Name the quantity before counting
Presence, occupancy, abundance, density and population trend are related but distinct. Presence is supported by a credible observation at a place and time. Occupancy usually describes the probability that a sampling unit is occupied or used under a stated design. Abundance concerns the number of animals within a defined population, while density relates numbers to area. A trend describes change in a chosen quantity over a specified period.
The distinction matters because a platform may display all detections as an impressive total. That total can include repeated photographs of the same animal, multiple frames from one visit and records gathered under unequal effort. Unless individual identities and sampling assumptions support it, the number is an observation count—not the size of the population.
Treat detection as a process
A species may be present yet remain unrecorded. An animal must move into the sensor’s effective range; the device must operate; the image or recording must be interpretable; and the processing workflow must retain the observation. Weather, vegetation, animal behaviour and classification errors can influence those stages. Improving the AI model addresses only part of the overall detection process.
Repeated surveys can help separate ecological state from imperfect detection. The occupancy framework described by MacKenzie and colleagues in 2002 formalised this distinction for detection–non-detection data. Its assumptions still need attention: repeat visits, a suitable closure period, relevant covariates and a meaningful definition of the sampling unit. For wide-ranging animals, “site use” may be a more careful interpretation than permanent occupancy.
OBSERVATION
No reviewed record of the target species appears during a survey visit.
RESPONSIBLE INFERENCE
The species was not detected during that visit. Absence needs further support from the design and estimated detection process.
Match the method to the available evidence
Individually identifiable animals may support capture–recapture methods when resightings can be linked reliably. Spatial capture–recapture uses where individuals are encountered to model density under explicit assumptions. Misidentifying one animal as several can inflate estimates, while merging distinct animals can reduce them. Identity review is therefore an analytical safeguard, not merely an image-management task.
Unmarked animals require different approaches. Encounter rates can be useful descriptive indices, but changes in movement and sensor placement can change those rates independently of abundance. Some methods estimate density from unmarked detections using additional information and assumptions; they are not a universal conversion from photographs to animals. Involve a quantitative ecologist before promising a population estimate that the survey was not designed to support.
| Evidence | Can support | Does not establish alone |
|---|---|---|
| One validated record | Presence at a recorded time and place | Population size or persistence |
| Repeated detection histories | Occupancy or use under a suitable model | Exact individual abundance |
| Reviewed individual resightings | Capture–recapture analysis | An assumption-free census |
| Effort-standardised encounter rate | A descriptive monitoring index | A direct measure of density |
Make time comparisons genuinely comparable
Maintain survey timing, device settings and placement rules where possible. Record exceptions rather than pretending that every field season was identical. A dry-season trail camera and a wet-season off-trail camera may observe different movement patterns. Additions to the camera network can also shift the sampled habitat and make an aggregate total look like ecological change.
Keep a denominator for effort. Camera-days should reflect actual functioning periods, with a documented rule for outages and uncertain failures. Audio surveys need comparable recording schedules and attention to calling activity. When a new recognition model is introduced, evaluate it against the earlier workflow or reprocess an appropriate archive consistently. Otherwise, an algorithmic improvement may masquerade as a biological recovery.
Show uncertainty alongside the headline
Report estimated quantities with intervals, sample sizes, model assumptions and the time window they describe. Wide uncertainty is not a reporting failure; hiding it is. A programme may have too little power to detect a modest change even when monitoring is well organised. A non-significant result should not be translated into proof that the population remained stable.
The IUCN Red List assesses extinction risk using defined criteria and evidence; a local dashboard trend does not automatically determine a species’ global category. Likewise, a detection inside a protected area does not establish that management caused recovery. Attribution needs an appropriate evaluation design and consideration of other drivers.
Write the conclusion the data can carry
A useful monitoring statement names the measured quantity, spatial scope, sampling period and uncertainty. It might report a change in estimated site use rather than announce “more animals”. That restraint helps managers choose the next survey, recognise evidence gaps and allocate limited resources without mistaking a persuasive interface for a population assessment.