Mortality and other determinants of bird divorce rate

Pair bonds can end in two different ways: either death of the partner or divorce, where the frequency of divorce varies enormously among bird species, from 0% to 100%. To better understand this variation, we created and analyzed the largest dataset so far, consisting of 158 species and 20 variables that quantify the species’ body size, life history, diet, and other characteristics. Our results suggest that species with a high divorce rate have a high mortality rate, tend to be ornamented and sexually dichromatic, live colonially, and form part-time rather than continuous partnerships. Traits quantifying body size or life history are often nonlinearly related with divorce rate. These nonlinearities might (partly) be caused by nonlinear relationships of the same traits with mortality rate which is, in turn, linearly related with divorce rate. In contrast to what has been found previously, mortality rate appears as a key factor for divorce rate in our study. This makes sense, as the likelihood that a partner survives from one year to the next decreases with increasing mortality rate, diminishing the likely success of a partner that attempts to locate its previous partner. We also found taxonomic differences in divorce rate. Specifically, Anseriformes have an exceptionally low divorce rate, which, however, can be explained by the generally important determinants of divorce rate: They also have a lower mortality rate, lower degree of ornamentation and coloniality, and more continuous partnerships than members of other orders. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Analyzing or explaining beta diversity? Understanding the targets of different methods of analysis

It has been actively discussed recently what statistical methods are appropriate when one is interested in testing hypotheses about the origin of beta diversity, especially whether one should use the raw-data approach (e.g., canonical analysis such as RDA and CCA) or the distance approach (e.g., Mantel test and multiple regression on distance matrices). Most of the confusion seems to stem from uncertainty as to what is the response variable in the different approaches. Here our aim is to clarify this issue. We also show that, although both the raw-data approach and the distance approach can often be used to address the same ecological hypothesis, they target fundamentally different predictions of those hypotheses. As the two approaches shed light on different aspects of the ecological hypotheses, they should be viewed as complementary rather than alternative ways of analyzing data. However, in some cases only one of the approaches may be appropriate. We argue that S. P. Hubbell's neutral theory can only be tested using the distance approach, because its testable predictions are stated in terms of distances, not in terms of raw data. In all cases, the decision on which method is chosen must be based on which addresses the question at hand, it cannot be based on which provides the highest proportion of explained variance in simulation studies.     

New procedures for simultaneous determination of mesotrione and atrazine in water and soil. Comparison of the degradation processes of mesotrione and atrazine

A method for the determination of residues of mesotrione, atrazine and its degradation products: deethylatrazine, hydroxyatrazine, deisopropylatrazine, desethyldesisopropylatrazine in a variety of water and soil matrices has been developed. Mesotrione is a new selective herbicide for use in corn, which has been substituted for atrazine, which has been banned in European Union countries since 2007. Although atrazine has not been used for three vegetative periods, it is still detected in the environment. The analysis was conducted by means of ultra-high-pressure liquid chromatography with ultraviolet detection and liquid chromatography with diode array detection. The procedures for analyte separation from water and soil matrices were also established. The optimal conditions for solid-phase extraction (SPE) were determined. The recoveries were compared with that obtained by means of SPE. Method fortification recoveries from water samples averaged 78–97% and for soil 80–97% depending on the analyte and type of sample. The limits of detection were 0.04–0.61 μg/L for water samples and for soil samples 0.02–0.88 μg/g. The soil samples were collected in spring 2009 from three different fields with water samples being made from effluents from these fields. Samples collection was conducted in the day of mesotrione (Callisto 100SC) application and then done weekly, until the mesotrione concentration was below the limit of quantification. The results enabled the monitoring of mesotrione degradation in soil and its permeability into surface waters; simultaneously, the same studies were conducted for atrazine.     

Linking fine-scale sub-arctic vegetation distribution in complex topography with surface-air-temperature modelled at 50-m resolution

Recent studies have shown that the complexities of the surface features in mountainous terrain require a re-assessment of climate impacts at the local level. We explored the importance of surface-air-temperature based on a recently published 50-m-gridded dataset, versus soil variables for explaining vegetation distribution in Swedish Lapland using generalised linear models (GLMs). The results demonstrated that the current distribution of the birch forest and snowbed community strongly relied on the surface-air-temperature. However, temperature alone is a poor predictor of many plant communities (wetland, meadow). Because of diminishing sample representation with increasing altitude, the snowbed community was under-sampled at higher altitudes. This results in underestimation of the current distribution of the snowbed community around the mountain summits. The analysis suggests that caution is warranted when applying GLMs at the local level.     

Removal of PCBs in contaminated soils by means of chemical reduction and advanced oxidation processes

Although the chemical reduction and advanced oxidation processes have been widely used individually, very few studies have assessed the combined reduction/oxidation approach for soil remediation. In the present study, experiments were performed in spiked sand and historically contaminated soil by using four synthetic nanoparticles (Fe⁰, Fe/Ni, Fe₃O₄, Fe_3???x Ni _x O₄). These nanoparticles were tested firstly for reductive transformation of polychlorinated biphenyls (PCBs) and then employed as catalysts to promote chemical oxidation reactions (H₂O₂ or persulfate). Obtained results indicated that bimetallic nanoparticles Fe/Ni showed the highest efficiency in reduction of PCB28 and PCB118 in spiked sand (97 and 79 %, respectively), whereas magnetite (Fe₃O₄) exhibited a high catalytic stability during the combined reduction/oxidation approach. In chemical oxidation, persulfate showed higher PCB degradation extent than hydrogen peroxide. As expected, the degradation efficiency was found to be limited in historically contaminated soil, where only Fe⁰ and Fe/Ni particles exhibited reductive capability towards PCBs (13 and 18 %). In oxidation step, the highest degradation extents were obtained in presence of Fe⁰ and Fe/Ni (18–19 %). The increase in particle and oxidant doses improved the efficiency of treatment, but overall degradation extents did not exceed 30 %, suggesting that only a small part of PCBs in soil was available for reaction with catalyst and/or oxidant. The use of organic solvent or cyclodextrin to improve the PCB availability in soil did not enhance degradation efficiency, underscoring the strong impact of soil matrix. Moreover, a better PCB degradation was observed in sand spiked with extractable organic matter separated from contaminated soil. In contrast to fractions with higher particle size (250–500 and <500 μm), no PCB degradation was observed in the finest fraction (≤250 μm) having higher organic matter content. These findings may have important practical implications to promote successively reduction and oxidation reactions in soils and understand the impact of soil properties on remediation performance.     

Mapping uncertainties in the future provision of ecosystem services in a mountain region in Switzerland

Regional Environmental Change - Ecosystem services (ES) management has to cope with a high degree of uncertainty related to changes in socio-economic and climatic conditions as well as in societal...     

Real-world vehicle emissions as measured by in situ analysis of exhaust plumes

Environmental Science and Pollution Research - We conducted a 60-day roadside measurement campaign on a busy street in Münster, Germany, during summer 2016. We used gas and particle...     

Minimum turbulence assumptions and u* and L estimation for dispersion models during low-wind stable conditions

The U.S. Environmental Protection Agency (EPA) short-distance dispersion model, AERMOD, has been shown to overpredict by a factor of as much as 10 when compared with observed concentrations from continuous releases at the Oak Ridge, TN (OR), and Idaho Falls, ID (IF), field experiments during stable periods when wind speeds often dropped below 1 m/sec. Some of this overprediction tendency can be reduced by revising AERMOD's meteorological preprocessor's parameterizations of the friction velocity, u _* , during low-wind stable conditions, thus increasing the calculated σ _v and σ _w and hence the lateral and vertical dispersion rates. Observations show that as the mean wind speed approaches zero at night, there is always significant σ _v and σ _w over time periods of 15 to 60 min, while standard Monin–Obukhov Similarity Theory (MOST) predicts that σ _v and σ _w will approach zero. This paper focuses on the u _* estimation methods and the minimum turbulence (σ _v and σ _w ) assumptions in AERMOD (beta option 4) and two widely used U.S. operational dispersion models, AERMOD (v12345) and SCICHEM. The U.S. EPA has provided results of its tests with the OR and IF data, with its base AERMOD version and its December 2012 modified versions, which assume adjustments to the low-wind u _* and increases in the minimum σ _v parameterization. SCICHEM has relatively small mean bias for both data sets. The revised AERMOD shows much less mean bias, agreeing more with SCICHEM.

Implications:

Suggestions are made for improvements to dispersion models such as AERMOD to correct overpredictions during light-wind stable conditions. Methods for estimating u_*, L, and the minimum turbulence parameters (σ_v and σ_w) are reviewed and compared. SCICHEM and the current operational version and an optional beta version (December 2012) of AERMOD are evaluated with tracer data from low-wind stable field experiments in Idaho Falls and Oak Ridge. It is seen that the operational version of AERMOD overpredicts by a factor of 2 to 10, while the optional beta version of AERMOD and SCICHEM have much less bias.