# Monitoring Carnivore Populations at the Landscape Scale: Occupancy Modelling of Tigers from Sign Surveys

> How do you map tigers across 38,000 km² of forest and farmland in India? Teams of trackers walked 4,174 km of forest trails, recording tiger tracks and scats, and the researchers modelled how often signs are missed. Tigers turned out to occupy about two-thirds of the available habitat, nearly twice what a simple tally of signs suggested, and were most likely to be present where prey was plentiful and livestock scarce.

Summary by Kersey Lawrence, Original Wisdom. Updated 9 November 2026. Page: https://www.originalwisdom.com/tracking-literature/2011-karanth-gopalaswamy-monitoring-carnivore-populations-landscape/

## Citation and access

- **Authors:** Kota Ullas Karanth, Arjun M. Gopalaswamy, Narayanarao Samba Kumar, Srinivas Vaidyanathan, James D. Nichols and Darryl I. MacKenzie
- **Year published:** 2011
- **Journal or source:** Journal of Applied Ecology
- **Full citation:** Karanth, K. U., Gopalaswamy, A. M., Kumar, N. S., Vaidyanathan, S., Nichols, J. D. and MacKenzie, D. I. 2011. Monitoring carnivore populations at the landscape scale: occupancy modelling of tigers from sign surveys. Journal of Applied Ecology 48(4): 1048-1056.
- **DOI:** 10.1111/j.1365-2664.2011.02002.x
- **Access status:** Hosted (freely available elsewhere)
- **PDF:** https://www.originalwisdom.com/wp-content/uploads/2026/11/2011_Karanth_and_Gopalaswamy_etal_monitoring-carnivore-populations-at-the-landscape-scale.pdf

## Tags

- **Species:** Mammals > Chital; Mammals > Dhole; Mammals > Gaur; Mammals > Leopard; Mammals > Muntjac; Mammals > Sambar; Mammals > Tiger; Mammals > Wild pig
- **Region:** Asia > India
- **Tracking:** Scat; Sign surveys; Track photographs; Tracking; Tracks; Tracks and signs identification
- **Behaviour and ecology:** Behaviour; Home range; Human disturbance; Movement
- **Methods and technology:** Akaike Information Criterion (AIC); Bootstrapping; Detectability; Distribution mapping; Occupancy modelling
- **Data type:** New field data
- **Observer qualification:** Reported but not validated
- **Discipline:** Behaviour and ecology; Wildlife tracking
- **Year published:** 2011

## Detailed keywords

- **Species:** Studied: tiger (Panthera tigris). Signs also recorded (as covariates for tiger presence): the main ungulate prey, gaur (Bos frontalis), sambar (Rusa unicolor), chital (Axis axis), wild pig (Sus scrofa) and muntjac (Muntiacus muntjak), and livestock (as a measure of human disturbance). Signs of leopard (Panthera pardus) and dhole (Cuon alpinus) were also recorded, but not analysed in this paper. Mentioned as other examples: wolverine (Gulo gulo), jaguar and lion.
- **Location and habitat:** The Malenad-Mysore Tiger Landscape (MMTL), central Western Ghats, Karnataka State, India. A landscape of 38,350 km² of moist-evergreen, moist-deciduous and dry-deciduous forest mixed with horticultural, agricultural and forestry crops, home to about 10.5 million people. Potential tiger habitat of 21,167 km², including 14 wildlife reserves (5,500 km²). Protection was described as high in Bandipur, Nagarahole and Bhadra reserves, moderate in BR Temple, Kudremukh and Dandeli-Anshi reserves, and somewhat poor elsewhere.
- **Topics:** landscape-scale sign surveys for large carnivores; imperfect detection of signs; occupancy (proportion of area occupied) versus naive presence-versus-absence estimates; spatial replicates along forest trails; dependence between detections on neighbouring trail sections; survey design based on tiger home range and movement along trails; prey density and human disturbance as drivers of tiger presence; source populations and metapopulations; mapping tiger distribution; prey depletion and local extinction; the “landscape approach” to tiger conservation; linking sign surveys with camera-trap density estimates.
- **Statistics or models:** Standard occupancy model (MacKenzie et al. 2002) compared with the Hines et al. (2010) spatial dependence occupancy model, which treats detections on successive trail sections as a first-order Markov process; program PRESENCE; model selection by Akaike Information Criterion (AIC) and model averaging over the top 10 models; parametric bootstrapping for variance; covariates: all prey, large prey, livestock and proportion of habitat in each cell; beta coefficients for each covariate. The authors note that PRESENCE offered no goodness-of-fit test for the spatial dependence model. Inconsistencies: (1) the survey is called “15-month-long”, but the dates given (February to May 2006 and December 2006 to May 2007) add up to about 10 months of fieldwork, spread over 16 months; (2) the text says “two covariates” contributed to variation in sign detection, while the top detection model has three (large prey, livestock and proportion of habitat); (3) the standard error for the occupancy estimate (0.0857) is identical to that given for theta-prime, which may be a copying error; (4) the text says the habitat includes 14 wildlife reserves, while the Figure 1 legend lists 16 protected areas; (5) the standard error of the detection probability is given as 0.17 in the Summary and 0.168 in the Results (rounding).
- **Data type:** New field data: sign surveys from February to May 2006 and December 2006 to May 2007, in dry seasons; 205 grid cells, 2,021 person-days, 4,174 km walked, 403 tiger signs.

## Synopsis

### 1. What they did

Tigers have lost about 93% of their range in two centuries, and their future depends on about 40 “source populations” where they breed above replacement level. Inside reserves, researchers can count tigers with camera traps or DNA from scats, but across whole landscapes they usually rely on finding tracks and scats. The problem is that not finding signs does not prove tigers are absent, so simple presence-versus-absence surveys underestimate where tigers live.

The authors set out to design a practical sign survey that corrects for missed signs, and to test what decides whether tigers are present: they expected prey numbers and protection from human disturbance to matter most.

- The study area. The Malenad-Mysore Tiger Landscape in Karnataka, India: about 38,000 km² of forest mixed with farms and plantations, with 21,167 km² of potential tiger habitat. It was thought to hold about 20% of India’s wild tigers.

- The grid. They divided the landscape into 205 square cells of 188 km² each, larger than the largest expected tiger home range (non-resident males, about 80 to 90 km²), and surveyed every cell.

- The walks. Teams of 2 to 3 skilled trackers walked forest trails from morning to dusk in the dry season, up to 40 km per cell (less where there was less habitat). Tigers often travel 1 to 20 km a day along forest trails, leaving tracks and the occasional scat. The teams photographed, located and recorded signs of tigers, leopards, dholes, prey species and livestock, recording only signs they could identify without doubt.

- The analysis. Each 1 km of trail became one “spatial replicate” (a repeat sample). Because a tiger may walk more than 1 km along a trail, finding signs on one kilometre makes signs on the next more likely. The authors compared a standard occupancy model with a newer one that allows for this, and tested the effect of prey signs, livestock signs (a measure of human disturbance) and the amount of habitat in each cell.

### 2. What they found

Survey effort

- 2,021 person-days, 4,174 km walked, and 403 tiger signs found: 223 scats and 180 sets of tracks.

Which model worked

- Signs on neighbouring kilometres were clearly not independent. The model that allows for this fitted far better than the standard model (a difference in AIC of 184.9).

Detection and occupancy

- The chance of finding tiger signs on a 1 km section, where tigers were present, was only 0.17 (17%).

- Tigers occupied an estimated 66% of the potential habitat: 14,076 km² (standard error 1,814 km²) of 21,167 km².

- The simple presence-versus-absence approach gave only 7,537 km², underestimating occupancy by about 47%.

What drives tiger presence

- More prey (all prey, and large prey: gaur, sambar, chital and wild pig) and more available habitat went with a higher chance of tigers being present.

- More livestock signs, the measure of human disturbance, went with a lower chance.

- These covariates had a strong effect on how easily signs were found, but the evidence for their effect on occupancy itself was weaker. All effects were in the predicted direction.

- Maps of occupancy probability picked out the source populations at higher tiger densities and matched known differences in tiger density.

### 3. What they concluded

- Landscape-scale sign surveys can efficiently show where large carnivores live and what governs their presence, as long as both the ecology and the way signs are detected are modelled.

- Ignoring missed signs can badly underestimate tiger distribution. The authors found it puzzling that other recent reports suggested sign surveys can estimate tiger distribution reliably without this correction.

- Designing surveys around the animal’s biology (home range size, movement along trails) and field logistics (distance walked, coverage of each cell) paid off. Three covariates (habitat, prey and human disturbance) were enough to explain the pattern of tiger distribution.

- The results support earlier findings that prey depletion and human disturbance drive local tiger extinctions. Tigers persisted better in this densely populated landscape than in larger, more remote forests elsewhere in India, apparently thanks to a few well-protected source populations.

- The method could be combined with camera-trap density estimates to estimate tiger numbers across large landscapes, and could suit many other wide-ranging carnivores whose signs are fairly easy to find.

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

The whole survey rested on tracks and signs identification. Teams walked forest trails and recorded fresh tiger tracks and scats, plus signs of leopards, dholes, five prey species and livestock. Only signs identified without doubt were recorded, and each was photographed and its location recorded. Each type of sign was logged once per 100 m of trail. No trailing was reported: teams followed trails, not individual animals. The design used what is known of tiger movement: tigers travel along forest trails and leave tracks and occasional scats as they go.

### 5. Tracker or observer certification

Reported but not validated. The survey teams are described as “skilled trackers” and “trained observers”, but no certification or test of their identification skill is reported. The field surveys were led by named team leaders, and many field assistants and volunteers took part. All results rest on these sign identifications.

### 6. Other technologies, skills or innovations

- A spatially replicated survey design: 1 km sections of a continuous trail used as repeat samples, in place of repeat visits over time, chosen mainly for practical reasons. Each cell was surveyed within about 12 to 36 hours.

- The Hines et al. (2010) occupancy model, which allows for the fact that signs on one stretch of trail make signs on the next more likely, run in program PRESENCE.

- Livestock signs used as an easily detected measure of human disturbance and poor protection.

- Maps of the probability that each cell is occupied by tigers.

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

- About 70% of wild tigers now survive in source populations on just 6% of their habitat, and these sources have so far been identified from anecdotal evidence. This survey approach can identify them quickly and objectively, so conservation effort can be targeted.

- Tiger presence depends strongly on prey density and protection, which cannot be seen by satellite, so landscape analyses without field data have limits.

- Combining intensive camera-trap surveys inside reserves with cheaper landscape-wide sign surveys offers a way to monitor tiger metapopulations. Repeated over time, the surveys could track range contractions or expansions.

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

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

- Tracks and scats found along trails can map the distribution of a rare, elusive carnivore across thousands of square kilometres, where camera traps or capture are not practical.

- Even with skilled teams, tiger signs were found on only about 17% of trail kilometres where tigers were present. Not finding signs is not the same as absence, which is why the authors model detection.

- The whole analysis depends on correct identification of tracks and scats, yet the trackers’ skill was not tested or reported. Tested observer skill would strengthen the data that such models rely on.

## Cite this summary

Lawrence, K. 2026. Summary of: Monitoring Carnivore Populations at the Landscape Scale: Occupancy Modelling of Tigers from Sign Surveys (Karanth and Gopalaswamy et al., 2011). Original Wisdom, Tracking Literature. https://www.originalwisdom.com/tracking-literature/2011-karanth-gopalaswamy-monitoring-carnivore-populations-landscape/