Camera trap records of urban wild mammal activity and habitat use in Freiburg, Germany
Data files
Feb 07, 2026 version files 1.23 MB
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NocturnalNeigborsData.xlsx
1.22 MB
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README.md
5.29 KB
Abstract
Wildlife is increasingly exposed to human-dominated environments, yet detailed data on species activity patterns and urban habitat use remain limited. This dataset documents camera-trap detections of ten wild mammal species - badger (Meles meles), foxes (Vulpes vulpes), rat (Rattus spp.), wild boar (Sus scrofa), and marten (Martes spp.), rabbit (Oryctolagus cuniculus), squirrel (Sciurus vulgaris), roe deer (Capreolus capreolus), hare (Lepus europaeus), and hedgehog (Erinaceus europaeus) - collected across urban and peri-urban sites in the city of Freiburg, Germany. Records were obtained from 24 camera-trap locations and include one row per independent capture event. For each detection, the dataset provides species identity, number of individuals, spatial identifiers (camera location ID and file path), and temporal information, including date, time, timestamp, season, and day–night classification. To characterize activity patterns, the dataset includes binary and categorical indicators of nocturnal versus diurnal activity as well as continuous circular time variables. Urban habitat context is quantified per ID by the proportion of impervious surface surrounding each camera location, serving as a proxy for urbanization intensity. Additional variables summarize sampling effort and detection frequency by camera and season. The dataset enables analyses of diel activity patterns, species-specific urban habitat preferences, and trait-based comparisons across mammal species in urban landscapes. It can be reused for studies on urban wildlife ecology, temporal niche partitioning, human–wildlife interactions, and spatial responses of mammals to urbanization gradients. All data were collected using non-invasive camera trapping and contain no personal or sensitive information. The dataset presents minimal ethical risk and complies with relevant wildlife monitoring and data protection guidelines.
Dataset DOI: 10.5061/dryad.73n5tb3bz
Description of the data and file structure
The data were collected through a systematic camera-trap survey conducted at 24 urban and peri-urban sites in the city of Freiburg, Germany. Camera traps were deployed to monitor the occurrence and activity patterns of medium-sized terrestrial mammals across a gradient of urbanization. Cameras operated continuously and recorded date- and time-stamped images of animal detections. Each independent capture event was identified and annotated with species identity, number of individuals, and temporal information. Spatial context was derived for each camera location to quantify surrounding impervious surface as a measure of urban intensity. The survey was designed to capture variation in diel activity and habitat use of mammal species in urban environments.
Files and variables
File: NocturnalNeigborsData.xlsx
Description:
This file contains camera-trap detection records of wild mammals collected at urban and peri-urban sites in Freiburg, Germany. Each row represents one independent capture event and includes species identification, number of individuals, spatial identifiers, temporal information, activity classification, urbanization context, and derived sampling and frequency variables.
Variables
ID
Unique identifier of the camera-trap location.
Label_ID
Numeric label of the camera location.
RelativePath
Relative directory path of the image file within the project folder.
RootFolder
Root directory of the camera-trap project.
File
Image file name associated with the detection.
Species
Primary species identified in the image.
Species2
Secondary species identified in the image, if present; “Empty” indicates no second species detected.
Individuals1
Number of individuals of the primary species detected in the image.
Individuals2
Number of individuals of the secondary species detected in the image.
DateTime
Original date and time of image capture as recorded by the camera (format: YYYY.MM.DD HH:MM:SS).
DatetimeCaptured
Standardized date and time of image capture.
Timestamp
Date and time used for temporal analyses (format: YYYY.MM.DD HH:MM:SS).
Season
Season of the year corresponding to the capture event.
Year
Calendar year of capture.
SeasonYear
Combined season and year identifier.
Time
Time of day of image capture (HH:MM:SS).
Seconds
Time of capture expressed as seconds since midnight.
IsNighttime
Binary indicator of nocturnal detection (“WAHR” = night, “FALSCH” = day).
Month
Month of capture (numeric).
Date
Calendar date of capture (DD.MM.YYYY).
day_night
Categorical classification of detection as day (“Tag”) or night (“Nacht”).
TimeOfDay
Continuous proportion of the day corresponding to capture time.
CircularTime
Time of day expressed as circular value (radians or scaled 0–2π) for circular statistical analyses.
avg_impervious
Mean proportion of impervious surface surrounding the camera location; proxy for urbanization intensity (unit: proportion, 0–1).
ID_freq
Number of detections recorded at the camera location.
MaxDays
Maximum number of sampling days for the camera location.
Adjusted_Frequency
Detection frequency standardized by sampling effort.
Code/software
The dataset is provided as a Microsoft Excel file (NocturnalNeighborsData.xlsx) and can be viewed using any software supporting the Open XML spreadsheet format, including Microsoft Excel (version 2016 or later), LibreOffice Calc, or Apache OpenOffice.
All data processing and statistical analyses were conducted using R (version 4). Mixed-effects models can be fitted using the lme4 package, with significance testing based on Satterthwaite’s approximation implemented in lmerTest. Multicollinearity diagnostics were performed using the car package. Activity pattern analyses and visualizations were conducted using the overlap, camTrap, and suncalc packages. Spatial analyses of urbanization were carried out using the sf and terra packages. Data manipulation and preparation were performed using dplyr, tidyr, and lubridate.
The workflow consisted of (1) importing camera-trap detection data into R, (2) cleaning and standardizing species and temporal variables, (3) deriving activity metrics including day/night classification, circular time, and weighted nocturnality indices corrected for sampling effort, (4) extracting impervious surface values within 500-m buffers around camera locations and computing weighted mean imperviousness per species.
No proprietary software is required to view, process, or analyze the data.
Access information
The dataset is openly available through the associated data repository and can be accessed without restriction. All files may be downloaded directly from the repository website. No registration or special permissions are required to access or reuse the data. The dataset is released under an open license as specified in the repository record.