Assimilating concentration observations for transport and dispersion modeling in a meandering wind field

Assimilating concentration data into an atmospheric transport and dispersion model can provide information to improve downwind concentration forecasts. The forecast model is typically a one-way coupled set of equations: the meteorological equations impact the concentration, but the concentration does not generally affect the meteorological field. Thus, indirect methods of using concentration data to influence the meteorological variables are required. The problem studied here involves a simple wind field forcing Gaussian dispersion. Two methods of assimilating concentration data to infer the wind direction are demonstrated. The first method is Lagrangian in nature and treats the puff as an entity using feature extraction coupled with nudging. The second method is an Eulerian field approach akin to traditional variational approaches, but minimizes the error by using a genetic algorithm (GA) to directly optimize the match between observations and predictions. Both methods show success at inferring the wind field. The GA-variational method, however, is more accurate but requires more computational time. Dynamic assimilation of a continuous release modeled by a Gaussian plume is also demonstrated using the genetic algorithm approach.     

Assessing sensitivity of source term estimation

Source term estimation algorithms compute unknown atmospheric transport and dispersion modeling variables from concentration observations made by sensors in the field. Insufficient spatial and temporal resolution in the meteorological data as well as inherent uncertainty in the wind field data make source term estimation and the prediction of subsequent transport and dispersion extremely difficult. This work addresses the question: how many sensors are necessary in order to successfully estimate the source term and meteorological variables required for atmospheric transport and dispersion modeling?The source term estimation system presented here uses a robust optimization technique – a genetic algorithm (GA) – to find the combination of source location, source height, source strength, surface wind direction, surface wind speed, and time of release that produces a concentration field that best matches the sensor observations. The approach is validated using the Gaussian puff as the dispersion model in identical twin numerical experiments. The limits of the system are tested by incorporating additive and multiplicative noise into the synthetic data. The minimum requirements for data quantity and quality are determined by an extensive grid sensitivity analysis. Finally, a metric is developed for quantifying the minimum number of sensors necessary to accurately estimate the source term and to obtain the relevant wind information.     

Effects of heavy metal exposure on the condition and health of nestlings of the great tit (Parus major), a small songbird species

In this study we examined the possible effects of heavy metal exposure on the condition and health of great tit nestlings (Parus major) at four study sites along a pollution gradient near a large non-ferrous smelter in Belgium during three consecutive breeding seasons. Our results showed that nestlings were indeed exposed to large amounts of heavy metals. Excrements contained significantly higher concentrations of several heavy metals (silver, arsenic, cadmium, mercury, lead) near the pollution source than at study sites farther away. When taking into account the number of young in the nest at the time of sampling, nestling body mass and condition were significantly reduced at the most polluted site. Nestlings at the two most polluted sites fledged significantly later than at the least polluted site. We also observed growth abnormalities of the legs near the pollution source. Tarsus length, wing length and haematocrit values did not differ significantly among study sites.     

Pilot-study on GIS-based risk modelling of a climate warming induced tertian malaria outbreak in Lower Saxony (Germany)

This project was designed to establish baseline aquatic biological community structure and physical habitat conditions in select wadeable streams within the California Central Valley. A secondary objective was to evaluate possible water quality differences between site types and seasons. Two agricultural and two urban streams were monitored in spring and fall for two consecutive years beginning in the fall of 2002. Bioassessment sampling was conducted according to modified US EPA methods. The study included physical habitat assessment, water and sediment chemical analysis and characterization of the benthic macroinvertebrate community at each site. Water samples were analyzed for selected organophosphate insecticides, pyrethroid insecticides and herbicides, while sediment samples were analyzed for pyrethroids only. All sites had substantial physical habitat and water quality impairments, and the absence of pollution intolerant macroinvertebrates and dominance of pollution tolerant macroinvertebrates were indications of biological impairment. Due to the limited amount of water quality and pesticide data collected, it was not possible to definitively demonstrate any cause and effect relationships between BMI community structure and water quality or pesticide concentrations. Though most physical habitat parameters were similar and EPA physical habitat scores revealed on no significant differences between urban and agricultural sites (P? = ?0.290), a significant difference was seen in substrate embeddedness (P? = ?0.020). Dominant taxon found at all sites were chironomids, amphipods, and oligochaetes. Benthic macroinvertebrate metrics were significantly different between both types of sites (P? = ?0.001) and seasons (P? = ?0.014). Chironomidae taxon and those of the functional feeding group scrapers were greater at urban sites, while those of the functional feeding group filterers were greater at agricultural sites. In addition, the metric groups Chironomidae, filterers, and predators were found in greater numbers in the spring than the fall.     

Population structure of humpback whales in the western and central South Pacific Ocean as determined by vocal exchange among populations

For cetaceans, population structure is traditionally determined by molecular genetics or photographically identified individuals. Acoustic data, however, has provided information on movement and population structure with less effort and cost than traditional methods in an array of taxa. Male humpback whales (Megaptera novaeangliae) produce a continually evolving vocal sexual display, or song, that is similar among all males in a population. The rapid cultural transmission (the transfer of information or behavior between conspecifics through social learning) of different versions of this display between distinct but interconnected populations in the western and central South Pacific region presents a unique way to investigate population structure based on the movement dynamics of a song (acoustic) display. Using 11 years of data, we investigated an acoustically based population structure for the region by comparing stereotyped song sequences among populations and years. We used the Levenshtein distance technique to group previously defined populations into (vocally based) clusters based on the overall similarity of their song display in space and time. We identified the following distinct vocal clusters: western cluster, 1 population off eastern Australia; central cluster, populations around New Caledonia, Tonga, and American Samoa; and eastern region, either a single cluster or 2 clusters, one around the Cook Islands and the other off French Polynesia. These results are consistent with the hypothesis that each breeding aggregation represents a distinct population (each occupied a single, terminal node) in a metapopulation, similar to the current understanding of population structure based on genetic and photo‐identification studies. However, the central vocal cluster had higher levels of song‐sharing among populations than the other clusters, indicating that levels of vocal connectivity varied within the region. Our results demonstrate the utility and value of using culturally transmitted vocal patterns as a way of defining connectivity to infer population structure. We suggest vocal patterns be incorporated by the International Whaling Commission in conjunction with traditional methods in the assessment of structure.     

Top-down control inhibits spatial self-organization of a patterned landscape

Regular, self-organized spatial patterns in primary producers have been described in a wide range of ecosystems and are predicted to affect community production and resilience. Although consumers are abundant in most systems, the effect of trophic interactions on pattern formation in primary producers remains unstudied. We studied the effects of top-down control by herbivores on a self-organized landscape of regularly spaced, diatom-covered hummocks alternating with water-filled hollows on an intertidal mudflat in The Netherlands. Spatial patterns developed during spring but were followed by a rapid collapse in summer, leading to a flat landscape with low diatom densities and little variation in sediment bed level. This dramatic decline co-occurred with a gradual increase of benthic herbivores. A manipulative field experiment, where benthic herbivores were removed from the sediment, revealed that both diatom growth and hummock formation were inhibited by the activity of benthic herbivores. Our study provides clear evidence of top-down control of spatial self-organized patterns by benthic herbivores within a biological-geomorphic landscape.     

Nestmate recognition in social insects: overcoming physiological constraints with collective decision making

Social insects rank among the most abundant and influential terrestrial organisms. The key to their success is their ability to form tightly knit social groups that perform work cooperatively, and effectively exclude non-members from the colony. An extensive body of research, both empirical and theoretical, has explored how optimal acceptance thresholds could evolve in individuals, driven by the twin costs of inappropriately rejecting true nestmates and erroneously accepting individuals from foreign colonies. Here, in contrast, we use agent-based modeling to show that strong nestmate recognition by individuals is often unnecessary. Instead, highly effective nestmate recognition can arise as a colony-level property from a collective of individually poor recognizers. Essentially, although an intruder can get by one defender when their odor cues are similar, it is nearly impossible to get past many defenders if there is the slightest difference in cues. The results of our models match observed rejection rates in studies of ants, wasps, and bees. We also show that previous research in support of the optimal threshold theory approach to the problem of nestmate recognition can be alternatively viewed as evidence in favor of the collective formation of a selectively permeable barrier that allows in nestmates (at a significant cost) while rejecting non-nestmates. Finally, this work shows that nestmate recognition has a stronger task allocation component than previously thought, as colonies can nearly always achieve perfect nestmate recognition if it is cost effective for them to do so at the colony level.