Type of GEM
Category 1a GeM Project Example: Estimating Abundance
Estimating abundance of grizzly bears in northwestern Montana
Background: The grizzly bear (Ursus arctos) population
in the Northern Continental Divide Ecosystem (NCDE) of northwestern
Montana has been managed for recovery since being listed under
the U.S. Endangered Species Act in 1975, yet no rigorous data
had been available to evaluate the program's success. In 2002,
members of the NCDE Managers Subcommittee identified an abundance
estimate as their top research priority. Study design, planning,
and initial field work were completed in 2002-2003, with ecosystem-wide
sampling occurring in 2004.
Noninvasive genetic sampling (NGS) was selected as the preferred
approach to estimate abundance in this area due to the remote,
rugged nature of the terrain, and the low sightability of bears
due to heavy forest cover. The USGS was selected to lead this
project based on extensive experience with NGS in this region
To avoid issues of geographic closure violation, Kendall et al.
(2009) sampled essentially all lands thought to be occupied by
grizzlies in this population, approximately 31,400 km2 (7.8 million acres) of occupied grizzly bear habitat in the greater
Lands were managed under numerous agencies, designations, and
regimes, including Glacier National Park, parts of five national
forests, parts of the two Indian Reservations, and significant
amounts of state and private land.
They used two concurrent sampling methods to collect bear hair
samples for genetic analysis and mark-recapture modeling of abundance.
Boulanger et al. (2008)
found through simulations based on empirical data that multiple
sampling methods decreased the bias of estimates, while increasing
the precision. Sampling was conducted between 15 June - 15 September,
primary sampling effort was systematically distributed hair traps
(Woods et al.
1999). A hair trap consists of a single strand of barbed-wire
strung around a series of trees or posts at a height of 50 cm
with a liquid lure applied in the middle. One hair trap was set
in each 49 km2 grid cell, four times. Hair traps were
subjectively placed to maximize detection rates of bears, factoring
in habitat quality, accessibility, proximity to other hair traps,
while maintaining a safe distance from areas used by humans.
Crews deployed 2,558 hair traps and collected 20,785 hair samples.
secondary sampling method was to collect hair samples left by
bears as they rub on trees, posts, power poles, etc. This is a
natural behavior, and no attractant was used. Most bear rubs were
located along trails and other recognized travel routes. Bear
rubs were surveyed on approximately 80% of the study area; the
majority of the unsampled area was east of the mountains on open
Crews conducted 18,021 visits to 4,795 bear rubs (avg. 3.8 surveys/rub),
and collected 12,956 hair samples.
To take advantage of three data types (hair trap, bear rubs,
and bears known through management activities, they used a stepwise
a priori approach to mark-recapture model development. To determine
the best structure for each data type, they initially modeled
hair trap and bear rub data separately, pooling the other two
data types and using them as the first sample occasion for each
exercise. For example, in the hair trap models, bear rub and physical
capture detections were pooled as the first sample session followed
by the four hair trap sessions. They then combined the most supported
hair trap and bear rub models into a single analysis which included
encounter histories for each of the 563 bears.
Support for candidate models was based on the sample size-adjusted
Akaike Information Criterion for small sample sizes (AICc; Burnham
and Anderson 2002). The model-averaged population estimates
(based on their support in the data as indexed by AICc weights)
was 765 (95% CI: 715-831) grizzly bears.
This study represents the first scientifically defensible abundance
estimate for this threatened population. Beyond abundance, the
large genetic dataset generated through NGS allowed the authors
to investigate genetic connectivity, distribution, and relative
density patterns, and demonstrates the utility of genetic monitoring
at the population scale.