# Power of Track Surveys to Detect Changes in Cougar Populations

> How many track transects does it take to know whether cougar numbers are falling? Using foot surveys of dry washes in Arizona and computer simulations, this study found that around 50 transects could catch a halving of numbers, but small yearly changes would need hundreds. Declines turned out to be easier to detect than increases.

Summary by Kersey Lawrence, Original Wisdom. Updated 9 November 2026. Page: https://www.originalwisdom.com/tracking-literature/1996-beier-cunningham-power-track-surveys-detect/

## Citation and access

- **Authors:** Paul Beier and Stanley C. Cunningham
- **Year published:** 1996
- **Journal or source:** Wildlife Society Bulletin
- **Full citation:** Beier, P. and Cunningham, S. C. 1996. Power of track surveys to detect changes in cougar populations. Wildlife Society Bulletin 24(3): 540–546.
- **Access status:** Publisher link only
- **Where to find it:** http://links.jstor.org/sici?sici=0091-7648%28199623%2924%3A3%3C540%3APOTSTD%3E2.0.CO%3B2-3

## Tags

- **Species:** Mammals > Mountain lion
- **Region:** North America > United States
- **Tracking:** Scat; Sign surveys; Track counts; Tracking; Tracks; Tracks and signs identification
- **Methods and technology:** Detectability; Kolmogorov–Smirnov test; Population trends; Power analysis; Simulation; Wilcoxon matched pairs test
- **Data type:** Existing data; Modelling or theoretical
- **Observer qualification:** Reported but not validated
- **Discipline:** Population trends; Wildlife tracking
- **Year published:** 1996

## Detailed keywords

- **Species:** Studied: cougar or mountain lion (Puma concolor). Mentioned: domestic cattle, whose tracks tended to wipe out cougar tracks in the lower parts of the washes; bighorn sheep, as an example of prey whose managers may want to detect a rise in cougars; grizzly bear (Ursus arctos horribilis) and black bear (U. americanus), from an earlier study of bear sign surveys.
- **Location and habitat:** Aravaipa-Klondyke study area (4035 km²), southeastern Arizona, United States. Twenty-eight first- and second-order dry stream washes (sand, dust and mud), walked from high to low elevation. Southern California and Florida are mentioned as places with small threatened cougar populations.
- **Topics:** track surveys as an index of population trend; statistical power; Type I and Type II error; number of transects needed; detecting increases versus decreases; patchy versus uniform population change; substrate and track detection along a wash; cattle trampling of tracks; presence-absence data per segment; transect placement in good and marginal habitat; comparison with hunter harvest, depredation and radiotelemetry methods.
- **Statistics or models:** Poisson model of track density; Kolmogorov-Smirnov tests (fit to normal and Poisson distributions); Wilcoxon matched-pairs signed-rank test and matched-pairs t-test; correlation between consecutive surveys (r² = 0.04, n = 51 pairs); simulation of 1,000 survey efforts per combination with Resampling Stats software, using a resampling (randomisation) test with 500 samples; power at two-sided α = 0.05, 0.10 and 0.20, with 80% as the target. Inconsistencies in the paper: (1) the abstract says about 110 transects at α = 0.10 and 85 at α = 0.20 to detect a 30% decrease, while the results say 120 and 90; (2) the methods text says the count was scaled for transects longer than 8 km, while the table footnote says shorter than 8 km (all transects in Table 1 are 8 km or less, so the footnote seems right); (3) the Figure 1 caption gives 1992-1993, while the four sampling periods ran from October 1991 to April 1993.
- **Data type:** Existing data: track surveys by Cunningham et al. (1995) in four sampling periods (October 1991, April 1992, October 1992, April 1993), 78 transect walks in all. Modelling: simulated Poisson data.

## Synopsis

### 1. What they did

Cougars are active at night, secretive and live at low densities, so changes in their numbers are hard to follow. Radio collaring many animals can give a population estimate, but it is costly and covers only a small area. Managers often use hunter kills, livestock losses or track surveys instead. The authors asked how many track transects would be needed to reliably detect a 30% or 50% change in cougar numbers between two surveys.

- Field data. They used existing data from 28 dry washes in the Aravaipa-Klondyke area of southeastern Arizona, walked in four periods between October 1991 and April 1993. Washes were chosen because tracks could only be seen in dust, sand or mud, and wind and traffic quickly wiped out tracks on dirt roads. A survey started only after 5 days without rain.

- The survey. One trained observer started at first light at the top of each wash and walked downstream for about 4 hours. For each 0.5-km segment, the observer counted cougar scats and track sets (a continuous set of tracks apparently made by one animal) and estimated how many different cougars were present.

- What they analysed. They reduced this to whether each segment had cougar tracks or not, and counted the segments with tracks on an 8-km transect (“track density”). They did not use scat counts, track set counts or estimated numbers of animals, because presence or absence depends less on the observer’s skill, and a high count usually meant a cougar had a kill nearby, not that more cougars were there.

- Simulations. Because track density followed a Poisson distribution, they simulated pairs of surveys and tested how statistical power changed with the number of transects (30 to 270), the accepted risk of a false alarm (α of 0.05, 0.10 or 0.20), the size of the change (30% or 50%), whether numbers rose or fell, and whether the change was even across transects or confined to half of them. They also tested a 10% decrease with 300 to 1,000 transects.

### 2. What they found

The field surveys

- On average, 7% of the 0.5-km segments had cougar tracks (mean 1.14 segments per 8-km transect).

- Tracks were rarely found in the first kilometre of a wash, where there was no well-developed scour zone with sand. In the lowest parts, cattle tracks tended to wipe out cougar tracks.

- More segments had tracks in the first period (12%) than later; only the drop from period 1 to period 2 (4%) was statistically significant.

- Track density followed a Poisson distribution, not a normal one, and a transect’s count did not predict its next count.

The simulations

- More transects gave more power. To reach 80% power for large changes needed from about 30 transects (a 50% decrease at α = 0.20) to about 190 transects (a 30% increase at α = 0.05).

- Decreases were easier to detect than increases.

- Detecting a 30% decline at α = 0.05 needed about 140 transects; at α = 0.10, about 120; at α = 0.20, about 90. A 50% decline needed only about 50 transects at α = 0.05.

- A small (10%) decrease was out of reach: 400 transects gave only 48% power, and 700 were needed for 80%.

- Most power curves levelled off at 75% to 90% power, so gains beyond that cost many more transects.

- When all the change happened on half the transects, power fell by 2% or less.

### 3. What they concluded

- About 30 to 190 eight-kilometre transects in dry washes would be needed to detect a 30% to 50% change in a cougar population. Track surveys will usually not detect small yearly changes, but may reveal large changes more efficiently than other methods.

- Even so, the authors believe track surveys are better than the alternatives: unlike hunter kills and livestock losses, they allow statistically valid conclusions, and they are cheap, easy to repeat, easy to standardise between observers, and do not disturb the animals.

- They suggest 80% power as a reasonable goal, and that an α above 0.05 is reasonable in most management situations: 0.10 to 0.20 for threatened populations, and 0.05 to 0.10 for hunted populations.

- Their results hold for any track counts that follow a Poisson distribution, and managers can easily check whether their own data do.

- Transects should be spread through the whole area used by the population, including marginal habitat, rather than only where cougars are expected to be common, and placed where cougars are most likely to travel and their tracks most likely to be visible.

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

The field data came from foot surveys of dry washes, in which an observer identified cougar tracks and scats in each 0.5-km segment, counted track sets, and estimated the number of individuals. The analysis used only whether each segment had cougar tracks. Track sets that clearly continued from the previous segment were not counted again. The paper also notes how substrate (sand, dust or mud) and cattle trampling affected where tracks could be found.

### 5. Tracker or observer certification

Reported but not validated. Each transect was walked by “a single trained observer”; the training and the observer’s skill were not described or tested. The authors chose presence-absence data partly because they are less affected by differences in observer skill. A. LeCount, J. Yarchin, R. Thompson, M. Peirce, K. Sergent and T. Waddell are thanked for developing the original field study and for help in the field.

### 6. Other technologies, skills or innovations

- Computer simulation of survey results with Resampling Stats software, using a resampling test that makes no assumptions about how the data are distributed.

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

- Surveys meant to catch a decline need less effort than surveys meant to catch an increase.

- For threatened populations, missing a real decline could mean extinction, so a higher risk of false alarm is acceptable if emergency steps would follow.

- If managers accept low power, the authors question whether the survey has any purpose other than making the public think the manager is “doing something.”

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

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

- The study shows how much the ground decides what a track survey can see: the same wash gave few tracks at its top (no sand) and at its bottom (cattle trampling), so where transects are placed shapes the counts.

## Cite this summary

Lawrence, K. 2026. Summary of: Power of Track Surveys to Detect Changes in Cougar Populations (Beier and Cunningham, 1996). Original Wisdom, Tracking Literature. https://www.originalwisdom.com/tracking-literature/1996-beier-cunningham-power-track-surveys-detect/