Films Made by Blending Poly(ε-Caprolactone) with Starch and Flour from Sagu Rhizome Grown at the Venezuelan Amazons

Starch-based composite films have been proposed as food packaging. In this context, the study of non-conventional starch sources (sagu, Canna edulis Kerr) has worldwide special attention, because these materials can impart different properties as carbohydrate polymers. A thorough study of the matrices used (sagu starch and flour) was carried out. In the same way, thermoplastic starch (TPS)/PCL blend and thermoplastic flour (TFS)/PCL blend were obtained by melt mixing followed by compression moulding containing glycerol as plasticizer. In this study, chemical composition of the matrices and their properties were related with the properties of the developed films. Moisture content, water solubility, X-ray diffraction, thermogravimetric analysis and mechanical and microstructural properties were evaluated in the films. Taking into account the results, the sagu flour has great potential as starchy source for food packaging applications. However, concretely the flour had lower compatibility with the PCL compared to the starch/PCL blend.     

Fourth-corner correlation is a score test statistic in a log-linear trait–environment model that is useful in permutation testing

Ecologists wish to understand the role of traits of species in determining where each species occurs in the environment. For this, they wish to detect associations between species traits and environmental variables from three data tables, species count data from sites with associated environmental data and species trait data from data bases. These three tables leave a missing part, the fourth-corner. The fourth-corner correlations between quantitative traits and environmental variables, heuristically proposed 20 years ago, fill this corner. Generalized linear (mixed) models have been proposed more recently as a model-based alternative. This paper shows that the squared fourth-corner correlation times the total count is precisely the score test statistic for testing the linear-by-linear interaction in a Poisson log-linear model that also contains species and sites as main effects. For multiple traits and environmental variables, the score test statistic is proportional to the total inertia of a doubly constrained correspondence analysis. When the count data are over-dispersed compared to the Poisson or when there are other deviations from the model such as unobserved traits or environmental variables that interact with the observed ones, the score test statistic does not have the usual chi-square distribution. For these types of deviations, row- and column-based permutation methods (and their sequential combination) are proposed to control the type I error without undue loss of power (unless no deviation is present), as illustrated in a small simulation study. The issues for valid statistical testing are illustrated using the well-known Dutch Dune Meadow data set.     

A graphical assessment and spatial clustering of the Deepwater Horizon oil spill impact on Laughing Gulls

The Deepwater Horizon oil spill occurred in the Gulf of Mexico on April 20, 2010. Considered the largest accidental marine oil spill in history, oil flowed for three months and approximately five million barrels of oil spilled through by mid-July 2010. In this article, we analyze bird data to assess the impact of the oil spill on the Gulf wildlife. Particularly, we want to determine in which regions Laughing Gulls were mostly affected by the oil spill, and whether those regions spatially shifted throughout the year 2010. Though our data sets have some limitations to apply statistical analysis methods, we obtained very interesting results. Our analyses showed the general consistency of the results based on two population data sets (from the 2011 Data Expo and from the eBird community) and justified the use of both publicly available data sets. We showed that the closer the surface oil spill area approached to the Laughing Gulls habitats, the more significant clusters of bird cases were observed.     

Modeling and data assessment of longitudinal salinity in a low-gradient estuarine river

Continuous data of vertical-profile salinity were analyzed for four stations located successively upriver in a macrotidal estuary, the lower St. Johns River (Northeast Florida, USA). The data analysis confirmed well-mixed salinity conditions in the river with at most 1.3 ppt of vertical variability at Dames Point (river km 20), where the main variations of salinity are along the longitudinal axis of the river. Given the well-mixed salinity conditions and dominant horizontal structure of salinity variations in the river, we present and apply a barotropic, two-dimensional modeling approach for hydrodynamic-salinity transport simulation in the lower St. Johns River. When properly forced by offshore surge, high-resolution wind fields and freshwater river inflows, the model replicated the salinity measurements remarkably well, including the separation into tidal and sub-tidal components. The data and model results show that, at times, offshore winds and surge can be more influential on longitudinal salinity variations than local winds over the river. We demonstrate the importance of using proper boundary conditions to force the model relative to the minimal sensitivity of the model to parameter adjustment of horizontal mixing and uncertainty-based perturbation of wind and inflow forcings.     

Does Zoning Winter Recreationists Reduce Recreation Conflict?

Environmental Management - Parks and protected area managers use zoning to decrease interpersonal conflict between recreationists. Zoning, or segregation, of recreation—often by non-motorized...     

Toxicity models of metal mixtures established on the basis of “additivity” and “interactions”

Observed effects of metal mixtures on animals and plants often differ from the estimates, which are commonly calculated by adding up the biological responses of individual metals. This difference from additivity is commonly referred to as being a consequence of specific interactions between metals. The science of how to quantify metal interactions and whether to include them in risk assessment models is in its infancy. This review summarizes the existing predictive tools for evaluating the combined toxicity of metals present in mixtures and indicates the advantages and disadvantages of each method. We intend to provide eco-toxicologists with background information on how to make good use of the tools and how to advance the methods for assessing toxicity of metal mixtures. It is concluded that statistically significant deviations from additivity are not necessarily biologically relevant. Incorporation of interactions between metals in a model does not on forehand mean that the model is more accurate than a model developed based on additivity only. It is recommended to first use a relatively simple method for effect prediction of uninvestigated metal mixtures. To improve the reliability of toxicity modeling for metal mixtures, further efforts should focus on balancing the relationship between the significance of statistics and the biological meaning, and unraveling the toxicity mechanisms of metals and their mixtures.

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Effect of ferrate on green algae removal

Green algae Cladophora aegagropila, present in cooling water of thermal power plants, causes many problems and complications, especially during summer. However, algae and its metabolites are rarely eliminated by common removal methods. In this work, the elimination efficiency of electrochemically prepared potassium ferrate(VI) on algae from cooling water was investigated. The influence of experimental parameters, such as Fe(VI) dosage, application time, pH of the system, temperature and hydrodynamics of the solution on removal efficiency, was optimized. This study demonstrates that algae C. aegagropila can be effectively removed from cooling water by ferrate. Application of ferrate(VI) at the optimized dosage and under the suitable conditions (temperature, pH) leads to 100% removal of green algae Cladophora from the system. Environmentally friendly reduction products (Fe(III)) and coagulation properties favour the application of ferrate for the treatment of water contaminated with studied microorganisms compared to other methods such as chlorination and use of permanganate, where harmful products are produced.

     

Analytic framework for assessing participation processes and outcomes of climate change adaptation tools

The assumptions and empirical evidence linking climate change adaptation to development cooperation have led to the emergence of a range of climate change adaptation tools aiming at improving the quality of development cooperation initiatives in times of global change. In order to assess the quality of these tools, we develop an analytic framework to critically assess the social learning and sustainable development outcomes of climate change adaptation tools. The analytic framework defines project objectives, participation typology, participation tools, participation stages, scenarios development, modelling exercises, stakeholder analysis and risk communication strategies that support quality participation outcomes.     

Fir decline and mortality in the southern Siberian Mountains

Regional Environmental Change - Increased dieback and mortality of “dark needle conifer” (DNC) stands (composed of fir (Abies sibirica), Siberian pine (Pinus sibirica) and spruce (Picea...     

The social and scientific values that shape national climate scenarios: a comparison of the Netherlands,Switzerland and the UK

This paper seeks to understand why climate information is produced differently from country to country. To do this, we critically examined and compared the social and scientific values that shaped the production of three national climate scenarios in the Netherlands, Switzerland and the UK. A comparative analysis of documentary materials and expert interviews linked to the climate scenarios was performed. Our findings reveal a new typology of use-inspired research in climate science for decision-making: (i) innovators, where the advancement of science is the main objective; (ii) consolidators, where knowledge exchanges and networks are prioritised; and (iii) collaborators, where the needs of users are put first and foremost. These different values over what constitutes ‘good’ science for decision-making are mirrored in the way users were involved in the production process: (i) elicitation, where scientists have privileged decision-making power; (ii) representation, where multiple organisations mediate on behalf of individual users; and (iii) participation, where a multitude of users interact with scientists in an equal partnership. These differences help explain why climate knowledge gains its credibility and legitimacy differently even when the information itself might not be judged as salient and usable. If the push to deliberately co-produce climate knowledge is not sensitive to the national civic epistemology at play in each country, scientist–user interactions may fail to deliver more ‘usable’ climate information.