Adaptive strategies of territory formation

How do territorial animals gain ownership of an area? Early modelling has considered the evolution of fighting when the winner can claim the right to the resource. Recently, alternative hypotheses have been offered where repeated interactions lead to division of space through 'nagging' instead of one decisive fight. However, these models assume that animals avoid areas in which they have taken part in aggressive interactions, but do not consider whether avoidance itself is adaptive. We aim to bridge this gap between mechanistic and adaptive explanations, by presenting a game-theory model where individuals choose whether to return to an area after a fight with a specific outcome (win, loss, draw). We show that avoidance of areas where fights have occurred can be adaptive, but only if benefits of access to the area are low compared to costs of fighting. Otherwise, one individual (typically the winner) responds by returning to the area, and the other (loser) avoids it. In such cases, space is gained by winning fights. We also consider the role of conventions. If responses to fights were purely conventional, paradoxical strategies where losers of fights gain ownership would be equally logical as common-sense ones where winners claim ownership. Paradoxical solutions can be stable but only when there is little difference in fighting ability between the competitors, when individuals adhere very strictly to a behavioural rule without much random variation, and when the population in its ancient state used a paradoxical strategy.     

Seasonal comparison of moss bag technique against vertical snow samples for monitoring atmospheric pollution

This is the first study seasonally applying Sphagnum papillosum moss bags and vertical snow samples for monitoring atmospheric pollution.Moss bags,exposed in January,were collected together with snow samples by early March 2012 near the Harjavalta Industrial Park in southwest Finland.Magnetic,chemical,scanning electron microscopy-energy dispersive X-ray spectroscopy(SEM-EDX),K-means clustering,and Tomlinson pollution load index(PLI)data showed parallel spatial trends of pollution dispersal for both materials.Results strengthen previous findings that concentrate and slag handling activities were important(dust)emission sources while the impact from Cu–Ni smelter's pipe remained secondary at closer distances.Statistically significant correlations existed between the variables of snow and moss bags.As a summary,both methods work well for sampling and are efficient pollutant accumulators.Moss bags can be used also in winter conditions and they provide more homogeneous and better controlled sampling method than snow samples.     

Experience with the use of LCA-modelling (EASEWASTE) in waste management.

Life-cycle assessment (LCA) models are becoming the principal decision support tools of waste management systems. This paper describes our experience with the use of EASEWASTE (Environmental Assessment of Solid Waste Systems and Technologies), a new computerized LCA-based model for integrated waste management. Our findings provide a quantitative understanding of waste management systems and may reveal consistent approaches to improve their environmental performances. EASEWASTE provides a versatile system modelling facility combined with a complete life-cycle impact assessment and in addition to the traditional impact categories addresses toxicity-related categories. New categories dealing with stored ecotoxicity and spoiled groundwater resources have been introduced. EASEWASTE has been applied in several studies, including full-scale assessments of waste management in Danish municipalities. These studies led to numerous modelling issues: the need of combining process-specific and input-specific emissions, the choice of a meaningful time horizon, the way of accounting for biological carbon emissions, the problem of stored ecotoxicity and aspects of crediting the waste management system with the savings inherent in avoided production of energy and materials. Interpretation of results showed that waste management systems can be designed in an environmentally sustainable manner where energy recovery processes lead to substantial avoidance of emissions and savings of resources.     

Bayesian methods for analyzing movements in heterogeneous landscapes from mark-recapture data

Spatially referenced mark-recapture data are becoming increasingly available, but the analysis of such data has remained difficult for a variety of reasons. One of the fundamental problems is that it is difficult to disentangle inherent movement behavior from sampling artifacts. For example, in a typical study design, short distances are sampled more frequently than long distances. Here we present a modeling-based alternative that combines a diffusion-based process model with an observation model to infer the inherent movement behavior of the species from the data. The movement model is based on classifying the landscape into a number of habitat types, and assuming habitat-specific diffusion and mortality parameters, and habitat selection at edges between the habitat types. As the problem is computationally highly intensive, we provide software that implements adaptive Bayesian methods for effective sampling of the posterior distribution. We illustrate the modeling framework by analyzing individual mark-recapture data on the Glanville fritillary butterfly (Melitaea cinxia), and by comparing our results with earlier ones derived from the same data using a purely statistical approach. We use simulated data to perform an analysis of statistical power, examining how accuracy in parameter estimates depends on the amount of data and on the study design. Obtaining precise estimates for movement rates and habitat preferences turns out to be especially challenging, as these parameters can be highly correlated in the posterior density. We show that the parameter estimates can be considerably improved by alternative study designs, such as releasing some of the individuals into the unsuitable matrix, or spending part of the recapture effort in the matrix.     

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.     

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...     

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.