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This study, carried out for the United States Bureau of Reclamation (BOR), demonstrated methods for surveying large landscapes using small-scale, habitat-based stratification, a common problem that has heretofore received little attention. The goal was to design a sampling plan for detecting change in the density of breeding birds of 6 species occurring along the Colorado River around and south of Lake Mead in the southwestern United States. The main problem in designing the study was that the focal species were concentrated in small, irregularly shaped patches of habitat. We partitioned the study area into >15,000 plots configured to enclose the high-quality habitat in the fewest possible plots with the constraint that plots could be surveyed in 1 morning by 1 person. Because of the irregular plot shapes and extremely dense vegetation, we used area searches to carry out the surveys. We used double-sampling, including a large sample of plots surveyed with a rapid method and a subsample of plots surveyed intensively, to estimate detection ratios. A simulation study helped allocate effort between rapid and intensive surveys and indicated that conducting 80 surveys per year would achieve high power to detect a 50% decline occurring during 20 years. This is one of the first studies to show how large landscapes can be sampled using small-scale stratification so that effort can be concentrated in the habitats of greatest interest. © 2012 The Wildlife Society. [ABSTRACT FROM AUTHOR]

Most studies that follow subjects over time are challenged by having some subjects who dropout. Double sampling is a design that selects and devotes resources to intensively pursue and find a subset of these dropouts, then uses data obtained from these to adjust naïve estimates, which are potentially biased by the dropout. Existing methods to estimate survival from double sampling assume a random sample. In limited-resource settings, however, generating accurate estimates using a minimum of resources is important. We propose using double-sampling designs that oversample certain profiles of dropouts as more efficient alternatives to random designs. First, we develop a framework to estimate the survival function under these profile double-sampling designs. We then derive the precision of these designs as a function of the rule for selecting different profiles, in order to identify more efficient designs. We illustrate using data from the United States President's Emergency Plan for AIDS Relief-funded HIV care and treatment program in western Kenya. Our results show why and how more efficient designs should oversample patients with shorter dropout times. Further, our work suggests generalizable practice for more efficient double-sampling designs, which can help maximize efficiency in resource-limited settings., (Copyright © 2014 John Wiley & Sons, Ltd.)