The nightlife of city birds: artificial light at night affects daily activity patterns of European
MATERIAL AND METHODS (a) Study populations
The study populations were located in the city of Munich, south-east Germany (48º 07´
N, 11º 34´ E; 518 m ASL) and in a rural forest near the village of Raisting (47º 53´ N, 11º 04´ E, 553 m ASL), 40 km south-west of Munich. Birds in the city were sampled in two different habitat types: i) “City park”: large urban parks (25-30 ha) with high tree and bush cover outside of the city centre and ii) “Business district”: medium-small green areas (1-4 ha) inside the old town, with little tree cover and larger density of people. The rural population inhabited a temperate mixed deciduous forest, with alder (Alnus spp.) and spruces (Picea spp.) as dominant species.
(b) Field recordings of activity patterns and light at night exposure
Between February and June of the year 2009, 2010 and 2011, we captured adult male European blackbirds by mist-nets at dawn (sample sizes: rural forest = 11, city park = 12, business district = 17). Each bird was colour-ringed for identification and equipped with a backpack attached with harnesses to its back. The backpack was composed of two electronic devices: a light logger and a radio-transmitter. Light loggers (Sigma Delta Tech., Australia, in year 2009 and 2010; weight = 2.8 g; custom made loggers by University of Konstanz, Germany, in year 2011; weight = 2.8 g) were individually calibrated during morning twilight against a photometer (Data-logger = LI-1400, pyranometer PY 56820, LI-COR, USA), to calculate irradiance (watt/m2) from frequency values. Loggers recorded and stored light intensity every two minutes. We used the data between 10 pm and 4 am (wintertime) to calculate median light irradiances for each night. Radio-transmitters (Sparrow Systems, USA, in 2009 and 2010, weight = 1.8 g;
Holohil Systems, Canada, in 2011, weight = 1.8 g) were used in combination with automated recording units (ARUs, Sparrow Systems, USA) to infer activity state. The ARU was placed close to the territory of a bird and connected to an H-antenna (Sparrow Systems, USA). The unit was programmed to scan every minute for the corresponding frequency of each bird and to record the signal strength of the radio-transmitter pulse. We used the change in signal strength over time to detect switches in activity status (Bisson et al. 2009). In particular, we applied the behavioural change point analysis, a novel method for identifying behavioural changes in animal movement data (Gurarie, Andrews,
& Laidre 2009), to detect the shift between active and non-active states. Our approach assumed that the signal strength data originates from two distributions, one for active and
one for non-active state. Because we were interested in the onset and end of daily activity, we used the signal strength data in a window of two hours before and after sunrise or sunset. The values in this temporal window were then divided in two different subsets for all possible changes in activity status between the first and last 20 values in this window. We fitted a Poisson distribution to both halves of the data (R package MASS (Venables & Ripley 2002)) and an AIC value was calculated for each time point.
The specific time that produced the lowest AIC value was identified as onset/offset of activity. We then calculated the duration of nocturnal inactivity as the difference in minutes between the time of activity offset in the evening and the time of activity onset in the following morning. We chose this parameter instead of the duration of daily activity because it can be directly related to light at night exposure. We corrected all these variables by relating them to information about local civil twilight obtained from the US Naval Observatory (http://www.usno.navy.mil/USNO), to account for seasonal variation in daylength.
(c) Noise
We conducted noise measurements between April and June 2010 in each of the urban sites. We recorded three times on weekdays (Monday to Friday) and three times on days during the weekend (Saturday and Sunday). A sound level meter (PCE-353, PCE Instruments UK Ltd., UK) was placed at a height of ~ 3 meters at random spots within each urban location. The recordings were made every three minutes using Leq correction and type-A frequency weighting. From these recordings we calculated the average noise
twilight and named it daytime noise. Night-time noise was calculated as the average noise level between evening and morning twilight. We used these measurements to test two hypotheses on potential effects of noise on activity timing. First, birds are more active at night to avoid acoustic masking of daytime noise. To test this hypothesis we calculated the difference between daytime and night-time noise and name it ∆ noise. For this hypothesis to be true, indeed, daytime noise has to be higher than the night-time noise, therefore a relative and not absolute noise value is needed. Second, we used night-time noise to test the hypothesis that noise during the night might disturb the sleep of birds and therefore might induce early awakening (Rabat et al. 2004; Muzet 2007).
(d) Weather variables
Weather conditions were obtained from the German Weather Service (Web Werdis, Deutscher Wetterdienst). We used data from two weather stations: one located within the city of Munich (48° 17´ N, 11° 54´ E, 515 m ASL) and one in the village of Wielenbach (47° 88´ N, 11° 16´ E, 550 m ASL), 8 km from the sampled rural population. We included temperature (°C), cloud cover (ocsa) and precipitation (ml) at the hour when morning twilight started or evening twilight ended on each day of activity recording for individual birds. To model the duration of night inactivity we used the mean temperature, cloud cover and precipitation between 00:00 h and 1:00 h, because data were not available for the whole night.
(e) Data analyses
Statistical analyses were conducted with software R 2.15.0 (R Development Core Team.
R: A language and environment for Statistical Computing 2011). All tests were two-tailed and we applied a significance level α = 0.05. All explanatory variables were centred and standardized to facilitate interpretation of the estimates. A preliminary analysis with linear mixed models (LMMs) revealed no significant within-site effect of year on variation in start and end of daily activity, or duration of nocturnal inactivity
(Pstart = 0.82, Pend = 0.42, Pduration = 0.27), so we pooled the data for the three years
together.
We tested differences in light at night and noise between sites using LMMs from the R package lme4 (Bates & Sarkar 2007). Light, daytime or night-time were included as dependent variables in different univariate models, whereas site and date were included as fixed effects. We modelled random intercept dependent on individual to correct for repeated measures. Pair-wise tests with Tukey correction were computed to assess the significance of the difference between pairs of sites with the R package multcomp (Hothorn, Bretz, & Westfall 2008). We used the same type of univariate models to test for differences in light at night, ∆ noise and night-time noise (dependent variables) between working and weekend days (categorical fixed effect), using the dataset for urban birds only. We corrected for potential seasonal trends by including date as fixed effect.
Variation in daily timing of activity was investigated by univariate LMMs. Since variance was highly heterogeneous between rural and urban birds, we ran models separately for the two populations. The independent variable was either onset of morning
were corrected based on the seasonal change in daylength. Site, date, day of the week, log-transformed median light intensity at night, precipitation, cloud cover and temperature were included as fixed effects. We first ran models with two-way interactions between site and all other variables. Interactions were then sequentially excluded if not significant. We retained all main fixed factors in the final model, even if non-significant, because we were interested in the partial effect of all these factors, that is, the effect of each factor after having corrected for all the other factors. We assessed the significance of model parameters using a Monte Carlo Marcov Chain (MCMC) approach through the function pvals.fnc in the R package languageR (Baayen 2007). We simulated values of the posterior distribution of model parameters (50000 iterations) and calculated the mean and the 95 % confidence intervals (CI) of each distribution. The effect was considered significant if zero was not included in the 95 % CI. P-values were calculated automatically by the function based on this assumption and named “pMCMC”.
Normality of error in all models was checked by visual inspection of Q-Q plots and homogeneity of variance was checked by plotting standardized residuals against fitted values. Between and within-individual variance was extracted from the fitted models and differences between sites were tested by means of F tests. The same models were used to estimate repeatability of activity traits. We defined repeatability as the proportion of phenotypic variance explained by the individual (Falconer & Mackay 1996). We used the R package rptr (Nakagawa & Schielzeth 2010) which allows the calculation of repeatability using REML estimation in a mixed model design.
RESULTS
(a) Exposure to light at night and noise
Exposure to light at night in birds in either city parks or business district was significantly higher than exposure in forest birds (Tukey’s test after LMM, rural-city parks: z = 3.23, P
= 0.004; rural-business district: z = 5.41, P < 0.001). A tendency was detected for the birds in the business district to be exposed to higher amounts of light intensity at night than birds in the city parks, but this was not significant (z = 2.07, P = 0.09). Forest birds always experienced the lowest light intensity detectable by each light logger (median = 0.000031 w/m², range = 0.000023 to 0.000045 w/m², Fig. 2a). In the urban population, exposure to light at night was highly variable between individuals (median = 0.000082 w/m², range = 0.000025 to 0.007 w/m², Fig 2a), but within-individual variation of urban birds was very low (median of individual variances = 2.3*e-9,range = 1*e-10 to 5.42*e-5).
Light intensity at night in the urban sites did not differ between working and weekend days (LMM, t = -0.20, P = 0.84; Fig. 3b). Day and night-time noise levels were significantly higher in the business district than in the city parks (Tukey’s test after LMM, day-time noise: z = 26.24, P < 0.001; night-time noise: z = 34.34, P < 0.001). Day-time noise was significantly lower during weekends than on week days (Tukey’s test after LMM, z = 34.34, P < 0.001), whereas night-time noise was higher on weekends than on weekdays (Tukey’s test after LMM, z = 33.47, P < 0.001). Finally, ∆ noise was lower on weekends than on weekdays (LMM, t = -70.12, P < 0.001; Fig. 3c).
(b) Onset of activity
Onset of activity was significantly earlier in both urban sites compared to rural individuals (Tukey’s test after LMM, rural-city parks: z = -6.26, P < 0.001; rural-business district: z = -5.49, P < 0.001), but no difference was detected between urban sites (z = 1.20, P = 0.45; Fig. 1a). In the urban population, light intensity at night was negatively correlated to the onset of morning activity: the higher the amount of light at night, the earlier the birds tended to start their activity (LMM, 95 % CI: -11.04, -2.70; pMCMC = 0.002; Fig. 2b). Temperature was related to time of onset morning activity by a linear (95
% CI: -11.67, -2.70; pMCMC = 0.008) and quadratic function (95 % CI: 0.15, 6.36;
pMCMC = 0.054). Date was related to time of onset of morning activity by a linear (95
% CI: 11.27, 33.75; pMCMC = 0.002) and quadratic function (95 % CI: 8.83, 28.54;
pMCMC = 0.004). That is, onset of morning activity was clearly advanced between mid-March and mid-April, while before and after this period birds tended to start their daily activity closer to morning civil twilight. Day of the week (95 % CI: -4.46, 8.42, pMCMC
= 0.59; Fig. 3a), precipitation (95 % CI: -3.47, 2.67; pMCMC = 0.71) and cloud cover (95 % CI: -4.95, 2.13; pMCMC = 0.46) were not significantly correlated to the time of activity onset. In the rural population no parameter (light at night, precipitation, temperature, cloud cover, date and day of the week) significantly explained onset of morning activity (Table 1). Instead, onset of morning activity was highly correlated to the onset of civil twilight (Pearson correlation, r = 0.74, P < 0.001).
Fig. 1. Variation in activity traits and exposure to light at night in adult male European blackbirds recorded at three study sites differing in the degree of urbanization. Onset of activity (a) and end of activity (b) are reported as minutes relative to onset of morning civil twilight and end of evening civil twilight. Negative numbers refer to time before civil twilight, positive numbers refer to time after civil twilight. Duration of inactivity at night (c) was calculated as the time between the end of activity in the evening and the start of activity in the following morning, and is reported as minutes relative to the length of the night. (d) Exposure to light at night was recorded by deploying light loggers on individual blackbirds. The median light intensity of every night was calculated and then log-transformed and centered. Box plots represent, from bottom to top: one standard deviation (s.d.) below the mean, lower quartile, median, upper quartile and one s.d. above the mean. Sample sizes: rural forest = 11, city park = 12, business district = 17.
Table 1. Variation in onset and end of daily activity and duration of nocturnal inactivity in the field.
Response variables were standardized, respectively, on onset of morning twilight, end of evening twilight and duration of night, to correct for the seasonal change in daylength. Reference for urban sites is city parks, for weekday is weekend. A parameter was considered to have a significant effect when the CI did not overlap zero. Significant effects are highlighted in bold.
Rural
Onset of activity End of activity Duration of night inactivity
Onset of activity End of activity Duration of night inactivity
(c) End of activity
End of activity was significantly different between birds in the business district and in the rural forest (Tukey’s test after LMM, z = -3.85, P < 0.001), but no difference was detected between the city park and the other two locations (rural-city parks: z = 1.99, P <
0.11; city parks-business district: z = -1.85, P = 0.15; Fig. 1b). In the urban population we detected a positive effect of light at night intensity on time of end of activity: the higher the light intensity at night, the later the birds ended their evening activity (LMM, 95 % CI: 0.73, 7.41; pMCMC = 0.018; Table 1). In the rural population we found a negative seasonal trend in the timing of end of activity (95 % CI: -11.90, -5.60; pMCMC < 0.001).
That is, birds advanced their offset of activity (relative to the end of evening twilight) later in the breeding season. Statistical results can be found in Table 1.
(d) Duration of nocturnal inactivity
The overall resting period at night was significantly different between rural individuals and birds in either city parks or business district (Tukey’s test after LMM, rural-city parks: z = -3.84, P < 0.001; rural-business district: z = -5.80, P < 0.001), but no difference was detected between the two urban sites (z = 1.88, P = 0.14; Fig. 1c). In the urban population, light at night was highly negatively correlated to the resting period at night:
the higher the light intensity at night, the shorter the period of nocturnal inactivity (LMM, 95 % CI: -20.07, -7.87; pMCMC = < 0.001). In addition, a quadratic effect of date was detected: the earlier in the breeding season, the shorter the duration of the resting period at night (95 % CI: 4.23, 36.04; pMCMC = 0.01). The same type of seasonal effect was
detected in the rural population, although the relationship was not quadratic, but linear (95 % CI: 3.47, 17.07; pMCMC = 0.004). Statistical results can be found in Table 1.
Fig. 2. Relationship between light at night and onset of activity. Onset of morning activity was measured via radio telemetry (see fig. 1) and is reported as minutes relative to onset of morning civil twilight.
Negative numbers refer to time before civil twilight, positive numbers refer to time after civil twilight.
Symbols (grey squares = rural; white circles = city parks; black triangles = business district) represent the mean for each individual. Error bars indicate SEM. Black line indicates a significant relationship between activity and light at night, but only for urban birds merged together. No significant relationship between the two variables was found for the rural birds. For sample sizes see Fig. 1.
(e) Between and within-individual variation in daily timing.
Between-individual variation in onset of morning activity was lower in rural areas compared to both urban sites (F test: rural-city parks: F = 9.9, P < 0.001; rural-business district: F = 9.4, P < 0.001), but there was no significant difference between urban sites (F = 1.0, P = 0.77). As far as end of activity, between-individual variation was higher in
the business district than in the city parks or in the rural forest (rural-business district: F = 2.3, P < 0.001; city parks-business district: F = 0.5, P < 0.001), while birds in the city parks and in the rural forest showed no significance difference (city parks-rural forest: F
= 1.1, P < 0.681). Between-individual variation in duration of resting period at night was lower in rural areas compared to both urban sites (rural-city parks: F = 2.6, P < 0.001;
rural-business district: F = 3.3, P < 0.001), but there was no difference between urban sites (F = 0.8, P = 0.168). Within-individual variation in onset of morning activity was lower in rural areas compared to both urban sites (F test: rural-city parks: F = 0.01, P <
0.001; rural-business district: F = 0.01, P < 0.001), but there was no significant difference between urban sites (F = 1.0, P = 0.98). Within-individual variation in end of evening activity was also lower in the rural forest than in both urban sites (F test: rural-city parks:
F = 0.04, P < 0.001; rural-business district: F = 0.1, P < 0.001), which did not significantly differ between each other while birds in the city parks and in the rural forest showed no significant difference (city parks-business district: F = 2.1, P < 0.189).
Finally, we found no significant difference in the duration of the resting period at night between all sites (rural-city parks: F = 0.8, P < 0.690; rural-business district: F = 0.8, P <
0.679; city parks-business district: F = 1.0, P < 0.998). Repeatability of all activity traits did not show any consistent pattern across sites or traits. All statistical results of variances and repeatabilities can be found in Table 2.
Table 2. Variability and repeatability of activity traits across study sites. Between and within-individual variation was obtained from the outputs of the models in Table 1. The same models were used to calculate repeatability of activity traits as the proportion of the phenotypic variance explained by the individual. For each repeatability estimate we calculated the standard error (SE) and the p-value.
Onset of activity
Site Between-ind. σ2 Within-ind. σ2 Repeatability
R SE P-value
Site Between-ind. σ2 Within-ind. σ2 Repeatability
R SE P-value
Site Between-ind. σ2 Within-ind. σ2 Repeatability
R SE P-value
rural 383.38 644.49 0.30 0.10 P < 0.001
city park 985.31 1408.52 0.38 0.10 P < 0.001
business district 1383.83 1706.60 0.35 0.09 P < 0.001
DISCUSSION
One caveat of previous studies which investigated the effect of light pollution on animals is the lack of precise information about what intensity of light animals are exposed to in the night. Our study is novel because we deployed for the first time light-loggers on individual free-ranging birds in rural and urban habitats. Our main purpose was to analyze the relationship between light at night and activity patterns, and to distinguish the effect of light to that of other environmental variables such as weather conditions and
urban European blackbirds, such as birds exposed to higher light intensities at night were active earlier in the morning and ceased their activity later in the evening. We could not gathered daily recordings of noise for each individual bird, however two additional results support the hypothesis that light at night is a major driver of daily timing in our study compared to noise. First, we found strong differences in noise levels between weekdays and weekends. If noise had an effect on daily timing we would have expected to see a difference in activity patterns between days of the week. On contrary, activity and light at night did not vary between weekdays and weekends. Second, we found differences in noise exposure between city parks and the business district, but both light at night and time of activity onset did not vary between the two urban sites. Therefore, all together we conclude that light at night might play a more important role than noise and weather in regulating timing of morning activity in blackbirds thriving in urban areas.
The time of onset of morning activity has been extensively studied by avian behavioural ecologists, as it is thought to be under selection by females (Catchpole & Slater 1995;
Poesel et al. 2006; Kempenaers et al. 2010). However, very little is known about the
Poesel et al. 2006; Kempenaers et al. 2010). However, very little is known about the