Accuracy of Short‐Term Demographic Data in Projecting Long‐Term Fate of Populations

Short‐term surveys are useful in conservation of species if they can be used to reliably predict the long‐term fate of populations. However, statistical evaluations of reliability are rare. We studied how well short‐term demographic data (1999–2002) of tartar catchfly (Silene tatarica), a perennial riparian plant, projected the fate and growth of 23 populations of this species up to the year 2010. Surveyed populations occurred along a river with natural flood dynamics and along a regulated river. Riparian plant populations are affected by flooding, which maintains unvegetated shores, while forest succession proceeds in areas with little flooding. Flooding is less severe along the regulated river, and vegetation overgrowth reduces abundance of tartar catchfly on unvegetated shores. We built matrix models to calculate population growth rates and estimated times to population extinction in natural and in regulated rivers, 13 and 10 populations, respectively. Models predicted population survival well (model predictions matched observed survival in 91% of populations) and accurately predicted abundance increases and decreases in 65% of populations. The observed and projected population growth rates differed significantly in all but 3 populations. In most cases, the model overestimated population growth. Model predictions did not improve when data from more years were used (1999–2006). In the regulated river, the poorest model predictions occurred in areas where cover of other plant species changed the fastest. Although vegetation cover increased in most populations, it decreased in 4 populations along the natural river. Our results highlight the need to combine disturbance and succession dynamics in demographic models and the importance of habitat management for species survival along regulated rivers. Precisión de Datos Demográficos de Corto Plazo en la Proyección del Destino de Poblaciones a Largo Plazo     

Assessment of four turbulence models in simulation of large-scale pool fires in the presence of wind using computational fluid dynamics (CFD)

Pool fires are the most common of all process industry accidents. Pool fires often trigger explosions which may result in more fires, causing huge losses of life and property. Since both the risk and the frequency of occurrence of pool fires are high, it is necessary to model the risks associated with pool fires so as to correctly predict the behavior of such fires.Among the parameters which determine the overall structure of a pool fire, the most important is turbulence. It determines the extent of interaction of various parameters, including combustion, wind velocity, and entrainment of the ambient air. Of the various approaches capable of modeling the turbulence associated with pool fires, computational fluid dynamics (CFD) has emerged as the most preferred due to its ability to enable closer approximation of the underlying physical phenomena.A review of the state of the art reveals that although various turbulence models exist for the simulation of pool fire no single study has compared the performance of various turbulence models in modeling pool fires. To cover this knowledge-gap an attempt has been made to employ CFD in the assessment of pool fires and find the turbulence model which is able to simulate pool fires most faithfully. The performance of the standard k–? model, renormalization group (RNG) k–? model, realizable k–? model and standard k–ω model were studied for simulating the experiments conducted earlier by Chatris et al. (2001) and Casal (2013). The results reveal that the standard k–? model enabled the closest CFD simulation of the experimental results.     

A Behaviorally Explicit Demographic Model Integrating Habitat Selection and Population Dynamics in California Sea Lions

Abstract: Although there has been a call for the integration of behavioral ecology and conservation biology, there are few tools currently available to achieve this integration. Explicitly including information about behavioral strategies in population viability analyses may enhance the ability of conservation biologists to understand and estimate patterns of extinction risk. Nevertheless, most behavioral‐based PVA approaches require detailed individual‐based data that are rarely available for imperiled species. We present a mechanistic approach that incorporates spatial and demographic consequences of behavioral strategies into population models used for conservation. We developed a stage‐structured matrix model that includes the costs and benefits of movement associated with 2 habitat‐selection strategies (philopatry and direct assessment). Using a life table for California sea lions (Zalophus californianus), we explored the sensitivity of model predictions to the inclusion of these behavioral parameters. Including behavioral information dramatically changed predicted population sizes, model dynamics, and the expected distribution of individuals among sites. Estimated population sizes projected in 100 years diverged up to 1 order of magnitude among scenarios that assumed different movement behavior. Scenarios also exhibited different model dynamics that ranged from stable equilibria to cycles or extinction. These results suggest that inclusion of behavioral data in viability models may improve estimates of extinction risk for imperiled species. Our approach provides a simple method for incorporating spatial and demographic consequences of behavioral strategies into population models and may be easily extended to other species and behaviors to understand the mechanisms of population dynamics for imperiled populations.     

Using structural equation modeling to investigate relationships among ecological variables

Structural equation modeling is an advanced multivariate statistical process with which a researcher can construct theoretical concepts, test their measurement reliability, hypothesize and test a theory about their relationships, take into account measurement errors, and consider both direct and indirect effects of variables on one another. Latent variables are theoretical concepts that unite phenomena under a single term, e.g., ecosystem health, environmental condition, and pollution (Bollen, 1989). Latent variables are not measured directly but can be expressed in terms of one or more directly measurable variables called indicators. For some researchers, defining, constructing, and examining the validity of latent variables may be the end task of itself. For others, testing hypothesized relationships of latent variables may be of interest. We analyzed the correlation matrix of eleven environmental variables from the U.S. Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program for Estuaries (EMAP-E) using methods of structural equation modeling. We hypothesized and tested a conceptual model to characterize the interdependencies between four latent variables-sediment contamination, natural variability, biodiversity, and growth potential. In particular, we were interested in measuring the direct, indirect, and total effects of sediment contamination and natural variability on biodiversity and growth potential. The model fit the data well and accounted for 81% of the variability in biodiversity and 69% of the variability in growth potential. It revealed a positive total effect of natural variability on growth potential that otherwise would have been judged negative had we not considered indirect effects. That is, natural variability had a negative direct effect on growth potential of magnitude –0.3251 and a positive indirect effect mediated through biodiversity of magnitude 0.4509, yielding a net positive total effect of 0.1258. Natural variability had a positive direct effect on biodiversity of magnitude 0.5347 and a negative indirect effect mediated through growth potential of magnitude –0.1105 yielding a positive total effects of magnitude 0.4242. Sediment contamination had a negative direct effect on biodiversity of magnitude –0.1956 and a negative indirect effect on growth potential via biodiversity of magnitude –0.067. Biodiversity had a positive effect on growth potential of magnitude 0.8432, and growth potential had a positive effect on biodiversity of magnitude 0.3398. The correlation between biodiversity and growth potential was estimated at 0.7658 and that between sediment contamination and natural variability at –0.3769.     

Sensitivity of normal theory methods to model misspecification in the calculation of upper confidence limits on the risk function for continuous responses

Normal theory procedures for calculating upper confidence limits (UCL) on the risk function for continuous responses work well when the data come from a normal distribution. However, if the data come from an alternative distribution, the application of the normal theory procedures may lead serious over- or under-coverage depending upon the alternative distribution. In this paper we conduct simulation studies to investigate the sensitivity of three normal theory UCL procedures to departures from normality. Data from several gamma, reciprocal gamma, and lognormal distributions are considered. The normal theory procedures are applied to both the raw data and the log-transformed data.     

Light-limited population dynamics of phytoplankton: modeling light and depth effects

Light has received very less attention as a selective factor, probably because the changing vertical light gradient makes light a rather complex factor when compared with the nutrient competition. In our study we modified an analytically tractable model based upon the Beer–Lambert law. The study aimed to predict the outcome of phytoplankton competition for light, on the basis of field measurements. When we considered competition theory, Fragilaria was found to be more competitive than other genera. The overall result shows that the trends flow in the following manner; Fragilaria > Gomphonema > Diatoma > Amphora > Achnanthes.     

An ecological perspective on theory, methods, and analysis in environmental psychology: Advances and challenges

Over the course of environmental psychology's brief history, there has been an interest in ecologically oriented approaches to theory and research. Based on this work, this paper identifies a set of six principles of ecological analysis that present theoretical, methodological, and analytic challenges to future research in environmental psychology. These challenges include the theoretical treatment of the multiple contexts within which human experience and behavior occurs, the need for sampling both persons and environments, the modeling of moderating and mediating processes, the issue of self-selection into and out of different settings, the necessity of considering temporal factors in environmental research, reliance on single methods (e.g., verbal report) in data generation, cross-sectional and longitudinal research designs, and the need for greater use of statistical techniques developed for contextual (multi-level) research. These issues are discussed and illustrated using recent developments in environmentally oriented research. The paper concludes with a set of 11 recommendations for the future.     

Household water use behavior: An integrated model

Water authorities are dealing with the challenge of ensuring that there is enough water to meet demand in the face of drought, population growth and predictions of reduced supply due to climate change. In order to develop effective household demand management programs, water managers need to understand the factors that influence household water use. Following an examination and re-analysis of current water consumption behavioral models we propose a new model for understanding household water consumption. We argue that trust plays a role in household water consumption, since people will not save water if they feel others are not minimizing their water use (inter-personal trust). Furthermore, people are less likely to save water if they do not trust the water authority (institutional trust). This paper proposes that to fully understand the factors involved in determining household water use the impact of trust on water consumption needs investigation.     

Zeolite formation from coal fly ash and heavy metal ion removal characteristics of thus-obtained Zeolite X in multi-metal systems

Zeolitic materials have been prepared from coal fly ash as well as from a SiO₂–Al₂O₃ system upon NaOH fusion treatment, followed by subsequent hydrothermal processing at various NaOH concentrations and reaction times. During the preparation process, the starting material initially decomposed to an amorphous form, and the nucleation process of the zeolite began. The carbon content of the starting material influenced the formation of the zeolite by providing an active surface for nucleation. Zeolite A (Na-A) was transformed into zeolite X (Na-X) with increasing NaOH concentration and reaction time. The adsorption isotherms of the obtained Na-X based on the characteristics required to remove heavy ions such as Ni²⁺, Cu²⁺, Cd²⁺ and Pb²⁺ were examined in multi-metal systems. Thus obtained experimental data suggests that the Langmuir and Freundlich models are more accurate compared to the Dubinin–Kaganer–Radushkevich (DKR) model. However, the sorption energy obtained from the DKR model was helpful in elucidating the mechanism of the sorption process. Further, in going from a single- to multi-metal system, the degree of fitting for the Freundlich model compared with the Langmuir model was favored due to its basic assumption of a heterogeneity factor. The Extended-Langmuir model may be used in multi-metal systems, but gives a lower value for equilibrium sorption compared with the Langmuir model.     

Long-Term Effects of Changing Land Use Practices on Surface Water Quality in a Coastal River and Lagoonal Estuary

The watershed of the Neuse River, a major tributary of the largest lagoonal estuary on the U.S. mainland, has sustained rapid growth of human and swine populations. This study integrated a decade of available land cover and water quality data to examine relationships between land use changes and surface water quality. Geographic Information Systems (GIS) analysis was used to characterize 26 subbasins throughout the watershed for changes in land use during 1992–2001, considering urban, agricultural (cropland, animal as pasture, and densities of confined animal feed operations [CAFOs]), forested, grassland, and wetland categories and numbers of wastewater treatment plants (WWTPs). GIS was also used together with longitudinal regression analysis to identify specific land use characteristics that influenced surface water quality. Total phosphorus concentrations were significantly higher during summer in subbasins with high densities of WWTPs and CAFOs. Nitrate was significantly higher during winter in subbasins with high numbers of WWTPs, and organic nitrogen was higher in subbasins with higher agricultural coverage, especially with high coverage of pastures fertilized with animal manure. Ammonium concentrations were elevated after high precipitation. Overall, wastewater discharges in the upper, increasingly urbanized Neuse basin and intensive swine agriculture in the lower basin have been the highest contributors of nitrogen and phosphorus to receiving surface waters. Although nonpoint sources have been emphasized in the eutrophication of rivers and estuaries such as the Neuse, point sources continue to be major nutrient contributors in watersheds sustaining increasing human population growth. The described correlation and regression analyses represent a rapid, reliable method to relate land use patterns to water quality, and they can be adapted to watersheds in any region.