Camera traps are a valuable tool for documenting large, ground-dwelling birds and studying nest predation, but they are not a universal substitute for traditional bird surveys. Detection is heavily biased by mounting height, bird size, and behavior; small, fast-moving, or arboreal species are frequently missed. Because detection probability is rarely 1.0, raw photo counts should be interpreted with caution and ideally analyzed using occupancy models that account for these biases.
Camera traps were originally developed and optimized for medium- and large-sized mammals. This design legacy creates a fundamental mismatch when applied to avian monitoring. Most standard camera surveys target mammals, so traps are often set at heights where birds are not reliably detected. A study of 91 nature reserves in China found that camera trapping recorded only approximately 42% of the region's forest bird species, with significant variation across taxonomic groups. The families most frequently detected were Phasianidae (pheasants), Muscicapidae (flycatchers and allies), Corvidae (crows and jays), and Leiothrichidae (bush warblers), while many other families were underrepresented.
Mounting height is a primary driver of detection bias. Traps set at 0.4–0.6 m above the ground, a common height for mammal monitoring, are often too high to detect small birds close to the trap or result in photographs focused on the background rather than the bird. A cross-continental study evaluating 172 paired sampling points across four biomes found that lower camera placements (30–50 cm above ground) significantly increased trapping rates for small (0–10 kg) and medium-sized species (11–50 kg), while higher placements favored larger animals. For multispecies monitoring that includes birds, lower cameras offer better overall performance, though this may compromise data for large ungulates or carnivores.
Beyond size, specific functional traits influence whether a bird is detected. Bayesian and random forest models from the Chinese forest study identified tarsometatarsal length (leg length) and feeding behavior as key predictors of detection probability. Birds with longer legs and those exhibiting herbivory behavior were more likely to be detected, while those with vertebrate-feeding behavior were less likely to be. This suggests that camera traps are biased toward birds that spend time on the ground or in low vegetation and move in ways that trigger passive infrared sensors effectively. For example, a ground-foraging pheasant with long legs is far more likely to trigger a sensor than a small, cryptic warbler foraging in the mid-canopy. Small, cryptic, or high-canopy species are systematically under-sampled.
For estimating bird abundance and diversity, camera traps should be viewed as a complementary tool rather than a replacement for point counts or acoustic monitoring. A study in the Afrotropical landscape of Jos, Nigeria, compared point counts, acoustic recorders, and camera traps for nocturnal birds. Camera traps detected zero of the 11 nocturnal bird species encountered, while acoustic recorders detected all 11. Species richness estimates from point counts and acoustic recorders were similar across habitat types, indicating that both methods are useful for nocturnal bird surveys in these environments, whereas camera traps were ineffective for this specific group.
In urban environments, camera trap "bycatch"—bird detections incidental to mammal surveys—can provide meaningful data. A study in Firenze, Italy, found that bird species richness from camera traps was strongly correlated with richness from standardized point counts, with a 70% species overlap. However, the methods detected different community compositions, reflecting method-specific biases. Camera traps are useful for documenting occurrence and behavior of ground-dwelling species but lack the sensitivity of point counts for estimating overall avian diversity.
Raw counts from camera traps are almost always an underestimate of abundance because detection probability is rarely 1.0. Researchers discourage the use of simple relative abundance indices based on trapping effort because these do not account for changes in detection probability over time or space. Instead, occupancy analysis and capture-recapture models that incorporate detection probability and covariates (such as vegetation density or time of day) are recommended for robust inference. For species that are individually identifiable, mark-resighting models can be used to estimate population size, but this requires prior marking of a subpopulation.
While the primary focus of this article is on ecological detection bias, field operators should also consider practical logistics. Deploying camera traps in semi-public or private areas requires coordination with landowners and adherence to local land-use regulations. Legal requirements vary significantly by jurisdiction; specific statutes governing wildlife monitoring, data privacy, and public land access differ across regions. Operators are advised to consult local wildlife agencies and privacy laws before deployment to ensure compliance. General operational best practices include positioning cameras to avoid direct views of residential windows or private trails and obtaining explicit permission from landowners. These steps help ensure that the survey is both ethically sound and legally compliant, though they are distinct from the statistical and ecological considerations discussed in the cited research.
Immediate Pilot Protocol: To start immediately, conduct a short pilot deployment in a mixed forest edge. This low-cost setup allows you to calculate local detection probability and calibrate placement strategies based on empirical data from your specific environment before scaling up to a larger survey.
Once your pilot deployment is complete, the next step is to move from raw images to statistical inference. Because detection probability is rarely 1.0, you should analyze the data using occupancy models that account for imperfect detection. Tools such as Occupancy (R package) or PREMIS can help estimate site occupancy and detection probability based on your replicated visit data. This analysis will reveal whether your camera placement and duration were sufficient to detect your target species reliably, guiding adjustments for any subsequent larger-scale survey.
