Computational protocol: Density- and trait-mediated effects of a parasite and a predator in a tri-trophic food web

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[…] Movement pattern, swimming speed, cell shape and cell size were extracted from video recordings of Paramecium cells with differing levels of infection – ‘overtly infected’, ‘covertly infected’ or ‘uninfected’. Cells categorized as overtly infected were drawn from the infected stock culture and were conspicuous due to the presence of massively inflated micronuclei (loaded with high numbers of infectious forms) that could already be identified as opaque spots in the cytoplasm at low magnification under the microscope. Covertly infected cells did not exhibit obvious outward symptoms of infection, and their less inflated micronuclei carried fewer, if any, infectious forms (, Supporting information; see also Kaltz & Koella ).Recordings were acquired, analysed and processed in accordance with the workflow proposed by Pennekamp & Schtickzelle (), using a stereomicroscope (Leica M205 C; Leica Mikrosysteme Vertrieb GmbH, Ernst-Leitz-Strasse 17-37, 35578 Wetzlar, Germany) and mounted digital CMOS camera (Hamamatsu C11440) in combination with the software programs ImageJ (Abramoff et al. ) and r (R Development Core Team ). Paramecium cells were transferred via micropipette into 1 mL of growth medium spread across a glass Sedgewick–Rafter counting cell. Videos were recorded for 5 s with a 40-ms field delay and 10-ms exposure (giving 25 frames per second) at low (7·8×) magnification. To minimize blur and achieve highest optical resolution and contrast, the image was set to grey scale, and high-intensity external illumination was placed around the stage plate (Schott VisiLED MC 1500; SCHOTT AG, Hattenbergstrasse 10, 55122 Mainz, Germany). So that the software could visually separate Paramecium from artefacts, the video recordings were converted to 8-bit format, and a size threshold of 10–255 pixel lengths was specified for Paramecium. Videos in which Paramecium ceased swimming, swam vertically or exited the field of view were discarded. ImageJ's Particle Analyzer and Particle Tracker returned x, y coordinates (pixel locations within the field of view) for cells within each frame of each video – along with estimates of length and width in pixels, aspect ratio (dimensionless ratio of length to width), and cross-sectional area in square pixels.Mobility was quantified in terms of the frequency of turns made by Paramecium and the average swimming speed of Paramecium in each video. Turns were defined as movements that caused Paramecium to deviate at least 45 degrees from its original trajectory. If, throughout the video, Paramecium deviated <45 degrees from its original trajectory, its movement pattern was defined as being linear (having no turns).Swimming speed was defined as spatial displacement over time. This was calculated by taking the square root of the sum of the squared change in position along the X-axis and the squared change in position along the Y-axis (Pythagorean Theorem). The resulting measure was converted from pixels per frame to millimetres per second and then averaged for each Paramecium cell. To ensure that this measure of swimming speed was not confounded by how often Paramecium made turns and how much it slowed down or sped up at the beginning or end of a turn, only linear parts of trajectories were used to make the calculation. Also omitted were videos in which Paramecium was undetected or misidentified by ImageJ's Particle Tracker (e.g. due to quick turns and loss of frames resulting from deletion of background artefacts), leaving a total of 55 videos upon which to base the calculation. Estimates of cross-sectional length, width, area and aspect ratio were calculated for these same remaining videos by taking the mean of the output of ImageJ's Particle Analyzer across frames for each cell. Effects of infection level on swimming speed, aspect ratio, total per capita turns and cross-sectional area were assessed using one-way analysis of variance (anova).To assess whether differences in swimming speed and cell length among infected and uninfected Paramecium were enough to create differences in Paramecium's predicted rate of encounter of Serratia, we employed two separate methods of estimating encounter rates: one developed by Fenchel (described in Shimeta & Jumars ), the other by Verity (). Details regarding the terms used in these equations are given in (Supporting information). […]

Pipeline specifications

Software tools ImageJ, Particle Tracker
Applications Laser scanning microscopy, Microscopic phenotype analysis
Organisms Paramecium caudatum, Toxoplasma gondii, Bacteria
Diseases Parasitic Diseases, Serratia Infections