Use GPS collars when you need to know where individual pronghorn travel. A collar can collect repeated locations for a tagged animal, allowing researchers to reconstruct routes, seasonal ranges, stopovers and migration timing. A camera trap records an animal only when it enters the camera’s detection zone, so it is best for determining whether pronghorn use a particular crossing, water source, fence opening or other fixed site.
Neither method is universally better. They observe different things: telemetry follows individuals across a landscape, while cameras sample visits at selected points. A well-designed project may use both, but camera detections should not be presented as continuous tracks or as proof of a route between cameras.
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What each method actually measures
| Question | GPS collars | Camera traps |
|---|---|---|
| Unit observed | A tagged individual pronghorn | An event recorded at a fixed camera |
| Best for | Routes, migration timing, seasonal range use and movements across broad areas | Presence, visitation, activity and passage at selected sites |
| Spatial coverage | Moves with the collared animal, subject to fix schedule, acquisition and project design | Limited to camera locations and their detection zones |
| Individual identity | Built into the collar record when the device is functioning and assigned correctly | May be unknown unless markings or image quality permit reliable identification |
| Main blind spot | Missed fixes, failed equipment, capture effects and missing or delayed data | Non-detection can mean the animal was missed, not that it was absent |
This distinction determines what can legitimately be inferred. A sequence of collar fixes can show an individual moving from one area to another between fixes. A sequence of photographs can show that pronghorn visited several camera sites, but it does not automatically establish which animal traveled between those sites or the path it took.
When GPS collars are the right choice
Mapping an individual route
GPS collars are the direct option when the central response variable is an animal’s location through time. Researchers can analyze travel corridors, road crossings, fence passages, seasonal range boundaries and migration dates from repeated fixes. The U.S. Geological Survey’s 2022 synthesis describes GPS-collar work as supporting detailed mapping of migration corridors, stopovers and winter ranges.
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In southwestern Wyoming, a Wyoming Department of Transportation and Federal Highway Administration study used GPS collars and GIS to examine the distribution and movements of 72 adult female pronghorn in relation to roads. The report linked fences and road conditions with movement patterns in that study area. Those findings describe that landscape and period; they should not be generalized to every herd, road network or fence design.
Measuring timing and sequence
A collar schedule gives every usable fix a time stamp. With an appropriate interval, analysts can estimate when an animal left a seasonal range, how long it remained at a stopover, and whether movements coincided with roads, fences, weather or other mapped features. The precision of those conclusions depends on the fix interval and the quality of the locations, so a “track” is a sampled sequence rather than an uninterrupted video of movement.
Receiving data over a large area
Collars can transmit locations by satellite, allowing a project to receive data even when animals move far beyond a field crew’s camera network. The Montana Fish, Wildlife & Parks 2024 final report describes one such configuration: Lotek collars programmed for hourly locations over three years, with satellite uploads and mortality alerts. That is a project-specific setup, not a standard capability or guaranteed battery life for every collar or service.
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What GPS collar data can and cannot tell you
Fix success is not perfect
The Wyoming report analyzed data collected from January 2002 through December 2003. Its collars produced 33,369 locations and had a reported mean successful location-acquisition rate of 93 percent. The 93 percent figure belongs to that project, equipment and period; it is not a current universal performance rate. A missing fix creates uncertainty about the exact path and timing between the surrounding successful locations.
Capture and equipment recovery matter
Collar studies require capture, animal handling, deployment and eventual retrieval or release. The same older Wyoming report documents collar release and retrieval problems, as well as an injury associated with a collar. It also records mortality for which the cause could not be determined. These are operational observations from a small historical project, not a general estimate of collar-related risk. Any new study needs appropriate permits, trained personnel, veterinary or animal-welfare oversight where required, and a plan for recovering or remotely releasing equipment.
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Sampling interval changes the question
Hourly fixes can describe fine-scale timing better than one fix per day, but they create greater data, power and battery demands. A long interval may be adequate for broad seasonal-range mapping while missing short road crossings or brief stopovers. Before fitting collars, define the shortest movement event that must be detected and select a schedule that can resolve it.
When camera traps are the right choice
Monitoring a fixed location
A camera trap is useful when the question concerns a place rather than a continuously tracked animal. Examples include:
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- When animals visit a water source, mineral site or migration bottleneck.
- Whether activity at a road edge changes by time of day or season.
- Which species pass a selected location and how often the site is triggered.
Because the camera remains in one place, it can monitor a site without capturing or marking animals. A wildlife trail camera is therefore a fixed-site detection instrument, not a consumer substitute for a GPS collar.
Collecting visual context
Images can show behavior, group composition, direction of travel, nearby vehicles or fence conditions that a location point alone cannot document. They may also reveal unmarked animals. Reliable individual identification is not guaranteed, however; coat appearance, angle, distance and lighting can make animals indistinguishable.
Scaling a network
Multiple cameras can sample several crossings or water sources simultaneously. The network’s effective coverage is still the sum of narrow detection zones. An animal that passes between cameras, approaches from outside the trigger area or moves too quickly for the settings can go unrecorded.
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Why a camera non-detection is ambiguous
Camera performance depends on more than whether a pronghorn was physically nearby. A 2022 field experiment on camera-trap models and settings found detection related to camera model, species, distance, time of day, mounting height and sensitivity. Vegetation, angle, weather, battery state and trigger delay can add further practical variation.
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Consequently, “no photograph” has at least two explanations: no pronghorn entered the effective detection zone, or a pronghorn was present but the camera failed to detect or retain a usable image. A study that treats every non-detection as absence will usually understate site use unless it models detection probability with repeated deployments or an appropriate estimator.
Can cameras reveal a migration route?
Not by themselves in the usual deployment. A camera array can establish the order and timing of detections at known sites, but it does not observe the path between those sites. If photographs cannot identify individuals, even the identity of the animal seen at one location may not match the animal seen at another.
To infer movement between cameras, a project would need additional evidence and a design built for that inference, such as individually recognizable markings, dense strategically placed cameras, or telemetry. Even then, the result is a model-based reconstruction with uncertainty, not a continuous GPS track. Regular-grid camera trapping should not be described as a direct replacement for telemetry when the research question requires individual routes.
Evidence from comparative methods research
Interaction detection is design-dependent
A 2024 paper in Ecological Indicators, “Camera trapping and telemetry for detecting and quantifying animal interactions: Not anything goes,” found that resource-monitoring cameras performed well for interaction detection when resources were suitable. The same comparison found that regular-grid camera trapping missed many direct interactions. Those results concern interaction detection, not a pronghorn movement trial, and they cannot be converted into a universal ranking of cameras versus collars.
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Daily travel distance may converge under strict conditions
A Movement Ecology study e-published July 31, 2026, reported similar estimates of daily travel distance from camera traps and GPS telemetry under specified analytical conditions. The comparison was cross-species rather than pronghorn-specific. It required correction for GPS error, adequate GPS sampling, estimation of activity from camera data and enough camera observations. Similar daily-distance estimates in those scenarios do not mean cameras reproduce individual collar tracks or migration histories.
Decision framework: choose the method from the question
| If your primary question is… | Preferred starting method | Why |
|---|---|---|
| Which route does an individual take? | GPS collars | Repeated locations are tied to a known animal. |
| When does migration begin or end? | GPS collars, potentially complemented by cameras | Collars provide landscape-wide timing; cameras can confirm use of key sites. |
| Does a fence opening or crossing receive pronghorn traffic? | Camera traps | The inference is about a fixed location and visual passage. |
| How much activity occurs at a water source? | Camera traps | Images provide time-stamped visits and context at the source. |
| What is daily travel distance? | Either, only with matched analysis | Define the response, correct GPS error, estimate camera activity and ensure adequate sampling. |
| Do animals interact across a broad landscape? | Question-specific design; often a combination | Camera placement and resource context strongly affect interaction detection. |
Start by writing the response variable in one sentence: route, migration date, site-use probability, number of detections, activity timing or daily distance. Then choose the instrument that measures that variable most directly.
How to design a GPS-collar study
- Define the movement event. Specify the spatial scale, season and minimum event duration you need to resolve.
- Select the study population. Decide whether the inference concerns adult females, another demographic group or the herd as a whole. The Wyoming road study, for example, involved 72 adult females; its sample should not be silently treated as all pronghorn.
- Obtain approvals and plan handling. Arrange wildlife permits, capture logistics, trained staff, welfare protocols and a release or retrieval plan.
- Set the fix schedule and transmission plan. Match interval, satellite upload frequency, battery expectations and mortality-alert needs to the question. The Montana project’s hourly, three-year Lotek configuration is an example, not a default.
- Predefine data-quality rules. Record failed fixes, implausible locations, dilution of precision and time gaps. Do not interpolate across a long gap as though the path were observed.
- Map explanatory features. Roads, fences, water, elevation and seasonal vegetation can be compared with fixes, but associations remain specific to the sampled landscape and design.
- Report the denominator. State animals collared, locations attempted, successful locations, study dates and any animals or devices lost from analysis.
How to design a camera-trap study
- Choose sites for the inference. Place cameras at crossings, fence gaps, water sources or other resources tied to the question instead of assuming a regular grid will answer every movement question.
- Standardize deployment. Record camera model, mounting height, orientation, sensitivity, trigger delay, detection distance and operating dates. These settings affect detection.
- Use repeatable sampling. Keep cameras active for comparable periods and document outages, empty batteries, blocked views and memory-card failures.
- Define an independent detection. Set a time threshold or event rule before counting photographs so bursts of triggers are not treated inconsistently.
- Measure detection probability. Use repeated occasions, calibration trials or a suitable occupancy or abundance estimator when the objective requires site-use or population inference.
- Record covariates. Note time of day, weather, vegetation, distance and image quality. These variables help explain why one site records fewer animals than another.
- State identification limits. If individuals are not reliably recognizable, report detections or visits rather than claiming individual movement histories.
Using collars and cameras together
A combined design can pair collar tracks with cameras at strategically important sites. Collars can identify when tagged animals approach a crossing or water source, while cameras can verify visual passage, document uncollared animals and record behavior or site conditions. This complementarity is a design option, not a demonstrated pronghorn-specific result established by the comparative studies above.
Keep the data streams conceptually separate. A collar fix near a camera is not automatically a photograph of the same animal, and a photograph is not evidence that a collared animal followed a particular route unless identity and timing support that link. Define matching windows, location tolerances and identification criteria before analysis.
How to compare results without misleading yourself
Align the time scale
Compare hourly collar fixes with camera observations only after deciding whether the target is hourly activity, daily distance, seasonal movement or another scale. A camera operating intermittently cannot be treated as continuous observation.
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Align the response variable
“Number of photographs,” “probability of site use,” “distance traveled” and “migration date” are different outcomes. Do not place them in one performance ranking. For distance, account for GPS location error, camera-derived activity estimates and the amount of data available to each method.
Report uncertainty and missingness
Give fix success, camera operating time, detection probability assumptions and excluded records. A method that produces more records is not necessarily more informative if those records answer a different question.
Practical limitations and common mistakes
- Calling cameras “GPS without collars.” Cameras do not locate an animal between detections or assign identity automatically.
- Treating a missed photograph as absence. Detection depends on model, settings, distance, height and conditions.
- Generalizing one collar project. The 93 percent acquisition rate, 33,369 locations and Wyoming road relationships belong to a 2002–2003 study. The 702 adult females and three-year hourly schedule belong to Montana’s 2019–2022 capture effort and 2024 report.
- Ignoring handling effects. Capture, fitting, release and retrieval require welfare safeguards and transparent reporting.
- Assuming a camera grid answers every question. Grid designs can miss direct interactions and are not a substitute for individual telemetry when routes are required.
- Comparing raw counts across unequal deployments. Different active days, settings or vegetation can change detection independently of animal use.
- Overinterpreting a cross-species result. Similar daily-distance estimates reported in 2026 apply to the studied scenarios, not automatically to pronghorn.
Equipment and operational requirements
GPS collars are specialist research systems rather than simple consumer purchases. A project needs capture capability, approved animal-handling procedures, collar programming, data transmission or retrieval, geospatial analysis and plans for failures and mortality alerts. The cited agency reports document such deployments but do not establish a general retail price or a universal service package.
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Camera projects require weather-resistant cameras, secure mounts, batteries or external power, memory, theft protection where relevant, field servicing and an image-management workflow. A wildlife trail camera can serve as the physical detector, but model choice and deployment settings influence what it records. No particular model, current price or availability is established here.
Bottom line for pronghorn movement studies
Choose GPS collars when the deliverable is an individual movement sequence: a route, migration timing, seasonal range or landscape-wide response to roads and fences. Choose camera traps when the deliverable is evidence of use, visitation or activity at a defined site. Use both when fixed-site visual confirmation adds value to telemetry, while keeping each method’s observation unit and uncertainty explicit.
There is no pronghorn-specific field trial showing that cameras and GPS collars are interchangeable. The strongest evidence supports a question-first design: collars for where a known animal goes, cameras for whether animals appear at places you can monitor, and matched analytical methods when estimating a shared quantity such as daily travel distance.
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