Quantifying structural physical habitat attributes using LIDAR and hyperspectral imagery

Structural physical habitat attributes include indices of stream size, channel gradient, substrate size, habitat complexity, and riparian vegetation cover and structure. The Environmental Monitoring and Assessment Program (EMAP) is designed to assess the status and trends of ecological resources at different scales. High-resolution remote sensing provides unique capabilities in detecting a variety of features and indicators of environmental health and condition. LIDAR is an airborne scanning laser system that provides data on topography, channel dimensions (width, depth), slope, channel complexity (residual pools, volume, morphometric complexity, hydraulic roughness), riparian vegetation (height and density), dimensions of riparian zone, anthropogenic alterations and disturbances, and channel and riparian interaction. Hyperspectral aerial imagery offers the advantage of high spectral and spatial resolution allowing for the detection and identification of riparian vegetation and natural and anthropogenic features at a resolution not possible with satellite imagery. When combined, or fused, these technologies comprise a powerful geospatial data set for assessing and monitoring lentic and lotic environmental characteristics and condition.     

Data fusion modeling for groundwater systems

Engineering projects involving hydrogeology are faced with uncertainties because the earth is heterogeneous, and typical data sets are fragmented and disparate. In theory, predictions provided by computer simulations using calibrated models constrained by geological boundaries provide answers to support management decisions, and geostatistical methods quantify safety margins. In practice, current methods are limited by the data types and models that can be included, computational demands, or simplifying assumptions. Data Fusion Modeling (DFM) removes many of the limitations and is capable of providing data integration and model calibration with quantified uncertainty for a variety of hydrological, geological, and geophysical data types and models. The benefits of DFM for waste management, water supply, and geotechnical applications are savings in time and cost through the ability to produce visual models that fill in missing data and predictive numerical models to aid management optimization. DFM has the ability to update field-scale models in real time using PC or workstation systems and is ideally suited for parallel processing implementation. DFM is a spatial state estimation and system identification methodology that uses three sources of information: measured data, physical laws, and statistical models for uncertainty in spatial heterogeneities. What is new in DFM is the solution of the causality problem in the data assimilation Kalman filter methods to achieve computational practicality. The Kalman filter is generalized by introducing information filter methods due to Bierman coupled with a Markov random field representation for spatial variation. A Bayesian penalty function is implemented with Gauss–Newton methods. This leads to a computational problem similar to numerical simulation of the partial differential equations (PDEs) of groundwater. In fact, extensions of PDE solver ideas to break down computations over space form the computational heart of DFM. State estimates and uncertainties can be computed for heterogeneous hydraulic conductivity fields in multiple geological layers from the usually sparse hydraulic conductivity data and the often more plentiful head data. Further, a system identification theory has been derived based on statistical likelihood principles. A maximum likelihood theory is provided to estimate statistical parameters such as Markov model parameters that determine the geostatistical variogram. Field-scale application of DFM at the DOE Savannah River Site is presented and compared with manual calibration. DFM calibration runs converge in less than 1 h on a Pentium Pro PC for a 3D model with more than 15,000 nodes. Run time is approximately linear with the number of nodes. Furthermore, conditional simulation is used to quantify the statistical variability in model predictions such as contaminant breakthrough curves.     

Amazonian Tree Mortality during the 1997 El Niño Drought

Abstract: In 1997, the Amazon Basin experienced an exceptionally severe El Niño drought. We assessed effects of this rare event on mortality rates of trees in intact rain forest based on data from permanent plots. Long-term (5- to 13-year) mortality rates averaged only 1.12% per year prior to the drought. During the drought year, annual mortality jumped to 1.91% but abruptly fell back to 1.23% in the year following El Niño. Trees dying during the drought did not differ significantly in size or species composition from those that died previously, and there was no detectable effect of soil texture on mortality rates. These results suggest that intact Amazonian rainforests are relatively resistant to severe El Niño events.     

Intracolonial genetic diversity in honeybee (<Emphasis Type="Italic">Apis mellifera</Emphasis>) colonies increases pollen foraging efficiency

Multiple mating by honeybee queens results in colonies of genotypically diverse workers. Recent studies have demonstrated that increased genetic diversity within a honeybee colony increases the variation in the frequency of tasks performed by workers. We show that genotypically diverse colonies, each composed of 20 subfamilies, collect more pollen than do genotypically similar colonies, each composed of a single subfamily. However, genotypically similar colonies collect greater varieties of pollen than do genotypically diverse colonies. Further, the composition of collected pollen types is less similar among genotypically similar colonies than among genotypically diverse colonies. The response threshold model predicts that genotypic subsets of workers vary in their response to task stimuli. Consistent with this model, our findings suggest that genotypically diverse colonies likely send out fewer numbers of foragers that independently search for pollen sources (scouts) in response to protein demand by the colony, resulting in a lower variety of collected pollen types. The cooperative foraging strategy of honeybees involves a limited number of scouts monitoring the environment that then guide the majority of foragers to high quality food sources. The genetic composition of the colony appears to play an important role in the efficiency of this behavior.     

A framework for understanding ecological traps and an evaluation of existing evidence

When an animal settles preferentially in a habitat within which it does poorly relative to other available habitats, it is said to have been caught in an "ecological trap." Although the theoretical possibility that animals may be so trapped is widely recognized, the absence of a clear mechanistic understanding of what constitutes a trap means that much of the literature cited as support for the idea may be weak, at best. Here, we develop a conceptual model to explain how an ecological trap might work, outline the specific criteria that are necessary for demonstrating the existence of an ecological trap, and provide tools for researchers to use in detecting ecological traps. We then review the existing literature and summarize the state of empirical evidence for the existence of traps. Our conceptual model suggests that there are two basic kinds of ecological traps and three mechanisms by which traps may be created. To this point in time, there are still only a few solid empirical examples of ecological traps in the published literature (although those few examples suggest that both types of traps and all three of the predicted mechanisms do exist in nature). Therefore, ecological traps are either rare in nature, are difficult to detect, or both. An improved library of empirical studies will be essential if we are to develop a more synthetic understanding of the mechanisms that can trigger maladaptive behavior in general and the specific conditions under which ecological traps might occur.     

Effects of El Niño on distribution and reproductive performance of Black Brant

Climate in low-latitude wintering areas may influence temperate and high-latitude breeding populations of birds, but demonstrations of such relationships have been rare because of difficulties in linking wintering with breeding populations. We used long-term aerial surveys in Mexican wintering areas and breeding areas in Alaska, USA, to assess numbers of Black Brant (Branta bernicla nigricans; hereafter brant) on their principal wintering and breeding area in El Ni?o and non-El Ni?o years. We used Pollock's robust design to directly estimate probability of breeding and apparent annual survival of individually marked brant at the Tutakoke River (TR) colony, Alaska, in each year between 1988 and 2001. Fewer brant wintered in Mexico during every El Ni?o event since 1965. Fewer brant were observed on the principal breeding area following each El Ni?o since surveys began in 1985. Probability of breeding was negatively related to January sea surface temperature along the subtropical coast of North America during the preceding winter. Between 23% (five-year-olds or older) and 30% (three-year-olds) fewer brant nested in 1998 following the strong El Ni?o event in the winter of 1997-1998 than in non-El Ni?o years. This finding is consistent with life history theory, which predicts that longer-lived species preserve adult survival at the expense of reproduction. Oceanographic conditions off Baja California, apparently by their effect on Zostera marina (eelgrass), strongly influence winter distribution of brant geese and their reproduction (but not survival), which in turn affects ecosystem dynamics in Alaska.     

Evaluating the vulnerability of surface waters to antibiotic contamination from varying wastewater treatment plant discharges

Effluents from three wastewater treatment plants with varying wastewater treatment technologies and design were analyzed for six antibiotics and caffeine on three sampling occasions. Sulfamethoxazole, trimethoprim, ciprofloxacin, tetracycline, and clindamycin were detected in the effluents at concentrations ranging from 0.090 to 6.0 microg/L. Caffeine was detected in all effluents at concentrations ranging from 0.19 to 9.9 microg/L. These findings indicate that several conventional wastewater management practices are not effective in the complete removal of antibiotics, and their discharges have a large potential to affect the aquatic environment. To evaluate the persistence of antibiotics coming from the wastewater discharges on the surrounding surface waters, samples were collected from the receiving streams at 10-, 20- and 100-m intervals. Ciprofloxacin, sulfamethoxazole, and clindamycin (0.043 to 0.076 microg/L) were found as far as 100 m from the discharge point, which indicates the persistence of these drugs in surface waters.