Plant Trait Diversity Buffers Variability in Denitrification Potential over Changes in Season and Soil Conditions

BACKGROUND: Denitrification is an important ecosystem service that removes nitrogen (N) from N-polluted watersheds, buffering soil, stream, and river water quality from excess N by returning N to the atmosphere before it reaches lakes or oceans and leads to eutrophication. The denitrification enzyme activity (DEA) assay is widely used for measuring denitrification potential. Because DEA is a function of enzyme levels in soils, most ecologists studying denitrification have assumed that DEA is less sensitive to ambient levels of nitrate (NO(3)(-)) and soil carbon and thus, less variable over time than field measurements. In addition, plant diversity has been shown to have strong effects on microbial communities and belowground processes and could potentially alter the functional capacity of denitrifiers. Here, we examined three questions: (1) Does DEA vary through the growing season? (2) If so, can we predict DEA variability with environmental variables? (3) Does plant functional diversity affect DEA variability? METHODOLOGY/PRINCIPAL FINDINGS: The study site is a restored wetland in North Carolina, US with native wetland herbs planted in monocultures or mixes of four or eight species. We found that denitrification potentials for soils collected in July 2006 were significantly greater than for soils collected in May and late August 2006 (p<0.0001). Similarly, microbial biomass standardized DEA rates were significantly greater in July than May and August (p<0.0001). Of the soil variables measured--soil moisture, organic matter, total inorganic nitrogen, and microbial biomass--none consistently explained the pattern observed in DEA through time. There was no significant relationship between DEA and plant species richness or functional diversity. However, the seasonal variance in microbial biomass standardized DEA rates was significantly inversely related to plant species functional diversity (p<0.01). CONCLUSIONS/SIGNIFICANCE: These findings suggest that higher plant functional diversity may support a more constant level of DEA through time, buffering the ecosystem from changes in season and soil conditions

Carpal Tunnel Syndrome: A Review of the Recent Literature

Carpal Tunnel Syndrome (CTS) remains a puzzling and disabling condition present in 3.8% of the general population. CTS is the most well-known and frequent form of median nerve entrapment, and accounts for 90% of all entrapment neuropathies. This review aims to provide an overview of this common condition, with an emphasis on the pathophysiology involved in CTS. The clinical presentation and risk factors associated with CTS are discussed in this paper. Also, the various methods of diagnosis are explored; including nerve conduction studies, ultrasound, and magnetic resonance imaging

Costs of travel time uncertainty and benefits of travel time information: Conceptual model and numerical examples

A negative effect of congestion that tends to be overlooked is travel time uncertainty. Travel time uncertainty causes scheduling costs due to early or late arrival. The negative effects of travel time uncertainty can be reduced by providing travellers with travel time information, which improves their estimate of the expected travel time, thereby reducing scheduling costs. In order to assess the negative effects of uncertainty and the benefits of travel time information, this paper proposes a conceptual model of departure time choice under travel time uncertainty and information. The model is based on expected utility theory, and includes the variation in travel time, the quality of travel time information and travellers’ perception of the travel time. The model is illustrated by an application to the case of the A2 motorway between Beesd and Utrecht in the Netherland

Whom to hang out with and where? Analysis of the influence of spatial setting on the choice of activity company

Over the past decade there has been an increasing interest into to role of social interactions and social networks for activities and travel. This coincides with a growing awareness that social and recreational trips make up a considerable share of total mobility and deserve more attention in order to understand trends in mobility. Given this trend remarkably little attention has been given to the investigation of the choice of company for social and recreational activities and travel. This paper contributes to filling this gap, by presenting estimation results of models of company choice for social activities, shopping, sport and recreation and cultural activities, based on activity diary data collected in 2007 in the Netherlands. Specific attention is given to the influence of urban form and accessibility of services on company choice. The estimation results suggest that accessibility of facilities has an impact on company choice. However, the mechanisms seem to differ between activity types. For social activities, shopping and sports/recreation, it seems that better access to facilities leads to more joint activity participation, presumably because coordination between involved parties in time and space becomes easier. In other cases (social and cultural activities), close access to facilities seems to lead to a higher probability of single activity engagement, possibly since impulsive activities (usually single) are easier to implement and pooling of facilities is not necessary

Towards an integrated luti model of long term and short-term mobilitye decisions of households using social learning

This paper presents a model of long-term household mobility decisions that was developed in the context of the PUMA model (an agent-based integrated model of land use and transportation). It extends that state-of-the-art in that it integrates households’ relocation decision in the allocation of monetary and temporal resources on the household level. It interacts with a micro-simulation model of daily activity patterns in order to improve the representation of accessibility effects in LUTI modelling. The model uses a social learning algorithm to represent households’ decision making in a large state space under limited information. The model is illustrated in a small scale numerical example

Estimating time, money and location effects in the organisation of leisure activities in time and space

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