DOI: 10.5061/dryad.crjdfn395
title: Data for: Passive and active parental food allocation in a songbird

description: Parent-offspring conflict over food allocation can be modeled using two
 theoretical frameworks: passive (scramble competition) and active choice
 (signaling) resolution models. However, differentiating between these
 models empirically can be challenging. One possibility involves
 investigating details of decision-making by feeding parents. Different
 nestling traits, related to competitive prowess or signalling cryptic
 condition, may interact additively or non-additively as predictors of
 parental feeding responses. To explore this, we experimentally created
 even-sized, small broods of pied flycatchers and manipulated nestling
 cryptic quality, independently of size, by vitamin E supplementation. We
 explored how interactions between nestling cryptic condition, size,
 signals, and spatial location predicted food allocation and prey-testing
 by parents. Parents created the potential for spatial scramble competition
 between nestlings by feeding from and to a narrow range of nest locations.
 Heavier supplemented nestlings grew faster and were more likely to access
 profitable nest locations. However, the most profitable locations were not
 more contested, and nestling turnover did not vary in relation to spatial
 predictability or food supply. Postural begging was only predicted by
 nestling hunger and body mass, but parents did not favor heavier
 nestlings. This suggests that size-mediated and spatial competition in
 experimental broods was mild. Pied flycatcher fathers allocated food in
 response to nestling position and begging order, while mothers seemingly
 followed an active choice mechanism involving assessment of more complex
 traits, including postural intensity interacting with order, position, and
 treatment, and perhaps other stimuli when performing prey-testings.
 Differences in time constraints may underlie sex differences in food
 allocation rules.
description: Experimental
 setup We monitored the nest boxes regularly to
 determine the exact start laying (±24 h) and hatching dates (±12 h; day 1
 was considered the hatching day). Initially, we included 32 broods (174
 nestlings) of monogamous pairs (Lundberg and Alatalo 1992) containing four
 to six nestlings that were five days old. Within each brood, we ranked the
 nestlings by mass and assigned them to either a vitamin E supplementation
 or control treatment, alternating the order between successive broods to
 ensure an unbiased sample with respect to hatching order and balanced
 across different brood sizes. As in a previous study conducted in the same
 population (Pérez-Rodríguez et al. 2019), supplemented nestlings were fed
 a dipteran larva (<em>Calliphora</em> sp.) soaked in a
 solution of vitamin E (DL-alpha-tocopherol acetate; Sigma-Aldrich ref.
 T3376) dissolved in organic coconut oil. The concentration of this vitamin
 E solution was adjusted across nestling development to provide a constant
 dose of 1.2 mg of vitamin E per kg per visit to the supplemented
 nestlings. Control nestlings were fed a larva soaked in coconut oil.
 Regardless of the treatment, nestlings received a new supplementation
 every other day, and we recorded their mass at each visit using an
 electronic balance (±0.01 g). There was no significant difference in
 initial (day 5) body mass between supplemented and control nestlings (β ±
 SE = -0.232 ± 0.155, t<sub> 1, 133</sub> = -1.500, P =
 0.136). Video recordings
 We recorded video sequences of
 parent and chick behavior when the broods reached 7 days old. This age was
 selected since it corresponds to the peak of daily mass gain (Siikamäki
 1996), and nestlings' feeding success still relies mostly on their
 begging behavior, before they can intercept food from adults at the nest
 entrance (Khayutin et al. 1988). On day 6, we attached a dummy camera to
 the roof of the nest-box to habituate the birds. In the morning of day 7,
 we fed the four nestlings with the most similar body masses (two from each
 treatment) to satiation with 1-5 larvae to equalize food deficit (Wright
 et al. 2002). We marked each nestling's head with a unique
 combination of blue adhesive kinesiology tape badges varying in shape
 (circle vs. triangle) and markings (closed vs. open dots) (Porkert and
 Špinka 2006). The badges were randomly assigned with respect to nestling
 treatment and relative mass. We used a Sony Go-Pro video camera to obtain
 video recordings for as long as the camera batteries allowed (about 2 h).
 We obtained video samples from 28 nests. Meanwhile, we kept the surplus
 chicks from each brood in heated containers, hand-fed them every 30 min,
 and returned them to their natal nest after
 recording.  We played recorded video sequences at 1/4 speed
 to accurately measure the behavior of parents and chicks during each
 parental feeding visit. A trained observer, blind to the aim of the
 experiment, computed behavioral rates. During each parental visit, we
 distinguished between parental feeding and prey-testing behavior. We
 defined a feeding as a parent introducing a food item into the mouth of a
 nestling and the nestling swallowing it. Prey-testing involved a parent
 introducing its beak with prey into a nestling mouth but failing to
 release any food item (Slagsvold and Wiebe 2007). Both parents feed
 nestlings with prey of a similar size at this age, and nestlings can
 efficiently handle most prey (Slagsvold and Wiebe 2007; Wiebe and
 Slagsvold 2009a). However, we discarded testing events (four visits) where
 prey items were of an unusually large size, which could be explained by
 gape size constraints rather than parental choice (Wiebe and Slagsvold
 2009a). We also recorded nestling behavior in a time window ranging from
 10 seconds before the adult entered the nest box to 10 seconds after the
 adult left it, to record nestling behavior while adults remained poking at
 the nest box from the outside during each parental
 visit. We divided the nest cup into 45° circular
 sectors, numbered clockwise from 1 to 8, to record parent and nestling
 behavior during feedings (Khayutin et al. 1988). We noted the sector from
 which each parent fed nestlings and the sector occupied by each nestling
 when parents entered the nest box. During each feeding visit, we recorded
 its duration, the spatial location of parents, the number of feedings and
 prey-testings received, the order in which nestlings begged (1 = first)
 and the maximum begging postural intensity of each nestling. We used a
 five-level ordinal scale to score the degree of body stretching as an
 indicator of begging postural intensity, following Redondo and Castro
 (1992). Additionally, we computed the rate of individual nestling movement
 as the average number of sector changes between parental visits. To
 calculate time of food deprivation for each nestling during a given visit,
 we measured the time elapsed since the previous visit when it was fed.
 Since initial deprivation times varied between nests, we computed
 deprivation times for each nestling as a fraction of the maximum value of
 deprivation time for each brood in the entire recording
 session. Nestling mouth coloration
 On day
 7, we photographed the mouth coloration of 72 vitamin E and 81 control
 nestlings from 32 broods. We gently opened each nestling's mouth in a
 standardized position and captured images using a Nikon D80 digital camera
 with a Sigma AF 50/2.8 objective and consistent illumination. To ensure
 accurate color representation, we included a ColorChecker Passport Photo 2
 (X-Rite, Regensdorf, Switzerland) color reference in a standard position
 in each image. Images in RAW format were processed using SpotEgg software
 (Gómez and Liñán-Cembrano 2017) to normalize and linearize them. We then
 used Adobe Photoshop CS6 v13.0 (Adobe Systems Incorporated, San Jose, CA)
 to quantify the color descriptors of the areas of interest. Color
 descriptors from digital pictures are a reliable and efficient method to
 assess nestling mouth coloration (Saino et al. 2003; Dugas and McGraw
 2011; Dugas and Dillow 2013), allowing us to obtain average color
 estimates of whole areas with minimal manipulation time, thus reducing
 interference on study subjects. However, this method does not capture
 ultraviolet reflectance. In pied flycatcher nestlings, the orange palates
 are bordered by white-yellowish fleshy flanges. To quantify nestling mouth
 coloration, we recorded the saturation value of the nestling's palate
 as an indicator of carotenoid pigmentation (Saino et al. 2003; Dugas and
 McGraw 2011) and the lightness (total reflectance) of the inner and outer
 parts of the mouth flanges as an indicator of conspicuousness (Kilner and
 Davies 1998; Wiebe and Slagsvold 2009b). To assess the repeatability of
 color variables, we measured a subset of 24 individuals twice. The results
 showed high repeatability for palate saturation (r = 0.69, F<sub>
 22, 25</sub> = 5.41, P &lt; 0.001), inner flange lightness (r =
 0.79, F<sub> 22, 25</sub> = 8.44, P &lt; 0.001), and outer
 flange lightness (r = 0.89, F<sub> 22, 25</sub> = 16.97, P
 &lt; 0.001). Statistical analyses
 We used linear mixed effects
 models with restricted maximum likelihood to perform statistical analyses
 (Zuur et al. 2009). We employed the “nlme” package (Pinheiro et al. 2020)
 in R 4.0.2 (R Development Core Team 2020) to fit the models (See Table S1
 for a description of all saturated models). To improve the stability of
 the models, likelihood of model convergence, and accuracy of parameter
 estimates (Harrison et al. 2018), we Z-transformed (mean centered with SD
 of 1) all numerical independent variables. Standardized beta coefficients
 were used to estimate standardized effect size for comparisons between
 fixed effects (Schielzeth 2010). In all models, we included original brood
 size at day 7 and hatching date (1 = June 8th) as covariates among fixed
 effects because the number of nestmates and differences in resource
 availability and parental condition as the season progresses could affect
 adult and nestling behavior. Validation of model assumptions were
 performed by inspecting residual plots generated by the “performance”
 package (Lüdecke et al. 2021) for linear mixed models and the “DHARMa”
 package for generalized mixed models (Hartig 2022). Predicted model values were plotted using the
 package “ggeffects” (Lüdecke 2018).
 To confirm that the treatment
 had the intended effect on nestling mass gain, we compared differences in
 nestling mass between the vitamin E and control groups on days 7 and 9,
 while controlling for initial mass at day 5 as a covariate. To analyze the
 effect of treatment on nestling flange conspicuousness, we conducted a
 principal component analysis (PCA) using the lightness values of the inner
 and outer areas of the flange with the prcomp() function from the “stats”
 package. We used individual scores on the first principal component (PC1),
 which explained 63.2% of the total variance, as a measure of overall
 lightness, as they were highly correlated with lightness values of both
 the inner and outer parts of the flange (PC1 loading value =
 0.707). To investigate the effect of vitamin E
 supplementation on nestling behavior, we built separate models for various
 dependent variables, including the probability of nestling begging (gaping
 or not), begging order, postural intensity, profitability of a nestling
 spatial location, inter-feeding intervals, and the rate of change between
 nest sectors. We included the sex of the feeding adult entering the nest
 box as a fixed effect (Wetzel et al. 2020), since nestlings may behave
 differently according to the parent's sex. To control for intra-brood
 differences in nestling body mass that could reflect differences in
 relative size or age, we used the mass difference of each nestling
 relative to the average of its brood rather than its absolute body mass.
 The probability of nestling begging was analyzed using a generalized
 linear mixed model with a binomial error distribution. Although begging
 order and postural intensity were measured as ordinal variables, they were
 treated as numeric in linear mixed models. We verified that conclusions
 did not differ from those from an ordinal regression (Christensen
 2022). Spatial profitability was computed as the
 fraction of total feedings given by each parent to the sector occupied by
 each nestling, relative to the parent's location in a given visit.
 Profitability values were transformed as logits prior to analysis. We also
 calculated the rate of spatial change between nest sectors for each
 nestling by dividing the number of changes between sectors by the number
 of feeding visits. To test whether nestling mobility was related to hunger
 levels, we computed the mean time of food deprivation for each chick and
 the average rate of change between sectors per brood by dividing the mean
 number of position changes by nestlings for the entire brood by the number
 of feeding visits (McRae et al. 1993). We included the number of caring
 parents and the degree of overlap between parent sexes in the use of nest
 sectors as fixed effects in this model to investigate whether the
 predictability of parental feeding position influenced nestling movement
 (Kölliker and Richner 2004). We calculated the degree of overlap between
 parent sexes in the use of nest sectors as the difference between the
 number of shared nest sectors multiplied by the number of feedings given
 from these sectors minus the number of feedings given from non-overlapping
 sectors, divided by the total number of feeding events. This resulted in
 an index ranging from 0 (no overlap) to 1 (maximum overlap). We assumed
 that uniparental broods had a maximum (1) overlap. 
 We analyzed parental feeding
 preferences using generalized linear mixed models with a binomial error
 distribution and a binary response variable (1 = fed, 0 = not fed). Since
 allocation patterns may vary by parental sex (Kilner 2002, Ryser et al.
 2016), we conducted separate analyses for females and males. To avoid
 convergence issues caused by a high number of predictors, we proceeded in
 two steps. First, we built models with all nestling traits, including
 relative mass, begging behavior (order and postural intensity), coloration
 (flange lightness and palate saturation), and spatial profitability,
 interacting with treatment and hatching date. Second, we built a second
 group of models considering only significant predictors from the first
 step (begging behavior, position, and treatment), including all pairwise
 interactions among them as well as with relative body mass (Table S1). To
 equalize the predictive potential and precision of spatial profitability
 with the other predictors, we transformed its raw values into 4-level
 ranks using the rank() function from the “base”
 package. We analyzed sex
 differences in the feeding rate of parents and the duration of each
 feeding visit to investigate whether differences between females and males
 in their degree of familiarity with nestlings could explain sex
 differences in allocation rules (Gottlander 1987; Lucass et al. 2016) (see
 Table S1). We explored the prey-testing behavior of parents
 in three steps. First, we looked for differences between visits where
 testing occurred and those where it did not. We compared the total number
 of begging nestlings and the intra-brood coefficient of variation in time
 of food deprivation, begging order, postural intensity and spatial
 profitability between visits where testing occurred and those where it did
 not, including the occurrence of testing (yes or no) as a categorical
 predictor (Table S1). Second, in a given visit, we compared the
 behavior of nestlings that were either tested, fed or neither fed nor
 tested. We built separate
 models for nestling behavior, relative body mass, and time of food
 deprivation as response variables. We also included treatment and a new
 three-level categorical predictor (“Testing”) depending on whether a
 nestling had been tested, fed, or neither fed nor tested in a given visit
 (Table S1). Third, we investigated whether the probability of a nestling
 being fed after having been tested varied according to its relative body
 mass, treatment, and time of food deprivation.
 We determined the optimal
 random- (intercepts and slopes) and fixed-effect structure for each model
 by comparing nested models with likelihood ratio tests, using a top-down
 strategy (Zuur et al. 2009) (Table S1). To find out the optimal random
 structure, we started with the most complex fixed and random structure
 (Barr et al. 2013) (or no fixed effects in the case of generalized linear
 mixed models), and progressively removed random effects one by one,
 comparing pairs of models with the anova() function from the “stats”
 package. Next, to determine the optimal fixed structure, we began with the
 most complex fixed-effect structure and the optimal random structure
 selected in the previous step. We then progressively removed fixed effects
 with lower β coefficients one by one, beginning with the interactions, and
 compared both models by likelihood ratio tests using the anova()
 function. 
description: R 4.0.2 (R Development Core
 Team 2020) software was used.