DOI: 10.5061/dryad.dncjsxm1s
title: Provisioning latency increases closer to roads and is associated with species-specific reproductive success in two urban adapters

description: Most research on urban avian ecology has focused on population- and
 community-level phenomena, whereas fewer studies have examined how
 urbanization affects individual behavioral responses to a sudden and novel
 stimulus, and how those translate to fitness. We measured
 between-individual variation in provisioning latency in two urban adapters
 - great tits and blue tits - in response to an infrared camera installed
 in the nestbox, encountered when offspring in the nest were at the peak of
 food demand (9–10-days old). For each nestbox, we quantified urbanization
 as intensity in human activity, distance to road and proportion of
 impervious surface area. In both species, provisioning latency increased
 significantly closer to roads. Moreover, increased provisioning latency
 when exposed to a novel object was associated with higher reproductive
 success in great tits whose nestboxes were surrounded by high amounts of
 impervious surface. In contrast, increased provisioning latency was
 consistently associated with lower reproductive success in blue tits. Our
 results suggest that provisioning latency changes in relation to the
 environment surrounding the nest, and may be context- and species-specific
 when exposed to a novel stimulus, such as a novel object in the nest. To
 better understand the role of initial behavioral responses towards novelty
 across an individual's lifetime and, ultimately, its impact on
 fitness in the urban mosaic, further research explicitly testing different
 behavioral responses across the entire breeding cycle in wild model
 systems is needed.
description: <strong> 2.2 Breeding data</strong>
 In both years, nestboxes were inspected at weekly intervals from
 mid-March until the end of June to determine first-egg date and clutch
 size. Hatching date (recorded as day 1) was determined by checking the
 nestboxes one day before the expected hatching (c. 12 days after the last
 egg of the clutch was laid), and every two days after that, until hatching
 occurred. When nestlings were between 9 and 10 days of age,
 infrared-cameras were installed inside the nestboxes as part of a project
 investigating avian diet across the urban mosaic (Corsini et al.,
 <em>in prep</em>). Parents were caught, measured and ringed
 when nestlings were ca. 11-13 days old, and aged on plumage
 characteristics as first-year breeders (<em>First year</em>)
 or older individuals (<em>Older</em>). At day 15, all
 nestlings were measured and ringed. Ca. 25 days after hatching, nestboxes
 were checked to record the number of nestlings that died before fledging,
 and to calculate fledging success as the proportion of eggs producing
 fledglings. <strong>2.3 Measuring individual
 neophobia </strong> Neophobia was measured as an
 individual’s latency time to first entrance (FE) following its first
 approach (FA) of the nestbox after installing the infrared camera (see
 Figure 2). To account for the influence of disrupting factors in the
 nestbox surroundings, we also recorded the number of times an individual
 flew off before its first entrance (<em>Number of
 flights</em>). The infrared-cameras (model SONY HK100427) were
 installed in the morning (between 6:26 and 9:33 AM) when nestlings were 9
 or 10 days old. The time (in minutes) between each bird’s first approach
 (FA, Figure 2, Figure S2) towards the camera (i.e., standing or flying
 next to the nestbox entrance, facing the camera) and its first entrance
 (FE, Figure 2, Figure S2), was used as a proxy of neophobia for comparison
 with previous studies (see <sup>37,50</sup>). Because of
 clear plumage differences between male and female great tit, we could
 record first entrances separately for each parent. As this was not
 feasible for blue tits – infrared cameras recorded black and white videos
 – we noted latency only for the first bird entering the nestbox. Total
 number of videorecorded nestboxes by species, study site and year is
 reported in Table S1. <strong>2.4 Characterizing
 urbanization </strong> We characterized the
 amount of urbanization surrounding each nestbox by measuring human or
 canine presence within 15m, distance to the closest road and percent area
 covered by impervious surface (i.e., % ISA). Briefly, human presence was
 obtained from repeated surveys on the ground aimed at detecting all humans
 and dogs within a 15m radius around each nestbox during 30 seconds long
 counts. This value was then standardized by dividing the total amount of
 humans and dogs detected by the number of counts performed within each
 study site (detailed in <sup>48</sup>). The distance to
 the closest road (i.e., for vehicular use) was recorded in meters using
 the <em>distance matrix </em>tool in QGIS (see methods in
 <sup>49</sup>). Percentage of ISA
 was derived from opensource remote sensing imagery data (Copernicus Land
 Monitoring, https://land.copernicus.eu/sitemap); specifically, we used the “imperviousness” layer - which included all built up areas and was quantified using a 20m-pixel resolution raster file - averaged in a 100m radius around each nestbox in QGIS using the <em>Zonal statistics</em> tool, as detailed in <sup>51</sup>. A Principal Component Analysis on these three proxies of urbanization after their standardization, revealed that PC1 explained 60% of the variance. PC1 comprehended the variables <em>Distance to Road</em> and ISA as main contributors (Figure S3a and b). <strong> 2.5 Weather data </strong> Weather data over the two-year period were provided by the Polish Institute of Meteorology and Water Management (IMGW – PIB). Daily temperature (°C) and rainfall (mm) were derived from two climatic stations: Warsaw Okecie and Legionowo (here used as references for study sites located within and outside the city, respectively). For each nest, we used the average-daily temperature and the rainfall on the date of recording. <strong>2.6 Statistical analyses</strong> All statistics were computed in R (v. 3.6.2, <sup>52</sup>), R-packages and functions are detailed below. Figures and plots were generated through the R-package “<em>ggplot2</em>” (v.3.2.1 <sup>53</sup>) and the opensource software <em>Inkscape </em>(v. 1.0.2 54). In the full dataset, two great tit nests (4 birds) and one blue tit nest (1 bird) were identified by ring numbers as second broods, thus, excluded from all the analyses, as birds were already exposed to the camera from the first breeding attempt that occurred in the same year. As in great tits, the second bird of the pair was often influenced by the partner while approaching the camera (i.e., the second bird entered the nestbox faster after the first one did, MC and PL personal obs., Figure S4), the behavioral dataset of both species was restricted to birds that accessed the nestbox first. Statistical analyses were performed as follows: 1)     We used the Test-Retest approach to infer whether neophobia was consistent within individuals measured in two distinct breeding years. Out of all birds measured, 9 banded great tits were caught in 2018 and 2019 (N = 18 observations). 2)     We used a series of One-Way ANOVAs to test whether neophobia (in min) differed between species, age-classes and sexes. We subsequently tested species-specific differences in terms of their exposure to human presence, distance to roads and % of ISA in the nestbox surroundings. We further tested whether neophobia was associated with the number of times a bird flew away from the nestbox before the first entrance. 3)     To test whether neophobia is mediated by urbanization, we built two distinct Linear Mixed – Effects Models (LMMs, “<em>lmer</em>” function in the R-package “<em>lme4</em>”, v. 1.1-21) where both species were analyzed jointly, and were characterised by the same model structure except for the proxy of urbanization, which was fitted as explanatory variable as either <em>Distance to Road</em> or <em>ISA</em>. In each model, <em>Neophobia </em>was log-transformed to meet models’ assumption and fitted as the response variable, while the variables: <em>Distance to Road</em> (or <em>ISA</em>), <em>Human presence</em>, rainfall in mm (<em>Rain</em>), <em>Temperature </em>and <em>Date of Recording</em> (1st of April coded as 1), were fitted as continuous predictors, and the variables <em>Species </em>and <em>Year </em>as two-levels factors. Site was set as random effect to control for non-independence of nestboxes sampled within the same study area. Because the variables Temperature and <em>Date of recording</em> were positively correlated (r<sub>Pearson</sub> (113) = 0.67, p &lt; 0.001), <em>Temperature </em>was removed from the global models. We used the <em>vif </em>function in the R-package “<em>car</em>” (v. 3.0-11) 55  to check possible multicollinearity issues between continuous predictors. Despite the fact that <em>Variance Inflation Factors </em>(<em>VIF</em>) were below 2 for <em>Distance to Road</em> and <em>ISA</em>, we detected a strong negative correlation between these two terms (r = -0.6; see Pearson’s correlation reported in Table S2). Consequently, these two proxies of urbanization were always analyzed separately; in contrast, because <em>Human presence </em>was only weakly correlated to <em>ISA </em>and <em>Distance to Road </em>(r = 0.4 and r = -0.2, respectively, see Figure S3a and Table S2), it was fitted as continuous predictor in each global model. Models comparing the separate effects of <em>Distance to Road</em> and <em>ISA </em>on neophobia were also used because roads may have a major impact on wildlife (e.g., via habitat loss, but also due to the increased levels of sound, light and chemical pollution associated to them, which may extend far away from their location 56). Thus, analyses and results relative to each proxy are reported and discussed in parallel. We performed backward model selection in each global model (<em>via </em>the <em>step </em>function in the R-package <em>lmerTest </em>v. 3.1-3 <sup>57</sup>). We used the <em>DHARM<sub>A</sub> </em>package to check the global models fit and assumptions <sup>58 </sup>(global model diagnostics shown in Figure S5). 4)     To test whether neophobia in parents was associated with reproductive success and whether this relationship was mediated by urbanization (here, again, separately tested as <em>Distance to Road</em> or <em>ISA</em>), analyses were run for the two species separately, to account for species-specific differences in life-history. We ran Generalized Linear Mixed-Effects Models (GLMMs, using the <em>glmer </em>function in <em>lme4</em>), where the number of fledglings weighed by clutch size (i.e., here referred to as <em>Fledging Success</em>) was fitted as response variable using a binomial distribution. In great tits, one nest was excluded from the analyses (due to missing information on clutch size, N = 64). The interaction-term between neophobia and the proxy of urbanization (as <em>ISA </em>or <em>Distance to Road</em>, as explained in point 2), along with the continuous variables <em>Date of Recording</em>, <em>Rain </em>and <em>Year</em>, were fitted as predictors in each model. The factor <em>Site </em>was fitted as random effect to control for non-independence of avian fitness in nests sampled within the same study site. With the only exception of the great tit global model testing whether neophobia and fledging success were mediated by <em>Distance to Roa</em>d (where the inter group site-variance was zero, thereby necessitating the removal of random effects in the model), all 3 remaining global models included Site as random factor (Table 1, Table S7). We checked for multicollinearity issues: VIF- scores were calculated in all models and never exceeded 2. Each global model was used to generate a set of models with all possible combinations of the predictors (R-package MuMIn v.1.43.15, see <sup>59</sup>). The best models were graded according to the Akaike's information criterion 60 (AIC<sub>c</sub>). Model-averaged coefficients for a subset of models (∆AIC<sub>c</sub> &lt; 2) were extracted. As some Akaike weights of best models were below 0.9, and therefore high model selection uncertainty existed, full-model averaging was used 61. We calculated upper and lower bounds of the 95% confidence intervals (CI) for each parameter. We applied Z-score scaling to each continuous predictor for clarity of parameters estimates.
description: The file entitled
 "<strong>Repeatability_data</strong>" contains data
 used to test repeatability in provisioning latency. (Statistical analyses,
 point 1) The file entitled
 "<strong>Data_Provisioning</strong>" contains all
 data used for the analyses described in 2.6 (Statistical analyses, points
 2,3 and 4). The file README contains detailed info on
 each variable reported in the datasets mentioned above.
 Missing data are recorded as "NA".