Field work on this project successfully began at the end of April 2008. During the summer, two wolves were captured and collared: the alpha female from the Crowsnest Pack and a sub-adult female from the Castle Carbondale Pack. In December, an adult female from the Bob’s Creek Pack was also collared. All wolves were fitted with download capable Lotek 7000SU GPS radiocollars, programmed to take a GPS location every hour. Data are downloaded from the ground approximately every seven to 10 days during the grazing season and every two to three weeks the remainder of the year. GPS data are analyzed for clusters of activity, and a portion of all clusters identified are visited in the field. Thus far, 214 GPS clusters have been visited. Of these, 74 were kill sites, 86 were bed sites, and eight were rendezvous sites. The remaining sites were areas where no sign of wolf activity was seen.

Kill site investigations from the Castle Carbondale and Crowsnest packs have resulted in several different species including moose, elk, deer, and cattle. Most of the cattle found by the GPS cluster analysis were during the grazing season. All cattle were reported immediately to Fish and Wildlife Officers who were then able to visit the carcass in the field to determine cause of death, sometimes resulting in compensation for the livestock owner. Using the GPS cluster analysis to identify kill sites is likely biased toward large bodied prey, and smaller animals such as deer fawns, beaver, and hare are likely missed. As such, we are also collecting scat samples to augment information collected from kill sites and provide a more complete picture of wolf diet composition. Thus far, 826 scats have been collected. Field work will continue through October 2009 so that seasonal variation in prey selection can be seen.

We have and will continue to work closely with the community and SRD, providing information on wolf predation and depredation events. Currently, no information on wolf diet composition is available in southwest Alberta, but it is of utmost importance given the potential threat to livestock. By studying the predator-prey dynamics along with the movement patterns and habitat selection of wolves in this region, we will be able to provide necessary information to various stakeholders so that appropriate management decisions can be made.

(Click on any map to view a larger image)
Selected bull elk range for the winter period North and South of Highway 3. The coloured outline illustrates the outer bounds of the animals' movements for that period. For example: the animals stayed within the polygon for the winter season.

Tyler Muhly has completed his fieldwork in southwestern Alberta and is beginning analyses. In April 2008, he deployed 40 "camera traps" on trails and roads in the region. These cameras have infrared sensors that when triggered take a picture. These cameras are providing thousands of pictures of people and animals that will be used to: (1) measure types and levels of human activity; and, (2) measure relative density of wildlife. The cameras have provided amazing pictures of wildlife, including wolves, grizzly bears, coyotes, cougars, moose, deer and elk.

Pictures of wildlife taken in southwestern Alberta using infrared camera traps.
(Click on any thumbnail to view a larger image)

Tyler is also continuing to measure herbivory in the study area using "exclosure" cages. These 2 m x 2 m cages prevent herbivores (i.e., cattle, elk, deer, etc.) from grazing within them. Grass within the plot is compared to an open plot outside of the cage to estimate the biomass of grass grazed by herbivores. Pellet counts are also completed at each site to assess relative density of herbivores at the location.

Photo of the researcher measuring herbivory in the Carbondale River area of southwestern Alberta.

The data collected over the past two years will be used to test hypotheses about the "top-down" effect of humans in southwest Alberta. Specifically, I will examine the effects of various levels and types of human activity on the food chain; from wolves, to elk and cattle, to plants. Please see my research proposal for further information.

In Alberta most grizzly bear mortalities occur near roads. Access management, the selective closure of roads, has been suggested as a way to reduce these mortalities. With the recent acceptance of the Grizzly Bear Recovery Plan, access management will begin to be implemented throughout the province. While access management has been linked to recovering bear populations in the United States, little is known about how it will specifically affect grizzly bear habitat use and movement. For access management to be successful in Alberta this information is needed, so as to optimize management efforts.

Goals

• Evaluate access management as a strategy for the recovery of grizzly bears in the province
• Determine the mechanisms driving the relationship between roads, and grizzly bear habitat selection and movement
• Determine the effects of access management on grizzly bear habitat selection and movement
• Evaluate mortality and grizzly bear/human conflict in relation to these effects, and
• Evaluate the efficacy of using gates to control access.

Progress

• Seven grizzly bears equipped with GPS radiocollars, set to obtain locations every hour
• Over 180 used and random sites visited in 2008 to determine bear activity and quantify fine-scale habitat
• Preliminary models of habitat use and movement created for grizzly bear collar data from 2004-2005
• Traffic counters deployed on roads in the study area

The data gained from this project will be shared with Alberta SRD and Fish and Wildlife officers in the area to facilitate with local grizzly bear issues, such as the occurrence of problem bears, and how best to begin mediating conflicts between grizzly bears and humans.

Carly Sponarski completed her course requirements for her degree this past winter. She is currently designing a questionnaire, which will be deployed in the late spring. At this time, she is doing interviews with government, landowners and conservation groups to help design her questionnaire but also to gain insight into the study area and Alberta’s wolf management program. She hopes to start her data analysis in the late summer and writing her thesis in the winter.

Determining the number of ungulates missed during surveys is a crucial component of interpreting survey results (Caughley 1974). Not accounting for sightability during aerial surveys can cause substantial errors in the resulting population calculations, including biases in sex and age structure and wide confidence intervals (Caughley 1974, McCorquodale 2001). Slight changes in weather patterns have proven to influence herding behaviour of elk, with potentially important implications for the proportion of elk observed during a survey (Boyce 1989, Allen 2005). Even trend-type surveys, which seek to estimate the trajectory of population change, may suffer when sightability differs within a survey or among surveys (Lancia et al. 1996).

Elk aerial surveys in southwestern Alberta are currently conducted as total trend counts during winter when elk are congregated and when snow cover provides good sightability. While these surveys provide a useful measure of elk relative abundance through time, they are a minimum count and do not allow estimates of the proportion of elk missed.

• Current Elk Surveys: Winter range trend counts; no correction for sightability. Bull:Cow ratios likely underestimated. Accurate sex ratios are necessary for management; mature bulls most important.
• Bull Sightability: May use separate winter ranges, travel in smaller herds, or spend more time in forest. Existing sightability model (Allen 2005) not compatible with non-random survey method.

Methods

Using previously collected Global Positioning System (GPS) collar location data from both mature bull (3+ antler points) and cow elk during 2008, we examined patterns of spatial overlap between the two sexes during the period when aerial surveys would be conducted (January - March). We developed 95% Minimum Convex Polygon and kernel home ranges in ArcGIS 9.2 for each collared elk and calculated the amount of within- and between-sex overlap. We tested for differences in space-use using one-tailed t-tests, ANOVA and Volume of Intersection calculations in Microsoft Excel and R.

Results

We compared percent overlap of five mature (3 point+) bull elk and 13 cow elk during winter 2008 (January - March). Elk locations were recorded by their collars every two hours and the number of fixes per animal over the winter period ranged from 665 to 1,056 locations. Our initial observations suggested mature bull elk winter home range size was half that of cow elk, on average. Individual bull and cow elk used consistent home range boundaries from January to March, allowing pooling of all GPS collar data from each animal across all three months. When pooled, mature bull elk home ranges overlapped less with cow elk home ranges than cows overlapped with each other (P = 0.017), indicating that bulls utilized different home ranges than cows during winter. Finally, volume of intersection analyses indicated that bulls and cows showed different patterns of space use within their home ranges (P = 0.040).

Conclusions

Bull and cow elk in southwestern Alberta exhibited different patterns of space use during the winter of 2008. In 2009/10, we will increase our dataset by incorporating 2009 winter range data. If spatial overlap between bull and cow elk continues to be low, we will develop a resource selection function for bull elk to identify areas of high probability of winter use. We will conduct aerial surveys of areas identified by the model as having a high probability of use by bulls to determine if these areas are occupied by bulls and should be included in winter range counts. If spatial overlap between bull and cow elk is high, we will develop a sightability model to correct the number of bulls and cows observed during winter range counts in order to improve estimates of sex ratios.