8 ± 4.3% [mean ± standard error (se); median = 50.2%; range: 31.7–69.1%] of the 5381 location points. All sleeping and food trees were identified at the species level and georeferenced with the Global Positioning System (GPS) unit. Only food trees in which the subgroup fed on for at least 5 min were selected for analysis

(cf. Link & Di Fiore, 2006). Given the 4-year span of the study, we considered these food trees as representative of the food sources available to the study community. In addition, the study area covered during subgroup follows did not change over these 4 years and a plateau was reached in the number of food trees used, emphasizing that our sampling effort was sufficient Z-VAD-FMK chemical structure ABT-263 in vivo to infer the variability in the quality of the habitat the monkeys typically use. The mean (±se) GPS accuracy was 8.8 ± 0.14 m based on 493 circular error probability readings given by the GPS unit at locations throughout the field site. Geographical coordinates were collected using the coordinate system (datum) WGS84 and projected into Universal Transverse Mercator (Zone 16N) units. We applied fixed-kernel estimators with least-squares cross-validation method to obtain the size of core areas within the 50%

isopleths and the home range within the 95% isopleths using ‘Hawth’s Tools for ArcGIS’ (Beyer, 2004). We calculated kernel areas based on data on the frequency of location use for the entire 4-year study period. A possible solution to reduce autocorrelation, that is, peudoreplication issues (Swihart & Slade, 1985), while having sufficient biologically realistic data, is to arbitrarily decide 3-mercaptopyruvate sulfurtransferase a minimum time interval when animals may likely switch locations (Willems & Hill, 2009). As the study monkeys are known to travel great distances rapidly (about 0.5 km h−1: Asensio et al., 2009) by setting the time interval between successive locations at 30 min, we reduced data autocorrelation while still maintaining biological validity. In addition to core areas based on frequency of location use, we calculated core areas based on intensity

of location use by weighing location use for subgroup size (cf. Spehar et al., 2010). As core areas based on intensity of location use were similar to those based on frequency, our analyses focused only on core areas based on frequency of location use. Core and non-core areas were divided into 1-ha hexagon cells using the Patch Analyst Extension for ArcGIS (Rempel & Kaufmann, 2003). Cell size may be less than 1 ha at the boundary of the home range and the boundary between core and non-core areas. We obtained 89 cells for core areas and 368 for non-core areas. For each cell, we calculated the value of the following variables of habitat quality. Sleeping tree density was the number of sleeping trees in a cell divided by cell size. Similarly, food tree density was the number of food trees in a cell divided by cell size.