Advantages of Geographically Weighted Regression for Modeling Benthic Substrate in Two Greater Yellowstone Ecosystem Streams

Stream habitat assessments are commonplace in fish management, and often involve nonspatial analysis methods for quantifying or predicting habitat, such as ordinary least squares regression (OLS). Spatial relationships, however, often exist among stream habitat variables. For example, water depth, water velocity, and benthic substrate sizes within streams are often spatially correlated and may exhibit spatial nonstationarity or inconsistency in geographic space. Thus, analysis methods should address spatial relationships within habitat datasets. In this study, OLS and a recently developed method, geographically weighted regression (GWR), were used to model benthic substrate from water depth and water velocity data at two stream sites within the Greater Yellowstone Ecosystem. For data collection, each site was represented by a grid of 0.1 m² cells, where actual values of water depth, water velocity, and benthic substrate class were measured for each cell. Accuracies of regressed substrate class data by OLS and GWR methods were calculated by comparing maps, parameter estimates, and determination coefficient r ². For analysis of data from both sites, Akaike’s Information Criterion corrected for sample size indicated the best approximating model for the data resulted from GWR and not from OLS. Adjusted r ² values also supported GWR as a better approach than OLS for prediction of substrate. This study supports GWR (a spatial analysis approach) over nonspatial OLS methods for prediction of habitat for stream habitat assessments.     

An Approach to Reaeration Coefficient Modeling in Local Surface Water Quality Monitoring

Reaeration coefficient (k ₂) for River Atuwara, Ogun State, Nigeria was calculated from dissolved oxygen and biochemical oxygen demand data collected over period of 3 months covering the two prevailing climatic seasons in the country. Both the Akaike and Bayesian information criteria were used in the selection and analysis of ten models to identify the most suitable reaeration coefficient (k ₂) model for Atuwara River. Models that passed the confidence limit were subjected to model evaluation using measures of agreement between observed and predicted data such as percent bias, Nash–Sutcliffe efficiency, and root mean square observation standard deviation ratio. The used approach yield better results than empirical models developed for local conditions while it is also useful in conserving scarce resources.     

Effects of northeast monsoon on trace metal distribution in the South China Sea off Peninsular Malaysia

Concentrations of trace metals in the South China Sea (SCS) were determined off the coast of Terengganu during the months of May and November 2007. The concentrations of dissolved and particulate metals were in the range of 0.019–0.194 μg/L and 50–365 μg/g, respectively, for cadmium (Cd), 0.05–0.45 μg/L and 38–3,570 μg/g for chromium (Cr), 0.05–3.54 μg/L and 21–1,947 μg/g for manganese (Mn), and 0.03–0.49 μg/L and 2–56,982 μg/g for lead (Pb). The order of mean log K _D found was Cd?>?Cr?>?Pb?>?Mn. The study suggests that the primary sources of these metals are discharges from the rivers which drain into the SCS, in particular the Dungun River, which flows in close proximity to agricultural areas and petrochemical industries. During the northeast monsoon, levels of particulate metals in the bottom water samples near the shore were found to be much higher than during the dry season, the probable result of re-suspension of the metals from the bottom sediments.