Table 1. The main features of the two GLIMMIXs testing for the differences of the white badge versus background and white badge versus owl body during the pre-calling period, when owls were calling and during the post-calling period (see text for additional information). Eagle Owl Dusk Chorus % deviance explained 74.1 73.4 Parameters Model: white badge vs. background Pre-calling period a Calling period a Post-calling period a Male owls b Intercept Post-hoc tests Pre-calling vs. calling periods Pre-calling vs. post-calling periods Calling vs. post-calling periods Model: white badge vs. body Pre-calling period a Calling period a Post-calling period a Male owls b Intercept Post-hoc tests pre-calling vs. calling periods pre-calling vs. post-calling periods calling vs. post-calling periods Parameter estimate6SE 116.865.4 126.665.5 37.765.7 559.16183.8 37.765.7 117.764.6 118.564.7 36.764.9 376.26124.1 36.764.9 P 0.0001 0.0001 0.0001 0.001 0.05 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.9 0.0001 0.0001 afixed effects, categorical variable brandom effect Differences among periods are shown as least square means6S.E. from the mixed models (LSMEANS statement in SAS). doi:10.1371/journal.pone.0004960.t001 start and the end of the call activity of each owl to define the temporal range of their dusk choruses. Image samples collected during call displays Based on previous information regarding the vocal behavior of the eagle owl [10,11,12], and for each of the 25 calling owls, we took a picture of the decoy every five minutes, from an hour before sunset to 15 minutes after the last call of the dusk display (overall sample size of pictures = 482). We selected this time bracket because, following previous observations [10,11,12]: (a) starting one hour before sunset allows us to have the time to take some pictures before the starting of eagle owl dusk chorus; and (b) generally, after the last call of the dusk chorus owls move to their hunting areas and temporally cease their call activities. The three supplemental pictures that we took during the 15 minutes after the last call allowed us to collect information on brightness after the cease of vocal displays. Pictures were taken with a digital camera Nikon D70 and a 300 mm lens (AF-S Nikkor 1:2.8 G ED) always placed on a tripod at the same distance from the decoy and with the diaphragm set to f.8. Measurements of the brightness contrasts Brightness values of the white badge, the body and the external environment was measured using the Photoshop CS2 software. Using this program, each pixel in an image can be identified across 256 levels of brightness from pure black (0) to pure white (255). A histogram of the intensity of the reflected light shows how all 256 possible levels of brightness are distributed in the image. The horizontal axis of the Photoshop histogram represents the range of brightness from 0 on the left to 255 on the right, i.e. a line with 256 spaces on which to stack pixels of the same brightness. Measurements were performed as follows in the overall sample size of 482 pictures. First, the digital pictures were modified to the grayscale mode and a Gaussian blur filter (radio = 5.0) was applied to diffuse the image and to avoid alteration of brightness by ‘‘outlier’’ pixels. Next, measurement patches were randomly selected from the white badge, the body and the external environment of each picture. For this purpose we applied a grid of 260 squares (size = 50650 pixels) to the photograph. Each square was numbered, squares on the badge- body and body-environment borderlines were excluded (as noise could bias measurements), and the measurement squares were chosen using an aleatory method. The mean brightness values of each square were obtained by superposing a rectangular tool of 50650 pixels over each measurement patch and using the eyedropper tool; results were reported on the histogram X-axis of the picture as mean6SD. Contrasts between the white badge and the background or body were measured as the differences between the values of mean brightness recorded for each square. Statistical analyses We used Generalized Linear Mixed Models [27] to test whether the contrasts of the white badge vs. background and white badge PLoS ONE | www.plosone.org 3 April 2009 | Volume 4 | Issue 4 | e4960