Camera traps have become essential tools in biodiversity monitoring. Whether tracking elusive carnivores or measuring the activity of herbivore populations, their ability to collect continuous, non-invasive data has reshaped ecological research. But the effectiveness of a camera trap network is not solely dependent on the technology—it is fundamentally influenced by where and how the cameras are deployed.

In this article, we explore the implications of camera placement strategies, drawing insights from a 2021 peer-reviewed study (Tanwar et al., Scientific Reports) and connecting them to the open-source tools available to the biodiversity community in 2025.

Why Placement Strategy Matters

Different placement strategies yield different kinds of data. The study conducted in Ranthambhore National Park (India) compared two sampling designs:

Trail Cameras: Placed on animal trails, forest roads, or fire lines to maximize captures of large carnivores like tigers and leopards.
Random Cameras: Positioned randomly, independent of visible tracks or habitat cues, to avoid sampling bias.

While both setups detected similar species richness (25 species), they showed significant differences in how they captured relative abundance, activity patterns, and detection probabilities.

Key Findings

Metric Trail Cameras Random Cameras Species Richness Similar to random, but accumulated faster Same richness, slower accumulation Abundance Index (RAI) Overestimated for large carnivores Strongly correlated with independent density estimates Activity Patterns Biased toward diurnal activity (especially herbivores) Captured nocturnal and crepuscular activity more realistically Group Size Detected larger herds more often Slightly smaller groups, similar averages Carnivore Detection High capture rates Very low detection probability

These findings demonstrate that placement affects ecological interpretation. For instance, trail cameras are effective at detecting wide-ranging carnivores but may misrepresent the daily activity of prey species that avoid trails at night due to predation risk.

🧠 Strategic Recommendations

Based on the evidence, we advocate for goal-driven deployment strategies:

Use trail-based cameras when the priority is monitoring flagship species, detecting illegal activities, or conducting mark-recapture studies.
Use random or systematic placements for unbiased estimation of species abundance, activity budgets, or community composition.
Combine both in a hybrid approach for ecosystem-level assessments.

This tailored strategy aligns with the core principles of evidence-based conservation planning and supports the development of robust biodiversity observatories.

Statistical Tools for Inference

To move from images to insights, the ecological community relies on advanced statistical models. Camera trap data can support:

Analysis Type Use Case Tools Occupancy Modeling Estimating presence/absence across habitats PRESENCE, unmarked (R), camtrapR Relative Abundance Index (RAI) Comparing population trends over time camtrapR, custom pipelines Capture-Mark-Recapture (CMR) Density of identifiable individuals (e.g. tigers) secr, SPACECAP Distance Sampling Estimating prey density from photos camtrapdp, Distance Activity Overlap Understanding temporal segregation overlap (R package), activity Species Accumulation Curves Measuring sampling effort vs. richness vegan (R package)

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Ecosystem of Open-Source Tools

To complement placement strategies and statistical analysis, the biodiversity community now has access to mature, open-source platforms that streamline the entire workflow—from raw images to ecological indicators.

Tool Strengths Use Case TRAPPER Spatial database, API access Collaborative, large-scale studies Declas AI-powered classification with MegaDetector Fast image triage and species filtering camtrapR Full R integration, modeling and visualization Advanced analytics and reproducibility PICT DIY, Raspberry Pi-based cameras Experimental or custom low-cost devices

Spotlight: EcoSecrets by Natural Solutions

EcoSecrets is our open-source, field-proven platform designed to manage multi-site, long-term camera trap deployments. It was built with field ecologists, NGOs, and protected area managers in mind.

Key Features:

📁 Centralized image and video storage with structured metadata.
🧠 Standardized annotation for species, behavior, and habitat variables.
🗺️ GIS integration, enabling full spatial analysis of detections.
🤖 AI-ready: Connect seamlessly with tools like MegaDetector.
🔄 Scalable and interoperable: Ideal for nationwide or regional programs.

EcoSecrets is particularly suited for programs that need to:

Compare biodiversity across multiple locations,
Monitor changes over time (climate, urbanization, conservation efforts),
Maintain data quality and consistency over several years and teams.

🔧 We provide custom deployments, technical assistance, and training to ensure your monitoring system is resilient, efficient, and ready for the future.

From Camera Traps to Nature Intelligence

At Natural Solutions, we are committed to helping institutions harness the full potential of biodiversity data. Whether through deploying camera trap networks, training AI models, or integrating analytics into GIS platforms, our mission is to make ecological insight actionable.

If you’re designing a new monitoring program, managing field teams, or building decision-support systems, we can help you:

Select the right sampling strategy
Implement AI-enhanced processing
Apply state-of-the-art statistical models
Maintain high standards of data quality and interoperability

→ Let’s work together to build smarter biodiversity observatories.

📩 Contact us: www.natural-solutions.world

References available upon request. Key scientific source: Tanwar et al. (2021). “Camera trap placement for evaluating species richness, abundance, and activity.” Scientific Reports, 11, 23050. https://doi.org/10.1038/s41598-021-02459-w

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