Dietary greenhouse gas emissions of meat-eaters,fish-eaters,vegetarians and vegans in the UK

The production of animal-based foods is associated with higher greenhouse gas (GHG) emissions than plant-based foods. The objective of this study was to estimate the difference in dietary GHG emissions between self-selected meat-eaters, fish-eaters, vegetarians and vegans in the UK. Subjects were participants in the EPIC-Oxford cohort study. The diets of 2,041 vegans, 15,751 vegetarians, 8,123 fish-eaters and 29,589 meat-eaters aged 20–79 were assessed using a validated food frequency questionnaire. Comparable GHG emissions parameters were developed for the underlying food codes using a dataset of GHG emissions for 94 food commodities in the UK, with a weighting for the global warming potential of each component gas. The average GHG emissions associated with a standard 2,000 kcal diet were estimated for all subjects. ANOVA was used to estimate average dietary GHG emissions by diet group adjusted for sex and age. The age-and-sex-adjusted mean (95 % confidence interval) GHG emissions in kilograms of carbon dioxide equivalents per day (kgCO₂e/day) were 7.19 (7.16, 7.22) for high meat-eaters (?>?=?100 g/d), 5.63 (5.61, 5.65) for medium meat-eaters (50-99 g/d), 4.67 (4.65, 4.70) for low meat-eaters (?<?50 g/d), 3.91 (3.88, 3.94) for fish-eaters, 3.81 (3.79, 3.83) for vegetarians and 2.89 (2.83, 2.94) for vegans. In conclusion, dietary GHG emissions in self-selected meat-eaters are approximately twice as high as those in vegans. It is likely that reductions in meat consumption would lead to reductions in dietary GHG emissions.     

Correlates of vulnerability to climate-induced distribution changes in European avifauna: habitat, migration and endemism

Accelerated climatic change will alter species’ distributions substantially by the end of the 21st Century and studies modeling distribution change using Climatic Envelope Modeling (CEM) are increasingly crucial for understanding long-term biotic implications of climate change. However, most CEM studies generate either all-species means, which are of limited practical use, or copious species-specific predictions that make it hard to draw general conclusions about those groups most vulnerable. Intermediate analyses that are half way between these two extremes are necessary to establish the relative vulnerability of species to change based on factors that can be related directly to policy and practice, including habitat associations and ecological traits such as endemism and migration status. Here we use species-specific CEM data to analyse changes in geographical distribution, range size, and overlap between current and potential ranges, for all 431 bird species breeding regularly in Europe. Future range sizes are predicted to be 80 % of current range sizes, with an average overlap of 39 %. However, we show that change varies significantly according to habitat, current range size, and endemism status, with no differences according to migration status. Coastal, wetland and upland birds will be significantly worse off under CEM scenarios than birds associated with woodland, farmland and heathland, while urban birds and those using multiple habitats doing best. Birds with small ranges show more severe, and spatially more complex, distribution shifts. The identification of species groups most vulnerable to climate change means that CEM predictions can now be used to inform policy and management, especially where initiatives are based on species grouped according to such variables or where habitat-specific policies are in place.     

Dynamic implication of biomass energy consumption on economic growth in Sub-Saharan Africa: evidence from panel data analysis

The present article examines the dynamic linkages between biomass energy consumption, capital stock, human capital and economic growth across selected Sub-Saharan African countries based on dynamic heterogeneous panels of a mean group (MG) and pooled mean group (PMG) techniques. The finding based on PMG as the preferred method reveals that biomass energy consumption, capital stock and human capital are statistically significant, which means aforementioned variables have positive significant impact on economic growth in the countries studied. When an alternative panel estimation techniques of panel cointegration, dynamic OLS (DOLS) and fully modified OLS (FMOLS) are applied, the result based on panel cointegration technique reveals that biomass energy consumption, capital stock, human capital and economic growth are cointegrated as null hypothesis of most statistics are rejected at 1 % level of significance. The finding based on FMOLS shows that biomass energy consumption, capital stock and human capital positively influences economic growth at 1 % level and same result is obtained from panel OLS. The result based on DOLS however reveals that biomass energy consumption and capital stock are significant at 1 % on economic growth while human capital is insignificant. Considering its positive effect on economic growth with little or no environmental degradation when compared with fossil fuel uses, consumption of biomass energy is more preferable in these countries therefore is the best option to adopt by the policy makers of Sub-Saharan African countries.     

Temporospatial dynamics and public health significance of bacterial flora identified on a major leatherback turtle (Dermochelys coriacea) nesting beach in the Southern Caribbean

Grande Riviere beach, on the island of Trinidad, supports the largest nesting population of leatherback turtles in the Caribbean region. Throughout the nesting season, nests are naturally disturbed by newly nesting females, resulting in egg breakage and loss of some nest viability. This environment is ideal for the growth and proliferation of microorganisms. The range of bacterial flora present in beach sand and egg shells was examined, with emphasis on bacteria that may pose a threat to public and animal health. The extent to which the bacterial load and genera on the beach changed throughout the season was also assessed. Twenty‐five genera were identified, with Pseudomonas spp. found to be the most predominant environmental bacteria. Four genera identified possess zoonotic potential, while five additional genera are known to be of public and animal health significance. Distinct shifts in the density and distribution of bacteria were observed along the beach from early to peak nesting season. Shifts were seen across heavily traversed zones, thus highlighting the potential exposure threats posed to beach visitors and animals alike. Further studies aimed at speciating this population of bacteria, as well as isolating potential fungal pathogens may mitigate this threat. Identification of bacterial agents that are specifically pathogenic to leatherback turtles, turtle eggs, hatchlings and those who may interact with these animals will serve to enhance and guide efforts to better conserve this species and protect the health of all who visit this ecologically significant site.     

Analysis of climate policy targets under uncertainty

Although policymaking in response to the climate change threat is essentially a challenge of risk management, most studies of the relation of emissions targets to desired climate outcomes are either deterministic or subject to a limited representation of the underlying uncertainties. Monte Carlo simulation, applied to the MIT Integrated Global System Model (an integrated economic and earth system model of intermediate complexity), is used to analyze the uncertain outcomes that flow from a set of century-scale emissions paths developed originally for a study by the U.S. Climate Change Science Program. The resulting uncertainty in temperature change and other impacts under these targets is used to illustrate three insights not obtainable from deterministic analyses: that the reduction of extreme temperature changes under emissions constraints is greater than the reduction in the median reduction; that the incremental gain from tighter constraints is not linear and depends on the target to be avoided; and that comparing median results across models can greatly understate the uncertainty in any single model.     

How well can we simulate complex hydro‐geomorphic process chains? The 2012 multi‐lake outburst flood in the Santa Cruz Valley (Cordillera Blanca,Perú)

Changing high‐mountain environments are characterized by destabilizing ice, rock or debris slopes connected to evolving glacial lakes. Such configurations may lead to potentially devastating sequences of mass movements (process chains or cascades). Computer simulations are supposed to assist in anticipating the possible consequences of such phenomena in order to reduce the losses. The present study explores the potential of the novel computational tool r.avaflow for simulating complex process chains. r.avaflow employs an enhanced version of the Pudasaini ( 2012 ) general two‐phase mass flow model, allowing consideration of the interactions between solid and fluid components of the flow. We back‐calculate an event that occurred in 2012 when a landslide from a moraine slope triggered a multi‐lake outburst flood in the Artizón and Santa Cruz valleys, Cordillera Blanca, Peru, involving four lakes and a substantial amount of entrained debris along the path. The documented and reconstructed flow patterns are reproduced in a largely satisfactory way in the sense of empirical adequacy. However, small variations in the uncertain parameters can fundamentally influence the behaviour of the process chain through threshold effects and positive feedbacks. Forward simulations of possible future cascading events will rely on more comprehensive case and parameter studies, but particularly on the development of appropriate strategies for decision‐making based on uncertain simulation results. © 2017 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.     

Analysis of growing season carbon and water fluxes of a subalpine wetland in the Canadian Rocky Mountains: Implications of shade on ecosystem water use efficiency

Mountain regions are an important regulator in the global water cycle through their disproportionate water contribution. Often referred to as the “Water Towers of the World”, mountains contribute 40%–60% of the world's annual surface flow. Shade is a common feature in mountains, where complex terrain cycles land surfaces in and out of shadows over daily and seasonal scales, which can impact water use. This study investigated the turbulent water and carbon dioxide (CO₂) fluxes during the snow-free period in a subalpine wetland in the Canadian Rocky Mountains, from 7 June to 10 September 2018. Shading had a significant and substantial effect on water and CO₂ fluxes at our site. When considering data from the entire study period, each hourly increase of shade per day reduced evapotranspiration (ET) and gross primary production (GPP) by 0.42 mm and 0.77 g C m^?2, equivalent to 17% and 15% per day, respectively. However, the variability in shading changed throughout the study, it was stable to start and increased towards the end. Only during the peak growing season, the site experienced days with both stable and increasing shade. During this time, we found that shade, caused by the local complex terrain, reduced ET and potentially increased GPP, likely due to enhanced diffuse radiation. The overall result was greater water use efficiency during periods of increased shading in the peak growing season. These findings suggest that shaded subalpine wetlands can store large volumes of water for late season runoff and are productive through short growing seasons.     

Changes in bark properties and hydrology following prescribed fire in Pinus taeda and Quercus montana

In the eastern United States, the use of prescribed fire as a silvicultural technique to manage for desirable upland tree species is increasing in popularity. Bark physical properties such as thickness, density, and porosity have known associations with fire tolerance among species. These physical properties simultaneously influence rainfall interception and canopy storage and thus are of interest across a range of disciplines. Furthermore, while these characteristics are innate to a species, it is unknown whether repeated exposure to fire facilitates physical change in bark structure and whether these changes are consistent among species. To answer these questions, bark samples were collected from mature pine (Pinus taeda L.) and oak (Quercus montana Willd.) trees from sites across the Bankhead National Forest in Alabama, USA under three different burn regimes: 3-year cycle, 9-year cycle, and no fire. Samples were analysed in the laboratory for bulk density, porosity, water storage capacity, and hygroscopicity (the amount of atmospheric water vapour absorbed by bark during non-rainfall conditions). Drying rates of saturated samples under simulated wetting conditions were also assessed. Oak bark had higher bulk density, lower porosity, and dried slower than pine bark. Interestingly, bark from both species had lower bulk density, higher porosity, greater water storage capacity, and dried faster in stands that were burned every 3 years compared to other fire regimes (p < 0.001). In summary, this study demonstrates that prescribed fire regimes in an eastern US forest alter bark structure and thus influence individual tree control on hydrological processes. The increase in bark water storage capacity, coupled with faster bark evaporation times may lead to less water inputs to the forest floor and drier overall conditions. Further investigation of this fire-bark-water feedback loop is necessary to understand the extent of these mechanisms controlling landscape-scale conditions.