CHANNEL FORM AND STREAM ECOSYSTEM MODELS1

A system is proposed to classify running water habitats based on their channel form which can be considered in three different sedimentological settings: a cobble and boulder bed channel, a gravel bed channel, or a sand bed channel. Three physical factors (relief, lithology, and runoff) are selected as state factors that control all other interacting parameters associated with channel form. When these factors are integrated across the conterminous United States, seven distinct stream regions are evident, each representing a most probable succession of channel forms downstream from the headwaters to the mouth. Coupling these different channel profiles with typical biotic community structures usually associated with each of the channel types should result in considerable refinement of the applicability of the River Continuum Concept and other holistic ecosystem models by realizing the nonrandomness of the effects of geo-morphology on stream ecosystems. Thus, this regional perspective of streams should serve to make persons concerned with water resources more aware of the geographical considerations that affect their study areas.     

Detection and decision-making in environmental effects monitoring

The majority of environmental effects monitoring (EEM) frameworks that have been proposed compare selected indicator variables as a means of assessing whether significant changes in stressed ecosystems have occurred. Most are deterministic in nature and do not appropriately account for the natural variability and dynamics within the systems being comapred. This suggests that the comparative procedures should be statistically based and immediately raises the issue of whether the selected comparative procedures are to be used as decision-making tools or conclusive procedures. Conclusive procedures require a significant body of evidence before rejecting the postulated null hypothesis. The costs and time involved in environmental data collection accordingly bias action toward the maintenance of a status quo approach to environmental management. if, however, EEM is treated as a decision-making procedure, risk functions that include consideration of type I and II statistical error may be developed and combined with costs to select a minimum expected loss strategy for environemental management. Examples of the interpretative difficulties and conclusion reversal phenomena caused when EEM is used as a conclusive procedure are presented. In addition, risk functions appropriate for environmental management within an EEM context are constructed and applied. Only when such tools are fully developed and applied can EEM expect to have significant impacts on minimizing environmental degradation.     

Hydrologic Model Framework for Water Resources Planning in the Santa Cruz River,Southern Arizona1

Abstract: The authors develop a model framework that includes a set of hydrologic modules as a water resources management and planning tool for the upper Santa Cruz River near the Mexican border, Southern Arizona. The modules consist of: (1) stochastic generation of hourly precipitation scenarios that maintain the characteristics and variability of a 45‐year hourly precipitation record from a nearby rain gauge; (2) conceptual transformation of generated precipitation into daily streamflow using varied infiltration rates and estimates of the basin antecedent moisture conditions; and (3) surface‐water to ground‐water interaction for four downstream microbasins that accounts for alluvial ground‐water recharge, and ET and pumping losses. To maintain the large inter‐annual variability of streamflow as prevails in Southern Arizona, the model framework is constructed to produce three types of seasonal winter and summer categories of streamflow (i.e., wet, medium, or dry). Long‐term (i.e., 100 years) realizations (ensembles) are generated by the above described model framework that reflects two different regimes of inter annual variability. The first regime is that of the historic streamflow gauge record. The second regime is that of the tree ring reconstructed precipitation, which was derived for the study location. Generated flow ensembles for these two regimes are used to evaluate the risk that the regional four ground‐water microbasins decline below a preset storage threshold under different operational water utilization scenarios.     

INFLUENCE OF HERBIVORY ON REGROWTH OF RIPARIAN SHRUBS FOLLOWING A WILDLAND FIRE1

Streamside vegetation frequently regenerates faster than upland vegetation following wildland fire and contributes to the recovery of riparian and stream ecosystems. Limited data are available, however, on the post‐fire growth of riparian species and the influence of herbivory on regeneration. To determine post‐fire regrowth of riparian vegetation, height, crown area, crown volume, and browse levels were measured for key riparian shrub species in streamside burned and unburned plots along second‐order streams in western Wyoming. Shrubs in the burned plots were subject to high levels of browse ‐ up to 84 percent of the leaders were browsed ‐ by native ungulates in 2002, the second post‐fire year (September 2001 to September 2002). In summer 2003, the burned watershed was also grazed by livestock, resulting in increased browse levels and decreased shrub heights for several species. In the third post‐fire year, September 2002 to September 2003, four of the six most common species showed no increase in crown area or crown volume, indicating that the combination of native ungulate and cattle browsing suppressed their growth. Potential impacts of grazing on post‐fire recovery of stream and riparian ecosystems are discussed.     

Unprecedented Low Levels of Genetic Variation and Inbreeding Depression in an Island Population of the Black-Footed Rock-Wallaby

Abstract: It has been argued that demographic and environmental factors will cause small, isolated populations to become extinct before genetic factors have a significant negative impact. Islands provide an ideal opportunity to test this hypothesis because they often support small, isolated populations that are highly vulnerable to extinction. To assess the potential negative impact of isolation and small population size, we compared levels of genetic variation and fitness in island and mainland populations of the black-footed rock-wallaby ( Petrogale lateralis [Marsupialia: Macropodidae]). Our results indicate that the Barrow Island population of P. lateralis has unprecedented low levels of genetic variation (  H _e = 0.053, from 10 microsatellite loci) and suffers from inbreeding depression (reduced female fecundity, skewed sex ratio, increased levels of fluctuating asymmetry). Despite a long period of isolation ( ∼ 1600 generations) and small effective population size (  N _e ∼ 15), demographic and environmental factors have not yet driven this population to extinction. Nevertheless, it has been affected significantly by genetic factors. It has lost most of its genetic variation and become highly inbred (  F _e = 0.91), and it exhibits reduced fitness. Because several other island populations of P. lateralis also exhibit exceptionally low levels of genetic variation, this phenomenon may be widespread. Inbreeding in these populations is at a level associated with high rates of extinction in populations of domestic and laboratory species. Genetic factors cannot then be excluded as contributing to the extinction proneness of small, isolated populations.     

Real-time PCR assay for detection and relative quantification of <Emphasis Type="Italic">Liocarcinus </Emphasis><Emphasis Type="Italic">depurator</Emphasis> larvae from plankton samples

Accurate species identification of decapod crustacean larvae is required to understand their population distributions, life cycle dynamics and interactions with their habitats. Analysis of plankton samples using morphological taxonomic methods and microscopy is time-consuming, requires highly skilled and trained operatives and may often be inaccurate. As complementary tools to classical identification methods, recent work has focused on the development of molecular approaches and shows their feasibility for species-specific identification. This study has developed real-time PCR assays utilising species-specific Taqman^® probes designed in the cytochrome oxidase I (COI) gene of Liocarcinus depurator, Necora puber, Carcinus maenas and Cancer pagurus. Our study then employed the probe and primers designed for L. depurator to obtain accurate identification and relative abundance estimates of L. depurator larvae in plankton samples collected between March 2005 and October 2006. Ranges of larval abundances were derived from a standard curve created from plankton samples spiked with a known number of larvae reared in the laboratory. Inhibition of the PCR reaction was shown to be an important factor and our results suggested that 0.1 ng of DNA as template provided accurate identification and avoided inhibition. Real-time PCR was shown to provide accurate species identification on unsorted plankton samples and could be suitable for the estimation of larval abundances in the plankton, although more work must be done to improve the accuracy of those estimations.     

Suitable environmental areas for commercial timber in the state of Rio de Janeiro,Southeastern Brazil

Environment, Development and Sustainability - In state of Rio de Janeiro, the Southeast Region of Brazil, 97.91% of the reforested areas contain exclusively species of Pinus and Eucalyptus. Due to...     

Forecasting air quality time series using deep learning

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O₃) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O₃ exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours.

Implications: Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.     

Approximate bias of the estimated proportion in group testing

The maximum likelihood estimator for estimating proportions by group testing is biased. An expression for the approximate bias has been previously presented, which enables the creation of a less biased estimator by removing the term of \(O(n^{-1})\). However, in this previous work the term of \(O(n^{-2})\) was incorrectly derived. This note gives a correct derivation, and examines the relative contribution of the two terms.