DOI: 10.5061/dryad.v41ns1rtz
title: Repeatable differences in exploratory behaviour predict tick infestation probability in wild great tits

description: Abstract Ecological factors and individual-specific traits affect parasite
 infestation in wild animals. Ixodid ticks are important ectoparasites of
 various vertebrate hosts, which include passerine bird species such as the
 great tit (Parus major). We studied various key ecological variables
 (breeding density, human disturbance) and phenotypic traits (exploratory
 behaviour, body condition) proposed to predict tick infestation
 probability and burden in great tits. Our study spanned 3 years and 12
 nest box plots located in southern Germany. Breeding, adult great tits
 were assessed for exploration behaviour, body condition, and tick burden.
 Plots were open to human recreation; human disturbance was quantified in
 each plot as a recreation pressure index from biweekly nest box
 inspections. Infested individuals were repeatable in tick burden across
 years. These repeatable among-individual differences in tick burden were
 not attributable to exploration behaviour. However, faster explorers did
 have a higher infestation probability. Furthermore, body condition was
 negatively correlated to tick burden. Recreation pressure was correlated
 to increased infestation probability, although this relationship was just
 above the threshold of statistical significance. Our study implies that
 avian infestation probability and tick burden are each determined by
 distinct phenotypic traits and ecological factors. Our findings highlight
 the importance of animal behaviour and human disturbance in understanding
 variation in tick burden among avian hosts. Significance statement Various
 abiotic and biotic factors, including personality type, influence tick
 parasitism in birds, but exactly how all these factors interplay remains
 unclear. We studied a wild population of great tits over a 3-year period
 and assessed birds for their exploration behaviour and tick infestation.
 We found that more explorative great tits were more likely to be infested
 with ticks. By contrast, faster explorers did not have higher tick
 burdens. Tick burden was nevertheless moderately repeatable among
 individuals. Our results imply that animal personality influences the
 probability of parasite infestation, and that infestation likelihood
 versus intensity are determined by distinct mechanisms. Our work
 highlights the importance of animal behaviour to understand parasite
 infestation in wild populations.
description: <strong>Material and
 Methods​​​​​​​</strong> <b>Study sites and data
 collection</b> Our study was performed in 12 nest box
 plots within a 10×15 km<sup>2</sup> area in southern Munich
 (47° 57'N, 11° 21'E) established in autumn 2009. Each plot was
 fitted with 50 nest boxes in a regular grid covering approximately 9
 hectares. Breeding parameters of great tits (detailed below) were
 monitored for three years (2017-2019) within all plots except one plot
 that was monitored only in 2017. Each nest box was inspected biweekly
 during the breeding season (April-July) for nesting activity. During plot
 inspections, the number of recreationists was counted as a measure of
 human disturbance (detailed by Hutfluss & Dingemanse,
 2019). When hatchlings were 10-12 days old,
 both parents were captured in the nest box using a spring trap. Birds were
 given a unique, numbered band if not previously banded. They were then
 tested for their exploration behaviour (detailed below), weighed, measured
 morphologically (Moiron et al. 2019), aged (based on plumage
 characteristic as first year breeder or older; Dingemanse et al. 2020),
 and screened for ticks (protocol detailed below). Most birds were captured
 in May and a few in June. These captures were used to calculate breeding
 density, defined as the number of breeding pairs producing first clutches
 per hectare. 
 <b>Exploration
 behaviour</b> Exploration
 behaviour was measured using a novel environment test adapted to the field
 using established protocols (see Stuber et al. 2013). Birds were
 transferred to a holding box attached to a cage (61L × 39W × 40 H cm)
 fitted with a mesh front and three perches, representing the novel
 environment. The holding box was covered with a cloth bag and the subject
 allowed to acclimatize for one minute. After acclimatization, the focal
 bird was released into the novel environment without handling and recorded
 for two minutes with the field observer located out of sight. Videos were
 subsequently scored by dividing the cage into six equal sections and three
 floor sections as described in Stuber et al. (2013). The exploration score
 was calculated as the sum of movements between all sections within the
 first two minutes of entering the novel environment (Abbey-Lee and
 Dingemanse 2019; Dingemanse et al. 2020). 
 <b>Tick burden and
 collection</b> Ticks tend to
 concentrate around the eyes and beak of great tits (Heylen and Matthysen
 2008; Fracasso et al. 2019). A regional patch examination protocol
 (reviewed in Lydecker et al. 2019) was developed to standardize the
 screening: We screened 1) around the eyes/ears and along the margin of the
 beak on both sides of the bird, 2) underneath the beak, 3) along the top
 margin of the beak, and 4) on the top of the head. We subsequently
 calculated the total number of ticks carried by each captured bird. In
 2018-2019, all captured birds were screened; in 2017 only a sample. In
 2018 and 2019, a sample of ticks were collected using fine tweezers and
 stored in 99% ethanol as part of another study. Those were subsequently
 morphologically identified to life-stage and species according to
 published taxonomic keys (Filippova 1977; Hillyard 1996; Estrada-Peña et
 al. 2014). 
 <b>Recreation
 pressure index</b> 
 During
 each plot inspection, all observers recorded each recreationist seen and
 their specific location (detailed by Hutfluss and Dingemanse 2019). Each
 recreationist was connected to the first observation to avoid double
 counts. The probability to observe a recreationist during a plot
 inspection is biased by various factors (Hutfluss and Dingemanse 2019). To
 obtain an unbiased index of recreation pressure, the binary probability to
 observe recreationists was calculated using all inspections conducted from
 2010-2019 (n = 3724 inspections). This probability was calculated using a
 binomial generalized linear mixed effects model (GLMM) fitting fixed
 effects for plot inspection duration, the number of observers, and
 starting time (in hours from sunrise) (Hutfluss and Dingemanse 2019). We
 fitted random intercepts for each unique combination of plot and year
 (termed plot-year, see Abbey-Lee et al. 2016; Araya-Ajoy et al. 2016;
 Araya-Ajoy and Dingemanse 2017) to acquire an average value of recreation
 pressure for each plot in each year, and for date of observation to
 control for date-specific environmental effects (Supplemental Table 1). We
 extracted best linear unbiased predictors (BLUPs) for each plot-year
 between 2017 and 2019 (n = 34) and used them in subsequent models as a
 recreation pressure index. The usage of BLUPs has been criticised when
 associated uncertainty is not taken forward; some have proposed to
 estimate a posterior distribution of possible BLUP values and thereby take
 forward uncertainty in subsequent analyses (Hadfield et al. 2010; Houslay
 and Wilson 2017). However, taking forward uncertainty in BLUP-values can
 result in biased estimates, whereas utilizing average BLUPs as fixed
 effects result in less precise but unbiased estimates (Dingemanse et al.
 2020). Therefore, we here present the estimated effect of the average BLUP
 values. 
 <b>Data
 Preparation</b> The scaled mass
 index (Peig and Green 2009) was used as our measure of body condition.
 Following Peig and Green (2009), this index was calculated separately for
 each sex according as follows: <i>1</i><i>     SM</i><sub><i>i</i></sub><i>=</i><i>M</i><sub><i>i</i></sub><i>×</i><i>L</i><sub><i>0</i></sub><i>L</i><sub><i>i</i></sub><sup><i>b</i><sub><i>SMA</i></sub></sup> where, <i>M<sub>i</sub></i> and <i>L<sub>i</sub> </i>are the mass and tarsus measurements of individual <i>i </i>respectively; <i>L<sub>0</sub> </i>is the arithmetic mean of all tarsus lengths of either all females or all males (dependent on the sex of individual <i>i</i>); and <i>b<sub>SMA</sub> </i>is the scaling exponent calculated as: <i>2</i><i>    b</i><sub><i>SMA</i></sub><i>=</i><i>b</i><sub><i>OLS</i></sub><i>R</i> where <i>b<sub>OLS</sub> </i>is the slope of linear regression of log-transformed body mass as a function of log-transformed tarsus length of either all females or all males (dependent on the sex of individual <i>i</i>) and <i>R </i>is the Pearson’s correlation coefficient of the regression. We used all birds recorded from 2010-2019 (n = 4273 records) to calculate the scaling coefficient (including any repeated measures). We calculated individual-mean exploration scores using a linear mixed effects model (LMM) fitting exploration behaviour as the response variable (Supplemental Table 2). We fitted test sequence as a fixed effect covariate to control for sequence effects ( Dingemanse et al. 2012, 2020), and included random intercepts for individual and plot-year. The model included data from all years (2010-2019) (n = 4251) and assumed a Gaussian error distribution. We extracted an average BLUP-value per individual, which we used in subsequent analyses as a measure of an individual’s average (experience-corrected) exploration score. <b>Statistical Analysis</b> Tick infestation of great tits was analysed in two parts using GLMMs. First, infestation probability was estimated using all recorded captures from 2017 through 2019 (n = 784 records). Second, tick burden (i.e. the absolute number of ticks observed) was modelled using only infested individuals (n = 520 records). For both response variables, the model set up was the same. Fixed effects were fitted for recreation pressure, breeding density, body condition, sex, age, and individual-mean exploration score. Sex and age were fitted because females (versus males) and older (versus younger) birds often have higher tick burdens (Heylen and Matthysen 2008; Heylen et al. 2013). Random intercepts were fitted for year, plot, plot-year, field observer, nest box, and individual. Prior to analysis, all fixed effects were mean-centred, and variance standardized, such that the statistical intercepts of our models reflected the value for the average individual in the average environmental condition. All statistical analyses were performed in R (version 3.5.3) (R Core Team 2019). Models were run using the functions <i>lmer</i> and <i>glmer</i> from the package <i>lme4</i> (Bates et al. 2015) assuming either binomial (infestation probability) or Poisson (tick burden) error distributions. Mean estimates and their 95% credible intervals (CI) were estimated based on 5,000 simulations using the <i>sim </i>function from the <i>arm</i> package (Gelman and Su 2016). Residual errors were calculated according to Nakagawa & Schielzeth, (2010). Adjusted repeatability values were calculated as the proportion of variance unexplained by the fixed effects that was explained by the focal random effect. Estimates with 95% CIs not overlapping zero were treated as statistically significant in the frequentist’s sense. Models were checked graphically for fit (Supplemental Fig. 1 & 2).
description: R-script for processing the data has been included to prepare all
 data (as described in Data Preparation) and run models (as described in
 Statistical Analysis).