# Estimating Lion Abundance using N-mixture Models for Social Species

> How many lions live in the southeastern Serengeti? By broadcasting roars and distress calls at night and reading fresh tracks on swept roads each morning, researchers estimated about 270 lions. Lions quickly grew used to the calls, and bright moonlight made them harder to detect, while track counts could not be turned into a lion count, partly because individual lions were hard to tell apart from their tracks.

Summary by Kersey Lawrence, Original Wisdom. Updated 9 November 2026. Page: https://www.originalwisdom.com/tracking-literature/2016-belant-bled-estimating-lion-abundance-n/

## Citation and access

- **Authors:** Jerrold L. Belant, Florent Bled, Clay M. Wilton, Robert Fyumagwa, Stanslaus B. Mwampeta and Dean E. Beyer, Jr.
- **Year published:** 2016
- **Journal or source:** Scientific Reports
- **Full citation:** Belant, J. L., Bled, F., Wilton, C. M., Fyumagwa, R., Mwampeta, S. B. and Beyer, D. E., Jr. 2016. Estimating lion abundance using N-mixture models for social species. Scientific Reports 6: 35920.
- **DOI:** 10.1038/srep35920
- **Access status:** Open access (CC BY 4.0)
- **PDF:** https://www.originalwisdom.com/wp-content/uploads/2026/11/2016_Belant_and_Bled_etal_estimating-lion-abundance-using-n-mixture-models-for-social-species.pdf

## Tags

- **Species:** Mammals > Leopard; Mammals > Lion
- **Region:** Africa > Tanzania
- **Tracking:** Individual identification; Track counts; Track measurements; Track photographs; Tracking; Tracks; Tracks and signs identification
- **Behaviour and ecology:** Behaviour
- **Methods and technology:** Bayesian statistics; Detectability; Geographic Information System (GIS); Linear regression; Markov chain Monte Carlo (MCMC); Population estimation; Road transects
- **Data type:** New field data
- **Observer qualification:** Reported but not validated
- **Discipline:** Behaviour and ecology; Wildlife tracking
- **Year published:** 2016

## Detailed keywords

- **Species:** Studied: African lion (Panthera leo). Present but rare in the study area, and told apart from lion by their tracks: leopard (Panthera pardus). Calls used to attract lions: female lion roar, wildebeest (Connochaetes taurinus) in distress, spotted hyena (Crocuta crocuta) whoop, and, in weeks 6 and 7 at some sites, buffalo (Syncerus caffer) distress calls. Mentioned as an earlier N-mixture study: clouded leopard (Neofelis diardi).
- **Location and habitat:** A 1,880 km² area in southeastern Serengeti National Park, Tanzania. Savanna running from short-grass savanna in the southeast through tall-grass savanna to woodland in the northwest, with woody vegetation along rivers and rock outcrops (kopjes) throughout. Land cover grouped into six classes: sparse grassland, closed grassland, dense grassland, shrub-grassland, shrubland and woodland. Track routes ran on dirt roads with clay substrate.
- **Topics:** lion abundance and density estimation; call-in (playback) surveys; track surveys on roads; imperfect detection; group versus individual detection in social species; habituation to broadcast calls; moonlight (lunar illumination) and lion detectability; observer effects; land cover; track density versus abundance; difficulty of telling individual lions apart from tracks; comparison with distance sampling and camera-trap estimates.
- **Statistics or models:** N-mixture models (Bayesian, run in WinBUGS) with a hierarchical (two-level) observation process for call-ins: a weekly group-response probability and an individual detection probability, similar to a zero-inflated binomial model. Random observer and site-week effects; Poisson abundance with land cover and other landscape covariates; variable selection for regression models (covariates kept if selected at least 10% of the time); 3 Markov chain Monte Carlo (MCMC) chains, Gelman-Rubin convergence statistic; Bayesian p-values for goodness-of-fit; simple linear regression of predicted track density against predicted abundance. Inconsistencies: (1) the text gives individual detection probabilities across weeks of 0.74 to 0.92, while Figure 2 appears to show a week 5 value of about 0.93; (2) figure captions describe “95% confidence intervals” for what the text reports as Bayesian credible intervals; (3) the Results call the track-to-abundance relationship “weak negative” (R² = 0.113, P = 0.012), while the Discussion calls it a “poor correlation” and a “lack of an observed relationship”.
- **Data type:** New field data: 7 weeks of call-in and track surveys from mid-September to November 2015.

## Synopsis

### 1. What they did

Lion numbers are falling across Africa, and managing them well depends on accurate counts. Lions are often counted directly, by call-in surveys, or by track counts, but few studies repeat their surveys in a way that allows for lions (or their tracks) that are present but missed. The authors tested N-mixture models, statistical models that estimate abundance from repeated counts at many sites, on lions in southeastern Serengeti National Park, Tanzania.

Over 7 weeks from mid-September 2015 they ran two surveys at the same time:

- Call-in survey. At 39 sites about 6 km apart, two crews broadcast a recording of a female lion roar, a wildebeest in distress and a spotted hyena whoop from four roof-mounted speakers, for 70 minutes starting at 7 pm. Each site was visited once a week. Lions that came in were counted through the roof hatch with a red-filtered spotlight and a thermal (forward-looking infrared) monocular. Because fewer lions came as the weeks went by, buffalo distress calls replaced wildebeest calls at some sites in weeks 6 and 7.

- Track survey. Ten road routes of about 25 km each (253 km in total) were swept with a tire drag each evening and checked the next morning from about 7 am, with an experienced tracker sitting on the hood of the vehicle. Each route was surveyed once a week.

Because lions live in groups, a whole pride may answer a call or not. The authors built this into the model: first, does a group respond in a given week; then, if it does, is each lion seen. They also included moonlight, land cover, rivers, roads, kopjes and which crew did the survey.

### 2. What they found

Call-in survey

- The model estimated 270 lions in the sampled area (median 242; 95% credible interval 170 to 551), or 14.4 lions per 100 km². Sites held between 2.0 and 17.9 lions.

- The chance of lions responding to a call fell sharply, from 0.93 in week 1 to 0.11 in week 5, then rose to 0.50 in week 7. The authors read this as rapid habituation to the calls.

- Once a group responded, the chance of seeing each lion was steadier (0.74 to 0.92 across weeks).

- Brighter moonlight lowered the chance of detecting individual lions. Land cover stayed in the model, but its effect on abundance could not be determined.

Track survey

- The crews recorded 456 lion track occurrences: 0 to 129 per route and 52 to 91 per week. Predicted density was 5.0 to 12.9 tracks per km.

- Land cover stayed in the track model too, with no clear effect.

- Predicted track density had a weak negative relationship with predicted lion numbers from the call-ins (R² = 0.113), so the authors could not estimate lion abundance from tracks.

Comparison with earlier counts

Their estimate was close to earlier distance-sampling estimates for larger areas that included theirs (314 lions in 2002; 247 in 2005) and a little higher than a camera-trap reference count of known females, which suggested to them that it is reasonable.

### 3. What they concluded

- This was the first estimate of lion abundance with N-mixture models, and the first model built to allow for the way social animals respond and are detected as groups.

- The estimate may be slightly high if the same lions came to neighbouring call-in sites, though earlier work suggests lions rarely come from more than 3 km away.

- Lions may move less on bright nights, when prey can see them more easily, which could make them less likely to approach calls.

- Changing the call sequence or site order between weeks, or leaving longer gaps between surveys, could reduce habituation and should be tested.

- Tracks did not track lion numbers well. Possible reasons are the small grid cells, missing covariates, and, more practically, not being able to tell individual lions apart from their tracks. Like an earlier study by Midlane and colleagues in Kafue National Park, they found call-ins more suitable than track surveys for estimating lion abundance.

- Call-ins work in both savanna and forest, can be done quickly, and with N-mixture models allow for observer differences and changing conditions such as moonlight.

- They suggest N-mixture models with a group-level observation process for other social, herding and grouping species, such as ungulates, birds and fish.

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

Track surveys ran alongside the call-ins. Roads were dragged clean the evening before, and each morning an experienced tracker on the vehicle hood looked for lion tracks while the driver drove at up to 10 km per hour, finishing by about noon to avoid harsh sunlight on the tracks. For each set of lion tracks the team:

- estimated the number of lions from track size, how the tracks lay in relation to each other, and direction of travel;

- measured the length and width of one representative track per lion and photographed it;

- counted tracks further along the route as new lions unless the measurements and photos showed they were the same animals.

The occasional leopard track was told apart from lion by size, pad shape, group size (leopards usually travel alone) and location (leopards mostly keep to wooded riverbanks). Any track that could not reliably be identified as lion was discarded. No trailing was reported.

The authors note that they did not measure all four paws, as recommended for identifying individuals, because of uneven road surfaces and varying substrate, and that overlapping measurements made telling lions apart hard. Lion numbers from tracks may therefore have been both over- and underestimated.

### 5. Tracker or observer certification

Reported but not validated. Each track crew had “an experienced tracker”, but no name, certification or test of skill is given. The model included a random observer effect for both surveys, and crews swapped routes and sites each week to allow for differences between them. Six people are thanked for field assistance.

### 6. Other technologies, skills or innovations

- A two-level (hierarchical) detection model for social species: group response, then individual detection.

- A commercial game-calling system (Foxpro) broadcasting at up to 116 decibels from four speakers, alternating between speaker pairs to spare the vehicle battery.

- A red-filtered spotlight (lions showed some aversion to unfiltered light) and a forward-looking infrared (FLIR) monocular for counting lions at night.

- Moonlight values for each night calculated with the R package lunar; landscape layers from the Serengeti-Mara database in a Geographic Information System (GIS).

- Tire drags to clear road routes before each track survey.

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

- Accurate estimates of large carnivore numbers are needed for harvest rules, conservation status assessments and understanding change.

- Allowing for animals that are present but missed, and for observer and moonlight effects, can improve abundance estimates.

- Reducing habituation to broadcast calls would improve the precision of call-in estimates.

- Better landscape measures tied to lion resource use, and monitoring of prey, could improve the models.

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

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

- The track survey’s main weakness was telling individual lions apart. The authors themselves name it: not all four paws were measured, the road surface was uneven, and overlapping measurements blurred individuals. Track counts for abundance depend on clear rules and skill in separating individuals, and on recording how that skill was checked.

- The tracker’s skill was described only as “experienced”. Without a reported evaluation, readers cannot tell how much of the track result reflects the method and how much reflects identification.

## Cite this summary

Lawrence, K. 2026. Summary of: Estimating Lion Abundance using N-mixture Models for Social Species (Belant and Bled et al., 2016). Original Wisdom, Tracking Literature. https://www.originalwisdom.com/tracking-literature/2016-belant-bled-estimating-lion-abundance-n/