DOI: 10.5061/dryad.dv41ns20h
title: Data for: The visual ecology of selective predation: Are unhealthy hosts less stealthy hosts?

description: Predators can strongly influence disease transmission and evolution,
 particularly when they prey selectively on infected hosts. Although
 selective predation has been observed in numerous systems, why predators
 select infected prey remains poorly understood. Here, we use a
 mathematical model of predator vision to test a longstanding hypothesis
 about the mechanistic basis of selective predation in a
 Daphnia-microparasite system, which serves as a model for the ecology and
 evolution of infectious diseases. Bluegill sunfish feed selectively on
 Daphnia infected by a variety of parasites, particularly in water
 uncolored by dissolved organic carbon. The leading hypothesis for
 selective predation in this system is that infection-induced changes in
 the transparency of Daphnia render them more visible to bluegill.
 Rigorously evaluating this hypothesis requires that we quantify the effect
 of infection on the visibility of prey from the predator's
 perspective, rather than our own. Using a model of the bluegill visual
 system, we show that three common parasites, Metschnikowia bicuspidata,
 Pasteuria ramosa and Spirobacillus cienkowskii, decrease the
 transparency of Daphnia, rendering infected Daphnia darker
 against a background of downwelling light. As a result of this increased
 brightness contrast, bluegill can see infected Daphnia at greater
 distances than uninfected Daphnia - between 19-33%
 further, depending on the parasite. Pasteuria and
 Spirobacillus also increase the chromatic contrast of Daphnia.
 These findings lend support to the hypothesis that selective predation
 by fish on infected Daphnia could result from the
 effects of infection on Daphnia's visibility. However, contrary to
 expectations, the visibility of Daphnia was not strongly
 impacted by water color in our model. Our work demonstrates that models of
 animal visual systems can be useful in understanding ecological
 interactions that impact disease transmission.
description: Data was collected as described in the paper. We discuss the data
 collection briefly below and note how the data were
 processed. <br><strong>Lake irradiance data</strong> -
 Data was collected in the upper part of the water column of two lakes,
 North Lake and Gosling Lake in Livingston County, Michigan. Measurements
 were taken at a depth of 50cm in the littoral zone and at 50cm &
 150cm in the pelagic zone. A spectroradiometer (Ocean Optics S2000)
 connected to a patch cord (Ocean Optics QP400 -2 UV-VIS), which was in
 turn connected to a cosine corrector (Ocean Optics CC-3 DA). 
 In the uploaded dataset, there is a column containing the raw
 data (raw_irradiance) and another with the smoothed data
 (smoothed_irradiance), which was used in the analysis. The raw data and
 smoothed data are plotted as transparent points and a smooth line in Fig.
 2B of the paper, respectively. Raw data was smoothed using the loess()
 function in R, with a degree of 2 and span of 0.12. After smoothing,
 negative values were rounded to zero. We made two measurements of the
 downwelling irradiance in the littoral part of Gosling lake. For this
 location, therefore the two spectra were averaged prior to smoothing and
 the mean of the raw spectra is given as the raw value in
 "raw_irradiance column" and is plotted as the raw data points in
 Figure 2B. We give the two different spectra measured in the littoral part
 of Gosling lake in an additional sheet.
 <strong>Daphnia data </strong>We measured light
 transmission through the thorax of <em>Daphnia
 dentifera</em> infected with three different parasites. Briefly, we
 connected a compound light microscope (Olympus BX53) to the aforementioned
 spectroradiometer via a patch cord and SMA connector, attached to the
 microscope's trinocular port. <em>Daphnia</em> were then
 illuminated from behind on the microscope and transmission of light
 measured.  In the uploaded data set, there is a column
 containing the raw data (raw_transmission) and another with the smoothed
 data (smoothed_transmission), which was used in the analysis. The raw data
 and smoothed data are plotted as transparent points and a smooth line in
 Fig. 2C of the paper, respectively. Raw data was smoothed using the
 procspec() function of the pavo2 package in R, with a span of 0.12.
 Negative values were rounded to 0 using the argument
 fix.neg="zero".