Camera-Trap Monitoring Report for GMU8 LEUVEN, Belgium
Ecological insights from camera-trap data for wildlife monitoring
Martijn Bollen, Niko Boone, Jim Casaer, Peter Desmet, Sander Devisscher, Sanne Govaert, Lynn Pallemaerts, Anneleen Rutten, and Jan Vercammen
Research Institute for Nature and Forest (INBO)
September 07, 2026
1 Introduction
Terrestrial wildlife populations play a fundamental role in ecosystem structure and function. Across taxonomic groups, wildlife contributes to key ecological processes such as nutrient cycling, energy flow, seed dispersal, trophic interactions, and habitat modification, thereby supporting biodiversity and strengthening ecosystem resilience (Dirzo et al., 2014). However, these populations are increasingly exposed to anthropogenic pressures, especially habitat alteration and land-use change, which may affect their distribution and abundance across space and time (Newbold et al., 2015). Understanding and monitoring these responses is therefore crucial for conservation planning, effective protected-area management, and evidence-based decision-making (Coad et al., 2015).
To better understand and monitor these responses, camera trapping has become a cost-effective, reliable, and non-invasive method for monitoring wildlife populations. By automatically capturing images or videos when animals move through the detection zone of infrared sensors, camera traps generate robust information on species occurrence, habitat use, activity patterns, and relative abundance while minimizing observer-related disturbance. Consequently, camera-trap data have become an important source of biodiversity information and are widely applied in ecological research, biodiversity monitoring, and adaptive conservation management (Kays et al., 2009). These data are valuable to both scientists and practitioners, as they support ecological analyses, advance understanding of species ecology, and provide critical insights into population status and trends (O’Connell et al., 2011).
This report presents the results of camera-trap surveys conducted in GMU8 LEUVEN, Belgium. The aim is to estimate and monitor large mammals over time and across deployments, and to provide insight into wildlife status at this site, including behaviour and changes in spatial and temporal patterns.
2 Methods
2.1 Study Area
The study was conducted in GMU8 LEUVEN, located in Belgium. The site is geographically defined by the coordinates 50.769°–50.845°N and 4.634°–4.674°E and covers an area of approximately 12.54 km². The site supports a diverse range of wildlife, with approximately 41 species recorded. The most frequently observed species include Capreolus capreolus, Sus scrofa, Vulpes vulpes, Columba palumbus, and Sciurus vulgaris.
2.2 Sampling
The sampling design at this site is systematic random. In a systematic random design, camera locations are initially chosen at random but then arranged in a regular pattern, such as a grid, providing more even spatial coverage while retaining a random starting point. Cameras were mounted at approximately 0.5 m above the ground. Media were captured using time-lapse and activity detection. This project was not specifically designed to identify individual animals, but rather to support broader wildlife monitoring. More detailed information on camera-trap deployments by survey year is provided in the summarized information table below.
| 📷 Camera Deployment Summary | ||||
| Table 1: Details of camera deployments per year | ||||
| Year | Camera Traps | Deployment Period | Setup Period | Pickup Period |
|---|---|---|---|---|
| 2018 | 8 | 2018-03-26 - 2018-12-12 | 26 March - 12 September | December 12 - December 12 |
| 2019 | 8 | 2019-02-25 - 2019-11-08 | 25 February - 07 September | November 06 - November 08 |
| 2020 | 8 | 2020-01-02 - 2020-11-05 | 02 January - 06 June | November 03 - November 05 |
| 2021 | 8 | 2021-01-06 - 2022-01-26 | 06 January - 01 April | January 26 - January 26 |
| 2022 | 8 | 2022-01-26 - 2022-10-27 | 26 January - 28 June | October 26 - October 27 |
| 2023 | 8 | 2023-01-02 - 2024-01-26 | 02 January - 31 March | January 26 - January 26 |
2.3 Camera Locations
The maps below display the locations of camera traps deployed during different years. Use the tabs above to explore data for each sampling year. The last tab shows the study area with all camera locations in this site. Locations are color-coded by habitat type. Click on the points for additional deployment information.
2018
2019
2020
2021
2022
2023
Total
Figure.1: Interactive map of camera trap locations. Click on a marker to view the corresponding location details and additional metadata.
2.4 Sampling Efforts
Sampling effort refers to the total number of camera trap operational days, accounting for both the number of active cameras and their duration of deployment. It provides critical context for interpreting species detection rates and comparing data across years. A summary of yearly sampling effort is provided below, including camera breakdowns, average operational days, and total effort per year:
- 2018: 6 cameras broke down during the study period, the cameras operated for an average of 94 days (range: 33 - 261 days), and the total sampling effort was 753 days, equivalent to 2.06 years.
- 2019: 5 cameras broke down during the study period, the cameras operated for an average of 120 days (range: 26 - 241 days), and the total sampling effort was 962 days, equivalent to 2.64 years.
- 2020: 5 cameras broke down during the study period, the cameras operated for an average of 183 days (range: 33 - 217 days), and the total sampling effort was 1466 days, equivalent to 4.02 years.
- 2021: 7 cameras broke down during the study period, the cameras operated for an average of 232 days (range: 202 - 385 days), and the total sampling effort was 1853 days, equivalent to 5.08 years.
- 2022: 5 cameras broke down during the study period, the cameras operated for an average of 189 days (range: 31 - 214 days), and the total sampling effort was 1513 days, equivalent to 4.15 years.
- 2023: 7 cameras broke down during the study period, the cameras operated for an average of 235 days (range: 210 - 389 days), and the total sampling effort was 1879 days, equivalent to 5.15 years.
The following figure provides detailed information on sampling effort over time, and the slider can be used to focus on specific time periods.
Figure.2: Number of active camera traps per survey year. Adjust the slider to focus on specific time periods.
2.5 Image Processing
To support standardized, reproducible, and interoperable workflows, camera-trap data are increasingly managed using dedicated platforms such as Agouti (Casaer et al., 2019) and structured according to community standards such as Camtrap DP (Bubnicki et al., 2024). Agouti supports sequence-based annotation, AI-assisted classification, secure archiving, and standardized data export. In this workflow, images recorded within 120 seconds of one another were grouped into sequences representing putative single events and annotated using a combination of AI-assisted classification and manual review; a subset was subsequently validated by experienced observers. Where relevant for density estimation, calibration images and species-specific movement paths were also annotated and analysed using the photogrammetric tools in the R package camtrapDensity (Rowcliffe, 2014).
The images used in this report were processed and archived in Agouti , and the data were structured according to the Camtrap DP standard.
2.6 Data Processing
The input data were provided in Camtrap DP standard format and pre-processed in camtrapReport, including cleaning, harmonisation, organisation, summarisation, and extraction of key attributes. Records were checked for completeness and internal consistency before being summarised into the descriptive statistics and ecological metrics presented in this report.
In the processing stage, ecological analysis modules operated on these standardised data to generate harmonised outputs, including tables, figures, text, and summary statistics. The report includes a broad range of ecological analyses, such as sampling effort, species richness, activity patterns, and species co-occurrence. These analyses draw on established ecological methods implemented in dedicated R packages, including activity and camtrapDensity.
In the post-processing stage, outputs generated by the selected modules were assembled into structured report sections and rendered into a coherent, article-style ecological report. Because sections can be customised by the user, the report structure remains flexible and can be adapted to different study objectives. This workflow provides a reproducible and extensible framework for transforming raw camera-trap data into standardised ecological insights.
3 Results
3.1 Captures
This section presents an overview of the camera-trap capture data collected during the study period. Across 2018, 2019, 2020, 2021, 2022, and 2023, the survey recorded 14 wild species and 2 domestic species. Human presence was also detected on 38 occasions, most likely associated with field visits for camera installation or maintenance.
The report focuses on large mammals recorded in at least 25 capture events during the study period. The following table summarizes these frequently detected species and provides the main capture-based metrics for each species, including capture events, Relative Abundance Index (RAI; captures per 100 trap-days), number of capture locations, and total photos.
| 🦌 Table 2. Summary of Frequently Detected large mammals | |||||
| Species with ≥ 25 capture events | |||||
| Common Name | Scientific Name | Captures | RAI | Capture Locations | Total Photos |
|---|---|---|---|---|---|
| roe deer | Capreolus capreolus | 1419 | 53.81 | 8 | 2985 |
| wild boar | Sus scrofa | 783 | 29.69 | 8 | 1615 |
| red fox | Vulpes vulpes | 715 | 27.11 | 8 | 1485 |
| beech marten | Martes foina | 98 | 3.72 | 8 | 196 |
3.2 Capture-Based and Abundance Trends
Camera-trap capture data do not directly measure true population size, but they can provide useful indicators of temporal variation in relative abundance and species detectability, provided that survey effort and detection conditions remain reasonably comparable among years.
The figures below summarize capture-based trends for frequently detected large mammals recorded in at least 25 capture events during the study period in GMU8 LEUVEN. The tabs present complementary metrics, including the number of captures, capture rate (captures per 100 trap-days), number of capture locations, and, where available, REM-based density estimates. Together, these metrics support comparison of temporal patterns in species records across survey years.
Number of Captures
Figure 3. Trends in camera-trap detections over the survey years for each study species in the large mammals group, based on the number of captures.
Capture Rate
Figure 4. Capture rates per 100 trap-days are shown on a log10 scale for each study species in the large mammals group across survey years.
Number of Locations
Figure 5. Trends in the number of camera-trap locations where each species was recorded in the large mammals group across survey years.
REM-Based Density
Figure 6. Trends in estimated REM-based population density for each study species in the large mammals group across survey years.
3.3 Population Density Estimation
Population density was estimated using the Random Encounter Model (REM) for species with sufficient data to support reliable parameter estimation (Palencia et al., 2022). All REM analyses were conducted using the camtrapDensity R package. Because detectability may vary among species, habitat types, and camera models, these estimates should be interpreted with reference to the assumptions of the model. The estimated model parameters and derived density values are presented in the following sections. For consistency, all density estimates were calculated using parameter values averaged across sampling rounds.
3.3.1 REM Model Parameters
The REM relies on three key parameters: movement speed, activity level, and day range. Movement speed represents the average distance travelled by an individual over time, whereas activity level reflects the proportion of time individuals are active and therefore available for detection by camera traps. Day range refers to the total distance an animal typically moves within a 24-hour period and is derived from movement speed and activity level (Rowcliffe et al., 2008; Rowcliffe et al., 2016). Together, these parameters underpin the REM density estimates presented in this section. The tabs below show species-specific estimates for each parameter.
Movement Speed
Figure 7. Estimated active movement speeds (km/h) across study species. Error bars show 95% confidence intervals.
Activity Level
Figure 8. Estimated activity levels, expressed as the proportion of the day active, for each study species. Error bars show 95% confidence intervals.
Day Range
Figure 9. Estimated day ranges for each study species, calculated as the product of movement speed and activity level scaled to a 24-hour period. Error bars show 95% confidence intervals.
3.3.2 Population Density Estimates
The plots below show annual population density estimates for each study species, together with 95% confidence intervals. These estimates allow comparison of temporal variation in density across survey years.
Capreolus capreolus
Figure.10: Estimated population density per sampling year for Capreolus capreolus. Whiskers represent the 95% confidence intervals.
Sus scrofa
Figure.11: Estimated population density per sampling year for Sus scrofa. Whiskers represent the 95% confidence intervals.
Vulpes vulpes
Figure.12: Estimated population density per sampling year for Vulpes vulpes. Whiskers represent the 95% confidence intervals.
3.4 Activity Patterns
Activity patterns describe the temporal distribution of animal activity across the 24-hour day and are typically inferred from the timing of camera-trap detections. These patterns help identify whether species are primarily diurnal, nocturnal, or crepuscular, and can provide insight into daily behavior, ecological roles, and potential responses to environmental factors or human disturbance. The figures below present estimated daily activity curves for each study species, based on camera-trap detections collected across the monitoring period and derived using functionality from the activity R package (Rowcliffe, 2016).
Capreolus capreolus
Figure.13: Estimated daily activity pattern for Capreolus capreolus, aggregated across all sampling rounds. Dashed lines indicate the average sunrise (yellow) and sunset (gray) times across survey years. The solid line shows the fitted activity model, and the shaded region shows the 95% CI.
Martes foina
Figure.14: Estimated daily activity pattern for Martes foina, aggregated across all sampling rounds. Dashed lines indicate the average sunrise (yellow) and sunset (gray) times across survey years. The solid line shows the fitted activity model, and the shaded region shows the 95% CI.
Sus scrofa
Figure.15: Estimated daily activity pattern for Sus scrofa, aggregated across all sampling rounds. Dashed lines indicate the average sunrise (yellow) and sunset (gray) times across survey years. The solid line shows the fitted activity model, and the shaded region shows the 95% CI.
Vulpes vulpes
Figure.16: Estimated daily activity pattern for Vulpes vulpes, aggregated across all sampling rounds. Dashed lines indicate the average sunrise (yellow) and sunset (gray) times across survey years. The solid line shows the fitted activity model, and the shaded region shows the 95% CI.
3.5 Richness
Species richness refers to the number of unique species recorded at each camera-trap location. It provides a useful indicator of local biodiversity and helps assess how effectively the survey captured variation in the wildlife community across the study area. The figure below shows the spatial distribution of species richness, where each point represents a camera-trap location. Circle size reflects the number of unique species detected at that site, and color indicates richness intensity, with warmer colors representing higher richness values. By selecting a point, users can view additional information on species composition, richness count, and habitat type, if available. These features help identify biodiversity hotspots and potential detection gaps across the study area.
2018
Figure.17: Species richness based on the species focus group in this report, across camera-trap locations for survey year 2018.
2019
Figure.18: Species richness based on the species focus group in this report, across camera-trap locations for survey year 2019.
2020
Figure.19: Species richness based on the species focus group in this report, across camera-trap locations for survey year 2020.
2021
Figure.20: Species richness based on the species focus group in this report, across camera-trap locations for survey year 2021.
2022
Figure.21: Species richness based on the species focus group in this report, across camera-trap locations for survey year 2022.
2023
Figure.22: Species richness based on the species focus group in this report, across camera-trap locations for survey year 2023.
Total
Figure.23: Species richness based on the species focus group in this report, across camera-trap locations for the cumulative dataset across all years.
3.6 Species Co-occurrence
Species co-occurrence analysis was used to assess pairwise associations among species recorded across camera-trap locations. Relationships were summarized in a correlation matrix, where positive values indicate species pairs that tended to co-occur more frequently across sites and negative values indicate lower co-occurrence. Such patterns can provide insight into shared space use or spatial segregation among species, although they should be interpreted cautiously because co-occurrence alone does not imply direct interaction. (Gotelli, 2000).
2018
Figure.24: Species co-occurrence patterns for the selected species focus group in this report, shown for survey year 2018.
2019
Figure.25: Species co-occurrence patterns for the selected species focus group in this report, shown for survey year 2019.
2020
Figure.26: Species co-occurrence patterns for the selected species focus group in this report, shown for survey year 2020.
2021
Figure.27: Species co-occurrence patterns for the selected species focus group in this report, shown for survey year 2021.
2022
Figure.28: Species co-occurrence patterns for the selected species focus group in this report, shown for survey year 2022.
2023
Figure.29: Species co-occurrence patterns for the selected species focus group in this report, shown for survey year 2023.
Total
Figure.30: Species co-occurrence patterns for the selected species focus group in this report, shown for the cumulative dataset across all years.
3.7 Spatial Density
Spatial density maps derived from point pattern analysis illustrate the spatial distribution of species detections across the study area. These visualizations highlight variation in detection intensity and help identify areas with relatively high or low concentrations of records. The following maps are presented for each species, either by survey year or across the full monitoring period. Warmer colors represent higher estimated detection density, whereas cooler colors indicate lower density. Together, these maps support comparison of spatial detection patterns among species and across survey years.
2018
Capreolus capreolus
Figure.31: Spatial point pattern density for Capreolus capreolus based on the species focus group in this report, shown for survey year 2018.
Sus scrofa
Figure.32: Spatial point pattern density for Sus scrofa based on the species focus group in this report, shown for survey year 2018.
Vulpes vulpes
Figure.33: Spatial point pattern density for Vulpes vulpes based on the species focus group in this report, shown for survey year 2018.
2019
Capreolus capreolus
Figure.34: Spatial point pattern density for Capreolus capreolus based on the species focus group in this report, shown for survey year 2019.
Martes foina
Figure.35: Spatial point pattern density for Martes foina based on the species focus group in this report, shown for survey year 2019.
Sus scrofa
Figure.36: Spatial point pattern density for Sus scrofa based on the species focus group in this report, shown for survey year 2019.
Vulpes vulpes
Figure.37: Spatial point pattern density for Vulpes vulpes based on the species focus group in this report, shown for survey year 2019.
2020
Capreolus capreolus
Figure.38: Spatial point pattern density for Capreolus capreolus based on the species focus group in this report, shown for survey year 2020.
Sus scrofa
Figure.39: Spatial point pattern density for Sus scrofa based on the species focus group in this report, shown for survey year 2020.
Vulpes vulpes
Figure.40: Spatial point pattern density for Vulpes vulpes based on the species focus group in this report, shown for survey year 2020.
2021
Capreolus capreolus
Figure.41: Spatial point pattern density for Capreolus capreolus based on the species focus group in this report, shown for survey year 2021.
Martes foina
Figure.42: Spatial point pattern density for Martes foina based on the species focus group in this report, shown for survey year 2021.
Sus scrofa
Figure.43: Spatial point pattern density for Sus scrofa based on the species focus group in this report, shown for survey year 2021.
Vulpes vulpes
Figure.44: Spatial point pattern density for Vulpes vulpes based on the species focus group in this report, shown for survey year 2021.
2022
Capreolus capreolus
Figure.45: Spatial point pattern density for Capreolus capreolus based on the species focus group in this report, shown for survey year 2022.
Sus scrofa
Figure.46: Spatial point pattern density for Sus scrofa based on the species focus group in this report, shown for survey year 2022.
Vulpes vulpes
Figure.47: Spatial point pattern density for Vulpes vulpes based on the species focus group in this report, shown for survey year 2022.
2023
Capreolus capreolus
Figure.48: Spatial point pattern density for Capreolus capreolus based on the species focus group in this report, shown for survey year 2023.
Martes foina
Figure.49: Spatial point pattern density for Martes foina based on the species focus group in this report, shown for survey year 2023.
Sus scrofa
Figure.50: Spatial point pattern density for Sus scrofa based on the species focus group in this report, shown for survey year 2023.
Vulpes vulpes
Figure.51: Spatial point pattern density for Vulpes vulpes based on the species focus group in this report, shown for survey year 2023.
Total
Capreolus capreolus
Figure.52: Spatial point pattern density for Capreolus capreolus based on the species focus group in this report, shown for the cumulative dataset across all years.
Martes foina
Figure.53: Spatial point pattern density for Martes foina based on the species focus group in this report, shown for the cumulative dataset across all years.
Sus scrofa
Figure.54: Spatial point pattern density for Sus scrofa based on the species focus group in this report, shown for the cumulative dataset across all years.
Vulpes vulpes
Figure.55: Spatial point pattern density for Vulpes vulpes based on the species focus group in this report, shown for the cumulative dataset across all years.
3.8 Habitat Preferences
Habitat preferences were assessed by comparing proportional capture rates across habitat types for each species. Capture rate, expressed as detections relative to sampling effort, serves here as an indicator of habitat use and relative habitat association rather than a direct estimate of habitat selection. Differences in the distribution of capture rates among habitat types therefore highlight habitats in which species were more or less frequently recorded. Together, these patterns provide insight into species-specific habitat use within the study area and help identify broad differences in habitat association among the focal species.
Figure.56: Capture rate distribution across habitat types based on the species focus group. Each bar represents a species, with sections indicating the proportion of each habitat type.
3.9 Species Accumulation Curves
Species accumulation curves show how recorded species richness changes with sampling effort, measured here as the number of camera-trap deployments. For each year available for this site, curves were derived from species presence–absence across deployments using the iNEXT R package. The tabs below show yearly curves with 95% confidence intervals, and the final Comparison tab overlays years with sufficient data.
2018
Figure.57: Species accumulation curve for the focal species group in 2018, with 95% confidence interval.
2019
Figure.58: Species accumulation curve for the focal species group in 2019, with 95% confidence interval.
2020
Figure.59: Species accumulation curve for the focal species group in 2020, with 95% confidence interval.
2021
Figure.60: Species accumulation curve for the focal species group in 2021, with 95% confidence interval.
2022
Figure.61: Species accumulation curve for the focal species group in 2022, with 95% confidence interval.
2023
Figure.62: Species accumulation curve for the focal species group in 2023, with 95% confidence interval.
Comparison
Figure.63: Species accumulation curves by year for the focal species group. Solid lines indicate rarefaction, dashed lines extrapolation, and points observed sample size.
4 Acknowledgements
This report was generated using the camtrapReport R package, developed by Elham Ebrahimi at Wageningen University & Research and Utrecht University, the Netherlands. The development of camtrapReport was supported by Biodiversa+ through the Big Picture project. We also gratefully acknowledge the European Observatory of Wildlife network for its contribution to package testing. Users are kindly requested to cite the package when using camtrapReport or publishing results derived from it.
Appendix
The following images showcase a selection of 3 species: Capreolus capreolus, Martes foina, Vulpes vulpes, captured at different camera locations within this study site.
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