Risk response towards roads is consistent across multiple species in a temperate forest ecosystem
Cite this dataset
Bastianelli, Matteo Luca et al. (2024). Risk response towards roads is consistent across multiple species in a temperate forest ecosystem [Dataset]. Dryad. https://doi.org/10.5061/dryad.3xsj3txp9
Abstract
Roads can have diverse impacts on wildlife species, and while some species may adapt effectively, others may not. Studying multiple species’ responses to the same infrastructure in a given area can help understand this variation and reveal the effects of disturbance on the ecology of wildlife communities. This study investigates the behavioural responses of four species with distinctive ecological and behavioural traits to roads in the protected Bohemian Forest Ecosystem in Central Europe: European roe deer (Capreolus capreolus), a solitary herbivore; red deer (Cervus elaphus) a gregarious herbivore; wild boar (Sus scrofa), a gregarious omnivore and Eurasian lynx (Lynx lynx), a solitary large carnivore. We used GPS data gathered from each species to study movement behaviour and habitat selection in relation to roads using an integrated step selection analysis. For all species and sexes, we predicted increased movement rates in response to roads, selection of vegetation cover near roads and open areas after road crossings, and increased road avoidance during the day. We found remarkably similar behavioural responses towards roads across species. The behavioural adaptations to road exposure, such as increased movement rates and selection for vegetation cover, were analogous to responses to natural predation risk. Roads were more strongly avoided during daytime, when traffic volume was high. Road crossings were more frequent at twilight and at night within open areas offering food resources. Gregarious animals exposed to roads favoured stronger road avoidance over faster movements. Ungulates crossed roads more at twilight, coinciding with commuter traffic during winter. Despite differences in the ecology and behaviour of the four species, our results showed similar adaptations towards a common threat. These insights can be used by managers to promote safer road crossings where roads interfere with animals' natural behaviour. The continuous expansion of the global transportation network should be accompanied by efforts to understand and minimise the impact of roads on wildlife to assist wildlife management and ensure conservation.
README: Risk response towards roads is consistent across multiple species in a temperate forest ecosystem
https://doi.org/10.5061/dryad.3xsj3txp9
Datasets contain the calculated animals' steps (i.e. trajectory between two consecutive GPS locations) derived from roe deer, red deer, wild boar and lynx GPS data. These datasets were used to perform integrated step selection analysis to investigate the movement behaviour and habitat selection of each species in relation to roads.
Each species is divided into females and males except for lynx data, which are divided into fine (one GPS fix per hour) and coarse (two GPS fixes per day) scale datasets.
The meaning of each column in the dataset can be found in the related article and the README file.
Description of the data and file structure
All datasets are provided as R objects (.rds) that can be directly loaded in R (see R scripts). Each dataset contains the following columns:
- id (numeric) -> Animal ID (identifier of each animal)
- id_step_id (character) -> Identifier of each used-available strata (ID of the step associated with each Animal ID)
- case_ (logical; true/false) -> Whether a step is used or available (True = used, False = available)
- y (numeric; 0/1) -> Same as case_ but used for modelling (case_ was used for descriptive plots)
- sex (factor; f/m) -> Sex of the animal (f = females, m = males)
- sl (numeric; meters) -> Step length, the distance between two consecutive GPS fixes
- log_sl (numeric) -> Logarithm of the step length, used for modelling
- ta_ (numeric; radiants) -> Turning angle, the angle between two consecutive steps.
- cos_ta (numeric) -> Cosine of the turning angle
- t1_ (POSIXct; timestamp) -> Start time, the time at which the GPS fix of the start location was taken
- t2_ (POSIXct; timestamp) -> End time, the time at which the GPS fix of the end location was taken
- tod_suncalc_end (factor; day, night, twilight) -> Time of Day calculated with the R package 'suncalc' at the end of each step
- night_suncalc_start & night_suncalc_end (numeric; 0/1) -> Dummy variable of time of day for the start and end of a step used in the model (0 = day, 1 = night)
- twilight_suncalc_start & twilight_suncalc_end (numeric; 0/1) -> Dummy variable of time of day for the start and end of a step used in the model (0 = day, 1 = twilight)
- season_start & season_end (numeric; 0/1) -> Season at the start and end of the step (0 = winter, 1 = summer)
- log_dist_road_start & log_dist_road_end (numeric; scaled and centred) -> logarithm of the distance to the nearest road from the start and end of a step in meters
- cover_end (numeric; scaled and centred) -> Canopy cover (LiDAR data), the proportion of high-stand vegetation density > 2.0 meters above the ground
- understory_end (numeric; scaled and centred) -> Understorey (LiDAR data), the proportion of forest undergrowth density < 2.0 meters above the ground
- ruggedness_end (numeric; scaled and centred) -> Terrain ruggedness index calculated from a 25-meter resolution digital elevation model.
Code/Software
All analyses were performed using R (version 4.1.2), with the recurrent use of the follwing packages:
- dplyr (version 1.0.7)
- glmmTMB (version 1.1.2.3)
- amt (version 0.1.6)
- ggplot2 (version 3.4.2)
Each R script has been numbered from 1 to 6 to clarify the workflow. The data provided are the basis to reproduce models, tables and figures presented in the manuscript (main text and supporting information), in details:
- 1_data_analysis_collinearity.R -> R code to test collinearity between the variables used in the model as in Supporting information 3 (Figure S3)
- 2_data_analysis_statistic.R -> R code to visualize animals' steps statistics as in Supporting information 4 (Table S1; Figure S4; Figure S5; Figure S6; Figure S7; Figure S8)
- 3_data_analysis_modelling.R -> R code to create and save models and visualize results as in Table 2 and Table S2
- 4_display_displacement.R -> R code to calculate animals' displacement and reproduce Figure 3
- 5_display_coefplot_and_logrss.R -> R code to reproduce Figure 4 and 5
- 6_display_interindividual_variability.R -> R code to explore interindividual variability in the responses to roads as in Supporting information 5 (Figure S9-S12; Table S3)
Methods
The locational data were collected from GPS-collared roe deer, red deer, wild boar and lynx in the Bohemian Forest Ecosystem (Germany and Czech Republic). The data were processed as described in detail in the Materials and Methods section of the article "Risk response towards roads is consistent across multiple species in a temperate forest ecosystem".
Funding
German Federal Ministry of Transport and Digital Infrastructure (BMVI), Award: 19F2014B
Deutsche Wildtier Stiftung
fct/mct
EU Program Interreg IV 44
Bavarian State Ministry of the Environment and Consumer Protection and the Bavarian Health and Food Safety