University of New Hampshire

About: University of New Hampshire is a education organization based out in Durham, New Hampshire, United States. It is known for research contribution in the topics: Population & Solar wind. The organization has 9379 authors who have published 24025 publications receiving 1020112 citations. The organization is also known as: UNH.

Directional selection has shaped the oral jaws of Lake Malawi cichlid fishes

Abstract: East African cichlid fishes represent one of the most striking examples of rapid and convergent evolutionary radiation among vertebrates. Models of ecological speciation would suggest that functional divergence in feeding morphology has contributed to the origin and maintenance of cichlid species diversity. However, definitive evidence for the action of natural selection has been missing. Here we use quantitative genetics to identify regions of the cichlid genome responsible for functionally important shape differences in the oral jaw apparatus. The consistent direction of effects for individual quantitative trait loci suggest that cichlid jaws and teeth evolved in response to strong, divergent selection. Moreover, several chromosomal regions contain a disproportionate number of quantitative trait loci, indicating a prominent role for pleiotropy or genetic linkage in the divergence of this character complex. Of particular interest are genomic intervals with concerted effects on both the length and height of the lower jaw. Coordinated changes in this area of the oral jaw apparatus are predicted to have direct consequences for the speed and strength of jaw movement. Taken together, our results imply that the rapid and replicative nature of cichlid trophic evolution is the result of directional selection on chromosomal packages that encode functionally linked aspects of the craniofacial skeleton.

Long-Term Nitrogen Additions and Nitrogen Saturation in Two Temperate Forests

Abstract: This article reports responses of two different forest ecosystems to 9 years (1988–96) of chronic nitrogen (N) additions at the Harvard Forest, Petersham, Massachusetts. Ammonium nitrate (NH4NO3) was applied to a pine plantation and a native deciduous broad-leaved (hardwood) forest in six equal monthly doses (May–September) at four rates: control (no fertilizer addition), low N (5 g N m-2 y-1), high N (15 g N m-2 y-1), and low N + sulfur (5 g N m-2 y-1 plus 7.4 g S m-2 y-1). Measurements were made of net N mineralization, net nitrification, N retention, wood production, foliar N content and litter production, soil C and N content, and concentrations of dissolved organic carbon (DOC) and nitrogen (DON) in soil water. In the pine stand, nitrate losses were measured after the first year of additions (1989) in the high N plot and increased again in 1995 and 1996. The hardwood stand showed no significant increases in nitrate leaching until 1995 (high N only), with further increases in 1996. Overall N retention efficiency (percentage of added N retained) over the 9-year period was 97–100% in the control and low N plots of both stands, 96% in the hardwood high N plot, and 85% in the pine high N plot. Storage in aboveground biomass, fine roots, and soil extractable pools accounted for only 16–32% of the added N retained in the amended plots, suggesting that the one major unmeasured pool, soil organic matter, contains the remaining 68–84%. Short-term redistribution of 15N tracer at natural abundance levels showed similar division between plant and soil pools. Direct measurements of changes in total soil C and N pools were inconclusive due to high variation in both stands. Woody biomass production increased in the hardwood high N plot but was significantly reduced in the pine high N plot, relative to controls. A drought-induced increase in foliar litterfall in the pine stand in 1995 is one possible factor leading to a measured increase in N mineralization, nitrification, and nitrate loss in the pine high N plot in 1996.

Estimating cognitive load using remote eye tracking in a driving simulator

Abstract: We report on the results of a study in which pairs of subjects were involved in spoken dialogues and one of the subjects also operated a simulated vehicle. We estimated the driver's cognitive load based on pupil size measurements from a remote eye tracker. We compared the cognitive load estimates based on the physiological pupillometric data and driving performance data. The physiological and performance measures show high correspondence suggesting that remote eye tracking might provide reliable driver cognitive load estimation, especially in simulators. We also introduced a new pupillometric cognitive load measure that shows promise in tracking cognitive load changes on time scales of several seconds.

Exploring deep microbial life in coal-bearing sediment down to ~2.5 km below the ocean floor

Abstract: Microbial life inhabits deeply buried marine sediments, but the extent of this vast ecosystem remains poorly constrained. Here we provide evidence for the existence of microbial communities in ~40° to 60°C sediment associated with lignite coal beds at ~1.5 to 2.5 km below the seafloor in the Pacific Ocean off Japan. Microbial methanogenesis was indicated by the isotopic compositions of methane and carbon dioxide, biomarkers, cultivation data, and gas compositions. Concentrations of indigenous microbial cells below 1.5 km ranged from <10 to ~10^4 cells cm^(−3). Peak concentrations occurred in lignite layers, where communities differed markedly from shallower subseafloor communities and instead resembled organotrophic communities in forest soils. This suggests that terrigenous sediments retain indigenous community members tens of millions of years after burial in the seabed.

Improving land surface models with FLUXNET data

Abstract: . There is a growing consensus that land surface models (LSMs) that simulate terrestrial biosphere exchanges of matter and energy must be better constrained with data to quantify and address their uncertainties. FLUXNET, an international network of sites that measure the land surface exchanges of carbon, water and energy using the eddy covariance technique, is a prime source of data for model improvement. Here we outline a multi-stage process for "fusing" (i.e. linking) LSMs with FLUXNET data to generate better models with quantifiable uncertainty. First, we describe FLUXNET data availability, and its random and systematic biases. We then introduce methods for assessing LSM model runs against FLUXNET observations in temporal and spatial domains. These assessments are a prelude to more formal model-data fusion (MDF). MDF links model to data, based on error weightings. In theory, MDF produces optimal analyses of the modelled system, but there are practical problems. We first discuss how to set model errors and initial conditions. In both cases incorrect assumptions will affect the outcome of the MDF. We then review the problem of equifinality, whereby multiple combinations of parameters can produce similar model output. Fusing multiple independent and orthogonal data provides a means to limit equifinality. We then show how parameter probability density functions (PDFs) from MDF can be used to interpret model validity, and to propagate errors into model outputs. Posterior parameter distributions are a useful way to assess the success of MDF, combined with a determination of whether model residuals are Gaussian. If the MDF scheme provides evidence for temporal variation in parameters, then that is indicative of a critical missing dynamic process. A comparison of parameter PDFs generated with the same model from multiple FLUXNET sites can provide insights into the concept and validity of plant functional types (PFT) – we would expect similar parameter estimates among sites sharing a single PFT. We conclude by identifying five major model-data fusion challenges for the FLUXNET and LSM communities: (1) to determine appropriate use of current data and to explore the information gained in using longer time series; (2) to avoid confounding effects of missing process representation on parameter estimation; (3) to assimilate more data types, including those from earth observation; (4) to fully quantify uncertainties arising from data bias, model structure, and initial conditions problems; and (5) to carefully test current model concepts (e.g. PFTs) and guide development of new concepts.