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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchNeither camera traps nor observer-led visual surveys are universally best for monitoring wildlife. Camera traps are one kind of camera survey; the practical choice is usually between unattended camera-trap sampling and a specific alternative, such as a visual encounter survey. Choose according to the species, the estimate you need—presence, occupancy, richness, abundance or density—and the coverage and effort your team can sustain.
First, clarify what “camera survey” means
A camera trap is a camera-based survey method, not the opposite of a camera survey. “Camera survey” can also refer to other camera-based protocols, so it is not a precise name for a single alternative. The useful comparison here is camera-trap sampling versus observer-led visual encounter surveys or another method suited to the animal being studied.
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Neither method records every animal that is present. A camera only produces a detection when an animal enters its detection zone and the sensor and settings yield a usable image. Visual observers can miss animals too, and their results depend on when, where and how people search.
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| Consideration | Camera traps | Observer-led visual surveys |
|---|---|---|
| Detection | Can record animals passing a camera’s detection zone while no observer is present; performance depends on placement, animal movement, sensor and settings. | Can detect animals a motion-triggered camera misses, depending on the observer, search protocol, timing and conditions. |
| Temporal coverage | Can collect repeated records over an unattended deployment, subject to power, equipment function and deployment schedule. | Usually limited to the times observers are in the field. |
| Spatial coverage | Requires cameras to be installed at selected locations; the placement scheme determines how representative the sample is. | Can offer flexibility in where observers search, though results still depend on a consistent and appropriate sampling design. |
| Effort and cost | Requires equipment, installation and image review. A long deployment can create a large image-processing workload. | Requires field visits and staff time; it may involve less camera equipment but is constrained by observer availability. |
| Best fit | Repeated monitoring where unattended temporal coverage is valuable and the target is detectable by the chosen camera setup. | Targets or conditions for which active searching is effective, including situations where standard passive-infrared cameras may miss small or less detectable animals. |
These are broad trade-offs, not a guarantee that one method will outperform the other for a particular species. In a North Carolina pilot on Carolina northern flying squirrels, estimated detection probability was 0.37 ± 0.06 standard error for acoustic surveys, 0.30 ± 0.06 for camera traps and 0.01 ± 0.005 for live traps. The study is a species-specific pilot, not a general ranking of cameras and visual surveys; its authors said seasonal effects and protocols needed further work. (Diggins et al., USGS record of a 2016 study.)
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Choose the method to match the question
Are the animals present?
A detection confirms that an animal was recorded at a sampled location and time. A non-detection does not establish absence: the animal may not have passed the camera, may have gone unseen by an observer, or may have been inactive during the survey. Repeated surveys and a method suited to the species help address imperfect detection.
What proportion of sites are occupied?
Occupancy estimates require a defined sampling unit, repeat-survey period and design that supports inference about the area of interest. Random or stratified camera placement is generally more appropriate for area-level inference than placing every camera on a trail or other high-use feature. Trail placement can raise detections for a target, but it can bias comparisons of occupancy or community richness across an area if the sampling objective is not accounted for.
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How many species occur in the area?
Species richness depends on both how many species are present and how likely the survey is to detect each one. A multi-site analysis by Kays and colleagues found that precise richness estimates used 25–35 camera sites depending on study scale. That is a result from the 41 areas and 2,225 deployments analyzed, not a universal site-count rule. Rare species may require substantially more sampling than common ones.
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Raw camera counts or detection rates should not be treated as abundance by default. They can reflect animal numbers, but also movement, behavior, camera placement and detectability. Use an analytical approach suited to the question and data; density estimates in particular need a design and assumptions that support them.
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What published camera-trap effort estimates can—and cannot—tell you
Kays and colleagues’ 2020 multi-site analysis offers useful planning context for the datasets and metrics they examined. It does not prescribe a universal camera layout or duration.
| Planning question | Finding in the 2020 analysis | How to interpret it |
|---|---|---|
| How many sites for richness? | 25–35 sites for precise species-richness estimates, depending on study scale. | Estimate from the study’s analyzed areas and deployments; required effort varies with scale and objective. |
| How many sites for occupancy? | Fewer than 20 sites could produce precise estimates for common species with occupancy above 0.75; rare species with occupancy below 0.25 could require more than 150. | These figures illustrate how rarity changes precision needs in that analysis; they are not fixed thresholds for every study. |
| How long should cameras run? | Two weeks was most efficient for detecting new species; three to four weeks were needed for precise local detection-rate estimates, with no precision gain after a month in the studied data. | Duration depends on the metric, species, season and setting. More operating time is not automatically more useful. |
| Does season matter? | Across the examined sites, 37%–50% of species had significant seasonal fluctuations in occupancy or detection rate. At temperate sites, season-sensitive species varied in relative abundance by an average factor of 4–5. | These are findings for the studied sites, not a global rate. Some species were absent in one season because of hibernation or migration. |
All figures in the table describe Kays and colleagues’ 2020 analysis, which used data from 41 areas and 2,225 camera deployments. They can help frame a pilot or power analysis, but local ecology and the intended estimate should guide the final design. (Kays et al., Methods in Ecology and Evolution.)
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Account for season, placement and processing
Survey when the species can be detected
Set survey timing around the target’s active season and expected behavior. A calendar-long deployment does not compensate for a species that is hibernating, migrating or outside the sampled area. Seasonal changes can alter both occupancy and detection rates, so comparisons between sites or years should use appropriate timing or explicitly account for season.
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Place cameras for the inference you want
For occupancy or community questions, use probability-based or stratified placement where possible. If the goal is to detect a particular animal, a trail or focal feature may be useful, but be explicit about how that choice affects the inference. Document the sampling unit, location-selection method, camera height, sensitivity, trigger delay, deployment dates, effort and downtime.
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Plan for the full workload
Unattended operation reduces the need for an observer to be present at every moment, but it does not eliminate field effort. Cameras need to be installed and maintained, and image volumes can take time to review. In the North Carolina flying-squirrel pilot, field effort was 9.6 hours for cameras and 8.4 hours for acoustic surveys, compared with 111.9 hours for live trapping; processing made total effort more similar. Those values describe that pilot’s methods and species, not a general labor comparison. (Diggins et al., USGS record.)
When another survey method may be better
Standard passive-infrared camera traps are not automatically the right tool for every animal. For small mammals, reptiles and amphibians, consider whether active visual searches, cover objects, drift fences or time-lapse approaches better match the animal’s size and activity. Acoustic surveys may also complement cameras for species that are easier to detect by sound.
In the flying-squirrel pilot, mean latency to first detection was 1.5 nights for acoustics and 3.25 nights for cameras. The result is specific to that pilot and should not be generalized to other species or protocols. More broadly, it illustrates why a complementary-method pilot can be valuable when no single method appears to meet both temporal and spatial needs. A recent threatened-species comparison describes camera traps’ temporal coverage alongside visual surveys’ spatial flexibility, while noting equipment, installation and processing costs for cameras. (Threatened-species method comparison.)
A practical way to choose
- Define the estimate. Decide whether you need detections, occupancy, richness, abundance or density. The design and analysis must support that specific result.
- Check how the target is detected. Consider body size, activity timing, movement, habitat and whether a camera’s sensor can capture a usable record. Consider active searching or acoustic methods where appropriate.
- Choose a representative sampling design. Use random or stratified placement for area-level questions where feasible. Use focal features only when they fit the target and intended inference.
- Set a season and repeat-survey period. Align fieldwork with activity, migration and hibernation patterns; specify the sampling unit and time window.
- Budget for the whole process. Include equipment, installation, travel, staff time, maintenance and image or observation processing.
- Pilot complementary methods if the trade-off is unclear. Compare what each method detects and the field and processing effort it requires before scaling up.
As Burton and coauthors put it in their 2015 review, “We remain optimistic about the technology’s promise, but call for more explicit consideration of underlying processes of animal abundance, movement and detection by cameras, including more thorough reporting of methodological details and assumptions.” (Burton et al., 2015 review.)
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