Experimental evidence for large carnivore risk cues reducing deer browsing intensity in a temperate forest
Data files
Nov 24, 2025 version files 627.54 KB
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Behavioural_data.csv
195.05 KB
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Browsing_data.csv
429.03 KB
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README.md
3.46 KB
Abstract
Intensive ungulate browsing represents a major challenge for managing temperate forests, and only a clear understanding of the underlying ecological processes can help to mitigate its impact. Predator-prey interactions play a crucial role in shaping browsing patterns; however, the lack of a comprehensive understanding of these dynamics hinders the development of functional policies. Using an experimental approach, we simulated large-carnivore presence using olfactory cues to examine the effects of predation risk on deer behaviour and its consequences for browsing intensity on tree saplings. We conducted this experiment in eleven locations in the Bavarian Forest National Park (Germany), each comprising four plots with olfactory cues of wolf, lynx, cow, and water (control). In each plot, we planted 30 saplings representing the five most common tree species in the area, which we regularly monitored to assess browsing intensity and selectivity. In addition, we set a camera trap at each plot to record deer species (red and roe deer), age, sex, and behavioural metrics like time spent vigilant, visitation duration, and visitation frequency. This experimental design allowed comparisons of the effects of different large carnivore on prey species while measuring browsing intensity. Our results demonstrated that deer modified their behaviour in response to large carnivore scent cues compared to non-risky cues, with more pronounced effects in lynx treatments compared to controls. Red and roe deer spent significantly less time in lynx treatments compared to controls, especially at dawn and dusk. In spring at dusk, we observed similar decreases in visitation duration in wolf treatments. Notably, our findings also show increased vigilance time of adult deer accompanied by juveniles at sites with lynx cues during winter nights. As a consequence of these behavioural changes, we recorded a significant reduction in browsing intensity in lynx treatments compared to controls. However, no signs of tree species selectivity were found in plots with large carnivores scent cues nor differences in visitation frequency. Synthesis and applications: By analysing multiple behavioural responses and considering relationships between different trophic guilds, we provide unique insights into the mechanisms of predator-prey interactions and their indirect impact on forest ecosystems. In particular, our findings highlight the role of large carnivores in mitigating ungulate browsing damages, which is important for managing temperate forests (both natural and commercial) where herbivore browsing is a significant pressure.
https://doi.org/10.5061/dryad.b2rbnzsrd
Description of the data and file structure
Data from: Experimental evidence for large carnivore risk cues reducing deer browsing intensity in a temperate forest
The repository contains the dataframes used to run the models explained in the Manuscript, all the information on the data collection can also be found in the Manuscript. In the Supporting Information of the Manuscript are available further details of the models.
The repository contains the following two dataframes: "Browsing_data" with all the data needed to run the Browsing intensity and the Browsing selectivity models, and "Behavioural_data" with all the data needed to run the Time Spent Vigilant model, Visitation Duration model and Visitation Frequency model.
Files and variables
File: Behavioural_data.csv
Description: dataframe needed to run the Time Spent Vigilant model, Visitation Duration model and Visitation Frequency model
Variables
- Plot_Nr: plot identifier
- Round: round when the Event occurred
- Block: area of the plot
- Season: Season the Event occurred
- Treatment: olfactory cues applied to the plot (wolf - Lynx - cow - water)
- Scent: the scent applied at the time of the Event (uring - Urine/scat)
- Prop_red_roe: Proportion of red deer over roe deer visiting the plot
- ID_SP: Event ID
- species_latin: Latin name of the species in the plot
- species_common: common name of the species in the plot
- Sex: sex of the animal in the plot
- Age: age of the animal in the plot
- Date: date of the animal in the plot
- Time: time of the animal in the plot
- Date_time: date and time of the animal in the plot
- X: x coordinate of the plot
- Y: y coordinate of the plot
- period: period of the day (Day - Dusk - Dawn - Night)
- Nr_Individuals_max: number of individuals in the plot
- Vigilant: time (s) spent vigilant by the animal
- Apprehension: time spent vigilant by the animal while chewing
- Freq_head_raise: number of times an individual raises their head
- Visitation_duration: duration of the time the animal spent in the plot
- Visitation_freq: number of times individuals visited the plot
File: Browsing_data.csv
Description: dataframe needed to run the Browsing intensity and the Browsing selectivity models
Variables
- Plot_Nr: plot identifier
- Round: round when the Event occurred
- Date: date of Control
- Date_planting: date saplings were planted in the plot
- Days_since_planting: number of days since saplings Plantation at the time of the control
- Treatment: olfactory cues applied to the plot (wolf - Lynx - cow - water)
- Row: row in which the sapling was planted (total of 5 rows per plot)
- Tree_Nr: individual number of the tree sapling in each plot
- Tree_Species: species of the tree sapling
- total_browsed: total number of apical and side shoots browsed
- total_available: total number of apical and side shoots available
- total_proportion: Proportion of the browsed shoots over the total available shoots
- Scent: the scent applied at the time of the Browsing Event
- Block: area of the plot
- Month: the month when the Browsing was checked
- Season: the season when the Browsing was checked
- Prop_red_roe: Proportion of red deer over roe deer visiting the plot