Long-term Monitoring Network Research Articles

Reorganisation of a long-term monitoring network using moss as biomonitor for atmospheric deposition in Germany

The determination of atmospheric deposition in forests can be accomplished using technical sampling devices (bulk samplers, wet only samplers), biomonitors or modelling. In Europe, since 1990 moss sampled every five years at up to 7300 places in up to 35 countries was used as biomonitor. In the moss specimens, heavy metals (HM), nitrogen (N, since 2005) and persistent organic pollutants (POPs, since 2010) were determined. Germany participated in all surveys with the exception of that in 2010. For the moss survey 2015, the biomonitoring network applied in the 2005 campaign should be reorganized. To this end, a complex statistically based methodology including a decision support system was developed and implemented. Its application yielded a network with a reduction of sample points from 726 to 402. By use of the data collected in 2005 the performance of the reorganized network did not reveal significant loss of statistical validity.

Detection of long term trends in underwater noise levels

The risk of adverse impact to marine ecosystems from underwater noise pollution is increasingly recognised by scientists, policymakers, and wider society. Deep water measurements from the Northeast Pacific indicate that ocean noise has increased substantially over recent decades. Policymakers are now considering establishing noise monitoring programs to determine noise levels and trends in their waters. However, the ability of noise monitoring to detect statistically significant trends is a function of the temporal extent, variance, and autocorrelation of the time series. This has implications for the feasibility of evaluating quantitative policy targets within prescribed time frames and hence should inform the formulation of such targets. The present work demonstrates that methods developed in other environmental science disciplines (e.g., atmospheric temperature measurement) to design long-term monitoring networks and assess their statistical power can be applied to noise monitoring programmes. Example datasets are used to show the application of these methods both to assess the significance of long-term trends in ambient noise, and the required monitoring period to detect a given magnitude of trend (e.g., 3 dB per decade). The implications for the design of noise monitoring networks and target setting for policy purposes are then discussed.

Impacts of land-use history on the recovery of ecosystems after agricultural abandonment

Abstract. Land-use changes have been shown to have large effects on climate and biogeochemical cycles, but so far most studies have focused on the effects of conversion of natural vegetation to croplands and pastures. By contrast, relatively little is known about the long-term influence of past agriculture on vegetation regrowth and carbon sequestration following land abandonment. We used the LPJ-GUESS dynamic vegetation model to study the legacy effects of different land-use histories (in terms of type and duration) across a range of ecosystems. To this end, we performed six idealized simulations for Europe and Africa in which we made a transition from natural vegetation to either pasture or cropland, followed by a transition back to natural vegetation after 20, 60 or 100 years. The simulations identified substantial differences in recovery trajectories of four key variables (vegetation composition, vegetation carbon, soil carbon, net biome productivity) after agricultural cessation. Vegetation carbon and composition typically recovered faster than soil carbon in subtropical, temperate and boreal regions, and vice versa in the tropics. While the effects of different land-use histories on recovery periods of soil carbon stocks often differed by centuries across our simulations, differences in recovery times across simulations were typically small for net biome productivity (a few decades) and modest for vegetation carbon and composition (several decades). Spatially, we found the greatest sensitivity of recovery times to prior land use in boreal forests and subtropical grasslands, where post-agricultural productivity was strongly affected by prior land management. Our results suggest that land-use history is a relevant factor affecting ecosystems long after agricultural cessation, and it should be considered not only when assessing historical or future changes in simulations of the terrestrial carbon cycle but also when establishing long-term monitoring networks and interpreting data derived therefrom, including analysis of a broad range of ecosystem properties or local climate effects related to land cover changes.

Sulphur speciation and availability in long-term fertilized soil: evidence from chemical fractionation and SK -edge XANES spectroscopy

Knowledge of sulphur (S) speciation in soil following applications of chemical and organic fertilizers is important for understanding the availability and fate of S in agricultural ecosystems. Sulphur speciation of the soil in three fields, which are part of the National Long-term Monitoring Network of Soil Fertility and Fertilizer Effects of China, was investigated with a combination of a wet-chemical fractionation scheme and S K-edge X-ray absorption near edge structure (XANES) spectroscopy. Treatments of the soil included no fertilizers (control), regular applications of nitrogen, phosphorus and potassium fertilizers (NPK), or of NPK plus organic manures (MNPK) since 1990. Soil samples were taken from the topsoil (0–20 cm) and subsoil (20–40 cm) horizons in 2011. Chemical fractionation showed that available sulphate was the predominant inorganic S species in the Eutric Cambisol, whereas HCl-extractable sulphate was the main inorganic S species in the Haplic Phaeozem and Calcaric Cambisol. Organic S accounted for up to 77% of total S in all soil samples. Results from the fractionation (inorganic S plus ester-S) and XANES analysis (oxidized S) were correlated (r = 0.585, P < 0.01), which confirmed the presence of HCl-extractable sulphate and the speciation of residual S in soil. Long-term application of organic manures facilitated significantly the accumulation of intermediate and reduced S species and increased total and organic S in the soil (P < 0.01). In contrast, the application of chemical fertilizers (NPK) had little effect on the distribution of organic S species compared with the control. Atmospheric deposition was identified as an important source of S. The combination of chemical fractionation and XANES spectroscopy provided complementary information on the availability and speciation of S in soil. Highlights Availability and species of S are characterized with chemical fractionation and XANES spectroscopy. Insoluble sulphate is identified as an important form of S in the soil regardless of soil pH. Organic manure facilitates the accumulation of organic S in long-term fertilized soil. Atmospheric deposition contributes importantly to the input and cycling of S in soil.

A multi-model intercomparison of halogenated very short-lived substances (TransCom-VSLS): linking oceanic emissions and tropospheric transport for a reconciled estimate of the stratospheric source gas injection of bromine

Abstract. The first concerted multi-model intercomparison of halogenated very short-lived substances (VSLS) has been performed, within the framework of the ongoing Atmospheric Tracer Transport Model Intercomparison Project (TransCom). Eleven global models or model variants participated (nine chemical transport models and two chemistry–climate models) by simulating the major natural bromine VSLS, bromoform (CHBr3) and dibromomethane (CH2Br2), over a 20-year period (1993–2012). Except for three model simulations, all others were driven offline by (or nudged to) reanalysed meteorology. The overarching goal of TransCom-VSLS was to provide a reconciled model estimate of the stratospheric source gas injection (SGI) of bromine from these gases, to constrain the current measurement-derived range, and to investigate inter-model differences due to emissions and transport processes. Models ran with standardised idealised chemistry, to isolate differences due to transport, and we investigated the sensitivity of results to a range of VSLS emission inventories. Models were tested in their ability to reproduce the observed seasonal and spatial distribution of VSLS at the surface, using measurements from NOAA's long-term global monitoring network, and in the tropical troposphere, using recent aircraft measurements – including high-altitude observations from the NASA Global Hawk platform. The models generally capture the observed seasonal cycle of surface CHBr3 and CH2Br2 well, with a strong model–measurement correlation (r ≥ 0.7) at most sites. In a given model, the absolute model–measurement agreement at the surface is highly sensitive to the choice of emissions. Large inter-model differences are apparent when using the same emission inventory, highlighting the challenges faced in evaluating such inventories at the global scale. Across the ensemble, most consistency is found within the tropics where most of the models (8 out of 11) achieve best agreement to surface CHBr3 observations using the lowest of the three CHBr3 emission inventories tested (similarly, 8 out of 11 models for CH2Br2). In general, the models reproduce observations of CHBr3 and CH2Br2 obtained in the tropical tropopause layer (TTL) at various locations throughout the Pacific well. Zonal variability in VSLS loading in the TTL is generally consistent among models, with CHBr3 (and to a lesser extent CH2Br2) most elevated over the tropical western Pacific during boreal winter. The models also indicate the Asian monsoon during boreal summer to be an important pathway for VSLS reaching the stratosphere, though the strength of this signal varies considerably among models. We derive an ensemble climatological mean estimate of the stratospheric bromine SGI from CHBr3 and CH2Br2 of 2.0 (1.2–2.5) ppt, ∼ 57 % larger than the best estimate from the most recent World Meteorological Organization (WMO) Ozone Assessment Report. We find no evidence for a long-term, transport-driven trend in the stratospheric SGI of bromine over the simulation period. The transport-driven interannual variability in the annual mean bromine SGI is of the order of ±5 %, with SGI exhibiting a strong positive correlation with the El Niño–Southern Oscillation (ENSO) in the eastern Pacific. Overall, our results do not show systematic differences between models specific to the choice of reanalysis meteorology, rather clear differences are seen related to differences in the implementation of transport processes in the models.

Optimal redesign of environmental monitoring networks by using software MSANOS

Redesign of environmental monitoring networks is a field rich in practical applications, mainly in the light of recent EU environmental directives. In the present work, an optimisation software application has been applied to reduce the size of an existing monitoring network affecting in minimal fashion its descriptive capability and making its maintenance costs more sustainable at the same time. The optimal reduction of a long-term monitoring network is a balance issue. In fact, redundancies among monitoring sites often load the manager with costs providing a negligible contribution in the decision-making processes. However, on the other hand, discarding erroneously monitoring sites could reduce the representativeness of the monitored system and, consequently, distort its informative framework. To avoid such risk, downstream of the optimisation stage, a retrospective analysis should be carried out in order to verify the correctness of the decision about the number and the locations of sites to be discarded. A case study is presented that focuses on the optimisation of a groundwater monitoring network located in the southern part of Italy. The investigated case is of particular interest for the criticality of the considered resource and for the recognised importance of the groundwater monitoring in the Water Framework Directive.

Long-term monitoring of soil quality changes in Northern Germany

The German regional long-term soil monitoring network (SMN) BDF-SH (Boden-Dauerbeobachtung Schleswig-Holstein) was assessed focusing on the quality of this soil monitoring network to detect and evaluate impacts of land-use change (LUC) from pasture to arable land on soil organic carbon (SOC) and biological soil properties. This included a review and evaluation of the monitoring methods to detect long-term SOC changes over time. Two loamy LUC study sites were selected from the monitoring program. The BDF-SH is not only focusing on monitoring of soil organic carbon but also on a wide range of data of soil chemistry, physics, biology and management at field scale. The quality of the SMN as a SOC monitoring was assessed using a catalogue of essential soil monitoring requirements which resulted in a classification of monitoring levels for each parameter. A SMN-specific method is given how to calculate SOC stock changes over time. Within seven and one year(s) respectively the conversion from pasture to arable land resulted in significant SOC losses of 19.4Mgha−1 (19.8%) at the sandy loam site 11 and 27.2Mgha−1 (20.2%) at the clay loam site 13. SOC measurements and microbiological parameters of the soil microbiological program confirmed and defined the results of the main program more precisely. Soil faunistic results underlined the impact of LUC on the soil ecosystem. Using evaluation schemes of the literature the quality of the SMN was evaluated as highly suitable to detect and evaluate SOC stock changes over time and further LUC impacts on the soil. Most of the assessed monitoring parameters of the SMN were evaluated as fulfilling the highest level. However, a SOC fractionation method could be included in the SMN to enable an even more thorough evaluation of SOC stock changes using SOC fraction measurements in process-based SOC modeling.

Ultrafine particles in four European urban environments: Results from a new continuous long-term monitoring network

To gain a better understanding on the spatiotemporal variation of ultrafine particles (UFPs) in urban environments, this study reports on the first results of a long-term UFP monitoring network, set up in Amsterdam (NL), Antwerp (BE), Leicester (UK) and London (UK). Total number concentrations and size distributions were assessed during 1–2 years at four fixed urban background sites, supplemented with mobile trailer measurements for co-location monitoring and additional short-term monitoring sites. Intra- and interurban spatiotemporal UFP variation, associations with commonly-monitored pollutants (PM, NOx and BC) and impacts of wind fields were evaluated. Although comparable size distributions were observed between the four cities, source-related differences were demonstrated within specific particle size classes. Total and size-resolved particle number concentrations showed clear traffic-related temporal variation, confirming road traffic as the major UFP contributor in urban environments. New particle formation events were observed in all cities. Correlations with typical traffic-related pollutants (BC and NOx) were obtained for all monitoring stations, except for Amsterdam, which might be attributable to UFP emissions from Schiphol airport. The temporal variation in particle number concentration correlated fairly weakly between the four cities (rs = 0.28−0.50, COD = 0.28−0.37), yet improved significantly inside individual cities (rs = 0.59−0.77). Nevertheless, considerable differences were still obtained in terms of particle numbers (20–38% for total particle numbers and up to 49% for size-resolved particle numbers), confirming the importance of local source contributions and the need for careful consideration when allocating UFP monitoring stations in heterogeneous urban environments.

Regional assessment of persistent organic pollutants in resident mussels from New Jersey and New York estuaries following Hurricane Sandy

Resident mussels are effective indicators of ecosystem health and have been utilized in national assessment and monitoring studies for over two decades. Mussels were chosen because contaminant concentrations in their tissues respond to changes in ambient environmental levels, accumulation occurs with little metabolic transformation and a substantial amount of historic data were available. Mussels were collected from 10 previously studied locations approximately a year after Hurricane Sandy. Regionally, concentrations of polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs) decreased significantly, while concentrations of organochlorine pesticides (OCPs) remained unchanged, and polybrominated diphenyl ethers (PBDEs) increased compared to historic concentrations. Although concentrations of PCBs, OCPs and PAHs were at or near record low concentrations, long-term trends did not change after Hurricane Sandy. To effectively measure storm-induced impacts it is necessary to understand the factors influencing changes in mussel body burdens and have a long-term monitoring network and an ability to mobilize post event.

Observational evidence of temperature trends at two levels in the surface layer

Abstract. Long-term surface air temperatures at 1.5 m screen level over land are used in calculating a global average surface temperature trend. This global trend is used by the IPCC and others to monitor, assess, and describe global warming or warming hiatus. Current knowledge of near-surface temperature trends with respect to height, however, is limited and inadequately understood because surface temperature observations at different heights in the surface layer of the world are rare especially from a high-quality and long-term climate monitoring network. Here we use high-quality two-height Oklahoma Mesonet observations, synchronized in time, fixed in height, and situated in relatively flat terrain, to assess temperature trends and differentiating temperature trends with respect to heights (i.e., near-surface lapse rate trend) over the period 1997 to 2013. We show that the near-surface lapse rate has significantly decreased with a trend of −0.18 ± 0.03 °C (10 m)−1 per decade indicating that the 9 m height temperatures increased faster than temperatures at the 1.5 m screen level and/or conditions at the 1.5 m height cooled faster than at the 9 m height. However, neither of the two individual height temperature trends by themselves were statistically significant. The magnitude of lapse rate trend is greatest under lighter winds at night. Nighttime lapse rate trends were significantly more negative than daytime lapse rate trends and the average lapse rate trend was three times more negative under calm conditions than under windy conditions. Our results provide the first observational evidence of near-surface temperature changes with respect to height that could enhance the assessment of climate model predictions.

Multi-objective optimization of long-term groundwater monitoring network design using a probabilistic Pareto genetic algorithm under uncertainty

Optimal design of long term groundwater monitoring (LTGM) network often involves conflicting objectives and substantial uncertainty arising from insufficient hydraulic conductivity (K) data. This study develops a new multi-objective simulation–optimization model involving four objectives: minimizations of (i) the total sampling costs for monitoring contaminant plume, (ii) mass estimation error, (iii) the first moment estimation error, and (iv) the second moment estimation error of the contaminant plume, for LTGM network design problems. Then a new probabilistic Pareto genetic algorithm (PPGA) coupled with the commonly used flow and transport codes, MODFLOW and MT3DMS, is developed to search for the Pareto-optimal solutions to the multi-objective LTGM problems under uncertainty of the K-fields. The PPGA integrates the niched Pareto genetic algorithm with probabilistic Pareto sorting scheme to deal with the uncertainty of objectives caused by the uncertain K-field. Also, the elitist selection strategy, the operation library and the Pareto solution set filter are conducted to improve the diversity and reliability of Pareto-optimal solutions by the PPGA. Furthermore, the sampling strategy of noisy genetic algorithm is adopted to cope with the uncertainty of the K-fields and improve the computational efficiency of the PPGA. In particular, Monte Carlo (MC) analysis is employed to evaluate the effectiveness of the proposed methodology in finding Pareto-optimal sampling network designs of LTGM systems through a two-dimensional hypothetical example and a three-dimensional field application in Indiana (USA). Comprehensive analysis demonstrates that the proposed PPGA can find Pareto optimal solutions with low variability and high reliability and is a promising tool for optimizing multi-objective LTGM network designs under uncertainty.

Simulation of atmospheric N&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O with GEOS-Chem and its adjoint: evaluation of observational constraints

Abstract. We describe a new 4D-Var inversion framework for nitrous oxide (N2O) based on the GEOS-Chem chemical transport model and its adjoint, and apply it in a series of observing system simulation experiments to assess how well N2O sources and sinks can be constrained by the current global observing network. The employed measurement ensemble includes approximately weekly and quasi-continuous N2O measurements (hourly averages used) from several long-term monitoring networks, N2O measurements collected from discrete air samples onboard a commercial aircraft (Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container; CARIBIC), and quasi-continuous measurements from the airborne HIAPER Pole-to-Pole Observations (HIPPO) campaigns. For a 2-year inversion, we find that the surface and HIPPO observations can accurately resolve a uniform bias in emissions during the first year; CARIBIC data provide a somewhat weaker constraint. Variable emission errors are much more difficult to resolve given the long lifetime of N2O, and major parts of the world lack significant constraints on the seasonal cycle of fluxes. Current observations can largely correct a global bias in the stratospheric sink of N2O if emissions are known, but do not provide information on the temporal and spatial distribution of the sink. However, for the more realistic scenario where source and sink are both uncertain, we find that simultaneously optimizing both would require unrealistically small errors in model transport. Regardless, a bias in the magnitude of the N2O sink would not affect the a posteriori N2O emissions for the 2-year timescale used here, given realistic initial conditions, due to the timescale required for stratosphere–troposphere exchange (STE). The same does not apply to model errors in the rate of STE itself, which we show exerts a larger influence on the tropospheric burden of N2O than does the chemical loss rate over short (&lt; 3 year) timescales. We use a stochastic estimate of the inverse Hessian for the inversion to evaluate the spatial resolution of emission constraints provided by the observations, and find that significant, spatially explicit constraints can be achieved in locations near and immediately upwind of surface measurements and the HIPPO flight tracks; however, these are mostly confined to North America, Europe, and Australia. None of the current observing networks are able to provide significant spatial information on tropical N2O emissions. There, averaging kernels (describing the sensitivity of the inversion to emissions in each grid square) are highly smeared spatially and extend even to the midlatitudes, so that tropical emissions risk being conflated with those elsewhere. For global inversions, therefore, the current lack of constraints on the tropics also places an important limit on our ability to understand extratropical emissions. Based on the error reduction statistics from the inverse Hessian, we characterize the atmospheric distribution of unconstrained N2O, and identify regions in and downwind of South America, central Africa, and Southeast Asia where new surface or profile measurements would have the most value for reducing present uncertainty in the global N2O budget.

Simulating the impact of climate change on the groundwater resources of the Magdalen Islands, Québec, Canada

Abstract Study region This study is conducted in the Magdalen Islands (Quebec, Canada), a small archipelago located in the Gulf of St. Lawrence. Study focus This work was undertaken to support the design of a long-term groundwater monitoring network and for the sustainable management of groundwater resources. This study relies mostly on the compilation of existing data, but additional field work has also been carried out, allowing for the first time in the Magdalen Islands, direct observation of the depth and shape of the transition zone between freshwater and seawater under natural conditions. Simulations were conducted along a 2D cross-section on Grande Entree Island in order to assess the individual and combined impacts of sea-level rise, coastal erosion and decreased groundwater recharge on the position of the saltwater–freshwater interface. The simulations were performed considering variable-density flow and solute transport under saturated-unsaturated conditions. The model was driven by observed and projected climate change scenarios to 2040 for the Magdalen Islands. New hydrological insights for the region The simulation results show that among the three impacts considered, the most important is sea-level rise, followed by decreasing groundwater recharge and coastal erosion. When combined, these impacts cause the saltwater–freshwater interface to migrate inland over a distance of 37 m and to rise by 6.5 m near the coast to 3.1 m further inland, over a 28-year period.

Metabarcoding is powerful yet still blind: a comparative analysis of morphological and molecular surveys of seagrass communities.

In the context of the sixth wave of extinction, reliable surveys of biodiversity are increasingly needed to infer the cause and consequences of species and community declines, identify early warning indicators of tipping points, and provide reliable impact assessments before engaging in activities with potential environmental hazards. DNA metabarcoding has emerged as having potential to provide speedy assessment of community structure from environmental samples. Here we tested the reliability of metabarcoding by comparing morphological and molecular inventories of invertebrate communities associated with seagrasses through estimates of alpha and beta diversity, as well as the identification of the most abundant taxa. Sediment samples were collected from six Zostera marina seagrass meadows across Brittany, France. Metabarcoding surveys were performed using both mitochondrial (Cytochrome Oxidase I) and nuclear (small subunit 18S ribosomal RNA) markers, and compared to morphological inventories compiled by a long-term benthic monitoring network. A sampling strategy was defined to enhance performance and accuracy of results by preventing the dominance of larger animals, boosting statistical support through replicates, and using two genes to compensate for taxonomic biases. Molecular barcodes proved powerful by revealing a remarkable level of diversity that vastly exceeded the morphological survey, while both surveys identified congruent differentiation of the meadows. However, despite the addition of individual barcodes of common species into taxonomic reference databases, the retrieval of only 36% of these species suggest that the remaining were either not present in the molecular samples or not detected by the molecular screening. This finding exemplifies the necessity of comprehensive and well-curated taxonomic reference libraries and multi-gene surveys. Overall, results offer methodological guidelines and support for metabarcoding as a powerful and repeatable method of characterizing communities, while also presenting suggestions for improvement, including implementation of pilot studies prior to performing full “blind” metabarcoding assessments to optimize sampling and amplification protocols.

Passive measurement of NO2 and application of GIS to generate spatially-distributed air monitoring network in urban environment

Nitrogen dioxide (NO2) plays an important role in atmospheric chemistry through formation of secondary particulate nitrate that contributes a major portion to the mass of particulate matter (PM) that has several impacts on the environment in the scales of local, regional and global. NO2 is regulated in most of the countries, because it acts as a precursor for nitrate formation and it shows significant negative effects on human health. The present study was conducted for measurement of NO2 concentration at 204 and 101 sampling locations in two Indian large cities (population more than 4 million), Delhi and Kanpur, respectively by using passive samplers. From the experimental results, an average concentration of 68.6±20.1μg/m3 was observed in Delhi and it was 36.9±12.1μg/m3 in Kanpur. The observed data from all sampling sites were utilized on the platform of Geographic Information System (GIS) to generate spatially distributed pollution maps (using Kriging interpolation method) and maps of probability of exceedences of air quality standards. Based on the survey results on emission activities, meteorology and pollution maps, this study proposed locations of air quality sampling sites for a long-term monitoring network in Delhi and Kanpur.

U.S. National PM2.5 Chemical Speciation Monitoring Networks—CSN and IMPROVE: Description of networks

The U.S. Environmental Protection Agency (EPA) initiated the national PM2.5 Chemical Speciation Monitoring Network (CSN) in 2000 to support evaluation of long-term trends and to better quantify the impact of sources on particulate matter (PM) concentrations in the size range below 2.5 μm aerodynamic diameter (PM2.5; fine particles). The network peaked at more than 260 sites in 2005. In response to the 1999 Regional Haze Rule and the need to better understand the regional transport of PM, EPA also augmented the long-existing Interagency Monitoring of Protected Visual Environments (IMPROVE) visibility monitoring network in 2000, adding nearly 100 additional IMPROVE sites in rural Class 1 Areas across the country. Both networks measure the major chemical components of PM2.5 using historically accepted filter-based methods. Components measured by both networks include major anions, carbonaceous material, and a series of trace elements. CSN also measures ammonium and other cations directly, whereas IMPROVE estimates ammonium assuming complete neutralization of the measured sulfate and nitrate. IMPROVE also measures chloride and nitrite. In general, the field and laboratory approaches used in the two networks are similar; however, there are numerous, often subtle differences in sampling and chemical analysis methods, shipping, and quality control practices. These could potentially affect merging the two data sets when used to understand better the impact of sources on PM concentrations and the regional nature and long-range transport of PM2.5. This paper describes, for the first time in the peer-reviewed literature, these networks as they have existed since 2000, outlines differences in field and laboratory approaches, provides a summary of the analytical parameters that address data uncertainty, and summarizes major network changes since the inception of CSN.ImplicationsTwo long-term chemical speciation particle monitoring networks have operated simultaneously in the United States since 2001, when the EPA began regular operations of its PM2.5 Chemical Speciation Monitoring Network (IMPROVE began in 1988). These networks use similar field sampling and analytical methods, but there are numerous, often subtle differences in equipment and methodologies that can affect the results. This paper describes these networks since 2000 (inception of CSN) and their differences, and summarizes the analytical parameters that address data uncertainty, providing researchers and policymakers with background information they may need (e.g., for 2018 PM2.5 designation and State Implementation Plan process; McCarthy, 2013) to assess results from each network and decide how these data sets can be mutually employed for enhanced analyses. Changes in CSN and IMPROVE that have occurred over the years also are described.

Impact of intensive residue removal on the fertility of forest soils and on their biodiversity

This PhD deals with the impact of logging residues removal on forest soil fertility and biodiversity. It takes place into an energy environment characterised by a policy of reducing fossil energy consumption and developing renewable energies market, particularly the fuel wood sector. This study focuses on two aspects: the interactions between carbon and nitrogen cycling in forest soils and the impacts of these types of forest management on soil microbial and fungal communities. It is conducted on an 18-site long-term monitoring network called Matieres Organiques des Sols (MOS). Three tree species are studied: oak, beech and Douglas fir. The PhD mostly consists in devising and establishing the baseline characteristics of the MOS network. Emila AKROUME INRA Centre de Nancy-Lorraine – AgroParisTech UR Biogeochimie des ecosystemes forestiers Interactions arbres-microorganismes F-54280 CHAMPENOUX (emila.akroume@agroparistech.fr)

Vulnerabilidad de los bosques españoles frente al Cambio Climático: evaluación mediante modelos

Ruiz-Benito, P., Herrero, A., Zavala, M.A. 2013. Vulnerability of Spanish forests under climatic change: evaluation through models. Ecosistemas 22(3):21-28. Doi.: 10.7818/ECOS.2013.22-3.04 Forest ecosystems contribute to human well-being providing critical ecosystem services. Global change (including climate and land-use changes), however, can alter ecosystem functioning and structure, even jeopardizing the future supply of ecosystem services. Vulnerability to global change depends on exposition (magnitude of the change), sensitivity (susceptibility to the change), and adaptive capacity (ability to adjust to the change) of the species. Here, we summarize diverse modeling approaches to analyze the different components of vulnerability, providing specific examples focused on Iberian forests. Despite of these advances, our empirical and theoretical ability to provide integrated assessments of potential impacts (i.e. including both exposition and sensitivity) of climate change and adaptive capacity is still very limited. An adequate estimation of vulnerability requires improving our knowledge about the adaptive capacity of species and their response to extreme environmental changes (e.g. through long-term monitoring networks), as well as integrating the knowledge obtained from models developed at different levels of biological organization, from physiological process-based models to aggregated species distribution models.

A review of management impacts on the soil productivity of South African commercial forestry plantations and the implications for multiple-rotation productivity

An overview of southern African research that has investigated the impact of management practices that can potentially affect the long-term site productivity of plantations is presented, with an emphasis of these impacts on soil productivity and with the implications for multiple-rotation site productivity. Activities that are most likely to cause long-term changes in soil productivity are identified as those causing high site disturbance during the inter-rotation period, notably ground-based mechanised operations and harvest residue management (including complete biomass removal) and, under some conditions, fertilisation. The impacts of these practices on soil and stand productivity are discussed from a southern African perspective. Knowledge gaps are identified and it is suggested that the immediate research priority is to identify meaningful indicators that are sensitive to change and that can be linked to stand production. Other key research focus areas are proposed and the need for a permanent long-term monitoring network is highlighted.

Spatial forest structure reconstruction as a strategy for mitigating edge-bias in circular monitoring plots

Forest ecosystem monitoring is an important task of forest science worldwide and its outcomes have important consequences both for national forest policies and local decision making. Often forest monitoring is implemented using circular plots of limited size. Edge effects can seriously bias the results of spatially explicit analyses of circular plot data and little research has been carried out on how to mitigate this problem. In this study, we have compared the method of spatial forest structure reconstruction to traditional plus-sampling, to the reflection method and to a situation where no edge-bias mitigation method is used at all. Reconstruction is a non-parametric modelling method based on simulated annealing. In the context of this study, the arithmetic means of structural summary characteristics are used to extrapolate spatial patterns previously measured in the core area of the monitoring plots to the margins outside. The computer experiments were based on 706 circular monitoring plots of the Estonian long-term monitoring network maintained by the Estonian University of Life Sciences, Tartu. The results clearly indicate the superiority of the reconstruction method and suggest that this approach has great potential for future spatially-explicit data analyses and modelling involving circular monitoring plots. Further improvements can be expected from using density functions and histograms of structural summary characteristics instead of arithmetic means.