AN EVALUATION OF THE AVAILABLE TECHNIQUES FOR ESTIMATING MISSING FECAL COLIFORM DATA1

ABSTRACT: This paper presents the findings of a study aimed at evaluating the available techniques for estimating missing fecal coliform (FC) data on a temporal basis. The techniques investigated include: linear and nonlinear regression analysis and interpolation functions, and the use of artificial neural networks (ANNs). In all, seven interpolation, two regression, and one ANN model structures were investigated. This paper also investigates the validity of a hypothesis that estimating missing FC data by developing different models using different data corresponding to different dynamics associated with different trends in the FC data may result in a better model performance. The FC data (counts/100 ml) derived from the North Fork of the Kentucky River in Kentucky were employed to calibrate and validate various models. The performance of various models was evaluated using a wide variety of standard statistical measures. The results obtained in this study are able to demonstrate that the ANNs can be preferred over the conventional techniques in estimating missing FC data in a watershed. The regression technique was not found suitable in estimating missing FC data on a temporal basis. Further, it has been found that it is possible to achieve a better model performance by first decomposing the whole data set into different categories corresponding to different dynamics and then developing separate models for separate categories rather than developing a single model for the composite data set.     

Assessing the status and trend of bat populations across broad geographic regions with dynamic distribution models

Bats face unprecedented threats from habitat loss, climate change, disease, and wind power development, and populations of many species are in decline. A better ability to quantify bat population status and trend is urgently needed in order to develop effective conservation strategies. We used a Bayesian autoregressive approach to develop dynamic distribution models for Myotis lucifugus, the little brown bat, across a large portion of northwestern USA, using a four-year detection history matrix obtained from a regional monitoring program. This widespread and abundant species has experienced precipitous local population declines in northeastern USA resulting from the novel disease white-nose syndrome, and is facing likely range-wide declines. Our models were temporally dynamic and accounted for imperfect detection. Drawing on species-energy theory, we included measures of net primary productivity (NPP) and forest cover in models, predicting that M. lucifugus occurrence probabilities would covary positively along those gradients. Despite its common status, M. lucifugus was only detected during -50% of the surveys in occupied sample units. The overall naive estimate for the proportion of the study region occupied by the species was 0.69, but after accounting for imperfect detection, this increased to -0.90. Our models provide evidence of an association between NPP and forest cover and M. lucifugus distribution, with implications for the projected effects of accelerated climate change in the region, which include net aridification as snowpack and stream flows decline. Annual turnover, the probability that an occupied sample unit was a newly occupied one, was estimated to be low (-0.04-0.14), resulting in flat trend estimated with relatively high precision (SD = 0.04). We mapped the variation in predicted occurrence probabilities and corresponding prediction uncertainty along the productivity gradient. Our results provide a much needed baseline against which future anticipated declines in M. lucifugus occurrence can be measured. The dynamic distribution modeling approach has broad applicability to regional bat monitoring efforts now underway in several countries and we suggest ways to improve and expand our grid-based monitoring program to gain robust insights into bat population status and trend across large portions of North America.     

Bankfull Regional Curves for the Inner and Outer Bluegrass Regions of Kentucky1

Brockman, Ruth R., Carmen T. Agouridis, Stephen R. Workman, Lindell E. Ormsbee, and Alex W. Fogle, 2012. Bankfull Regional Curves for the Inner and Outer Bluegrass Regions of Kentucky. Journal of the American Water Resources Association (JAWRA) 48(2): 391‐406. DOI: 10.1111/j.1752‐1688.2011.00621.x Abstract: Bankfull regional curves that relate channel dimensions and discharge to watershed drainage area are useful tools for assisting in the correct identification of bankfull elevation and in stream restoration and reconstruction. This study assessed 28 stable streams located in two physiographic regions of Kentucky: the Inner Bluegrass and the Outer Bluegrass. Bankfull channel dimensions, discharge, and return period as well as average channel slope, median bed material size, sinuosity, Rosgen stream classification, and percent impervious area were determined. Significant relationships were found between drainage area and the bankfull characteristics of cross‐sectional area, width, mean depth, and discharge for both the Inner Bluegrass and Outer Bluegrass regions (α = 0.05). It was also found that the percent impervious area in a watershed had minimal effect on bankfull dimensions, which is attributed to the well‐vegetated nature of the streambanks, cohesive streambank materials, and bedrock control. No significant differences between any of the Inner Bluegrass and Outer Bluegrass regional curves were found (α = 0.05). Comparisons were made between the Inner Bluegrass and Outer Bluegrass curves and others developed in karst‐influenced areas in the Eastern United States. Although few significant differences were found between the regional curves for bankfull discharge and width, a number of the curves differed with regards to bankfull cross‐sectional area and mean depth.