# Monitoring wildlife abundance through track surveys: A capture-mark-recapture inspired approach to assess track detection by certified trackers in the Kalahari, Botswana

> How many tracks do expert trackers miss from a moving vehicle? In Botswana's Kalahari, two pairs of certified trackers drove the same transects one after the other, and a capture-mark-recapture approach showed they detected about three-quarters of fresh tracks. They agreed on the species for 92% of fresh tracks, and their few disagreements cancelled out rather than biasing the counts.

Summary by Kersey Lawrence, Original Wisdom. Updated 5 November 2026. Page: https://www.originalwisdom.com/tracking-literature/2024-gielen-johannes-monitoring-wildlife-abundance-through/

## Citation and access

- **Authors:** Marie-Charlotte Gielen, Xiko Johannes, Njoxlau Kashe, Glam Khumo, Zoro Zoronxhogo and Nicolas Schtickzelle
- **Year published:** 2024
- **Journal or source:** Global Ecology and Conservation
- **Full citation:** Gielen, M.-C., Johannes, X., Kashe, N., Khumo, G., Zoronxhogo, Z. and Schtickzelle, N. 2024. Monitoring wildlife abundance through track surveys: A capture-mark-recapture inspired approach to assess track detection by certified trackers in the Kalahari, Botswana. Global Ecology and Conservation 51: e02924.
- **DOI:** 10.1016/j.gecco.2024.e02924
- **Access status:** Open access (CC BY-NC 4.0)
- **PDF:** https://www.originalwisdom.com/wp-content/uploads/2026/11/2024_Gielen_and_Johannes_etal_monitoring-wildlife-abundance-through-track-surveys.pdf

## Tags

- **Species:** Mammals > African wild dog; Mammals > Blue wildebeest; Mammals > Brown hyena; Mammals > Cheetah; Mammals > Eland; Mammals > Gemsbok; Mammals > Greater kudu; Humans; Mammals > Leopard; Mammals > Lion; Birds > Ostrich; Mammals > Red hartebeest; Mammals > Spotted hyena
- **Region:** Africa > Botswana
- **Tracking:** Footprints; Spoor age estimation; Track counts; Tracking; Tracks and signs identification
- **Behaviour and ecology:** Behaviour; Movement
- **Methods and technology:** Capture-mark-recapture; CyberTracker; CyberTracker software; CyberTracker Tracker Evaluation; Detectability; Double sampling; Formozov–Malyshev–Pereleshin (FMP) formula; Global Positioning System (GPS); Indigenous knowledge; Logistic regression; Observer reliability
- **Data type:** New field data
- **Observer qualification:** Certified trackers
- **Discipline:** Wildlife tracking
- **Year published:** 2024

## Detailed keywords

- **Species:** Studied (tracks recorded): eland (Taurotragus oryx), gemsbok (Oryx gazella), red hartebeest (Alcelaphus buselaphus caama, spelled "Alcephalus" in the paper), greater kudu (Tragelaphus strepsiceros), blue wildebeest (Connochaetes taurinus), ostrich (Struthio camelus), African wild dog (Lycaon pictus), brown hyaena (Hyaena brunnea), cheetah (Acinonyx jubatus), leopard (Panthera pardus), lion (Panthera leo) and spotted hyaena (Crocuta crocuta). The six large carnivores and ostrich were grouped as "followers" (they tend to walk along roads), and the five large antelopes as "crossers". Livestock tracks near cattleposts were a disturbance factor. Detection probabilities from other studies, cited for comparison: the butterfly Boloria eunomia, the mouse Pseudomys novaehollandiae, river birds and reef fish.
- **Location and habitat:** Four Wildlife Management Areas in Ghanzi District, Botswana (GH10, GH11, GH13 and the southern part of GH3), about 22,000 km², forming the northern part of an ecological corridor between the Central Kalahari Game Reserve and the Kgalagadi Transfrontier Park. The Kalahari is a semi-arid sand-covered plain with grasses, shrubs and scattered trees. The area contains four villages and 45 cattleposts, with 41 more within 10 km. Transects ran on sand cutlines, sand roads, gravel roads and off-road on vegetated sand. The tracker co-authors are from East Hanahai and Bere.
- **Topics:** the two parts of track detectability: the chance that an animal's path crosses the transect (p1) and the chance that trackers detect a track that is there (p2); measuring p2 with two teams driving the same transect one after the other; effects of tracker pair, substrate, livestock track density, track age, and crossing versus following behaviour; agreement between tracker pairs on species identification and track ageing; confusion versus directional bias; implications for the Formozov–Malyshev–Pereleshin formula, track indices and community-based conservation; certification of tracking skills.
- **Statistics or models:** a two-session capture-mark-recapture approach for a closed population (detection p2 = m2/n2 and m2/n1); a track-matching procedure based on location, species and track age; logistic regression models (SAS PROC LOGISTIC) with chi-square tests.
- **Data type:** New field data: a driven track survey over 7 days in May 2022, at the beginning of the dry season, on 40 transect segments (2–10 km long, 175 km in total).

## Synopsis

### 1. What they did

The Formozov–Malyshev–Pereleshin (FMP) formula, which turns track counts into density, assumes that trackers detect every track on the transect. That may hold on foot, but large surveys are usually driven, and tracks can be missed. The authors measured how likely trackers are to detect a track that is there, what affects it, and how often two tracker pairs disagree on species and track age.

- Design, inspired by capture-mark-recapture: two teams drove each transect one after the other. Each team had a vehicle driven by a recorder and a pair of certified trackers on seats on the bull bar. The first team "captured and marked" the tracks; the second "recaptured" them. The teams kept a gap between vehicles so the second team could not easily see where the first had stopped.

- The tracker pairs: the NG pair was led by a Master Tracker, with a Tracker III. The XZ pair was led by a Tracker III, with a tracker certified in Track & Sign I and Trailing III. The two pairs alternated between the first and second vehicle.

- Survey: seven days in May 2022 on 40 transect segments (175 km). Driving was capped at 10 km per hour and slowed where visibility was poor. Surveys avoided the midday hours, so shadows made tracks stand out. Trackers stopped the vehicle, and stepped down when needed, to confirm identification and age.

- Recording: for each track, location, species, number of individuals and age (from 24 hours to over a month) were recorded in CyberTracker on a smartphone. A trail crossing the transect counted as one track interception, and every crossing was counted, whoever made it. Identifications were not checked by an independent assessor; the aim was to measure disagreements in real conditions, where the truth is unknown.

- Factors tested: tracker pair; substrate (sand cutline, sand road, gravel road, off-road); livestock track density; track age (24 hours or less versus older); and whether a species tends to cross roads (the five antelopes) or follow them (the six carnivores and ostrich).

- Matching: tracks detected by both teams were matched by location, species and age, then analysed with logistic regression.

### 2. What they found

Detection is high but not perfect

- The teams recorded 1612 tracks: 1251 by the first team, 1127 by the second, and 766 by both.

- The probability of detecting a track was 0.68 for the first team and 0.61 for the second, rising to 0.77 and 0.75 for fresh tracks (24 hours or less).

- They estimated there were about 1841 tracks in total, so 229 were missed by both teams.

Track age matters most

- Fresh tracks were more detectable (0.77) than older tracks (0.61).

- Older tracks of followers (carnivores and ostrich) were the hardest to detect (0.55, against 0.67 for crossers). The authors suggest antelope cross in groups, leaving more obvious track sets than solitary carnivores.

Other conditions mattered little

- Detection was slightly higher on clear sand (0.73) than on gravel roads and off-road transects (0.65), a marginal difference.

- The pair led by the Master Tracker detected slightly more tracks, but only in the hardest conditions: fresh and antelope tracks off-road, and older tracks on gravel roads. The difference was very small (5%).

- Detection by both teams was marginally higher when the NG pair went first (0.52 versus 0.44), which may mean slight information transfer from the first vehicle.

Agreement on species

- The two pairs agreed on the species for 81% of the 766 tracks both detected, and 92% for fresh tracks.

- Agreement was 95% for carnivores and ostrich but 77% for antelope, mainly because gemsbok and red hartebeest, which leave very similar tracks, were confused in 29% of cases (119 of 412).

- The only carnivore mismatches were 4 leopard–cheetah, 1 lion–leopard and 2 spotted hyena–brown hyena.

Agreement on age

- The pairs agreed on whether a track was fresh or older for 88% of tracks. Disagreement was higher on gravel roads (0.23), whose fine sand and dust erode quickly.

No overall bias

- Disagreements took the form of two-way confusion rather than one pair consistently favouring one species or age class, so errors largely cancelled out in the total counts.

- One pair recorded proportionally more spotted hyena and blue wildebeest tracks, but the counts were too small to conclude a bias.

### 3. What they concluded

- The main finding: track detection was high compared with many other wildlife survey methods, and remarkably constant across tracker pairs and sampling conditions. Fresh tracks were slightly more detectable and more reliably identified than older ones.

- A first: to their knowledge, this is the first published study to use a capture-mark-recapture approach to measure track detection in real field conditions.

- For the FMP formula: detection of fresh tracks was below 1 (0.77), so absolute densities from driven surveys will be underestimated to some extent. Because detection was largely constant, the formula can at least give reliable relative densities. Practitioners must decide whether measuring detection to correct absolute estimates is worth the effort.

- Survey design: to keep detection high in driven surveys, survey where and when disturbance is mild, in daytime with good track visibility, at low speed set by the observers, and with observers whose skills have been assessed and maintained to an excellent level. Clearing transects by dragging 24 hours before may not be worth the cost, given that age confusions cancel out.

- Skill: track ageing is one of the hardest parts of tracking. The results emphasize training to reach and maintain excellent skills. Standardizing and quantifying tracking skills, for example through CyberTracker Conservation's Tracker Certification System, is necessary to ensure reliable data.

- Limitations: only two tracker pairs were compared, so formal proof that pairs do not differ would need more pairs. In walking surveys, detection is likely close to 1, but this was not tested.

- Communities: tracking knowledge is eroding. Involving communities in track-based work can maintain tracking skills, value cultural identity and Indigenous knowledge, and provide much-needed employment.

### 4. How tracking was or was not used

- Track counts: two pairs of certified trackers, driving at up to 10 km per hour, detected, identified and aged every track of 12 large species crossing the transect.

- Rules: a single trail crossing counted as one interception, an animal walking along the road counted once, and every separate crossing was counted, regardless of individual.

- Checking: trackers stopped the vehicle, and stepped down when needed, for a closer look.

- No trailing was done.

### 5. Tracker or observer certification

- Certified trackers. The NG pair was led by a Master Tracker, assisted by a Tracker III. The XZ pair was led by a Tracker III, assisted by a tracker with Track & Sign I and Trailing III certifications. The certifications follow the CyberTracker Tracker Certification System (Liebenberg et al. 2018).

- The four Indigenous tracker co-authors took part in the fieldwork. The paper states that they did not take part in the data analysis and did not influence its conclusions about their tracking skills.

### 6. Other technologies, skills or innovations

- Capture-mark-recapture applied to tracks: two teams driving the same transect one after the other, with matching by location, species and age instead of physically marking tracks.

- CyberTracker: a smartphone app used to record location, species, number of individuals and track age.

- Timing and speed: avoiding midday so shadows show the tracks, and capping speed at 10 km per hour.

### 7. Implications for conservation (from the paper)

- Value of track surveys: where the substrate is suitable, track surveys are cost-effective. They can cover many species at once, including elusive large carnivores, over large areas.

- Further uses: once both parts of detectability are measured or controlled, track counts can also help identify key habitats, distribution and landscape connectivity.

- Communities: involving local communities in track-based work can bring them benefits from wildlife conservation, for example through Community Based Natural Resource Management and Payments for Ecosystem Services.

- Recognition: certified skills could be formally recognized by governments and the private sector.

### 8. Implications for tracking (Original Wisdom's observations)

_These are Original Wisdom's observations, not the authors'._

- Even highly skilled certified trackers miss some tracks from a moving vehicle, and older tracks are harder both to see and to identify.

- Gemsbok and red hartebeest tracks were the most commonly confused pair, which may be worth particular attention in tracks and signs training in the Kalahari.

## Cite this summary

Lawrence, K. 2026. Summary of: Monitoring wildlife abundance through track surveys: A capture-mark-recapture inspired approach to assess track detection by certified trackers in the Kalahari, Botswana (Gielen and Johannes et al., 2024). Original Wisdom, Tracking Literature. https://www.originalwisdom.com/tracking-literature/2024-gielen-johannes-monitoring-wildlife-abundance-through/