DOI: 10.5061/dryad.63xsj3vbq
title: Noise pollution as a major disturbance of avian predation in Amsterdam

description: Trophic interactions play a key role in maintaining ecological balance. In
 urban environments, avian predation has been demonstrated to be
 particularly important due to its effects on community structure, pest
 control, and nutrient cycling. As humanity relies upon ecosystem services
 for sustenance, and with 70% of the global population projected to reside
 in urban areas by 2050, understanding the impact of urbanization on avian
 predation is becoming increasingly important. This study investigates the
 impacts of urban microclimates, shaped by impervious surfaces and
 green/blue infrastructure, and human-induced disturbances, on avian
 predation in urban areas, with the aim of testing the increased
 disturbance hypothesis. To assess the avian predation rate, plasticine
 caterpillars were placed in Quercus robur trees in the city of Amsterdam
 for a period of two months. The analyses evaluated the impact of
 artificial lighting at night, human population density, the urban heat
 island effect, impervious surfaces, vegetation, noise pollution, and water
 bodies on predation rates. The results indicated a substantial increase in
 predation during the second month, which was likely caused by an increase
 in naïve fledglings or elevated ambient temperatures. Noise pollution was
 identified as the most frequent and robust predictor of predation,
 consistently leading to a reduction in predation rates, possibly due to
 avoidance behavior. Other predictors exhibited substantial temporal and
 spatial variability. The variables related to urbanization increased
 predation in the initial month, suggesting that insectivorous birds prey
 on areas with higher illumination and temperature. However, the effect
 diminished in the subsequent month, potentially due to the increased
 daylight hours or a reduction in heating effects. During the second month,
 all predictors exhibited a negative effect on predation, thereby
 supporting the increasing disturbance hypothesis. These findings
 underscore the complex relationship between urban factors and avian
 predation, emphasizing the necessity for mitigation efforts in urban
 planning.
description: <em>Study Site</em> The city of
 Amsterdam (The Netherlands) was selected as the study site for this
 experiment due to several factors. The city has a high human population
 density with 5.336 people per km<sup>2 </sup>(CBS 2023), a
 significant vegetation presence, numerous water canals, and a negligible
 elevation difference throughout the city that might affect trophic
 interactions (Dean et al. 2024). In consideration of the findings
 presented by Hernández-Agüero et al. (2020), which demonstrated predation
 differences between tree species, this study exclusively utilized
 <em>Quercus robur </em>trees. The trees were selected from a
 georeferenced list of all trees in Amsterdam. The selection criteria
 included species and a maximum height of twelve meters, which was
 necessary for us to reach the branches. A total of 2,882 <em>Q.
 robur</em> trees were identified as eligible from a list of 259,431
 trees in Amsterdam. The human population density, NDVI index and water
 percentage surrounding each tree were estimated, and the resulting ranges
 were divided into five categories. Only one tree per category of each was
 randomly selected. Ultimately, 38 trees were chosen based on variations in
 HPD, vegetation, and water presence (<em>Figure
 1</em>). <em>Avian Predation Data
 Collection</em> The field experiment involved the
 placement of artificial plasticine caterpillars (N = 114) of three
 distinct colors as a proxy for prey organisms in <em>Q. robur
 </em>trees (N = 38) throughout Amsterdam. The coloration of prey
 organisms can influence the detection and selection of prey by avian
 species, as these animals primarily detect prey through visual cues
 (Ruxton et al. 2018). By using three different colors in our experiment,
 we ensured sufficient variability in predation pressure among study sites,
 at least for one color, independent of the time interval between tree
 visits, as is common in similar studies (e.g. Alonso-Crespo &
 Hernández-Agüero 2023, Hernández-Agüero et al 2024b). This approach
 permitted the comparison of predation levels between trees, even in
 instances where predation for a particular color was either nearly absent
 or so high as to preclude the observation of differences in the number of
 attacks. All colors demonstrated sufficient variability among trees, and
 thus no color was excluded from the analysis. The
 models were placed in week 15 (2024), with two reviews occurring at
 four-week intervals. This was done to account for potential temporal
 variability, with avian predation rates being highest in the summer
 months, thereby strengthening the potential for seasonal variability
 (Hernández-Agüero et al. 2020). During the reviews, we identified attack
 marks at the coarse taxonomic level with the assistance of the
 standardization proposed by Low et al. (2014). First, the avian predation
 marks were recorded, and the caterpillars were molded back to their
 original shape. The methodology employed was similar to that used by
 Alonso-Crespo and Hernández-Agüero (2023) (see Appendix Figure A1), with
 the caterpillars attached to tree branches with metal wires (diameter 0.5
 mm). The average length of each caterpillar was approximately 30mm, with a
 diameter of 4mm. Given that invertebrate predation rates are higher when
 placed near the ground (Lövei and Ferrante 2017), and our objective was to
 observe avian predation, the caterpillars were placed at heights ranging
 between 1.5 and 2 meters. The plasticine caterpillar models were non-toxic
 and unscented, consisting of a mixture of waxes, inert substances, and
 colored pigments (STAEDTLER MARS GmbH & Co KG 2017). This material
 addresses the concerns raised by of Rößler et al. (2018) regarding the
 avoidance of polyvinyl chloride due to potential ingestion hazards. All
 models were molded exclusively by hand, resulting in slight differences
 between the models. However, this is the most reasonable method for
 creating caterpillar-like shapes with plasticine (Bateman et al.
 2017). <em>Spatial Analyses</em>
 The averages of the urbanization-related predictors, vegetation
 presence, and water presence were calculated through zonal statistics in
 QGIS version 3.34.3 (QGIS 2023). This was conducted for buffers of 200
 meters surrounding the trees where measurements were taken, in accordance
 with the methodology described by Valdés-Correcher et al. (2022). In
 addition to the 200-meter buffers, larger buffer zones of 400, 600, 800,
 and 1,000 meters were constructed in order to account for the dynamic
 nature of avian movement, and to examine the effects of these predictors
 across spatial scales. We requested satellite imagery
 from the Sentinel-II satellite through Copernicus, and the European Space
 Agency (ESA 2023) processed all satellite imagery. The MultiSpectral
 Instrument (MSI), comprising 13 spectral bands, and the high spatial
 resolution of Sentinel-II (10m/20m/60m) facilitate precise remote sensing
 analyses. The selection of imagery was based on several criteria to ensure
 its usability for reliable remote sensing. These criteria included the
 necessity for the imagery to originate from the same satellite, for the
 sensing periods to fall as close to the measurements as possible, and for
 cloud coverage to be limited to below 5%. These criteria adhere to the
 guidelines for remote sensing set forth by Lefsky and Cohen (2003) and
 Rembold et al. (2020). As this study incorporated
 remote sensing analyses for both vegetation and water infrastructures
 through the Normalized Difference Vegetation Index/NDVI (see
 <em>Appendix Equation A1</em>)  and the Normalized Difference
 Water Index/NDWI (see <em>Appendix Equation A2</em>), and
 given that it was necessary to isolate vegetation and water areas to
 prevent misguided averages in their respective analyses, a remote sensing
 analysis on impervious surfaces through the Normalized Difference Built-up
 Index/NDBI (see <em>Appendix Equation A3</em>) was also
 conducted. The isolation of water, impervious surfaces, and vegetation was
 achieved by identifying the predictors and reclassifying them.
 Subsequently, inversions of the predictors were employed to isolate the
 respective predictors. Moreover, we acquired population
 data for Amsterdam from WorldPop (2018) to assess HPD (people/hectare),
 with a resolution of 100 x 100 meters. For ALAN, we extracted the modelled
 light emissions at night in 2022
 (Watt/cm<sup>2</sup>/steradian) provided by the National
 Oceanic and Atmospheric Administration (NOAA) using the Visible Infrared
 Imager Radiometer Suite (VIIRS). This model was subsequently modified by
 the National Institute for Public Health and the Environment (RIVM in
 Dutch; RIVM 2023). We acquired noise data from RIVM (2020), which provides
 estimated noise pollution/Lden (level day-evening-night) in 2021 in dB.
 Anthropogenic noise pollution is defined as artificial noise originating
 from air traffic, industry, neighborhoods, rail traffic, and road traffic
 (Radford et al. 2012). The model encompassed all widely accepted primary
 sources of noise pollution, with the exception of noise from
 neighborhoods. <em>Data
 Analyses</em> We assessed the associations
 between standardized averages of the outlined predictors and the avian
 predation rates recorded with the plasticine caterpillars in R environment
 version 4.2.2 (R Core Team 2022), including packages ‘car’ (Fox and
 Weisberg 2019), ‘ggplot2’ (Wickham 2016), ‘Hmisc’ (Harrell 2024), ‘lme4’
 (Bates et al. 2015), ‘MuMIn’ (Barton 2021), and ‘visreg’ (Breheny and
 Burchett 2017). A correlation analysis was conducted between variables
 within the 600-meter buffer zone, as this is the buffer zone of
 intermediate size. An additional check for multicollinearity was conducted
 using the Variance Inflation Factor to confirm the efficacy of the
 multicollinearity correction (see <em>Appendix Table A2)</em>.
 As HPD, impervious surface, UHI effect, ALAN and NDVI exhibited high
 correlation coefficients (above ± 0.8; <em>Table A1 in
 Appendix</em>), and thus were not viable to be included in the model
 without violating the assumptions of the model, principal component
 analyses were used to group all those correlated variables with the
 ‘prcomp’ function. We evaluated the extent to which the predictors exerted
 a significant influence on avian predation by employing generalized linear
 mixed models (GLMM) with a Poisson distribution. In each model, we
 included the first two axes of the PCA analysis, along with noise, water
 proportion and caterpillar color (to account for color predation
 differences) as prediction variables. We selected the best fitting GLMMs
 using the corrected Akaike information criterion (AICc) with the ‘dredge’
 function. AICc corrects for small sample sizes and should be used instead
 of AIC when the ratio between the sample size and the number of parameters
 (including the intercept) falls below 40 (Burnham & Anderson,
 2002), which is the case in our study. Models with a difference in AICc ≤
 2 were selected as best fitting models and the other models were omitted.
 By using dredge from the ‘MuMIn’ package, we conducted a systematic
 analysis that runs all possible model configurations and ranks them based
 on AICc values and model complexity.
description: # Noise pollution as a major disturbance of avian predation in Amsterdam
 [https://doi.org/10.5061/dryad.63xsj3vbq](https://doi.org/10.5061/dryad.63xsj3vbq) ## Description of the data and file structure This repository contains resources for replicating the analysis and methodology described in the article **"Noise pollution as a major disturbance of avian predation in Amsterdam"** published in *Wildlife Biology* by Bas Krijnen and Juan Antonio Hernández-Agüero. #### Contents 1\. **`R_script_13_11.R`**: This R script contains the full analysis pipeline used in the study. It includes data preparation, statistical analysis, and visualization steps. Users can follow the script to reproduce all results presented in the article. 2\. **`Data_script_caterpillars.RData`**: This file contains all the datasets required to replicate the analysis. The data are pre-processed and formatted to be compatible with the `R_script_13_11.R`. \## Instructions 1\. Ensure you have R (version 4.2. or higher) installed on your system. You may also need to install additional R packages as indicated in the `analysis_script.R`. 2\. Download both the `R_script_13_11.R` and `Data_script_caterpillars.RData` files to the same directory. 3\. Open `R_script_13_11.R` in R or RStudio, and run the script step by step or entirely to reproduce the results. \## Citation If you use this repository for your research or teaching, please cite the original article: Krijnen, B., & Hernández-Agüero, J. A. (2024) **Noise pollution as a major disturbance of avian predation in Amsterdam.** *Wildlife Biology*. For questions or feedback, please contact the authors of the article in: [j.a.hernandezaguero@vu.nl](mailto:j.a.hernandezaguero@vu.nl) or [hernandezaguer@gmail.com](mailto:hernandezaguer@gmail.com)