# Linking landscape characteristics to local grizzly bear abundance using multiple detection methods in a hierarchical model

> Where do grizzly bears actually live in greatest numbers? By collecting hair from baited wire traps and from trees that bears rub on around Glacier National Park, Montana, researchers identified 222 individual bears by their DNA. Both males and females were fewer where roads were dense, and females were more common where there was more moist habitat.

Summary by Kersey Lawrence, Original Wisdom. Updated 9 November 2026. Page: https://www.originalwisdom.com/tracking-literature/2011-graves-kendall-linking-landscape-characteristics-local/

## Citation and access

- **Authors:** T. A. Graves, K. C. Kendall, J. A. Royle, J. B. Stetz and A. C. Macleod
- **Year published:** 2011
- **Journal or source:** Animal Conservation
- **Full citation:** Graves, T. A., Kendall, K. C., Royle, J. A., Stetz, J. B. and Macleod, A. C. 2011. Linking landscape characteristics to local grizzly bear abundance using multiple detection methods in a hierarchical model. Animal Conservation 14(6): 652-664.
- **DOI:** 10.1111/j.1469-1795.2011.00471.x
- **Access status:** Publisher link only
- **Where to find it:** https://doi.org/10.1111/j.1469-1795.2011.00471.x

## Tags

- **Species:** Mammals > Brown bear
- **Region:** North America > United States
- **Tracking:** Hair; Sign surveys; Tracking
- **Behaviour and ecology:** Behaviour; Habitat selection; Human disturbance
- **Methods and technology:** Bayesian statistics; Capture-mark-recapture; Detectability; DNA; Hair sampling; Markov chain Monte Carlo (MCMC); Population estimation
- **Data type:** New field data
- **Observer qualification:** Not reported
- **Discipline:** Behaviour and ecology; Wildlife tracking
- **Year published:** 2011

## Detailed keywords

- **Species:** Studied: grizzly bear (brown bear, Ursus arctos), males and females analysed separately. 222 individual bears identified from hair: 108 males and 114 females. Other species named only as examples of earlier studies using the same kind of model: amphibians, fish, wild turkeys and shrubland birds.
- **Location and habitat:** Greater Glacier National Park study area (7933 km², centred on Glacier National Park, 4079 km²), northwestern Montana, United States, covering the northern third of the Northern Continental Divide Ecosystem and over 30% of its grizzly bear habitat. Pacific maritime-influenced coniferous forest west of the Continental Divide, grading to drier prairie grassland and farmland along the eastern boundary; elevations 960 to 3190 m. Land under different owners with different bear protection: the national park, US Forest Service land, other state and federal land, Plum Creek land and private land. Human density generally low, mostly along road corridors, with several large towns.
- **Topics:** relative abundance of grizzly bears; landscape and habitat characteristics; road density; building and activity density; per cent mesic (moist) habitat; avalanche chutes; greenness; precipitation; terrain ruggedness; solar radiation; three-dimensional area; bear protection level (food storage rules and enforcement by landowner); historic human-caused mortality (1970 to 1999); non-invasive genetic sampling; hair traps (barbed wire and scent lure); bear rub trees; multiple detection methods; detection probability and sampling effort; spatial autocorrelation; habitat selection versus abundance; road closures; habitat monitoring for recovery of a threatened population.
- **Statistics or models:** N-mixture model (a site-based mark-recapture model with an abundance level and a detection level), extended to two detection methods with separate detection sub-models; Poisson abundance, binomial detection with logit link; conditional autoregressive (CAR) term for spatial autocorrelation; Bayesian variable selection with indicator weights (Kuo and Mallick method), variables with weights over 50% considered important; posterior model probabilities; Markov chain Monte Carlo (MCMC) in WinBUGS run from R (R2WinBUGS); three chains, 10,000 burn-in, 120,000 iterations, thinned by 20; priors with variance 10 or 100 compared; correlations (r) between predicted abundance and raw counts. Inconsistencies in the paper: the methods describe one hundred and twenty-six 8 x 8 km grid cells, but the model performance section says 125 grid cells (and 1250 rub cell sessions, which matches 125 cells x 10 sessions). The abundance equation is printed as “Nt ~ Poisson(λj)” though the text defines Ni and λi. The article header says “Received 30 September 2010; accepted 6 May 2010”, which puts acceptance before receipt. The text layer drops minus signs in Tables 3 and 5; the text and Figure 3 confirm the road density coefficients are negative.
- **Data type:** New field data: hair traps in five 14-day sessions, 22 May to 9 August 2000 (633 trap sites); 907 rub trees along 1185 km of trails, surveyed 22 May to 27 October 2000 (10 sessions). The same dataset was used by Kendall et al. (2008) and Boulanger et al. (2008). Landscape layers from existing maps and remote sensing (some from 1998 or 2001).

## Synopsis

### 1. What they did

Most habitat management for grizzly bears has been based on where radio-collared bears choose to go (habitat selection). Few studies have linked habitat to how many bears actually live in an area, and those did not account for bears being present but not detected. The authors wanted to find out which landscape features best explain how many grizzly bears live in different parts of a large area.

In the summer of 2000, they collected bear hair in and around Glacier National Park in northwestern Montana, using two methods:

- Hair traps. A single strand of barbed wire around several trees with scent lure poured on forest debris in the middle. They aimed for one trap in each 8 x 8 km grid cell in each of five 14-day sessions, moving traps between sessions (633 trap sites in all).

- Rub trees. Trees that bears naturally rub on, fitted with short pieces of barbed wire to catch hair. They surveyed 907 rub trees along 1185 km of trails, mostly in the park, visiting each about five times.

Genetic analysis of the hair gave the species, sex and identity of each bear. The authors then built a model that estimated the number of bears in each grid cell while separately estimating how likely each method was to detect a bear there. They tested 11 landscape features, such as road density, building density, moist (mesic) habitat, avalanche chutes, greenness, precipitation, terrain ruggedness, how strongly bears were protected on each type of land, and past human-caused bear deaths. They also tested whether bear numbers in neighbouring cells were linked (spatial autocorrelation). Males and females were analysed separately.

### 2. What they found

- The hair identified 222 grizzly bears: 108 males and 114 females. 71% of grizzly bear samples (809) could be matched to an individual.

- Females: road density and per cent mesic habitat best explained how many females lived in a cell. There were more females where road density was lower and where there was more mesic habitat. Spatial autocorrelation was not supported (included in only 34.6% of models). Detection of females rose with rub tree sampling effort.

- Males: road density best explained how many males lived in a cell, with more males where road density was lower. Spatial autocorrelation was supported (63% of models). Detection of males rose with both rub tree and hair trap effort, and fell over the course of the summer. When the authors used narrower priors, precipitation became important and spatial autocorrelation dropped out, so they kept the results from the less informative priors.

- Predicted numbers of bears per cell matched the raw counts of bears detected fairly well (r = 0.72 for females and 0.78 for males).

- Male and female predicted numbers were similar even though there are about 40 males for every 60 females, because males have larger home ranges and are counted in more cells. The average seasonal home range of adult females was 231 km² (a personal communication), larger than one grid cell.

- West-central Glacier National Park held a cluster of males beyond what the landscape features predicted.

- The number of hair samples in a hair trap was a poor guide to the number of bears caught there (r = 0.459).

- The data were sparse (64% of rub tree cell sessions had no rub tree sampling), so the results show only relative numbers of bears, not absolute counts.

### 3. What they concluded

- Road density was the most important landscape feature for both sexes, consistent with earlier studies that found grizzly bears avoid areas with many roads.

- The study supports the assumption that closing and removing roads may increase the number of bears where mesic habitat and areas of low road density are nearby.

- Landscape features worked the same way for abundance as in earlier habitat selection studies, suggesting habitat selection analyses give a good index of grizzly bear habitat quality in this ecosystem.

- Their approach offers a new way to combine several detection methods in one abundance model and to test whether spatial autocorrelation belongs in the model. It is most useful where detection varies with sampling effort, time or habitat, and where different methods detect different parts of the population.

- They recommend sampling as evenly as possible across sessions and space.

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

No tracks or trailing were used. The study relied on one kind of natural sign: hair left on trees that grizzly bears rub on. Field crews surveyed rub trees along trails and collected hair from them, alongside hair from baited hair traps. All identifications of species, sex and individual came from genetic analysis, not from reading the signs. The paper does not describe how rub trees were found or recognised; it refers to Kendall et al. (2008).

### 5. Tracker or observer certification

Not reported. The paper thanks more than 450 biologists, field and laboratory technicians, data specialists and many volunteers, but does not say who found and surveyed the rub trees or what their experience was. Because every identification was made genetically, the findings do not depend on observers’ sign identification skills.

### 6. Other technologies, skills or innovations

- Non-invasive genetic sampling: species from the G10J microsatellite, sex from the SRY and amelogenin markers, and individual identity from six microsatellite loci, with up to 10 more loci used to confirm close matches and careful error checking.

- An extended N-mixture model that handles two detection methods with their own detection sub-models, in one hierarchical Bayesian model.

- Bayesian variable selection with indicator weights, and a conditional autoregressive term to test spatial autocorrelation.

- Landscape layers from remote sensing, including the LANDFIRE vegetation cover map (for mesic habitat), DAYMET precipitation, and a new avalanche chute index from remote imagery.

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

- Identifying the landscape features that best explain local bear numbers can help prioritise conservation actions for threatened populations, and those features can be monitored as an early warning of decline. Habitat monitoring is one requirement for removing this population from the threatened list.

- Closing and removing roads, as some land managers already do, may increase bear numbers where mesic habitat and low road density are nearby.

- Managers cannot easily add mesic habitat, but could manage for bear foods. Because mesic habitat may change with climate change and fire, monitoring it may give early insight into changes in bear numbers.

- Better records of mortality, measures of people’s attitudes toward bears and better detection information could explain more of the variation, especially where fewer bears were found than expected.

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

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

- Rub trees are a natural sign that trackers know well. This study shows how a sign can be turned into a systematic survey method: when hair from rub trees was added to baited hair traps, more bears and more sessions were sampled, and male and female detection responded differently to each method.

## Cite this summary

Lawrence, K. 2026. Summary of: Linking landscape characteristics to local grizzly bear abundance using multiple detection methods in a hierarchical model (Graves and Kendall et al., 2011). Original Wisdom, Tracking Literature. https://www.originalwisdom.com/tracking-literature/2011-graves-kendall-linking-landscape-characteristics-local/