Linking Models of Land Use,Resources, and Economy to Simulate the Development of Mountain Regions (ALPSCAPE)

We present a framework of a scenario-based model that simulates the development of the municipality of Davos (Swiss Alps). We illustrate our method with the calculation of the scenario for 2050 “Decrease in subsidies for mountain agriculture and liberalization of markets.” The main objective was to link submodels of land-use allocation (regression-based approach), material and energy flows submodels (Material and Energy Flux Analysis), and economic submodels (Input–Output Analysis). Letting qualitative and quantitative information flow from one submodel to the next, following the storyline describing a scenario, has proven to be suitable for linking submodels. The succession of the submodels is then strongly dependent on the scenario. Qualitative information flows are simulated with microsimulations of actor choices. Links between the submodels show different degrees of robustness: although the links involving microsimulations are the weakest, the uncertainty introduced by the land-use allocation model is actually advantageous because it allows one possible change in the landscape in the future to be simulated. The modeling results for the scenario here presented show that the disappearance of agriculture only marginally affects the region’s factor income, but that the consequences for the self-sufficiency rate, for various landscape-related indicators and ecosystem services, and for the economy in the long term may be considerable. These benefits compensate for agriculture’s modest direct economic value. The framework presented can potentially be applied to any region and scenario. This framework provides a basis for a learning package that allows potential detrimental consequences of regional development to be anticipated at an early stage.     

Assessing the components of adaptive capacity to improve conservation and management efforts under global change

Natural‐resource managers and other conservation practitioners are under unprecedented pressure to categorize and quantify the vulnerability of natural systems based on assessment of the exposure, sensitivity, and adaptive capacity of species to climate change. Despite the urgent need for these assessments, neither the theoretical basis of adaptive capacity nor the practical issues underlying its quantification has been articulated in a manner that is directly applicable to natural‐resource management. Both are critical for researchers, managers, and other conservation practitioners to develop reliable strategies for assessing adaptive capacity. Drawing from principles of classical and contemporary research and examples from terrestrial, marine, plant, and animal systems, we examined broadly the theory behind the concept of adaptive capacity. We then considered how interdisciplinary, trait‐ and triage‐based approaches encompassing the oft‐overlooked interactions among components of adaptive capacity can be used to identify species and populations likely to have higher (or lower) adaptive capacity. We identified the challenges and value of such endeavors and argue for a concerted interdisciplinary research approach that combines ecology, ecological genetics, and eco‐physiology to reflect the interacting components of adaptive capacity. We aimed to provide a basis for constructive discussion between natural‐resource managers and researchers, discussions urgently needed to identify research directions that will deliver answers to real‐world questions facing resource managers, other conservation practitioners, and policy makers. Directing research to both seek general patterns and identify ways to facilitate adaptive capacity of key species and populations within species, will enable conservation ecologists and resource managers to maximize returns on research and management investment and arrive at novel and dynamic management and policy decisions.     

Temporal and Spatial Variations in Nitrogen Dioxide Concentrations Across an Urban Landscape: Cambridge, UK

The acquisition of a comprehensive air quality dataset for a small city environment is described for use in statistical modelling of dispersion processes and micro-scale assessment of polluted zones. The dataset is based on a nitrogen dioxide diffusion tube survey for Cambridge where up to 80 roadside and background sites have been monitored continuously over two years, using a two week exposure period. Site categories are defined by their function within the urban landscape. Spatial and temporal features of the data set are explained in terms of urban location, street geometry, meteorology and traffic behaviour. The highest levels of NO2 are found in central canyon streets which are narrow with enclosing architecture and slow-moving traffic. In contrast lower levels are found for the wider, more open radial routes where traffic is free-flowing. The influence of street geometry on NO2 levels for central streets is demonstrated, where canyon sections adjacent to open sections having the same traffic flow record higher concentrations. Whilst all roadside sites are affected by a photochemical pollution 'episode', the greater potential for elevated NO2 concentrations within the canyon sections is significant. The close proximity of low background levels of NO2 to roadside 'hot-spots' is important for public exposure assessment. The variation in background levels across the urban landscape is very small and unrelated to location; whether central, suburban or outer city. Seasonal variation, not seen in roadside data, is clearly apparent in background data with a winter maximum and summer minimum.     

Space-time stick-breaking processes for small area disease cluster estimation

We propose a space-time stick-breaking process for the disease cluster estimation. The dependencies for spatial and temporal effects are introduced by using space-time covariate dependent kernel stick-breaking processes. We compared this model with the space-time standard random effect model by checking each model’s ability in terms of cluster detection of various shapes and sizes. This comparison was made for simulated data where the true risks were known. For the simulated data, we have observed that space-time stick-breaking process performs better in detecting medium- and high-risk clusters. For the real data, county specific low birth weight incidences for the state of South Carolina for the years 1997–2007, we have illustrated how the proposed model can be used to find grouping of counties of higher incidence rate.     

Transcending scale dependence in identifying habitat with resource selection functions

Multi-scale resource selection modeling is used to identify factors that limit species distributions across scales of space and time. This multi-scale nature of habitat suitability complicates the translation of inferences to single, spatial depictions of habitat required for conservation of species. We estimated resource selection functions (RSFs) across three scales for a threatened ungulate, woodland caribou (Rangifer tarandus caribou), with two objectives: (1) to infer the relative effects of two forms of anthropogenic disturbance (forestry and linear features) on woodland caribou distributions at multiple scales and (2) to estimate scale-integrated resource selection functions (SRSFs) that synthesize results across scales for management-oriented habitat suitability mapping. We found a previously undocumented scale-specific switch in woodland caribou response to two forms of anthropogenic disturbance. Caribou avoided forestry cut-blocks at broad scales according to first- and second-order RSFs and avoided linear features at fine scales according to third-order RSFs, corroborating predictions developed according to predator-mediated effects of each disturbance type. Additionally, a single SRSF validated as well as each of three single-scale RSFs when estimating habitat suitability across three different spatial scales of prediction. We demonstrate that a single SRSF can be applied to predict relative habitat suitability at both local and landscape scales in support of critical habitat identification and species recovery.     

Leveraging foodways for health and justice

ABSTRACT

The global industrial food system is increasingly recognised as a source of poor health that deepens social and economic inequity. Health advocates, policy makers, and food activists strive to improve nutrition and food access across racial and ethnic divides; however, given established approaches, they may miss fundamental pathways for improving health and justice comprehensively. While food access and nutrition are often identified as primary concerns for marginalised communities and the reason for food insecurity and food-related illness, critical food justice scholars use a more expansive lens to suggest a democratised food system is needed, and that solutions based solely in access to healthy food can undermine more systemic approaches. Our research extends this analysis, highlighting the importance of endemic food culture (foodways) as a tool for retaining identity, building community, and maintaining health among refugee populations in one community in Salt Lake City, Utah. Further, this work suggests that community engagement and expertise is essential in leveraging foodways such that marginalised communities can effectively resist cheap, unhealthy, and placeless calories.     

Matrix-immobilized organoclay for the sorption of polycyclic aromatic hydrocarbons and pentachlorophenol from groundwater

Sorbent materials consisting of organoclay immobilized onto the surface of a solid support were evaluated for use in pentachlorophenol (PCP) and polycyclic aromatic hydrocarbon (PAH) remediation of groundwater at a creosote-contaminated Superfund site. Cetylpyridinium-exchanged low pH montmorillonite clay (CP-LPHM) was bonded to either sand (CP-LPHM/sand) or granular activated carbon (GAC) (CP-LPHM/GAC) using the free acid form of carboxymethylcellulose as an adhesive. Effluent from an oil-water separator was eluted through equal bed volumes of composite (4 g 3:2 CP-LPHM/GAC or 13 g CP-LPHM/sand), affinity-extracted, and quantitatively analyzed by GC/MS. PCP, naphthalene, fluorene, phenanthrene, pyrene, and total PAHs were initially reduced by both CP-LPHM/GAC (> or =99%, 61%, 99%, > or =99%, 97%, and 94%, respectively) and CP-LPHM/sand (90%, 70%, 94%, 95%, 93%, and 86%, respectively). Complete breakthrough of naphthalene occurred after approximately 15 h of elution through 3:2 CP-LPHM/GAC and 22 h through CP-LPHM/sand. PCP showed complete breakthrough following 18 h of elution through 3:2 CP-LPHM/GAC and 26 h through CP-LPHM/sand. However, 50% breakthrough was not attained for higher molecular weight PAHs, as fluoranthene, pyrene, benzo[a]anthracene, and chrysene continued to be greatly reduced with both 3:2 CP-LPHM/GAC (98%, 95%, 94%, and 95%, respectively) and CP-LPHM/sand (75%, 73%, 76%, and 78%, respectively) after 48 h of continuous elution. Results confirm prior studies, indicating that these organoclay-containing composites have a high capacity for contaminants found in wood preserving waste. Further, results suggest that the inclusion of CP-LPHM may be useful as part of an effective strategy for groundwater remediation of high concentrations of PCP and PAHs, in particular high molecular weight and carcinogenic PAHs.