Modeling leaf dispersal in mixed hardwood forests using a ballistic approach

In mixed-species stands, modeling leaf litter dispersal is important to predict the physical and chemical characteristics of the forest floor, which plays a major role in nutrient cycling and in plant population dynamics. In this study, a spatially explicit model of leaf litterfall was developed and compared with two other models. These three models were calibrated for a mixed forest of oak and beech using litterfall data from mapped forest plots. All models assumed that an allometric equation described individual leaf litter production, but they strongly differed in the modeling of the probability density of leaf shedding with distance from source trees. Two models used a negative exponential function to account for leaf dispersal with distance, and this function was allowed to vary according to wind direction in one of them. In contrast, our approach was based on a simple ballistic equation considering release height, wind speed, wind direction, and leaf fall velocity; the distributions of wind speeds and wind directions were modeled according to a Weibull and a Von Mises distribution, respectively. Using an independent validation data set, all three models provided predictions well correlated to measurements (r > 0.83); however, the two models with a direction-dependent component were slightly more accurate. In addition, parameter estimates of the ballistic model were in close agreement with a foliar litter production equation derived from the literature for beech and with wind characteristics measured during leaf litterfall for both species. Because of its mechanistic background, such a spatially explicit model might be incorporated as a litterfall module in larger models (nutrient cycling, plant population dynamics) or used to determine the manner in which patch size in mixed-species stands influences litter mixture.     

Light at night reduces digestive efficiency of developing birds: an experiment with king quail

The Science of Nature - Gravity is very important for many organisms, including web-building spiders. Probably the best approach to study the relevance of gravity on organisms is to bring them to...     

Exploring simulated driving performance among varsity male soccer players

Background: It is documented that male athletes display riskier behaviors while driving (as well as in life in general) than female athletes and nonathletes. However, the literature has reported that athletes show better driving ability than nonathletes. This paradox between behaviors and abilities motivated the present study to further understand the collision risk of varsity athletes.

Objective: The current study estimates the performance differences between varsity male soccer players and male undergraduate nonathletes on (1) a driving task and (2) three perceptual–cognitive tasks (associated with collision risk prediction; i.e., Useful Field of View [UFOV] test).

Methods: Thirty-five male undergraduate students (15 varsity soccer players, 20 undergraduate nonathletes) took part in this study. Driving performance was assessed during 14?min of urban commuting using a driving simulator. While completing the simulated driving task and UFOV test, the physiological responses were monitored using an electrocardiograph (ECG) to document heart rate variability (HRV).

Results: Varsity soccer players showed more risky behaviors at the wheel compared to their nonathlete student peers. Varsity soccer players spent more time over the speed limit, committed more driving errors, and adopted fewer safe and legal behaviors. However, no difference was observed between both groups on driving skill variables (i.e., vehicle control, vehicle mobility, ecodriving). For subtests 1 and 2 of the UFOV (i.e., processing speed, divided attention), both groups performed identically (i.e., 17?ms). The nonathlete group tended to perform better on the selective attention task (i.e., subtest 3 of UFOV test; 63.2?±?6.2?ms vs. 87.2?±?10.7?ms, respectively; this difference was not significant, P = .76).

Conclusion: Preventive driving measures should be enforced in this high-risk population to develop strategies for risk reduction in male team athletes.     

Effects of Exposure Conditions on Antioxidant Depletion,Tensile Strength,and Creep Modulus of Corrugated HDPE Pipes Made With or Without Recycled Resins

Journal of Polymers and the Environment - The service lifetime of corrugated high-density polyethylene (HDPE) pipes used in transportation infrastructure applications has been the subject of...     

Modeling forest floor contribution to phosphorus supply to maritime pine seedlings in two-layered forest soils

The quantitative contribution of the forest floor to P nutrition of maritime pine seedlings was experimentally determined by Jonard et al. (2009) in a greenhouse experiment using the radio-isotopic labeling. To extend the results of the experiment on a known mineral soil, a modeling approach was developed to predict P uptake of maritime pine seedlings growing in a mineral soil covered with a forest floor layer. The classical nutrient uptake model based on the diffusion/mass-flow theory was extended to take into account mineralization of P in dead organic matter, microbial P immobilization and re-mineralization and P leaching. In addition, the buffer power characterizing the P retention properties of the mineral soil was allowed to vary with time and with the P-ion concentration in solution. To account for increasing root competition with time, a moving boundary approach was implemented. According to the model, the forest floor contributed most of the P supply to the seedlings (99.3% after 130 days). Predicted P uptake was consistent with observed P uptake and modeling efficiency was 0.97. The uptake model was then used to evaluate the impact of the P retention properties of the mineral soil on the contribution of the forest floor to P uptake. Simulations showed that the contribution of the forest floor was much lower in the quasi non-reactive soil (45.7%) but rapidly increased with soil P reactivity.     

Colony nutritional status modulates worker responses to foraging recruitment pheromone in the bumblebee Bombus terrestris

Foraging activity in social insects should be regulated by colony nutritional status and food availability, such that both the emission of, and response to, recruitment signals depend on current conditions. Using fully automatic radio-frequency identification (RFID) technology to follow the foraging activity of tagged bumblebees (Bombus terrestris) during 16,000 foraging bouts, we tested whether the cue provided by stored food (the number of full honeypots) could modulate the response of workers to the recruitment pheromone signal. Artificial foraging pheromones were applied to colonies with varied levels of food reserves. The response to recruitment pheromones was stronger in colonies with low food, resulting in more workers becoming active and more foraging bouts being performed. In addition to previous reports showing that in colonies with low food successful foragers perform more excited runs during which they release recruitment pheromone and inactive workers are more prone to leave the nest following nectar influx, our results indicate that evolution has shaped a third pathway that modulates bumblebee foraging activity, thus preventing needless energy expenditure and exposure to risk when food stores are already high. This new feedback loop is intriguing since it involves context-dependent response to a signal. It highlights the integration of information from both forager-released pheromones (signal) and nutritional status (cue) that occurs within individual workers before making the decision to start foraging. Our results support the emerging view that responses to pheromones may be less hardwired than commonly acknowledged. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Spatial structure effects on the detection of patches boundaries using local operators

Landscapes exhibit various degrees of spatial heterogeneity according to the differential intensity and interactions among processes and disturbances that they are subjected to. The management of these spatially dynamical landscapes requires that we can accurately map them and monitor the evolution of their spatial arrangement through time. Such a mapping requires first the delineation of various spatial features present in the landscape such as patches and their boundaries. However, there are several environmental (spatial variability) as well as technical (spatial resolution) factors that impair our ability to accurately delineate patches and their boundaries as polygons. Here, we investigate how the spatial structure and spatial resolution of the data affect the accuracy of detecting patches and their boundaries over simulated landscapes and real data. Simulated landscapes consisted of two patches with parameterized spatial properties (patches’ level of spatial autocorrelation, mean value and variance) separated by a boundary of known location. Real data allowed the investigation of a more complex landscape where there is a known transition between two forest domains with unknown spatial properties. Boundary locations are defined using the lattice-wombling edge detector at various aggregation levels and the degree of patch homogeneity is determined using Getis-Ord’s G*. Results show that boundary detection using a local edge detector is greatly affected by the spatial conditions of the data, namely variance, abruptness of the spatial gradient between two patches and patches’ level of spatial autocorrelation. They also suggest that data aggregation is not a panacea for bringing out the ecological process creating the patches and that indicators derived from local measures of spatial association can be complementary tools for analysing spatial structures affecting boundary delineation.

Individual characteristics,work perceptions,and affective reactions influences on differentiated absence criteria

The present study provides support for the utility of studying absence as a multi-dimensional criterion. Survey responses were collected from 194 bus drivers and paired with categorized archival absences. Seven absence indices were created and linked with three categories of predictors: (1) affective reactions to the work environment; (2) work-related perceptions; and (3) individual resource characteristis. The relationships between the multiple absence criteria and the three sets of predictors were examined both separately and combined using part canonical, and canonical correlation analyses. Affective responses fully mediated the influence of work perceptions on absence, and partially mediated the influence of individual resource variables. Redundancy coefficients and a rotated structure matrix were employed to identify two significant dimensions labeled, nonwork obligations and stress reactions, that linked the combined predictor sets with the set of absence measures. Together these dimensions accounted for 15 per cent of the variance in absence, with predictors differing in their explanatory power. Implications for the management of employee absence programs were discussed.