-related cognitive decline and dementia remain poorly understood. Here, we take advantage of the Diversity Outbred mouse population to utilize quantitative trait loci mapping and identify Dlgap2 as a positional candidate responsible for modifying working memory decline. To evaluate the translational relevance of this finding, we utilize longitudinal cognitive measures from human patients, RNA expression from post-mortem brain tissue, data from a genome-wide association study (GWAS) of Alzheimer\u27s dementia (AD), and GWAS results in African Americans. We find an association between Dlgap2 and AD phenotypes at the variant, gene and protein expression, and methylation levels. Lower cortical DLGAP2 expression is observed in AD and is associated with more plaques and tangles at autopsy and faster cognitive decline. Results will inform future studies aimed at investigating the cross-species role of Dlgap2 in regulating cognitive decline and highlight the benefit of using genetically diverse mice to prioritize novel candidates

Vegetation Type Dominates the Spatial Variability in CH<inf>4</inf> Emissions Across Multiple Arctic Tundra Landscapes

Methane (CH4) emissions from Arctic tundra are an important feedback to global climate. Currently, modelling and predicting CH4 fluxes at broader scales are limited by the challenge of upscaling plot-scale measurements in spatially heterogeneous landscapes, and by uncertainties regarding key controls of CH4 emissions. In this study, CH4 and CO2 fluxes were measured together with a range of environmental variables and detailed vegetation analysis at four sites spanning 300 km latitude from Barrow to Ivotuk (Alaska). We used multiple regression modelling to identify drivers of CH4 flux, and to examine relationships between gross primary productivity (GPP), dissolved organic carbon (DOC) and CH4 fluxes. We found that a highly simplified vegetation classification consisting of just three vegetation types (wet sedge, tussock sedge and other) explained 54% of the variation in CH4 fluxes across the entire transect, performing almost as well as a more complex model including water table, sedge height and soil moisture (explaining 58% of the variation in CH4 fluxes). Substantial CH4 emissions were recorded from tussock sedges in locations even when the water table was lower than 40 cm below the surface, demonstrating the importance of plant-mediated transport. We also found no relationship between instantaneous GPP and CH4 fluxes, suggesting that models should be cautious in assuming a direct relationship between primary production and CH4 emissions. Our findings demonstrate the importance of vegetation as an integrator of processes controlling CH4 emissions in Arctic ecosystems, and provide a simplified framework for upscaling plot scale CH4 flux measurements from Arctic ecosystems

Pulse‐labeling studies of carbon cycling in Arctic tundra ecosystems: The contribution of photosynthates to methane emission

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94952/1/gbc790.pd

Probabilistic downscaling of remote sensing data with applications for multi-scale biogeochemical flux modeling

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes