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A characteristic feature of the eastern Bering Sea (EBS) is a subsurface layer linked to seasonal sea ice (SSI) and defined by bottom temperatures less than 2 °C, which is termed the cold pool. Cold pool variability is directly tied to regional zooplankton and fish dynamics. Multifrequency (200 and 460 kHz) acoustic backscatter data were collected remotely using upward looking echosounders along the EBS shelf from 2008 and 2018 and used as a proxy of biological abundance. Acoustic data were coupled with bottom temperature and regional SSI data from the cold (2006–2013) and warm (2014–2018) regimes to assess the relationship between biological scattering communities and cold pool variation. Acoustic backscatter was 2 orders of magnitude greater during the cold regime than during the warm regime, with multifrequency analysis indicating a shift in the warm regime frequency-dependent scattering communities. Cold pool proxy SSI was a stronger predictor for biological scattering than bottom temperature in the cold regime, while warm regime bottom temperature and SSI were equal in predictive power and resulted in improved predictive model performance. Results suggest coupled cold pool and frequency-dependent scattering dynamics are a potential regime shift indicator and may be useful for management practices in surrounding Arctic ecosystems.

The purpose of this paper is to provide a robust process to statistically analyze reflective cracking field performance data. There is often a lack of consistency and transparency in statistical analysis of pavement field performance data, which may not satisfy ANOVA or regression modeling assumptions. Twelve full-scale asphalt concrete (AC) overlay pavement test sections located at the MnROAD test facility are used to demonstrate the statistical framework. The percentage of cracking reported at joint locations (%RC) is used to represent reflective cracking performance, and its relationships to pre-overlay load transfer efficiency (LTE), truck traffic, overlay thickness, and common performance indices determined from laboratory tests are investigated. The three laboratory tests considered in this study are the disk-shaped compact tension (DCT), semi-circular bend (SCB) and overlay tester (OT). Logistic regression models are used for estimation. Predictive abilities of various models are compared in relation to the percentage odds (%odds) of reflective cracking. Model output is binary: it estimates not the relative amount of reflective cracking but instead the probability of a given pavement structure cracking. Varying ability to perform asphalt mixture laboratory performance testing is assumed. One such model, where no laboratory performance testing variables are included, shows that a one-unit increase (1-in.) in AC overlay thickness may result in approximately a third decrease in the %odds of reflective cracking. A logistic regression model developed that considers laboratory performance data from DCT, SCB, and OT results in the optimal model balancing best fit and best prediction properties without overfitting.

The impact of multibeam echosounder (MBES) operations on marine mammals has been less studied compared to military sonars. To contribute to the growing body of MBES knowledge, echolocation clicks of foraging Cuvier's beaked whales were detected on the Southern California Antisubmarine Warfare Range (SOAR) hydrophones during two MBES surveys and assembled into foraging events called group vocal periods (GVPs). Four GVP characteristics were analyzed Before, During, and After 12 kHz MBES surveys at the SOAR in 2017 and 2019 to assess differences in foraging behavior with respect to the mapping activity. The number of GVP per hour increased During and After MBES surveys compared with Before. There were no other differences between non-MBES and MBES periods for the three other characteristics: the number of clicks per GVP, GVP duration, and click rate. These results indicate that there was not a consistent change in foraging behavior during the MBES surveys that would suggest a clear response. The animals did not leave the range nor stop foraging during MBES activity. These results are in stark contrast to those of analogous studies assessing the effect of Naval mid-frequency active sonar on beaked whale foraging, where beaked whales stopped echolocating and left the area.

Availability of essential food resources is one of the most important drivers of survival and to persist in environments where critical resource abundance is changing, animals must either relocate or adapt in place. Testing an animal’s ability to respond to alternative conditions can reveal differences in physiological responses. We used flow-through respirometry to characterize metabolic phenotypes of the desert-adapted cactus mouse (Peromyscus eremicus) under diurnally variable environmental conditions that mimic the Sonoran Desert and treated mice with two different diets; a standard diet and a low-fat diet. We found significant diet-specific differences in the rate of water loss and serum electrolyte values. Mice fed the low-fat diet lost more water relative to those eating the standard diet and patterns of de novo lipogenesis are not limited by dietary composition. Our results are consistent with the hypothesis that diet contributes to water homeostasis. Compared to other desert animals, rodents have limited capacity to dissipate heat using evaporative cooling, limiting thermoregulatory performance at higher temperatures. We predict that a mismatch in physiological requirements and environmental conditions could significantly impact small mammal survival in desert environments if optimal foods become less abundant, as may be the case given climate change.

The hemlock woolly adelgid (HWA) (Hemiptera: Adelges tsugae Annand) is an invasive insect causing damage to Eastern ( Tsuga canadensis (L.) Carr.) and Carolina ( Tsuga caroliniana Engelm.) hemlock trees in the eastern United States. Maine and New Hampshire are currently the northernmost front of HWA's range. Developing methods to locate newly infested stands is paramount in the effort to monitor HWA range expansion; presently, the most reliable method of detection requires extensive on-the-ground manual surveying. Field surveys for invasive pests like HWA consume multiple resources, limiting the amount of area that can be surveyed, and the results often misrepresent the true extent or patchiness of an invasion. Satellite based remote sensing, using vegetation indices, enables us to detect insect driven changes in forest health at a landscape scale. Our objectives were to classify HWA infested hemlock stands along the infestation front and demonstrate how the classification product could be used to improve HWA survey planning. Our workflow consisted of 1) modeling hemlock habitat suitability using a Maximum Entropy algorithm; 2) developing decision tree rules to classify likely infested stands from a Landsat time series, using the habitat suitability model to mask out non-hemlock areas; and 3) field check the final classification product. The hemlock habitat suitability model attained an overall accuracy of 68.2%. Partitioning of leaf-on multi-year (1995–2013) Landsat 5 and 8 data resulted in seven probability of infestation classes with a training R 2 = 0.782. Agreement between the classification and previously reported HWA infestations was 75.0% in conifer forests, 33.3% in mixed forests and 50.0% in deciduous forests. Agreement between the classification and test-survey locations was 78.6%; verified new infestations were detected as far as 19 km away from previously reported infestations. The methods presented outline how Landsat could be used to detect low-level HWA infestations. Classification products such as this ultimately could be used by federal and state agencies to target specific areas for efficient survey, suppression, and eradication efforts.

To create appropriate marine mammal protection regulations, a better understanding of potential impacts of anthropogenic noise on marine mammals is needed. To contribute to the growing body of knowledge on this subject, echolocation clicks of foraging Cuvier's beaked whales were detected on the Southern California Antisubmarine Warfare Range (SOAR) hydrophones during 12 kHz multibeam echosounder (MBES) ocean mapping surveys in 2017 and 2019, and assembled into foraging events called group vocal periods (GVPs). Four GVP characteristics were analyzed Before, During, and After the MBES surveys to assess differences in foraging behavior with respect to the mapping activity. The number of GVP per hour increased During and After MBES surveys compared with Before. There were no other differences between non-MBES and MBES periods for the other characteristics: number of clicks per GVP, GVP duration, and click rate. These results indicate that the animals did not leave the range or stopforaging during MBES activity. [Funded by NOAA Grant No. NA15NOS4000200 through the UNH Joint Hydrographic Center.]

How climate and rising carbon dioxide concentrations (pCO2) have influenced competition between C3 and C4 plants over the last 50 years is a critical uncertainty in climate change research. Here, we used carbon isotope (δ13C) values of the saprotrophic lawn fungus Amanita thiersii to integrate the signal of C3 and C4 carbon in samples collected between 1982 and 2009 from the Midwestern USA. We then calculated 13C fractionation (Δ) to assess the balance between C3 and C4 photosynthesis as influenced by mean annual temperature (MAT), mean annual precipitation over a 30-year period (MAP-30), and pCO2. Sporocarp Δ correlated negatively with MAT (-1.74‰ °C-1, 79% of variance) and positively with MAP (9.52‰ m-1, 15% of variance), reflecting the relative productivity of C3 and C4 grasses in lawns. In addition, Δ values correlated positively with pCO2 (0.072‰ ppm-1, 5% of variance). Reduced photorespiration with rising pCO2 accounted for 20% of this increased Δ, but the remaining 80% is consistent with increased assimilation of C3-derived carbon by Amanita thiersii resulting from increased productivity of C3 grasses with rising pCO2. Between 1982 and 2009, pCO2 rose by 47 ppm and the relative contribution of C3 photosynthesis to Amanita thiersii carbon increased 18.5%. The δ13C value of Amanita thiersii may integrate both lawn maintenance practices and the physiological responses of turf grasses to rising CO2 concentrations.

Lakes have been suggested as an indicator of climate change; however, long-term, systematic records of lake temperature are limited. Satellite remote sensing is capable of supporting lake temperature mapping with the advantage of large-area and systematic observations. The goal of this research application was to assess spatiotemporal trends in lake skin temperature for all lakes over 8 ha across northern New England for the past three decades. Nearly 10 000 Landsat scenes for July, August, and September from 1984 to 2014 were processed using MODTRAN and MERRA parameterizations to generate atmospherically corrected lake skin temperature records. Results show, on average, lakes warmed at a rate of 0.8°C decade−1, with smaller lakes warming at a faster rate. Complementing regression and space–time analyses showed similar results (R2 = 0.63) for lake temperature trends and found lakes, on average, are warming faster than daily maximum or minimum air temperature. No major hot spots were found as lake temperature changes were heterogeneous on a local scale and evenly distributed across the region. Maximum and minimum daily temperature, lake size, and elevation were found as significant drivers of lake temperature. This effort provides the first regionally focused and comprehensive spatiotemporal assessment of thousands (n = 3955) of lakes concentrated in one geographic region. The approach is scalable and adaptable to any region for assessing lake temperature trends and potential drivers.

The hemlock woolly adelgid (HWA) (Adelges tsugae Annand) is an invasive insect in the eastern United States. Since its initial detection in Richmond, Virginia, in 1951, HWA has spread to half of the eastern hemlock (Tsuga canadensis (L.) Carr.) natural range. Detection of early infestation symptoms via remote sensing requires the knowledge of the changes in reflectance resulting from physiological changes in the host as inflicted by the insect and the selection of equipment with the appropriate sensor characteristics. Laboratory-based reflectance measurements of infested and non-infested hemlock foliage collected from four sites in southern New Hampshire and Maine occurred biweekly over 6 months in 2012 and weekly over 5 weeks in 2013. Vegetation indices (red edge inflection point (REIP), normalized difference vegetation index (NDVI), moisture stress index (MSI), and near infrared (NIR) 3/1 ratio) were associated with concurrent chlorophyll and moisture content data. Infested first-year foliage contained greater concentrations of chlorophyll and moisture, resulting in reduced visible spectral reflectance, greater REIP and NDVI values, and lower MSI and NIR 3/1 values than non-infested foliage. Furthermore, fluorescence measurements indicated greater photosystem function during the early stages of infestation, suggesting a possible compensatory response by hemlock to infestation. Significant differences in reflectance between infested and non-infested foliage were observed in late June and July in the weeks immediately following HWA settlement on new growth. Implementing these observations during remote sensing mission planning may increase the likelihood of detecting early HWA infestation symptoms at landscape scales.

This paper describes a new method to monitor end milling tool wear in real-time by tracking force model coefficients during the cutting process. The behavior of these coefficients are shown to be independent from the cutting conditions and correlated with the wear state of the cutting tool. The tangential and radial force model coefficients are normalized and combined into a single parameter for wear monitoring. A number of experiments with different workpiece materials are run to investigate the feasibility of tool wear monitoring using this method. We show that this method can be used in real-time to track tool wear and detect the transition point from the gradual wear region to the failure region in which the rate of wear accelerates.

Background: The dynamics of gaps plays a role in the regimes of tree mortality, production of coarse woody debris (CWD) and the variability of light in the forest understory. Aims: To quantify the area affected by, and the carbon fluxes associated with, natural gap-phase disturbances in a tropical lowland evergreen rain forest by use of ground measurements and high-resolution satellite images. Methods: We surveyed two large forest inventory plots of 114 and 53 ha of the Tapajos National Forest (TNF) in the Brazilian Amazon during 2008 and 2009, respectively. We mapped all gaps and collected data on light availability, CWD stocks and tree mortality in the field. Gap location, canopy openness (CO) and leaf area index (LAI) estimated in the field were compared with two IKONOS–2 high-resolution satellite images acquired at approximately the time of the field measurements. Results: In the two large plots (167 ha total area) we found 96 gaps. The gaps represented 1.42% of the total area and gaps

Reoccurring seasonal cyanobacterial harmful algal blooms (CHABs) persist in many waters, and recent work has shown links between CHAB and elevated risk of amyotrophic lateral sclerosis (ALS). Quantifying the exposure levels of CHAB as a potential risk factor for ALS is complicated by human mobility, potential pathways, and data availability. In this work, we develop phycocyanin concentration (i.e., CHAB exposure) maps using satellite remote sensing across northern New England to assess relationships with ALS cases using a spatial epidemiological approach. Strategic semi-analytical regression models integrated Landsat and in situ observations to map phycocyanin concentration (PC) for all lakes greater than 8 ha (n = 4117) across the region. Then, systematic versions of a Bayesian Poisson Log-linear model were fit to assess the mapped PC as a risk factor for ALS while accounting for model uncertainty and modifiable area unit problems. The satellite remote sensing of PC had strong overall ability to map conditions (adj. R2, 0.86; RMSE, 11.92) and spatial variability across the region. PC tended to be positively associated with ALS risk with the level of significance depending on fixed model components. Meta-analysis shows that when average PC exposure is 100 μg/L, an all model average odds ratio is 1.48, meaning there is about a 48% increase in average ALS risk. This research generated the first regionally comprehensive map of PC for thousands of lakes and integrated robust spatial uncertainty. The outcomes support the hypothesis that cyanotoxins increase the risk of ALS, which helps our understanding of the etiology of ALS.

The primary purpose of this study is to understand better the roadway performance and the conditions that trigger congestion. Incidents of recurrent congestion were encountered at a location on New Hampshire Interstate 93 northbound, where radar measurements of speed were continuously recorded from April through November 2010. The root cause for the onset of congestion, both recurrent and nonrecurrent, is impossible to discern and explain by using exploratory data analyses alone; the data are too noisy. A time series modeling approach suggests that the magnitude of traffic flow and volatility, measured as the second (variance) and third (skewness) moments of flow residuals, can explain the triggering of congestion events for a highly variable environment that changes time by the time and day. The approach includes two types of mathematical models: generalized additive binomial probability models for roadway congestion that use functions of traffic flow and volatility as explanatory variables and functional data models that decompose and smooth traffic data to add insight to the roles that flow and volatility play in the congestion process. Most notably, the probability of congestion is shown to be a function of the short-term history of flow and volatility. The changes in these values—the first derivatives of flow and second and third moments of flow residuals derived from functional data model—are shown as well. Model selection, parameter estimation, and checking are presented.

A method to assess the impacts of forecasted climate change on pavement deterioration is presented. Traditional methods of pavement design use historic climate data and assume that climate is stationary with time. Climate change challenges this assumption of stationarity (i.e., natural driving forces of engineering have a variability described by a time-invariant probability density function). Therefore, the use of historic climate data is insufficient for the prediction of climate conditions. The focus is on the preparation and the use of climate model data sets as inputs to the Mechanistic–Empirical Pavement Design Guide (MEPDG) model to simulate flexible pavement performance and deterioration over time. The method is illustrated with a case study that uses future climate model temperature data from three North American Regional Climate Change Assessment Program scenarios at four sites across New England. Pavement distress predicted with future temperature scenarios is compared with that from MEPDG temperature data. Application of the method demonstrates the importance of matching the overlapping periods before using climate forecast output in the MEPDG. Although the simulated impact of future temperature changes on pavement performance was negligible for alligator cracking at the four study sites, asphalt concrete rutting differences were great enough to warrant additional consideration and to suggest that climate change and variability in future climate scenarios could affect pavement design and evaluation. The proposed method can be used to evaluate the impact of other climate variables alone or in combination. The method also can readily use new climate model output and be adapted for new downscaling methods.

A Bayesian statistical approach for determining the parameter uncertainty of a storm-water treatment model is reported. The storm-water treatment technologies included a sand filter and a subsurface gravel wetland. The two field systems were loaded and monitored in a side-by-side fashion over a two-year period. The loading to each system was storm-water runoff generated by ambient rainfall on a commuter parking lot. Contaminant transport is simulated by using a one-dimensional advection-dispersion model. The unknown parameters of the model are the contaminant deposition rate and the hydrodynamic dispersion. The following contaminants are considered in the study: total suspended solids, total petroleum hydrocarbons–diesel range hydrocarbons, and zinc. Parameter uncertainties are addressed by estimating the posterior probability distributions through a conventional Metropolis-Hastings algorithm. Results indicate that the posterior distributions are unimodal and, in some instances, exhibit some level of skew...

Geographic variability in abundance can be driven by multiple physical and biological factors operating at multiple scales. To understand the determinants of larval trematode prevalence within populations of the marine snail host Littorina littorea, we quantified many physical and biological variables at 28 New England intertidal sites. A hierarchical, mixed-effects model identified the abundance of gulls (the final hosts and dispersive agents of infective trematode stages) and snail size (a proxy for time of exposure) as the primary factors associated with trematode prevalence. The predominant influence of these variables coupled with routinely low infection rates (21 of the 28 populations exhibited prevalence

In this paper, we present an improved procedure for collecting no or little atmosphere- and snow-contaminated observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The resultant time series of daily MODIS data of a temperate deciduous broadleaf forest (the Bartlett Experimental Forest) in 2004 show strong seasonal dynamics of surface reflectance of green, near infrared and shortwave infrared bands, and clearly delineate leaf phenology and length of plant growing season. We also estimate the fractions of photosynthetically active radiation (PAR) absorbed by vegetation canopy (FAPARcanopy), leaf (FAPARleaf), and chlorophyll (FAPARchl), respectively, using a coupled leafcanopy radiative transfer model (PROSAIL-2) and daily MODIS data. The Markov Chain Monte Carlo (MCMC) method (the Metropolis algorithm) is used for model inversion, which provides probability distributions of the retrieved variables. A two-step procedure is used to estimate the fractions of absorbed PAR: (1) to retrieve biophysical and biochemical variables from MODIS images using the PROSAIL-2 model; and (2) to calculate the fractions with the estimated model variables from the first step. Inversion and forward simulations of the PROSAIL-2 model are carried out for the temperate deciduous broadleaf forest during day of year (DOY) 184 to 201 in 2005. The reproduced reflectance values from the PROSAIL-2 model agree well with the observed MODIS reflectance for the five spectral bands (green, red, NIR1, NIR2, and SWIR1). The estimated leaf area index, leaf dry matter, leaf chlorophyll content and FAPARcanopy values are close to field measurements at the site. The results also showed significant differences between FAPARcanopy and FAPARchl at the site. Our results show that MODIS imagery provides important information on biophysical and biochemical variables at both leaf and canopy levels.

We develop an error model to understand the reliability and accuracy of river discharge datasets that are now being used for a variety of important global change questions. The developed error model for cold region river discharge uses standard hydrometric data along with information on the frequency and precision of measurements, characteristics of river channel capacity, and method of discharge computation. The uncertainties of daily, monthly and annual discharge data for the downstream gauges of the six largest Eurasian rivers (Severnaya Dvina, Pechora, Ob', Yenisei, Lena and Kolyma) in the pan-Arctic drainage along with uncertainty of aggregated annual time series are evaluated using the suggested methodology. The study shows that uncertainties associated with discharge determination significantly change from year to year and strongly depend on the computational methods used and frequency of discharge measurements. Recent work by Peterson et al. (2002) has shown increases in river discharge to the Arctic Ocean of the six largest Eurasian rivers of 7% from 1936 to 1999. This paper focuses on determination of reliability in the discharge data which provided such conclusion. The obtained results further confirm the findings of Peterson et al. (2002) concerning the rise in river discharge. We found that errors of the total annual discharge for the six rivers over the period 1950–2000 are in the range 1.5–3.5%. The long-term trend of the observed discharge from these six rivers into the Arctic Ocean for 1936–2000, along with uncertainty associated with discharge data, is 2.0±0.4 km 3 /year.

In this paper, we present a theoretical and modeling framework to estimate the fractions of photosynthetically active radiation (PAR) absorbed by vegetation canopy (FAPAR canopy ), leaf (FAPAR leaf ), and chlorophyll (FAPAR chl ), respectively. FAPAR canopy is an important biophysical variable and has been used to estimate gross and net primary production. However, only PAR absorbed by chlorophyll is used for photosynthesis, and therefore there is a need to quantify FAPAR chl . We modified and coupled a leaf radiative transfer model (PROSPECT) and a canopy radiative transfer model (SAIL-2), and incorporated a Markov Chain Monte Carlo (MCMC) method (the Metropolis algorithm) for model inversion, which provides probability distributions of the retrieved variables. Our two-step procedure is: (1) to retrieve biophysical and biochemical variables using coupled PROSPECT + SAIL-2 model (PROSAIL-2), combined with multiple daily images (five spectral bands) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor; and (2) to calculate FAPAR canopy , FAPAR leaf and FAPAR chl with the estimated model variables from the first step. We evaluated our approach for a temperate forest area in the Northeastern US, using MODIS data from 2001 to 2003. The inverted PROSAIL-2 fit the observed MODIS reflectance data well for the five MODIS spectral bands. The estimated leaf area index (LAI) values are within the range of field measured data. Significant differences between FAPAR canopy and FAPAR chl are found for this test case. Our study demonstrates the potential for using a model such as PROSAIL-2, combined with an inverse approach, for quantifying FAPAR chl , FAPAR leaf , FAPAR canopy , biophysical variables, and biochemical variables for deciduous broadleaf forests at leaf- and canopy-levels over time.

We performed a synthetic analysis of Harvard Forest net ecosystem exchange of CO2 (NEE) time series and a simple ecosystem carbon flux model, the simplified Photosynthesis and Evapo-Transpiration model (SIPNET). SIPNET runs at a half-daily time step, and has two vegetation carbon pools, a single aggregated soil carbon pool, and a simple soil moisture sub-model. We used a stochastic Bayesian parameter estimation technique that provided posterior distributions of the model parameters, conditioned on the observed fluxes and the model equations. In this analysis, we estimated the values of all quantities that govern model behavior, including both rate constants and initial conditions for carbon pools. The purpose of this analysis was not to calibrate the model to make predictions about future fluxes but rather to understand how much information about process controls can be derived directly from the NEE observations. A wavelet decomposition enabled us to assess model performance at multiple time scales from diurnal to decadal. The model parameters are most highly constrained by eddy flux data at daily to seasonal time scales, suggesting that this approach is not useful for calculating annual integrals. However, the ability of the model to fit both the diurnal and seasonal variability patterns in the data simultaneously, using the same parameter set, indicates the effectiveness of this parameter estimation method. Our results quantify the extent to which the eddy covariance data contain information about the ecosystem process parameters represented in the model, and suggest several next steps in model development and observations for improved synthesis of models with flux observations.

We studied soil moisture dynamics and nitrous oxide (N2O) fluxes from agricultural soils in the humid tropics of Costa Rica. Using a split-plot design on two soils (clay, loam) we compared two crop types (annual, perennial) each unfertilized and fertilized. Both soils are of andic origin. Their properties include relatively low bulk density and high organic matter content, water retention capacity, and hydraulic conductivity. The top 2–3 cm of the soils consists of distinct small aggregates (dia.

Nitric oxide (NO), nitrous oxide (N2O), and methane (CH4) are naturally produced and consumed by soil biogeochemical processes. Naturally high variation between trace gas fluxes may temporarily increase due to agricultural management. We studied spatial and temporal variability of fluxes in the context of a 3-year field experiment established to identify and quantify N2O fluxes and controlling factors using automated field measurements. We measured trace gas fluxes, soil temperature, and moisture from fertilized and unfertilized balsa (Ochroma lagopus) plantations. Combining spatial and temporal sampling we evaluate if automatically measured time series of N2O emissions are representative of overall mean fluxes from fertilized loam under balsa. Soil trace gas fluxes were measured manually at 36 randomly distributed sampling locations per plot. Mean plot emissions were evaluated against fluxes measured by seven chambers commonly used for routine bimonthly manual measurements and against N2O emissions measured by two automated chambers at 4.6-hour sampling intervals. Trace gas fluxes were highly variable over 40 × 40 m plots. Nitrogen oxide fluxes were mainly spatially independent. Fertilization increased nitrogen oxide emissions but did not introduce spatial dependency of flux data. Within about 6 weeks fluxes approached pre-fertilization level again. Given high spatial variation of nitrogen oxide fluxes we find that automatically measured N2O fluxes represent the nature of the flux response well and are in the range of fluxes indicated by spatial sampling. When soils were relatively dry fertilization inhibited CH4 uptake.

Net ecosystem exchange (NEE) of CO2 was measured in four peatlands along plant community, hydrologic, and water chemistry gradients from bog to rich fen in a diverse peatland complex near Thompson, Manitoba, as part of the Boreal Ecosystem-Atmosphere Study (BOREAS). A simple model for estimating growing season net ecosystem productivity (NEP) using continuous measurements of photosynthetically active radiation (PAR), and peat temperature was constructed with weekly chamber measurements of NEE from May to October 1996. The model explained 79–83% of the variation in NEE across the four sites. Model estimation and parameter uncertainty calculations were performed using nonlinear regression analyses with a maximum likelihood objective function. The model underestimated maximum NEE and respiration during the midseason when the standard errors for each parameter were greatest. On a daily basis, uncertainty in the midday NEE simulation was higher than at night. Although the magnitude of both photosynthesis and respiration rates followed the trophic gradient bog less than poor fen less than intermediate fen less than rich fen, NEP did not follow the same pattern. NEP in the bog and rich fen was close to zero, while the poor and intermediate fens had higher NEP due to a greater imbalance between uptake and release of CO2. Although all sites had a positive growing season NEP, upper and lower 95% confidence limits showed that the bog and rich fen were either a source or sink of CO2 to the atmosphere, while the sedge-dominated poor and intermediate fens were accumulating approximately 20–100 g CO2 C m−2 over the 5 month period in 1996. Peatland ecosystems may switch from a net sink to a source of carbon on short timescales with small changes in soil temperature or water table position. Since the difference between production and decomposition is small, it is important to understand and quantify the magnitude of uncertainty in these measurements in order to predict the effect of climatic change on peatland carbon exchange.

This paper presents a complete Bayesian methodology for analyzing spatial data, one which employs proper priors and features diagnostic methods in the Bayesian spatial setting. The spatial covariance structure is modeled using a rich class of covariance functions for Gaussian random fields. A general class of priors for trend, scale, and structural covariance parameters is considered. In particular, we obtain analytic results that allow easy computation of the predictive distribution for an arbitrary prior on the parameters of the covariance function using importance sampling. The computations, as well as model diagnostics and sensitivity analysis, are illustrated with a set of precipitation data.

Measurements of atmospheric carbon dioxide and satellite-derived measurements of temperature and the vegetation index were used to investigate relationships among climate, carbon dioxide, and ecosystems. At the global scale, lagged correlations between temperature and carbon dioxide growth rate were found, indicating modulation by biogeochemical feedbacks. Spatial analysis of the temperature and vegetation index data suggests that the global correlations are a composite of individualistic responses of different ecosystems. The existence of biome-specific time scales of response implies that changes in global ecosystem distributions could indirectly alter the relationships between climate and carbon storage.

Major chemical species (Cl−, NO−3, SO2−4, Na+, K+, Mg2+, Ca2+) from 24 snowpits (sampled at a resolution of 3 cm, total 2995 samples) collected from northern, central, and southern Greenland were used for this investigation. The annual and seasonal (winter and summer) concentration of each chemical species was calculated and used to study the spatial distribution of chemical species over the central portion of the Greenland Ice Sheet. A two-sided t-distribution test (α = 0.05) suggests that concentrations of major chemical species in snow do not vary significantly over this portion of central Greenland. The relationship between chemical concentration and snow accumulation rate was investigated using annual data from two groups of snowpits: those from coastal sites (northern and southern Greenland); and those from high-altitude inland sites (central Greenland). The snowpit data from a single group, when examined independently of the other group, show that chemical concentrations do not vary with snow accumulation rate. However, when data from the two groups are integrated into a single data set, pseudorelationships appear, with NO−3 concentration decreasing and Na+, K+, Mg2+, and Cl− increasing as snow accumulation rate increases. Therefore we suggest that it is improper to study the relationship between chemical concentration and snow accumulation rate by using data collected from different geographic sites. The relationship between elevation and chemical concentration was investigated using the same suite of annual data sets. We find that Cl−, Na+, and Mg2+ concentrations decrease, while NO−3 concentration increases, with increasing elevation on the Greenland Ice Sheet.

The possibility of a bimodal log-likelihood function arises with certain data when the combined removal and signs-of-activities estimator is used. Bimodal log-likelihoods may, in turn, yield disjoint confidence intervals for certain confidence levels. The hypothesis that bimodality is caused by the violation of the equal catchability assumption of the removal model, leading to the combination of contradictory data/models in the combined estimator is set forth. Simulations exploring the effect of the violation of removal model assumptions on estimation and inference showed that the assumption of unequal capture probability influenced the frequency of bimodal likelihoods; similarly, extreme parameter values for probability of capture influenced the number of excessively large confidence intervals produced. A sex-specific combined estimator is developed as a remedial model tailored to the problem. The simulations suggest that both the signs-of-activities estimator and the sex-specific estimator perform equally well over the range of simulations presented, though the signs-of-activities estimator is easier to implement.

Our previous research has demonstrated the feasibility of monitoring tool wear during milling by continuously updating the coefficients of a cutting force model. The method requires a robust method for on-line model coefficient estimation. Estimation using Least Square Regression (LSR) is easily implemented but requires that the data points come from different cutting conditions which is not always possible. In this paper, a method for coefficient estimation and wear tracking based on Bayesian updating is described. The Bayesian method has the advantage that it can be used when the cutting conditions are constant.Copyright © 2012 by ASME

2.6-1000 km 2 ), large rivers, lakes, and reservoirs, using a 30 0 latitudelongitude river network to route and process material from continental source areas to the coastal zone. Mean annual aquatic TN removal (for the mid-1990s time period) is determined by the distributions of aquatic TN inputs, mean annual hydrological characteristics, and biological activity. Model-predicted TN concentrations at basin mouths corresponded wellwithobservations(medianrelativeerror= � 12%,interquartile rangeofrelativeerror= 85%), an improvement over assumptions of uniform aquatic removal across basins. Removal by aquatic systems globally accounted for 14% of total N inputs to continental surfaces, but represented 53% of inputs to aquatic systems. Integrated aquatic removal was similar in small rivers (16.5% of inputs), large rivers (13.6%), and lakes (15.2%), while large reservoirs were less important (5.2%). Bias related to runoff suggests improvements are needed in nonpoint N input estimates and/or aquatic biological activity. The within-basin approach represented by FrAMES-N will improve understanding of the freshwater nutrient flux response to anthropogenic change at global scales.

The strong influence of subsurface heterogeneity on contaminant migration and in situ remediation calls for an improved understanding of its origins and more efficient methods of characterization. Accordingly, an outcrop study of physical and chemical heterogeneity was conducted in a glaciofluvial deposit in Deerfield, New Hampshire, in order to uncover processes controlling the spatial variation of sediment properties and evaluate the extent to which geologic information can be used to characterize the observed variation. The results indicate that physical and chemical properties at the Deerfield site have distinctly different spatial correlation structures. Lithologic facies explain 31% to 60% of the variation in permeability, dithionite citrate (DC)-extractable manganese, and DC-extractable aluminum. Lithofacies bounding surfaces do not separate regions of significantly different DC-extractable iron; instead, 49% of its variation is explained by sediment color. Color also accounts for 34% of the variation in DC-extractable aluminum and 60% of the variation in DC-extractable manganese. Strong relationships with sediment facies and/or color enable detailed mapping of permeability, extractable iron, and extractable manganese. Differences in the geometries of iron and manganese enrichment, petrographic observations, and scanning electron microscope analyses indicate that (hydr)oxide grain coatings originated from the postdepositional weathering of biotite and garnet, coupled with local, redox-driven redistribution of the liberated iron and manganese. The findings suggest that lithofacies and color information can aid the characterization and modeling of heterogeneity at similar carbon-poor glaciofluvial sites.

[1] We present an uncertainty analysis of gross ecosystem carbon exchange (GEE) estimates derived from 7 years of continuous eddy covariance measurements of forest-atmosphere CO2 fluxes at Howland Forest, Maine, USA. These data, which have high temporal resolution, can be used to validate process modeling analyses, remote sensing assessments, and field surveys. However, separation of tower-based net ecosystem exchange (NEE) into its components (respiration losses and photosynthetic uptake) requires at least one application of a model, which is usually a regression model fitted to nighttime data and extrapolated for all daytime intervals. In addition, the existence of a significant amount of missing data in eddy flux time series requires a model for daytime NEE as well. Statistical approaches for analytically specifying prediction intervals associated with a regression require, among other things, constant variance of the data, normally distributed residuals, and linearizable regression models. Because the NEE data do not conform to these criteria, we used a Monte Carlo approach (bootstrapping) to quantify the statistical uncertainty of GEE estimates and present this uncertainty in the form of 90% prediction limits. We explore two examples of regression models for modeling respiration and daytime NEE: (1) a simple, physiologically based model from the literature and (2) a nonlinear regression model based on an artificial neural network. We find that uncertainty at the half-hourly timescale is generally on the order of the observations themselves (i.e., ∼100%) but is much less at annual timescales (∼10%). On the other hand, this small absolute uncertainty is commensurate with the interannual variability in estimated GEE. The largest uncertainty is associated with choice of model type, which raises basic questions about the relative roles of models and data.

[1] To more fully understand the role of precipitation in observed increases in freshwater discharge to the Arctic Ocean, data from a new archive of bias-adjusted precipitation records for the former USSR (TD9813), along with the CRU and Willmott-Matsuura data sets, were examined for the period 1936–1999. Across the six largest Eurasian river basins, snowfall derived from TD9813 exhibits a strongly significant increase until the late 1950s and a moderately significant decrease thereafter. A strongly significant decline in derived rainfall is also noted. Spatially, snowfall increases are found primarily across north-central Eurasia, an area where the rainfall decreases are most prominent. Although no significant change is determined in Eurasian-basin snowfall over the entire 64 year period, we note that interpolation from early, uneven station networks causes an overestimation of spatial precipitation, and that the local snowfall trends determined from gridded TD9813 data are likely underestimated. Yet, numerous uncertainties in historical Arctic climate data and the sparse, irregular nature of Arctic station networks preclude a confident assessment of precipitation-discharge linkages during the period of reported discharge trends.

The relationships between the concentration and the flux of chemical species (Cl{sup {minus}}, NO{sub 3}{sup {minus}}, SO{sub 4}{sup 2{minus}}, Na{sup +}, K{sup +}, NH{sub 4}{sup +}, Mg{sub 2+}, Ca{sup 2+}) versus snow accumulation rate were examined at GISP2 and 20D in Greenland, Mount Logan from the St. Elias Range, Yukon Territory, Canada, and Sentik Glacier from the northwest end of the Zanskar Range in the Indian Himalayas. At all sites, only nitrate flux is significantly ({alpha}=0.05) related to snow accumulation rate. Of all the chemical series, only nitrate concentration data are normally distributed. Therefore the authors suggest that nitrate concentration in snow is affected by postdepositional exchange with the atmosphere over a broad range of environmental conditions. The persistant summer maxima in nitrate observed in Greenland snow over the entire range of record studied (the last 800 years) may be mainly due to NO{sub x} released from peroxyacetyl nitrate by thermal decomposition in the presence of higher OH concentrations in summer. The late winter/early spring nitrate peak observed in modern Greenland snow may be related to the buildup of anthropogenically derived NO{sub y} in the Arctic troposphere during the long polar winter. 58 refs., 3 figs., 4 tabs.

Risk analysis can be defined as the evaluation of the probability of end events interpreted in terms of sequences of earlier events. In fisheries science, stock assessment provides future projections on which management decisions can be based, Uncertainties in estimating input parameters for such projections are considerable. For the assessment problem we propose an event tree analysis that couches the uncertainty of projections in terms of relative risk associated with various management options. As an illustration we rephrase the stock assessment advice given in 1980 for the Georges Bank haddock stock (Melanogrammus aiglefinus L.) in the framework of event tree analysis. This method is also examined for a second species: the Gulf of Mexico menhaden (Brevoortia patronus). We also evaluate the results and their sensitivity relative to choices of end events.

The concept of chronic toxicity has caused confusion in fish toxicology because it has developed four connotations: long duration, inclusion of all life stages, low severity, and high sensitivity. To compare alternate chronic tests and expressions of test results, we extracted concentration-response data from published life-cycle, partial-life-cycle, and early life-stage tests and derived concentration-response relationships by nonlinear regression. The effects examined were reductions in parental survival, fecundity, hatching success, larval survival, weight of early juveniles, and weight of early juveniles per egg. On the average, the most sensitive effect was reduction in fecundity, not effects on early life stages. We also found that maximum acceptable toxicant concentrations (MATCs) corresponded to fairly high levels of effect; mean reductions at the MATC were parental survival, 20%; fecundity, 42%; hatching, 12%; larval survival, 19%; weight, 20%; and weight/egg, 35%. These results indicate that, on average, MATCs are concentrations that cause substantial effects and that MATCs estimated from early life-stage tests are not good substitutes for life-cycle tests. We suggest that the acute-chronic dichotomy be abandoned in favor of tests and benchmarks based on concentration-duration-response dynamics.

The present paper constitutes a preliminary report on the formulation end development of statistical approaches to partition the effects of fishing mortality, natural mortality and pollution mortality caused by habitat degradation on fishery stocks in estuarine waters. The selected approaches are illustrated using a data set involving striped bass in the Potomac river.

Data extrapolations are required to assess the effects of toxic pollutants on aquatic resources. We present a procedure that uses maximum likelihood methods of estimating a functional linear relationship for extrapolation. Optimal estimates for the variance of the extrapolation parameters as well as the fitted values do not exist. We propose several alternative variance estimates which are used for computations of the extrapolated chronic benchmark and its distribution. Improved estimates can be obtained by applying computer-intensive resampling methods.