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Vegetation, water, and carbon dioxide have complex interactions on carbon mitigation in vegetation–water ecosystems. As one of the major global change drivers of carbon sequestration, flooding disturbance is a fundamental but poorly discussed topic to date. The aquatic and associated riparian systems are highly dynamic vegetation–water carbon capture systems driven by fluvial processes such as flooding. However, their global carbon offset potential is largely unknown. This study examines daily CO2 perturbations under flooding disturbance in the river (fluvial area) and associated riparian areas with 2 year in situ observations along the Lijiang. We find that, though the submerged riparian area behaved as a carbon source during the flooding season (CO2 flux: 2.790 gm-2d-1), the riparian area and the fluvial area as a whole transformed from a carbon source in pre-flooding season (1.833 gm-2d-1) to a carbon sink after recovery in post-flooding season (−0.592 gm-2d-1). The fluvial area sequestered carbon (−0.619 gm-2d-1) in post-flooding season instead of releasing carbon as in pre-flooding season (2.485 gm-2d-1). Also, the carbon sequestration capacity of the riparian area was enhanced in post-flooding season (pre-flooding season: −0.156 gm-2d-1, post-flooding season: −0.500 gm-2d-1). We suggest that post-disturbance recovery of riparian vegetation played a vital role in this transformation, due to its stronger carbon uptake capacity after recovery from the flooding disturbances. The findings shed light on the quantitative modelling of the riparian carbon cycle under flooding disturbance and underlined the importance of the proper restoration of riparian systems to achieve global carbon offset.

International audience; In this paper, we propose an innovative metaheuristic characterized by a multi-dimensional chromosome where each dimension is driven by a rational agent. These agents have to communicate in order to implement evolving and adaptive genetic operators to accelerate the convergence towards the optimal solution. This cooperative approach is applied to solve the patient scheduling problem in emergency department (ED). This problem is NP-difficult due to the permanent interference between three types of arrival: already programmed patients, non-programmed patients and urgent non-programmed patients. Our scheduling problem has to integrate several dimensions such as medical dimensional, patient dimensional, temporal dimensional. The multi-dimensional aspect of the chromosome is crucial to model the different dimensions of the ED. The main goal of the simulation results is to assess the performance of the proposed agent driven multidimensional chromosome. The simulation results confirm that the intra and inter chromosomal interactions allow to avoid the blind aspect of the genetic operators and impacts the quality of solutions. The agents' cooperation and its ability to improve efficiently the quality of the solutions by exploring intelligently the research space are confirmed by the drop in average total patient waiting time by 15.09%

Though substantial research has been conducted on possible historical, physiological, and symbiotic mechanisms that permit monodominance to occur within tropical lowland rainforests, less is known about the successional rates at which monodominance exerts itself on surrounding forest structures. Here we extend efforts to evaluate the longitudinal dynamics of Gilbertiodendron dewevrei-dominated forest in Central Africa by considering this species’ spatial dynamics. Using three 10-ha censused field plots measured across three time periods, we present the first quantitative estimates of the spatial propagation of Gilbertiodendron into adjacent mixed species forest. Using three analytical strategies, we demonstrate that Gilbertiodendron is increasing in dominance and that monodominant forest patches are expanding into the surrounding forest at a statistically significant rate. The rates of successional advance vary by patch and direction, but average 0.31 m year−1, with speeds greatest in the direction of the prevailing winds. We show that the advancement of Gilbertiodendron is significantly slower than documented rates from other forest ecotones across Central Africa. When paired with stress tolerance traits and ectomycorrhizal associations, these findings help to clarify the means by which Gilbertiodendron dewevrei gains dominance in otherwise species-diverse regions.

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Background With the objective of increasing knowledge on biomass and carbon stocks, and thus improving the accuracy of published estimates, the present study explored wood density and carbon concentration of coarse woody debris (diameter ≥10) by decay class in a Seasonal Semi-deciduous Forest (SSF) area in the Atlantic Rain Forest and in a Cerrado sensu-stricto (CSS) area (Brazilian savanna), in Brazil. Two strata were identified in each area and ten sampling units were systematic located in each stratum. Data were collected according to the line intersect sampling method. Each tallied element, the diameter, length, and perpendicular width were recorded at the transect intersection point. Each element was classified into a decay class, and the species was identified when possible. Sample discs were cut from each element, from which cylindrical samples were extracted and oven-dried to determine density. These cylinders were milled and analyzed using a LECO-C632 to determine carbon concentration as percentage of mass. Results In both areas, wood density decreased as the decay class increased. For SSF the mean carbon concentration of all analyzed samples was 49.8% with a standard deviation of 3.3, with a range of 27.9–57.0% across 506 observations. For CSS the general mean was 49.6% with a standard deviation of 2.6, with a range of 31.2–54.5% over 182 observations. Carbon concentration barely change between decay classes. Carbon stock was estimated at 3.3 and 0.7 MgC/ha for the SSF and the CSS, respectively. Similar results were obtained when using a 50% conversion constant. Conclusions The present study concludes that wood density decreases as the woody debris becomes more decomposed, a pattern found in many previous studies. The carbon concentration, however, barely changes between decay classes, and that result is consistent with most of the literature reviewed. Our carbon concentrations are very close to the 50% used most commonly as a conversion factor. We strongly recommend that future studies of CWD evaluate wood density and carbon concentration by decay class to address the uncertainty still found in the literature.

Selective logging causes at least half of the emissions from tropical forest degradation. Reduced-impact logging for climate (RIL-C) is proposed as a way to maintain timber production while minimizing forest damage. Here we synthesize data from 61 coordinated field-based surveys of logging impacts in seven countries across the tropics. We estimate that tropical selective logging emitted 834 Tg CO2 in 2015, 6% of total tropical greenhouse gas emissions. Felling, hauling, and skidding caused 59%, 31%, and 10% of these emissions, respectively. We suggest that RIL-C incentive programs consider a feasible target carbon impact factor of 2.3 Mg emitted per Mg of timber extracted. Operational modifications are needed to achieve this target, such as reduced wood waste, narrower haul roads, and lower impact skidding equipment. Full implementation would reduce logging emissions by 44% (366 Tg CO2 year−1) and deliver 4% of the nationally determined contributions to the Paris Climate Agreement from tropical countries, while maintaining timber supplies.

To estimate carbon emissions from selective logging in Central Africa, we employed the reduced-impact logging for carbon emissions reductions (RIL-C) protocol to quantify baseline carbon emissions from legal timber harvests by source (i.e., hauling, skidding, and felling). We modeled the relationships between emissions and biophysical conditions, logging practices, and forest policies and then used these models to estimate potential emission reductions from full implementation of RIL-C practices. We applied the method in 8 forest management enterprises (FMEs; i.e., concessions) in the Democratic Republic of Congo (DRC), 9 in Gabon, and 6 in the Republic of Congo (RoC). Committed logging emissions expressed per cubic meter of timber harvested (to control for differences in logging intensities) ranged from 0.63 Mg C m−3 in a FME in RoC to 4.8 Mg C m−3 in a FME in Gabon, with an overall average of 2.1 Mg C m−3. Logging emissions were dominated by damage caused by road and log landing construction (i.e., hauling; 50%) and felling (43%; includes carbon in extracted logs). Total emissions represented only about 9% of unlogged forest biomass carbon stocks. Average emissions were highest in Gabon (2.65 Mg C m−3) followed by DRC (1.84 Mg C m−3) and RoC (1.54 Mg C m−3). Emissions from concessions certified by the Forest Stewardship Council (FSC, N = 6) and those that were not certified (N = 17) did not differ. Nearly half (51%) of logging emissions could be avoided without reducing timber yields if all best examples of RIL-C logging practices observed were applied in the same FME. At the country level, if all FMEs were to utilize these practices, emissions reductions would be 34% in RoC, 45% in DRC, and 62% in Gabon. When combined with country-level logging statistics, emissions from selective logging as currently practiced in the six countries of the Congo Basin are equivalent to 40% of the region’s total emissions from deforestation.

Motivation: Trait variation within species can reveal plastic and/or genetic responses to environmental gradients, and may indicate where local adaptation has occurred. Here, we present a dataset of rangewide variation in leaf traits from seven of the most ecologically and economically important tree species in Europe. Sample collection and trait assessment are embedded in the GenTree project (EU‐Horizon 2020), which aims at characterizing the genetic and phenotypic variability of forest tree species to optimize the management and sustainable use of forest genetic resources. Our dataset captures substantial intra‐ and interspecific leaf phenotypic variability, and provides valuable information for studying the relationship between ecosystem functioning and trait variability of individuals, and the response and resilience of species to environmental changes. Main types of variable contained: We chose morphological and chemical characters linked to trade‐offs between acquisition and conservation of resources and water use, namely specific leaf area, leaf size, carbon and nitrogen content and their ratio, and the isotopic signature of stable isotope ¹³C and ¹⁵N in leaves. Spatial location and grain: We surveyed between 18 and 22 populations per species, 141 in total, across Europe. Time period: Leaf sampling took place between 2016 and 2017. Major taxa and level of measurement: We sampled at least 25 individuals in each population, 3,569 trees in total, and measured traits in 35,755 leaves from seven European tree species, i.e. the conifers Picea abies, Pinus pinaster and Pinus sylvestris, and the broadleaves Betula pendula, Fagus sylvatica, Populus nigra and Quercus petraea. Software format: The data files are in ASCII text, tab delimited, not compressed.

Background: Progress in the field of evolutionary forest ecology has been hampered by the huge challenge of phenotyping trees across their ranges in their natural environments, and the limitation in high-resolution environmental information. Findings: The GenTree Platform contains phenotypic and environmental data from 4,959 trees from 12 ecologically and economically important European forest tree species: Abies alba Mill. (silver fir), Betula pendula Roth. (silver birch), Fagus sylvatica L. (European beech), Picea abies (L.) H. Karst (Norway spruce), Pinus cembra L. (Swiss stone pine), Pinus halepensis Mill. (Aleppo pine), Pinus nigra Arnold (European black pine), Pinus pinaster Aiton (maritime pine), Pinus sylvestris L. (Scots pine), Populus nigra L. (European black poplar), Taxus baccata L. (English yew), and Quercus petraea (Matt.) Liebl. (sessile oak). Phenotypic (height, diameter at breast height, crown size, bark thickness, biomass, straightness, forking, branch angle, fructification), regeneration, environmental in situ measurements (soil depth, vegetation cover, competition indices), and environmental modeling data extracted by using bilinear interpolation accounting for surrounding conditions of each tree (precipitation, temperature, insolation, drought indices) were obtained from trees in 194 sites covering the species’ geographic ranges and reflecting local environmental gradients. Conclusions: The GenTree Platform is a new resource for investigating ecological and evolutionary processes in forest trees. The coherent phenotyping and environmental characterization across 12 species in their European ranges allow for a wide range of analyses from forest ecologists, conservationists, and macro-ecologists. Also, the data here presented can be linked to the GenTree Dendroecological collection, the GenTree Leaf Trait collection, and the GenTree Genomic collection presented elsewhere, which together build the largest evolutionary forest ecology data collection available.

The concept of Perpendicular Shape Anisotropy STT-MRAM (PSA-STT-MRAM) has been recently proposed as a solution to enable the downsize scalability of STT-MRAM devices beyond the sub-20 nm technology node. For conventional p-STT-MRAM devices with sub-20 nm diameters, the perpendicular anisotropy arising from the MgO/CoFeB interface becomes too weak to ensure thermal stability of the storage layer. In addition, this interfacial anisotropy rapidly decreases with increasing temperature which constitutes a drawback in applications with a large range of operating temperatures. Here, we show that by using a PSA based storage layer, the source of anisotropy is much more robust against thermal fluctuations than the interfacial anisotropy, which allows considerable reduction of the temperature dependence of the coercivity. From a practical point of view, this is very interesting for applications having to operate on a wide range of temperatures (e.g. automotive -40 °C/+150 °C).

The purpose of this note is to propose a variance estimator under non-measurable designs that exploits the existence of an auxiliary variable well correlated with the survey variable of interest. Under non-measurable designs, the Sen–Yates–Grundy variance estimator generates a downward bias that can be reduced using a calibration weighting based on the auxiliary variable. Conditions of approximate unbiasedness for the resulting calibration estimator are given. The application to systematic sampling is considered. The proposal proves to be effective for estimating the variance of the forest cover estimator in remote sensing-based surveys, owing to the strong correlation between the reference data, available from a systematic sample, and the satellite map data, available for the whole population and hence exploited as an auxiliary variable. Supplementary materials accompanying this paper appear online.

Binary variables have two possible outcomes: occurrence or non-occurrence of an event (usually with 1 and 0 values, respectively). Binary data are common in ecology, including studies of presence/absence, alive/dead, and change/no-change. Logistic regression analysis has been widely used to model binary response variables. Unbalanced data (i.e., an extremely larger proportion of zeros than ones) are often found across a variety of ecological datasets. Sometimes the data are balanced (i.e., same amount of zeros and ones) before fitting the model, however, the statistical implications of balancing (or not) the data remain unclear. We assessed the statistical effects of balancing data when fitting a logistic regression model by studying both its statistical properties of the estimated parameters and its predictive capabilities. We used a base forest-mortality model as reference, and by using stochastic simulations representing different configurations of 0/1 data in a sample (unbalanced data scenarios), we fitted the logistic regression model by maximum likelihood. For each scenario we computed the bias and variance of the estimated parameters and several prediction indexes. We found that the variability of the estimated parameters is affected, with the balanced-data scenario having the lowest variability, thus, affecting the statistical inference as well. Furthermore, the prediction capabilities of the model are altered by balancing the data, with the balanced-data scenario having the better sensitivity/specificity ratio. Balancing, or not, the data to be used for fitting a logistic regression models may affect the conclusion that can arise from the fitted model and its subsequent applications.

Large-area forest inventories are often undertaken following a stratified random or systematic design. Yet the strata rarely correspond to the reporting areas of interest (domains) over which the country wants to report specific variables. The process is exemplified by a country aiming to use national forest inventory data to obtain average biomass estimates per forest type for GHGI international reporting, where activity data (areas of land use or land use changes) and emission factors (carbon coefficients) are typically compiled from disparate sources and estimated using different sampling schemes. This study aims to provide a decision tree for the choice of the estimator to be used in forest surveys to draw conclusions about population sub-groups created after (and independently of) the sample selection. This manuscript describes two unbiased estimators that can be used to estimate reporting-strata means, regardless of the sampling design adopted, and extends the result to the common situation in which the reporting-strata are spatially explicit, where a nested group estimator outperforms in terms of both bias and precision other more traditional estimators. From this estimator, an optimal sample allocation scheme is also derived.

Estimates of global carbon stocks in standing forests are subject to uncertainty because of regional and tree species differences that are usually ignored in global allometric equations. The absence of appropriate site-specific and individual tree allometric equations has led to broad use of pan moist tropical equations, which use has raised questions on the accuracy of the resulting predictions of standing biomass. Here we develop site-specific individual tree allometric equations for estimating biomass of Gilbertiodendron dewevrei, a canopy tree that dominates extensive areas of forest in the Congo basin region. We applied both antithetic and randomized branch sampling to sample for total aboveground biomass components. We evaluated a series of regression models (linear and non-linear) for predicting total aboveground biomass as a function of commonly measured variables including diameter, basal area and total height of 43 sample trees for Ituri and Yoko forests in the Democratic Republic of Congo. We found the best model for total aboveground biomass to be a linear, 3-knot cubic spline model that used only basal area as a predictor with the lowest AIC and BIC of 602 and 610 respectively. The incorporation of height in biomass equations did not significantly improve model performance, while models with diameter alone or in combination with height perform poorly. Our results show that models using only basal area are sufficient to accurately estimate biomass for Gilbertiodendron dewevrei and carbon stock, which is an important outcome given that height measurements are usually difficult to acquire. We also demonstrate that general models used can significantly overestimate amounts of carbon for Gilbertiodendron dewevrei as compared to the site and species-specific regression model that we have developed.

Pericopsis elata (a.k.a. African teak) is one of the most valuable timber species in Central Africa. Like other shade intolerant tropical tree species, P. elata could play a vital role in economic development, and ecological sustainability, but regenerates poorly following selective logging. Now endangered, there is a critical need for sustainable silvicultural systems to restore this once prominent timber species. To assess management options for P. elata we analyzed growth performance and survival in primary and secondary forest plots under burning and weeding treatments in Yoko Forest Reserve, Ubundu Democratic Republic of Congo. We transplanted nursery-grown seedlings of P. elata to experimental gaps and followed their growth and survival for 1 year. Seedlings in large canopy gaps 50 × 50 m were taller (mean difference; P = 0.006) and more likely to survive (mean difference; P

Post-stratified model-assisted (MA) and hybrid (HY) estimators are used with repeated airborne laser scanning (ALS) strip sampling and national forest inventory field data for stratum-wise and overall estimation of aboveground biomass (AGB) stock and change. The study area covered the southern portion of the Hedmark County in Norway. Both MA and HY estimation substantially reduced the uncertainty in AGB change when compared with estimation using the field survey only. Relative efficiencies (relative variance) of 4.15 (MA) and 3.36 (HY) for overall estimates were found. The results suggest the MA estimator for single-time estimation and the HY as more appropriate for change estimation by cover class. With the HY estimator, a nested post-stratification scheme is demonstrated, combining cover classes with change classes, which enables detailed reporting for change according to cause within each cover class, and has the potential to improve the estimation precision. Finally, parametric bootstrapping is demonstrated as an empirical alternative to estimate the model-error component in the HY estimator. The model error estimated with parametric bootstrapping converged to the analytically determined value of the HY estimator within 1000 bootstrap samples.

Forest resource assessments utilizing remotely sensed auxiliary data are becoming increasingly important due to their ability to provide precise estimates of forest parameters at low cost. In presenting results from such surveys, it is important to provide not only estimates of the target parameters, but also their confidence intervals, which provide the range of values wherein the true value is located with a certain level of confidence. If such an interval is narrow the point estimates from the survey can be considered very reliable. In estimating the confidence interval the variance of an estimator must first be estimated. Unbiasedness, i.e. that an estimator on average coincides with the true value, is an important property also for variance estimators. Another important property is that the variance estimator itself has low variance, not least in cases when the variance estimates obtained with the estimator may not be strictly positive. One such important case is when two-stage designs are used to first allocate sample clusters in the form of strips from which auxiliary data, such as metrics derived from airborne laser scanning, are obtained; field data are then derived from sample plots beneath each sample strip in a second stage. In this article we compare two variance estimators for such surveys. The first estimator is a standard estimator suggested in reference textbooks on model-assisted sampling. The second estimator is proposed by the authors, and utilizes the auxiliary data to a larger extent. Through Monte Carlo simulation we show that both variance estimators are approximately unbiased, but that the new estimator is more stable (i.e., has lower empirical variance) and provides empirical confidence interval coverage rates that coincide more closely with the nominal coverage rates.

As a non-Annex 1 Party to the United Nations Framework Convention on Climate Change (UNFCCC), Tanzania has committed to submit the results of the measurement, reporting, and verification of greenhouse gas (GHG) emissions reductions and removals by sinks under the Biennial Update Reports of national GHG inventory and mitigation actions. Thus, implementation of a robust and cost-efficient carbon accounting system is one of the prerequisites for receiving financial benefits under the REDD + mechanism. For estimation targeting large areas (i.e., country, region, and sub-regional), remote sensing techniques like airborne laser scanning (ALS) enable precise and timely estimation of biomass stocks and biomass changes. The main objective of the study was to compare alternative sampling strategies using ALS for estimating aboveground biomass (AGB) changes in miombo woodlands that would be suitable components of the National Forestry Resources Monitoring and Assessment (NAFORMA) program of Tanzania and of the Biennial Update Reports under UNFCCC. NAFORMA is the national forest inventory of Tanzania. The study area of 15,867 km2 was located in the administrative district of Liwale, south-eastern Tanzania and dominated by miombo woodlands. A longitudinal survey was carried out during two temporally consistent field and ALS data acquisition campaigns in 2012 and 2014. The field data were collected on permanent plots of the NAFORMA program. ALS measurements were acquired along 32 parallel strips spaced 5 km apart and covering approximately 25% of the study area. Estimation methods (1) solely based on the ground inventory data provided by NAFORMA and (2) by incorporating field observations and auxiliary variables derived from ALS data were pursued for assessing AGB change as the difference between estimated AGB in 2012 and 2014, together with the uncertainties related to sampling variability. The AGB estimates varied from 58.48 to 59.55 Mg ha− 1 in 2012 and between 58.20 and 59.29 Mg ha− 1 in 2014. The sampling variability (in terms of standard errors) was between 1.37 and 4.79 Mg ha− 1 in 2012, and 1.77 and 5.03 Mg ha− 1 in 2014. The mean AGB change varied from 0.11 to 0.35 Mg ha− 1 with standard errors of 0.31 to 0.85 Mg ha− 1. Compared to direct estimation based on field data only, the efficiency gain under ALS-assisted (design-based, model-assisted) estimation was 5–22%, and approximately 66% when using ALS data in a model-dependent estimation. The results provide statistically significant evidence of change in AGB over the 2-yr period spanned by the study only under model-dependent estimation at 50% confidence level, illustrating the great challenges of change estimation over such short time periods as requested for UNFCCC reporting.

Laurine Robert,1 Grégoire Ficheur,2 Sophie Gautier,3 Alexandre Servais,1 Michel Luyckx,4 Julien Soula,1 Bertrand Decaudin,5 François Glowacki,6 François Puisieux,1 Emmanuel Chazard,2 Jean-Baptiste Beuscart1 1Univ. Lille, CHU Lille, EA2694, Lille, F-59000, France; 2Univ. Lille, CHU Lille, EA2694, Public Health Department, Lille, F-59000, France; 3Univ. Lille, CHU Lille, UMR 1171, Department of Pharmacology, Lille, F-59000, France; 4Univ. Lille, EA7365, F-59000 Lille, CH Denain, Department of Pharmacy, Denain, F-59220, France; 5Univ. Lille, CHU Lille, EA7365, Department of Pharmacy, Lille, F-59000, France; 6Univ. Lille, CHU Lille, EA4483 IMPECS, Department of Nephrology, Lille, F-59000, FranceCorrespondence: Laurine RobertCERIM – EA 2694, Faculté de Médecine - Pôle Recherche, 1 Place de Verdun, Lille Cedex 59045 FranceEmail laurine.robert.etu@univ-lille.frPurpose: Community-acquired acute kidney injury (CA-AKI) is a frequent and severe adverse drug reaction (ADR) among older patients. The combination of drugs and other CA-AKI risk factors was barely evaluated. The objectives of our study were to both accurately identify CA-AKI induced by drugs in older patients, and to describe their combination with other risk factors.Patients and methods: We conducted a retrospective, single-center study in a general hospital over a two-year period. An automated detection identified CA-AKI according to KDIGO criteria, amongst 4,767 eligible inpatient stays among patients aged 75 years or older. Two independent experts reviewed all CA-AKI events to adjudicate drug involvement (Naranjo scale), identify inappropriate prescriptions (STOPP criteria), evaluate avoidability (Hallas criteria) and identify combined risk factors.Results: An expert review confirmed 713 CA-AKI (15.0% of inpatient stays) and determined that 419 (58.8%) CA-AKI were induced by drugs. A multifactorial cause (i.e., at least one drug with a precipitating factor) was found in 63.2% of drug-induced CA-AKI. Most of the drug-induced events were avoidable (66.8%), mainly in relation to a multifactorial cause.Conclusion: Drug-induced CA-AKI were frequent, multifactorial events in hospitalized older patients and their prevention should focus on combinations with precipitating factors.Keywords: adverse drug reactions, elderly, prevention, acute kidney injury

International audience

A spatiotemporal calibration and resolution refinement model was fitted to calibrate nitrogen dioxide (NO$_2$) concentration estimates from the Community Multiscale Air Quality (CMAQ) model, using two sources of observed data on NO$_2$ that differed in their spatial and temporal resolutions. To refine the spatial resolution of the CMAQ model estimates, we leveraged information using additional local covariates including total traffic volume within 2 km, population density, elevation, and land use characteristics. Predictions from this model greatly improved the bias in the CMAQ estimates, as observed by the much lower mean squared error (MSE) at the NO$_2$ monitor sites. The final model was used to predict the daily concentration of ambient NO$_2$ over the entire state of Connecticut on a grid with pixels of size 300 x 300 m. A comparison of the prediction map with a similar map for the CMAQ estimates showed marked improvement in the spatial resolution. The effect of local covariates was evident in the finer spatial resolution map, where the contribution of traffic on major highways to ambient NO$_2$ concentration stands out. An animation was also provided to show the change in the concentration of ambient NO$_2$ over space and time for 1994 and 1995., Comment: 23 pages, 8 figures, supplementary material

Patterns of structural change associated with monodominant tropical forest complexes have remained enigmatic for decades. Here, we extend previous efforts in presenting a longitudinal, local-scale analysis of forest dynamics in central Africa. Using four 10-ha census plots measured across three time periods (959,312 stems ≥1 cm DBH), we analyzed changes in a number of biometrical attributes for four distinct forest types capturing the developmental gradient from mixed species forest to Gilbertiodendron dewevrei-dominated forest. We modeled above-ground biomass (AGB), basal area (BA), and stem density across all species, and diameter at breast height (DBH), recruitment, and mortality for Gilbertiodendron dewevrei. We hypothesized that trends in these attributes are consistent with a slow spread of Gilbertiodendron dewevrei into adjacent mixed species forest. We identified statistically significant increases in AGB and BA across sites and positive, though nonsignificant, increases in AGB and BA for most forest types. DBH and relative recruitment increased significantly for Gilbertiodendron dewevrei stems, while relative mortality did not. When looking from mixed species to transitional to monodominant forest types, we found a statistically significant pattern of developmental aggradation and net expansion of monodominant forest. We do not attribute this to atmospheric forcing but to a combination of (a) landscape-scale recovery or response to widespread disturbance (primarily historical fires), (b) Gilbertiodendron dewevrei’s ectomycorrhizal association, and (c) Gilbertiodendron dewevrei’s exceptional stress tolerance traits.

Constant improvements of the computational power and methods as well as demands of accurate and reliable measurements for reactor operation and safety require a continuous upgrade of the instrumentation. In particular, nuclear sensors used in nuclear fission reactors (research or power reactors) or in nuclear fusion facilities are operated under intense mixed neutron and gamma-ray fields, and need to be calibrated and modeled to provide selective and accurate neutron and gamma-ray measurements. The French Atomic Energy and Alternative Energies Commission (CEA) and the Jožef Stefan Institute (JSI) have started an experimental program dedicated to a detailed experimental benchmark with analysis using Monte Carlo particle transport calculations and a series of neutron and gamma-ray sensor types used in the JSI TRIGA Mark II reactor. CEA has setup a simplified TRIPOLI-4® modeling scheme of the JSI TRIGA reactor based on the information available in the IRPhEP benchmark in order to facilitate analysis of future neutron and gamma-ray measurements. These allow the CEA to perform a TRIPOLI-4 instrumentation calculation scheme benchmarked with the JSI MCNP model. This paper presents the main results of this CEA calculation scheme application and the analysis of their comparison to the JSI results obtained in 2012 with the MCNP5 & ENDF/B-VII.0 calculation scheme. This paper will conclude with some information about the new experimental program to be carried out in 2022 in the TRIGA reactor core.

A recent experimental irradiation and measurement campaign using containers made from boron nitride (BN) at the Jožef Stefan Institute (JSI) TRIGA Mark II reactor in Ljubljana, Slovenia, has shown the applicability of BN for neutron spectrum characterization and cross-section validation in the epithermal range through integral activation measurements. The first part of the paper focuses on the determination of the transmission function of a BN container through Monte Carlo calculations and experimental measurements. The second part presents the process of tayloring the sensitivity of integral activation measurements to specific needs and a selection of suitable radiative capture reactions for neutron spectrum characterization in the epithermal range. A BN container used in our experiments and its qualitative effect on the neutron spectrum in the irradiation position employed is displayed in the Graphical abstract.

For many decades remotely sensed data have been used as a source of auxiliary information when conducting regional or national surveys of forest resources. In the past decade, airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool for sample surveys aimed at improving estimation of above-ground forest biomass. This technology is now employed routinely in forest management inventories of some Nordic countries, and there is eager anticipation for its application to assess changes in standing biomass in vast tropical regions of the globe in concert with the UN REDD program to limit C emissions. In the rapidly expanding literature on LiDAR-assisted biomass estimation the assessment of the uncertainty of estimation varies widely, ranging from statistically rigorous to ad hoc. In many instances, too, there appears to be no recognition of different bases of statistical inference which bear importantly on uncertainty estimation. Statistically rigorous assessment of uncertainty for four large LiDAR-assisted surveys is expounded.

Spatiotemporal calibration of output from deterministic models is an increasingly popular tool to more accurately and efficiently estimate the true distribution of spatial and temporal processes. Current calibration techniques have focused on a single source of data on observed measurements of the process of interest that are both temporally and spatially dense. Additionally, these methods often calibrate deterministic models available in grid-cell format with pixel sizes small enough that the centroid of the pixel closely approximates the measurement for other points within the pixel. We develop a modeling strategy that allows us to simultaneously incorporate information from two sources of data on observed measurements of the process (that differ in their spatial and temporal resolutions) to calibrate estimates from a deterministic model available on a regular grid. This method not only improves estimates of the pollutant at the grid centroids but also refines the spatial resolution of the grid data. The modeling strategy is illustrated by calibrating and spatially refining daily estimates of ambient nitrogen dioxide concentration over Connecticut for 1994 from the Community Multiscale Air Quality model (temporally dense grid-cell estimates on a large pixel size) using observations from an epidemiologic study (spatially dense and temporally sparse) and Environmental Protection Agency monitoring stations (temporally dense and spatially sparse). Copyright © 2016 John Wiley & Sons, Ltd.

Los modelos de crecimiento de bosques son herramientas claves para el manejo y la comprensión de la dinámica de los bosques. Estos modelos han evolucionado en complejidad desde las tablas de rendimiento a modelos que simulan procesos fisiológicos y ecológicos. Dada la actual multiplicidad de aproximaciones para modelar el crecimiento de los bosques, es difícil entender sus diferencias, fortalezas y debilidades. En este artículo se presenta una revisión del estado del conocimiento sobre modelos de crecimiento de bosques considerando los siguientes tipos de modelos: crecimiento y rendimiento, sucesión, basados en procesos, e híbridos. Además, se hace énfasis en que los modelos anteriores pueden, o no, incluir componentes estocásticos. A nivel mundial existe una tendencia hacia construir modelos híbridos por sus bondades al poder representar más naturalmente el efecto del cambio climático en el crecimiento de los árboles, sin embargo, la evidencia empírica no ha demostrado mayores diferencias predictivas con modelos más simples. Finalmente, se enfatiza que no existen tipos de modelos mejores que otros, sino que cada cual se emplea para responder diversas preguntas de investigación o de manejo.

The need to provide transparent and reliable Greenhouse Gas (GHG) emission estimates is strongly emphasized in the context of international reporting under the United Nations Framework Convention on Climate Change (UN-FCCC) and the Paris Agreement. Yet it is difficult to find specific guidance about what information is really needed to evaluate the quality of the emission factors or activity data used for GHG emission estimates. The most commonly used indicator of the reliability of an estimation procedure (and one of the few indicators explicitly mentioned in the 2006 IPCC guidelines) is the so-called confidence interval, usually at a confidence level of 90% or 95%. This interval, however, is unlikely to be a meaningful indicator of the quality of the estimate, if not associated with additional information about the estimation and survey procedures (such as on the sampling design, measurement protocols or quality control routines, among others). We provide a review of the main sources of error that can have an impact on the precision and accuracy of the estimation of both emission factors and activity data and a list of the essential survey features that should be reported to properly evaluate the quality of a GHG emission estimate. Such list is also applicable to the reporting of national forest inventories and of area estimation of activity data, and includes the case in which confidence intervals are obtained using error propagation techniques.

In felled-tree studies, ratio and regression estimators are commonly used to convert more readily measured branch characteristics to dry crown mass estimates. In some cases, data from multiple trees are pooled to form these estimates. This research evaluates the utility of both tactics in the estimation of crown biomass following randomized branch sampling (RBS). Synthetic generalized regression (GREG) estimators are developed, and their properties examined against standard estimators. It is shown that synthetic GREG estimators with zero or low design-bias can be obtained, and that the variance of a design-unbiased class of GREG estimators can be unbiasedly estimated. Simulated sampling from 20 censused crowns of two Rocky Mountain species indicated that improvements in accuracy can be obtained through GREG estimation following RBS. Simulations also showed that synthetic GREG estimators that pool data from multiple trees can stabilize coefficients of multivariate regression models to provide improved accuracy over direct GREG estimators. However, for the univariate regression models that proved most adept for the censused crowns, direct GREG estimation provided the lowest average root mean squared error (RMSE) for RBS. Simulations also showed that model-based branch aggregation estimators have generally low RMSE but can be heavily design-biased. For the crown forms studied, use of branch auxiliary information at both the design and estimation stages through RBS and GREG estimation appears to be more efficient than using the information only at the estimation stage following simple or stratified random sampling.

Individual tree heights are needed in many situations, including estimation of tree volume, dominant height, and simulation of tree growth. However, height measurements are tedious compared to tree diameter measurements, and therefore height–diameter (H–D) models are commonly used for prediction of tree height. Previous studies have fitted H–D models using approaches that include plot-specific predictors in the models and those that do not include them. In both these approaches, aggregation of the observations to sample plots has usually been taken into account through random effects, but this has not always been done. In this paper, we discuss four alternative model formulations and report an extensive comparison of 16 nonlinear functions in this context using a total of 28 datasets. The datasets represent a wide range of tree species, regions, and ecological zones, consisting of about 126 000 measured trees from 3717 sample plots. Specific R-functions for model fitting and prediction were developed to enable such an extensive model fitting and comparison. Suggestions on model selection, model fitting procedures, and prediction are given and interpretation of the predictions from different models are discussed. No uniformly best function, model formulation, or model fitting procedure was found. However, a 2-parameter Näslund and Curtis function provided satisfactory fit in most datasets for the plot-specific H–D relationship. Model fitting and height imputation procedures developed for this study are provided in an R-package for later use.

Large scale forest inventories are often undertaken following a stratified random or systematic design. Yet the strata rarely correspond to the reporting areas of interest (domains) over which the country wants to report specific variables. The process is exemplified by a country aiming to use national forest inventory data to obtain average biomass estimates per forest type for GHGI international reporting, where activity data (areas of land use or land use changes) and emission factors (carbon coefficients) are typically compiled from disparate sources and estimated using different sampling schemes. This study aims to provide a decision tree for the use of data obtained from forest surveys to draw conclusions about population sub-groups created after (and independently of) the sample selection. While bias can arise whenever activity data and emission factors are calculated independently, it can be eliminated in case of a simple random or simple systematic design if properly weighted estimators are provided. This manuscript describes two unbiased estimators that can be used to estimate reporting-strata means, regardless of the sampling design adopted, and extends the result to the common situation in which the reporting-strata are spatially explicit, where a nested group estimator outperforms in terms of both bias and precision other more traditional estimators. From this estimator, an optimal sample allocation scheme is also derived.

Lidar provides critical information on the three-dimensional structure of forests. However, collecting wall-to-wall laser altimetry data at regional and global scales is cost prohibitive. As a result, studies employing lidar for large area estimation typically collect data via strip sampling, leaving large swaths of the forest unmeasured by the instrument. The goal of this research was to develop and examine the performance of a coregionalization modeling approach for combining field measurements, strip samples of airborne lidar and Landsat-based remote sensing products to predict aboveground biomass (AGB) in interior Alaska's Tanana Valley. The proposed modeling strategy facilitates mapping of AGB density across the domain. Additionally, the coregionalization framework allows for estimation of total AGB for arbitrary areal units within the study area—a key advance to support diverse management objectives in interior Alaska. This research focuses on characterization of prediction uncertainty in the form of posterior predictive coverage intervals and standard deviations. Using the framework detailed here, it is possible to quantify estimation uncertainty for any spatial extent, ranging from point-level predictions of AGB density to estimates of AGB stocks for the full domain. The lidar-informed coregionalization models consistently outperformed their counterpart lidar-free models in terms of point-level predictive performance and total (mean) AGB precision. Additionally, including a Landsat-derived forest cover covariate further improved precision in regions with lower lidar sampling intensity. Findings also demonstrate that model-based approaches not explicitly accounting for residual spatial dependence can grossly underestimate uncertainty, resulting in falsely precise estimates of AGB. The inferential capabilities of AGB posterior predictive distribution (PPD) products extend beyond simply mapping AGB density. We show how PPD products can provide insight regarding drivers of AGB heterogeneity in boreal forests, including permafrost and fire, highlighting the range of potential applications for Bayesian geostatistical methods to integrate field, airborne and satellite data.

Miniature fission chambers are suited tools for instrumenting experimental reactors, allowing online and in-core neutron measurements of quantities such as fission rates or reactor power. A new set of such detectors was produced by CEA to be used during the next experimental program at the EOLE facility starting in 2013. Some of these detectors will be employed in pulse mode for absolute measurements, thus requiring calibration. The calibration factor is expressed in mass units and thus called “effective mass”. A calibration campaign was conducted in December 2012 at the SCK·CEN BR1 facility within the framework of the scientific cooperation VEP (VENUS-EOLE-PROTEUS) between SCK·CEN, CEA and PSI. Two actions were conducted in order to improve the calibration method. First a new characterisation of the thermal flux cavity and the MARK3 neutron flux conversion device performed by SCK.CEN allowed using calculated effective cross sections for determining detectors effective masses. Dosimetry irradiations were performed in situ in order to determine the neutron flux level and provide link to the metrological standard. Secondly two fission chambers were also calibrated at the CEA CALIBAN reactor (fast neutron spectrum), using the same method so that the results can be compared with the results obtained at the SCK.CEN. In this paper the calibration method and recent improvements on uncertainty reduction are presented. The results and uncertainties obtained in the two reactors CALIBAN and BR1 are compared and discussed.

Although differences in canopy openness, herbivory and their interaction may promote species coexistence, how these factors affect pioneer tree species and potentially limit growth, and survival has been poorly studied, particularly in tropical South Asia. We monitored the effect of canopy openness and herbivore damage on seedling survival and growth of 960 individuals of six pioneer tree species: Dillenia triquetra, Macaranga indica, Macaranga peltata, Schumacheria castaneifolia, Trema orientalis, and Wendlandia bicuspidata. Seedlings were placed in four gap-understory positions—center, outer gap edge, inner forest edge, and understory—in four large, natural gaps within the Sinharaja World Heritage Reserve, Sri Lanka. Canopy openness positively affected survival probability beyond the 550-d experiment, while herbivory decreased survival and was highest in understory conditions. The relative order of species survival stayed fairly consistent between gap-understory positions and followed their known shade tolerance rankings. When averaged across all experimental conditions, T. orientalis had the lowest survival probability estimate beyond the 550-d experiment (0.05), but the greatest capacity for growth where it successfully established, while the species with highest averaged survival probability (0.79), D. triquetra, showed the lowest growth. One species, W. bicuspidata, responded positively to herbivory by re-sprouting. Coexistence of D. triquetra, T. orientalis, and W. bicuspidata can be explained by a trade-off among species in survival, growth, and response to herbivory. In addition to variation in canopy light environment, herbivory may be important in determining pioneer species distribution through fine-scale niche partitioning and should be carefully considered in reforestation efforts.

A new concept to increase the downsize scalability of perpendicular spin transfer torque magnetic random-access memory (p-STT-MRAM), called perpendicular shape anisotropy (PSA) STT-MRAM is presented. This approach consists of significantly increasing the thickness of the storage layer in p-STT-MRAM to values comparable to the cell diameter so as to induce a PSA in this layer which comes on top of the MgO/FeCoB interfacial anisotropy. This PSA-STT-MRAM is provided by depositing a thick ferromagnetic (FM) layer on top of an MgO/FeCoB based magnetic tunnel junction (MTJ) so that the thickness of the storage layer becomes of the order or larger than the diameter of the MTJ pillar. In contrast to conventional spin transfer torque (STT) magnetic random access memory, wherein the demagnetizing energy opposes the interfacial perpendicular magnetic anisotropy (iPMA), in these novel memory cells, both PSA and iPMA contributions favor out-of-plane orientation of the storage layer magnetization. Using thicker storage layers in these PSA-STT-MRAM has several advantages. Thanks to this robust source of bulk anisotropy, PSA-STT-MRAM offers a greatly improved downsize scalability over conventional perpendicular p-STT-MRAM. Despite the large thickness of the storage layer, PSA-STT-MRAM cells can still be written by STT provided their thermal stability factor Δ is adjusted in the same range as in conventional p-STT-MRAM, i.e. Δ of the order of 60–100 depending on the memory capacity and required bit error rate. Moreover, a low damping material can be used for the thick FM material, thus leading to a reduction of the write current. Thanks to the PSA, very high and easily tunable thermal stability factors can be achieved, even down to sub-10 nm diameters. The paper describes this new PSA-STT-MRAM concept, practical realization of such memory arrays, magnetic characterization demonstrating thermal stability factor above 200 for MTJs as small as 8 nm in diameter and the possibility to maintain thermal stability factor above 60 down to 4 nm diameter. We also show that thanks to the increased thickness of the storage layer, the anisotropy and therefore the memory retention are much less sensitive on temperature than in conventional p-STT-MRAM. This is very interesting for applications operating on a wide range of temperatures (e.g. automotive −40 °C to +150 °C), as well as to fulfill solder reflow compliance.

Parks are developing nature play areas to improve children's health and “connect” them with nature. However, these play areas are often located in protected natural areas where managers must balance recreation with associated environmental impacts. In this exploratory study, we sought to describe these impacts. We also investigated which ages, gender, and play group sizes most frequently caused impact and where impacts most frequently occur. We measured the lineal and aerial extent and severity of impacts at three play areas in the eastern United States. Methods included soil and vegetation loss calculations, qualitative searches and tree and shrub damage classifications. Additionally, we observed 12 h of play at five play areas. Results showed that measurable negative impacts were caused during 33% of the time children play. On average, 76% of groundcover vegetation was lost at recreation sites and 100% was lost at informal trails. In addition, approximately half of all trees and shrubs at sites were damaged. Meanwhile, soil exposure was 25% greater on sites and trails than at controls. Boys and small group sizes more frequently caused impact, and informal recreation sites were most commonly used for play. No statistically significant correlations were found between age or location and impact frequency. Managers interested in developing nature play areas should be aware of, but not deterred by these impacts. The societal benefits of unstructured play in nature may outweigh the environmental costs. Recommended management strategies include selecting impact-resistant sites, improving site resistance, promoting low impact practices, and managing adaptively.

Auxiliary information provided by airborne laser scanners (ALS) is expected to increase the accuracy of biomass estimation in large-scale forest surveys. Because acquisition of “wall-to-wall” ALS data over large areas is not economically feasible, a systematic sampling approach using ALS as a strip sampling tool was used to supplement a conventional field-based inventory in a large-scale biomass survey in Hedmark County (HC), Norway. However, the complexities of these surveys render prohibitive the analytical determination of the properties of the resulting estimators and of the estimators of their sampling variances. To overcome the problem, the statistical properties of the estimators were empirically investigated in this paper using simulated sampling from an artificial population. Through this approach, estimators with desirable properties can be identified and used for inference in real applications. By combining biomass estimates from Norwegian National Forest Inventory plots in HC, ALS measurements and Landsat 5 TM imagery, an artificial population at the scale of HC was created. Using this artificial population as “ground-truth”, we demonstrate how simulated sampling can be used for assessing the statistical properties of regression estimators and of their variance estimators under two-phase post-stratified systematic sampling (SYS) and simple random sampling without replacement (SRSwoR) designs, considering design- and model-dependent inferential frameworks. The results were assessed using a purely ground-based systematic design with a Horvitz–Thompson (HT) estimator as benchmark. The real overall precision of the ALS-aided systematic survey was nearly five times overestimated when using the design-based variance estimators developed for SRSwoR, while under model-dependent inference the overestimation of the real standard errors was around 40%. Compared to ground-based inventory, the estimated standard errors of the systematic ALS survey doubled while in reality the standard errors were 55% lower. Using successive differences variance estimators greatly improved the precision of the systematic ALS-aided survey and produced valid 95% confidence intervals under the design-based inference. The most satisfactory results for the ALS-aided survey in terms of analytical variances occurred under design-based inference with successive difference variance estimator, closely followed by the model-dependent estimators. Using simulations, the cost efficiency of the ground based and ALS-aided surveys was assessed by evaluating accuracy against inventory cost for various sampling intensities. The results indicated that the ALS-aided surveys can be a cost-efficient alternative to traditional field inventories.

To estimate the aboveground biomass (AGB) for large areas, two-stage sampling designs using airborne laser scanning (ALS) as a strip sampling tool in combination with subsampling of field plots hav...

∙ Key message The study presents novel model-based estimators for growing stock volume and its uncertainty estimation, combining a sparse sample of field plots, a sample of laser data, and wall-to-wall Landsat data. On the basis of our detailed simulation, we show that when the uncertainty of estimating mean growing stock volume on the basis of an intermediate ALS model is not accounted for, the estimated variance of the estimator can be biased by as much as a factor of three or more, depending on the sample size at the various stages of the design. ∙ Context This study concerns model-based inference for estimating growing stock volume in large-area forest inventories, combining wall-to-wall Landsat data, a sample of laser data, and a sparse subsample of field data. ∙ Aims We develop and evaluate novel estimators and variance estimators for the population mean volume, taking into account the uncertainty in two model steps. ∙ Methods Estimators and variance estimators were derived for two main methodological approaches and evaluated through Monte Carlo simulation. The first approach is known as two-stage least squares regression, where Landsat data were used to predict laser predictor variables, thus emulating the use of wall-to-wall laser data. In the second approach laser data were used to predict field-recorded volumes, which were subsequently used as response variables in modeling the relationship between Landsat and field data. Results ∙ The estimators and variance estimators are shown to be at least approximately unbiased. Under certain assumptions the two methods provide identical results with regard to estimators and similar results with regard to estimated variances. ∙ Conclusion We show that ignoring the uncertainty due to one of the models leads to substantial underestimation of the variance, when two models are involved in the estimation procedure.

This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes designbased and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data..We review studies on large-area forest surveys based on model-assisted, modelbased, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters. Keywords: Design-based inference, Model-assisted estimation, Model-based inference, Hybrid inference, National forest inventory, Remote sensing, Sampling