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The whooping crane (Grus americana), an endangered species, has been counted on its winter grounds in Texas, USA, since 1950 using fixed-wing aircraft. Many shortcomings of the traditional survey technique have been identified, calling into question its efficacy, defensibility, repeatability, and usefulness into the future. To improve and standardize monitoring effort, we began investigating new survey techniques. Here we focus on efficacy of line transect-based distance sampling during aerial surveys. We conducted a preliminary test of distance sampling during winter 2010–2011 while flying the traditional survey, which indicated that detectability within 500 m of transects was 0.558 (SE = 0.031). We then used an experimental decoy survey to evaluate impacts of observer experience, sun position, distance from transect, and group size on detectability. Our results indicated decoy detectability increased with group size and exhibited a quadratic relationship with distance likely due to pontoons on the aircraft. We found that detectability was 2.704 times greater when the sun was overhead and 3.912 times greater when the sun was at the observer's back than when it was in the observer's eyes. We found that an inexperienced observer misclassified non-target objects more often than an experienced observer. During the decoy experiment we used marks on the struts to categorize distances into intervals, but we found that observers misclassified distances 46.7% of the time (95% CI = 37.0–56.6%). Also, we found that detectability of individuals within detected groups was affected by group size and distance from transect. We discuss how these results inform design and implementation of future whooping crane monitoring efforts. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

Core areas are important descriptors of animal space-use patterns, but current estimation methods rely on arbitrary rules and potentially lead to imprecise or erroneous area estimates. We proposed a Bayesian statistical model that incorporates an individual-based method for estimating core area boundaries. The model accounts for boundary uncertainty and multiple scales of clustering by partitioning a home range into L 2 completely spatially random point patterns defined by a kernel density isopleth. We used data from coyotes (Canis latrans), bobcats (Lynx rufus), and red-shouldered hawks (Buteo lineatus) to estimate core areas for individual animals. We also estimated core areas from simulated point patterns with known boundaries, varying numbers of points, and relative densities of points inside core areas, and compared estimates to those obtained using the 50% isopleth. Optimal isopleths for the empirical data ranged between 18.7% and 71.5%. We found no species-specific range of core area isopleths. Across all simulated scenarios, our method outperformed the 50% isopleth-based estimate, which consistently overestimated core areas. Minta overlap values were 20-40% higher across all scenarios for our method compared to the 50% isopleth. Minta overlap values were .75% in 90% of scenarios using our method. Objectively estimating core areas using our individual-based method may lead to improved inference about which behavioral and ecological processes underlie observed space-use patterns because of greater estimate precision.

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