DOI: 10.5061/dryad.t76hdr8dz
title: Testing the sentinel method: live and artificial prey display contrasting patterns of predation across an urban gradient

description: Assessing changes in the intensity of biotic interactions across
 environmental gradients is a central issue in ecology. The sentinel method
 has been widely adopted to study predator-prey interactions by
 establishing patches of prey under different conditions that predators can
 attack. Sentinels, proxies for prey, are frequently worm-shaped prey
 resembling caterpillars and are specifically used to assess predation by
 arthropod-feeding predators, with predation measured as the rate of
 disappearance or evidence of predation after a certain period of exposure.
 While it has been suggested that artificial sentinel prey might produce
 divergent results to live prey, previous studies showed mixed results in
 the difference between these two prey types. Results are likely to vary
 with context, and the assessment of different prey types along urban
 gradients is still lacking. Here, we performed an experiment at ten sites
 across a natural-to-urban gradient in Suzhou (East China) combining live
 prey and artificial prey to determine differences in predation intensity
 between these prey types. We released 2,575 artificial prey and 3,825 live
 prey, either separately (artificial or live prey alone) or combined, in a
 randomized sequence. We found a positive relationship between our index of
 predation and the level of urbanization using both types of prey. However,
 predation rate using artificial prey was lower than with live prey and
 showed a different pattern with urbanization. The predation rate using
 live prey was higher for avian predators and lower for insect predators
 with increasing urbanization rate. Our results suggest that artificial and
 live prey produce strongly divergent estimates of predation intensity.
 Thus, while artificial prey might be used as a rapid-screening tool, live
 prey should be favored in comprehensive studies to assess this fundamental
 ecosystem service.
description: # Testing the sentinel method: live and artificial prey display
 contrasting patterns of predation across an urban gradient Dataset DOI:
 [10.5061/dryad.t76hdr8dz](https://doi.org/10.5061/dryad.t76hdr8dz) ##
 Description of the data and file structure This dataset contains the raw
 observational data from a field experiment designed to compare predation
 intensity using two methods—live prey and artificial plasticine
 prey—across a natural-to-urban gradient in Suzhou, Jiangsu Province, East
 China. The study tested the hypothesis that artificial sentinel prey
 (plasticine caterpillars) produce divergent estimates of predation
 intensity and patterns compared to live prey. Data was collected at ten
 primary sites, with some sites containing multiple sub-location
 replicates, resulting in a total of 16 location IDs. Predation events were
 identified and attributed to either avian or insect predators based on
 distinct attack marks (e.g., beak pecks vs. mandible marks). ####
 **Methodology Summary** * **Sites:** Ten sites were selected along a
 gradient from natural/wetland to urban park in Suzhou. * **Prey:** Two
 prey types were used: (1) **Live prey** (mealworms), and (2) **Artificial
 prey** (plasticine models shaped like caterpillars). * **Design:** Prey
 were deployed in a randomized sequence, both separately and in
 combination, on vegetation. * **Census:** After retrieval, each prey item
 was examined. A predation event was recorded if clear attack marks were
 present. Marks were classified as from **avian predators** or
 **invertebrate/insect predators**. * **Urbanization Metric:** The
 `UrbanizationRate` was derived from remote sensing data (e.g., the
 proportion of impervious surface within a buffer around the site) ###
 Files and variables #### File: Yu_et_al._2025._Ecol_Evol.csv
 **Description:** This dataset contains the raw observational data from a
 field experiment designed to compare predation intensity using two
 methods—live prey and artificial plasticine prey—across a natural-to-urban
 gradient in Suzhou, Jiangsu Province, East China. Variables | Column Name
 | Data Type | Description | | :--------------- | :---------------- |
 :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Site | String | Unique identifier for the study location (e.g., DushuhuPark, YangjiaVillage). Sites with numbers (e.g., DushuhuPark1) denote sub-location replicates within a primary site. | | UrbanizationRate | Numeric (Decimal) | A continuous index quantifying the level of urbanization based on impervious surface area for the location. Higher values indicate more urbanized environments. | | DayN | Integer | The experimental day or trial sequence number (1 to 4) within the location. | | PredationRate | Numeric (Decimal) | The proportion of prey attacked. Calculated as P / Total. Range: 0 to 1. | | PreyType | String | Type of prey deployed: Live prey or Plasticine prey. | | PredatorType | String | Broad category of predator based on attack marks: Avian predators or Insect predators. | | P | Integer | Count of prey items that were attacked/predated upon (successes). | | N | Integer | Count of prey items that were not attacked (failures). | | Total | Integer | Total number of prey items deployed in that experimental unit (P + N). | ## Code/software All the analyses were carried out in R v 4.2.3 (R Core Team, 2023), using the packages lme4 (Douglas *et al*., 2015), glmmTMB (Mollie *et al*., 2017), MuMin v 1.47.5 (Bartoń, 2023), car v 3.1.2 (Fox & Weisberg, 2019), DHARMa v 0.4.6 (Hartig, 2022) and the figures were produced using ggplot2 (Wickham, 2015). Bartoń, K. (2023). MuMIn: Multi-Model Inference (R package version 1.47.5). [https://CRAN.R-project.org/package=MuMIn](https://CRAN.R-project.org/package=MuMIn) Douglas, B., Martin, M., Ben, B., & Steve, W. (2015). Fitting linear mixed-effects models using lme4. *Journal of Statistical Software*, 67(1), 1–48. [https://doi.org/10.18637/jss.v067.i01](https://doi.org/10.18637/jss.v067.i01) Fox, J., & Weisberg, S. (2019). *An R Companion to Applied Regression* (3rd ed.). Sage Publications. [https://socialsciences.mcmaster.ca/jfox/Books/Companion/](https://socialsciences.mcmaster.ca/jfox/Books/Companion/) Hartig, F. (2022). *DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models* (R package version 0.4.6). [https://CRAN.R-project.org/package=DHARMa](https://CRAN.R-project.org/package=DHARMa) Mollie, E. B., Kasper, K., Koen, J. van B., Arni, M., Casper, W. B., Anders, N., Hans, J. S., Martin, M., & Benjamin, M. B. (2017). glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. *The R Journal*, 9(2), 378–400. [https://doi.org/10.32614/RJ-2017-066](https://doi.org/10.32614/RJ-2017-066) Wickham, H. (2016). *ggplot2: Elegant Graphics for Data Analysis*. Springer-Verlag New York. ## Access information Other publicly accessible locations of the data: * N/A Data was derived from the following sources: * N/A