Spatio-temporal variability of solid, total dissolved and labile metal: passive vs. discrete sampling evaluation in river metal monitoring

In order to obtain representative dissolved and solid samples from the aquatic environment, a spectrum of sampling methods are available, each one with different advantages and drawbacks. This article evaluates the use of discrete sampling and time-integrated sampling in illustrating medium-term spatial and temporal variation. Discrete concentration index (CI) calculated as the ratio between dissolved and solid metal concentrations in grab samples are compared with time-integrated concentration index (CI) calculated from suspended particulate matter (SPM) collected in sediment traps and labile metals measured by the diffusive gel in thin films (DGT) method, collected once a month during one year at the Seine River, upstream and downstream of the Greater Paris Region. Discrete CI at Bougival was found to be significantly higher than at Triel for Co, Cu, Mn, Ni and Zn, while discrete metal partitioning at Marnay was found to be similar to Bougival and Triel. However, when using time-integrated CI, not only was Bougival CI significantly higher than Triel CI, CI at Marnay was also found to be significantly higher than CI at Triel which was not observed for discrete CI values. Since values are time-averaged, dramatic fluctuations were smoothed out and significant medium-term trends were enhanced. As a result, time-integrated concentration index (CI) was able to better illustrate urbanization impact between sites when compared to discrete CI. The impact of significant seasonal phenomenon such as winter flood, low flow and redox cycles was also, to a certain extent, visible in time-integrated CI values at the upstream site. The use of time-integrated concentration index may be useful for medium- to long-term metal studies in the aquatic environment.     

Methodological uncertainties in multi-regression analyses of middle-atmospheric data series

Multi-regression analyses have often been used recently to detect trends, in particular in ozone or temperature data sets in the stratosphere. The confidence in detecting trends depends on a number of factors which generate uncertainties. Part of these uncertainties comes from the random variability and these are what is usually considered. They can be statistically estimated from residual deviations between the data and the fitting model. However, interferences between different sources of variability affecting the data set, such as the Quasi-Biennal Oscillation (QBO), volcanic aerosols, solar flux variability and the trend can also be a critical source of errors. This type of error has hitherto not been well quantified. In this work an artificial data series has been generated to carry out such estimates. The sources of errors considered here are: the length of the data series, the dependence on the choice of parameters used in the fitting model and the time evolution of the trend in the data series. Curves provided here, will permit future studies to test the magnitude of the methodological bias expected for a given case, as shown in several real examples. It is found that, if the data series is shorter than a decade, the uncertainties are very large, whatever factors are chosen to identify the source of the variability. However the errors can be limited when dealing with natural variability, if a sufficient number of periods (for periodic forcings) are covered by the analysed dataset. However when analysing the trend, the response to volcanic eruption induces a bias, whatever the length of the data series. The signal to noise ratio is a key factor: doubling the noise increases the period for which data is required in order to obtain an error smaller than 10%, from 1 to 3-4 decades. Moreover, if non-linear trends are superimposed on the data, and if the length of the series is longer than five years, a non-linear function has to be used to estimate trends. When applied to real data series, and when a breakpoint in the series occurs, the study reveals that data extending over 5 years are needed to detect a significant change in the slope of the ozone trends at mid-latitudes.     

Economic Analysis of Summer Fallow Management to Reduce Take-All Disease and N Leaching in a Wheat Crop Rotation

This paper addresses the question of summer cover-crop adoption by farmers in presence of a risk of yield loss due to take-all disease and climate variability. To analyze the public incentives needed to encourage farmers to adopt summer cover crops as a means of reducing N leaching, we combine outputs from an economic, an epidemiological and an agronomic model. The economic model is a simple model of choice under risk. The farmer is assumed to choose among a range of summer fallow managements and input uses on the basis of the expected utility criterion (HARA assumption) in presence of both climate and take-all risks. The epidemiological model proposed by Ennaïfar et al. (Eur J Plant Pathol 118:127–143, 2007) is used to determine the impact of take all on yields and N uptake. The crop-soil model (STICS) is used to compute yield developments and N leaching under various management options and climatic conditions. The input parameters are chosen to match the conditions prevailing in Grignon, located in the main wheat-growing area in France. Eight management systems are examined: four summer fallow managements: ‘wheat volunteers’ (WV), ‘bare soil’ (BS), ‘early mustard’, ‘late mustard’, and two input intensities. We show that the optimal systems are BS (WV) when the take-all risk is (not) taken into account by agents. We then compute the minimum payment to each system such that it emerges in the optimum. We thus derive the required amounts of transfer needed to trigger catch-crop adoption. The results of the Monte Carlo sensitivity analysis show that the ranking of management systems is robust over a wide range of input parameters.     

Testing hypotheses in evolutionary ecology with imperfect detection: capture-recapture structural equation modeling

Studying evolutionary mechanisms in natural populations often requires testing multifactorial scenarios of causality involving direct and indirect relationships among individual and environmental variables. It is also essential to account for the imperfect detection of individuals to provide unbiased demographic parameter estimates. To cope with these issues, we developed a new approach combining structural equation models with capture-recapture models (CR-SEM) that allows the investigation of competing hypotheses about individual and environmental variability observed in demographic parameters. We employ Markov chain Monte Carlo sampling in a Bayesian framework to (1) estimate model parameters, (2) implement a model selection procedure to evaluate competing hypotheses about causal mechanisms, and (3) assess the fit of models to data using posterior predictive checks. We illustrate the value of our approach using two case studies on wild bird populations. We first show that CR-SEM can be useful to quantify the action of selection on a set of phenotypic traits with an analysis of selection gradients on morphological traits in Common Blackbirds (Turdus merula). In a second case study on Blue Tits (Cyanistes caeruleus), we illustrate the use of CR-SEM to study evolutionary trade-offs in the wild, while accounting for varying environmental conditions.     

Marine habitat mapping in support of Marine Protected Area management in a subarctic fjord: Gilbert Bay, Labrador, Canada

This paper presents an approach that allows production of benthic substrate and habitat maps in fjord environments. This approach is used to support the management of the Gilbert Bay Marine Protected Area (MPA) in southeastern Labrador, Atlantic Canada. Multibeam sonar-derived bathymetry, seabed slope, and acoustic reflectance (backscatter) were combined using supervised classification methods and GIS with ground-truthed benthic sampling in order to derive maps of the substrates and main benthic habitats. Six acoustically distinct substrate types were identified in the fjord, and three additional substrate types without a unique acoustic signature were recognized. Ordination by multidimensional scaling and analysis of similarity generalized these to four acoustically distinct habitat types. Greatest within-habitat (alpha) diversity was found in the coralline-algae encrusted gravel habitat. Greatest between-habitat (beta) diversity was found in the management Zones 1 and 2, which have the highest level of protection. The study confirmed that the zoning plan for the MPA, which was designed to protect spawning and juvenile fish habitat for a local genetically distinct population of Atlantic cod, afforded highest levels of protection to areas with highest habitat diversity.     

Carbon leakage and capacity-based allocations: Is the EU right?

Competitiveness and carbon leakage are major concerns for the design of CO₂ emissions permits markets. In the absence of a global carbon tax and of border carbon adjustments, output-based allocation is a third-best solution and is actually implemented (Australia, California, New Zealand). The EU has followed a different route; free allowances are allocated to existing or new capacities in proportion to a benchmark, independent of actual production. This paper compares these two schemes in a formal setting and shows that the optimal one is in fact a combination of both schemes, or output-based allocation alone if uncertainty is limited. A key assumption of our analysis is that the short-term import pressure depends both on the existing capacities and the level of demand, which is typical in capital intensive and internationally traded sectors. A calibration of the model is used to discuss the EU scheme for the cement sector in the third phase of the EU-ETS (2013–2020). This allows for a quantification of various policies in terms of welfare, investment, production, company profits, public revenues and leakage.     

Influence of ozone on the sampling and storage of volatile organic compounds in canisters

A dynamic dilution system was used for the study of the influence of ozone on the sampling and storage of volatile organic compounds (VOCs) at 0, 4, 7 and 14 days in “TO-Can” canisters at two ozone concentrations, 60 and 150 ppbv. Among the 31 ozone precursors VOCs, a representative mixture containing five alkenes, five aromatics, acetylene and 1,3 butadiene was selected using the Clausius–Clapeyron equation. Here, we demonstrate that the presence of ozone have no influence on the concentrations of VOCs after 14 days storage period and consequently no problem of representativity of the sampling appears after 14 days of storage. The main explanation is the degradation of ozone in contact with deactivated walls of canisters.