Conservation Assessments of Arboreal Mammals in Difficult Terrain: Occupancy Modeling of Pileated Gibbons (Hylobates pileatus)

Long-term monitoring programs, wildlife surveys, and other research involving species population assessment require reliable data on population status. Given the logistically challenging nature of some species’ habitats and cryptic behaviors, collecting these data can prove to be a considerable barrier. We used detection/nondetection data from pileated gibbons (Hylobates pileatus) in the Cardamom Mountains of southwest Cambodia to estimate their population occupancy and detectability. We modeled occupancy using elevation, tree height, tree density, tree diversity, and disturbance covariates. Modeling demonstrated that 83% of the sites are occupied by Hylobates pileatus and that the detectability of the species varies positively with elevation. No clear relationship between habitat quality covariates and occupancy of Hylobates pileatus emerged. Effort analysis based on model estimates demonstrated that at high elevations, less than half the number of site visits is needed to attain the same detectability estimate precision as across all elevations. We suggest that human activities at low elevations, which affect forest composition, are the central factors impacting the detectability and occupancy of Hylobates pileatus. Longer sampling durations and/or a higher number of site visits, especially at lower elevations, increase precision of the occupancy estimator for the least effort. For effective future monitoring and research for this and similar species, using this relatively simple method, applied with repeat site visits, would allow a longitudinal comparison of detection at sites in difficult terrain.     

Less label, more free: approaches in label-free quantitative mass spectrometry

In this review we examine techniques, software, and statistical analyses used in label-free quantitative proteomics studies for area under the curve and spectral counting approaches. Recent advances in the field are discussed in an order that reflects a logical workflow design. Examples of studies that follow this design are presented to highlight the requirement for statistical assessment and further experiments to validate results from label-free quantitation. Limitations of label-free approaches are considered, label-free approaches are compared with labelling techniques, and forward-looking applications for label-free quantitative data are presented. We conclude that label-free quantitative proteomics is a reliable, versatile, and cost-effective alternative to labelled quantitation.