Technical Report

TECHNICAL REPORT

Using Snow-track Surveys to Evaluate Wildlife

Use of Forests Affected by Mountain Pine Beetle

in North-central British Columbia

FRASER MACDONALD1 AND R. SCOTT MCNAY1

MARCH 19, 2010

1Wildlife Infometrics Inc., PO Box 308, Mackenzie, BC, V0J 2C0, [email protected]

Prepared for Abitibi-Consolidated Company of Canada - Mackenzie, under contract # ACCC2010FIA01

CITA TION: MacDonald, F. and R.S. McNay 2010. Using Snow-track Surveys to Evaluate Wildlife Use of Forest Affected by Mountain Pine Beetle in North-central British Columbia. Wildlife Infometrics Inc. Report No. 346. Wildlife Infometrics Inc., Mackenzie, British Columbia, Canada.

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ABSTRACT

The winter time use of pine forests affected by the Mountain Pine Beetle (MPB, Dendroctonus ponderosae) by wildlife within the Defined Forest Area of the Mackenzie Timber Supply Area in north-central British Columbia were assessed using wildlife snow- track surveys during the winter of 2010. The snow track surveys were completed in partnership with the Tsay Keh Dene and Kwadacha First Nations during two sessions: January 15th to 24th and February 14th to 23rd. Transects were surveyed within moose (Alces alces) and woodland caribou (Rangifer tarandus caribou) Ungulate Winter Ranges (UWR) in close proximity to the communities of Tsay Keh and Kwadacha in north-central British Columbia.

We hypothesized that there would be a significant difference in species use between caribou UWR and moose UWR. The level of species use was defined as the number of tracks observed per day, per 100 meters of transect (NT/D100m). Analysis determined that the factors of session and strata (UWR types) were not significant in determining the amount of tracks observed. The factors of study area and wildlife species were significant, and shared no discernable interaction.

Tracks of the following species were observed: red squirrel (Tamiasciurus hudsonicus), snowshoe hare (Lepus americanus), marten (Martes americana), fisher (Martes pennanti), lynx (Lynx canadensis), ermine (Mustela erminea), least weasel (Mustela nivalis), wolf (Canis lupus), wolverine (Gulu gulo), deer mouse (Peromyscus maniculatus), spruce grouse (Falcipennis canadensis), ruffed grouse (Bonasa umbellus), rocky mountain elk (Cervus elaphus), caribou, and moose. Red squirrel and snowshoe hare tracks were observed at higher rates than the other species. Tracks of deer mice, fisher, lynx, ermine and weasel were observed at lower rates than all the other species.

It is our hopes to continue and expand the snow-track project for the winter of 2010- 2011. The project represents a unique opportunity to combine traditional snow-tracking knowledge with science to effectively monitor the effects of ecosystem change on local wildlife resources as the level of MPB attack progresses.

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TABLE OF CONTENTS

ABSTRACT ...... i LIST OF TABLES...... iii LIST OF FIGURES ...... iii ACKNOWLEDGMENTS ...... 1 INTRODUCTION ...... 2 STUDY AREA...... 3 The Communities ...... 3 Kwadacha First Nation ...... 3 Tsay Keh Dene...... 4 Ungulate Winter Range...... 5 Caribou ...... 5 Moose...... 5 METHODS...... 6 Ungulate Winter Range Determination...... 6 Caribou ...... 6 Moose...... 6 Field Sampling...... 7 Snow Track Surveys...... 8 Snow Measurements...... 8 Habitat Plots ...... 10 Weather...... 11 Data Analysis ...... 11 RESULTS...... 12 Snow and Weather Observations...... 12 Data Analysis ...... 13 DISCUSSION ...... 15 LITERATURE CITED...... 19 APPENDIX A. Species codes...... 21

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LIST OF TABLES

Table 1. Randomly-selected caribou and moose UWR polygons by study area...... 7 Table 2. The significance of species and study area (r-square = 0.207660) as factors of the number of tracks observed per day per 100 meters of snow-track transect. Tsay Keh and Kwadacha, North-central BC, January-March 2010...... 14

LIST OF FIGURES

Figure 1. Wildlife snow-track study area including caribou and moose winter range polygons, North-central British Columbia, January-March 2010...... 4

Figure 2. Randomly selected caribou and moose winter range polygons for snow-track surveys within proximity the communities of Tsay Key (A) and Fort Ware (B), North-central British Columbia, January-March 2010...... 9

Figure 3. Snow-track transect, displaying snow stations, habitat plots, and tracks encountered within a randomly selected caribou winter range polygon, Tsay Keh, BC, January-March 2010 (refer to Appendix A for wildlife species codes)...... 10

Figure 4. Number of tracks observed by species, per day, per 100 meters of snow-track transect near Tsay Keh and Kwadacha, North-central BC, January-March 2010. Wildlife species codes in the figure are as follow (refer to Appendix A for all species): TAHU (red squirrel), SMMU (small Mustelid), RUGR (grouse), PEMA (deer mouse), OTHR (other species), MAPE (fisher), MAAM (pine marten), LYCA (lynx), LEAM (snowshoe hare), CALU (wolf), and ALAL (moose)...... 14

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ACKNOWLEDGMENTS

The study was funded by the BC Forest Investment Account Land-Based Investment Program and completed under contract to Abitibi-Consolidated Company of Canada – Mackenzie Region. We’d specifically like to thank Shaun Kuzio for his management and administration of the project funding. We would also like to thank the Kwadacha and Tsay Keh Dene First Nations for their help and support of the project, specifically, Danny Case for his help with organization of the project in Kwadacha and Robert Tomah for his assistance in Tsay Keh. Our thanks also go out to Jacolyn Mattiew and Atheena Wald for being alternative safety contacts and also for their administration help. The Kwadacha field crew included Carolyn McCook, Charlotte Boya, Belinda Pierre, and Ian McCook. The Tsay Keh field crew included Brent Belcourt, Nathan Pierre, Alisha Abou, and Micheal Tarry.

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INTRODUCTION

Lodgepole Pine (Pinus contorta var. latifolia) is the primary component of low-elevation Ungulate Winter Ranges (UWR) used by threatened herds of woodland caribou (Rangifer tarandus caribou) in north-central British Columbia (BC) (McNay et al. 2008). Pine dominated forests are generally located in landscape positions that provide ease of access for timber harvesting and have recently been subjected to high levels of attack by the Mountain Pine Beetle ((MPB) Dendroctonus ponderosae). Ecosystem change associated with MPB attack is an important factor to consider in: 1) monitoring sustainable forest management, 2) understanding the dynamics of accessible Ghudi (edible game) and fur for the sustainable health and prosperity of local aboriginal people, and 3) successful implementation of recovery actions and habitat restoration for woodland caribou. Past investment of over $7 million has allowed for precise spatial identification of UWR and for the implementation of conservation measures to sustain habitat for caribou. As a result, the conservation measures are a major component in the sustainable forest management plans and certification agendas of the local forest industry. However, the MPB epidemic has raised concern about the efficacy of the conservation measures (Sulyma 2010) and this has led to other questions about the potential change in the more general biodiversity value of these forests. For example, members of the aboriginal communities in the area have expressed concern regarding their access to edible game both as a result of the MPB epidemic and historic industrial activity. This concern expressed by the aboriginal community and the general interest in dynamics of biodiversity values in MPB-killed pine forests prompted our general goal to begin gathering information to help address the concern.

Inventory of wildlife using pine forests has typically been difficult since the dense forest canopy makes aerial methods inefficient and little effort has been made to assess other inventory techniques. In such cases elsewhere, snow-track surveys have been used effectively (Alberta Biodiversity Monitoring Institute (ABMI) 2009, D’Eon et al. 2006, D’Eon R.G. 2001). We also envisioned snow-track surveys as an opportunity to address local concerns while insuring direct community involvement and building technical capacity within the communities of Tsay Keh and Kwadacha. Both communities have substantial basis in existing traditional knowledge of wildlife tracking.

Monitoring the wildlife use of pine forests prior to the presence of MPB is no longer a possibility in most of BC due to the status of the current attack but opportunities still exist in the northern extent of the Williston Reservoir in north-central BC (BC Ministry of Forests and Range (MOFR) 2009). Furthermore, the area of the Rocky Mountain Trench that the communities of Tsay Keh and Kwadacha occupy provided us with both an area to sample that was accessible by road and pine forests that were in their first year of MPB attack and still green. By using snow-track surveys to monitor the difference in wildlife use between caribou UWR and moose UWR it will be possible to assess the possible difference in the level of wildlife use within different ecosystems as the level of MPB attack progresses and the landscape changes.

Although not a threatened species, moose (Alces alces), is considered an important food source for many members of the local aboriginal communities. Compared to caribou UWR, moose UWR is characterized by a more diverse blend of forest types, in both age and species structure. As such moose UWR will respond differently to the MPB epidemic than caribou UWR. The contrast between the two UWR types not only provides a comparison of wildlife use but also provides a non-pine dominated baseline

Snow-tracks survey in MPB 2009-10 2 MACDONALD AND MCNAY WILDLIFE INFOMETRICS INC. for monitoring change in wildlife use as the MPB outbreak progresses and ecosystem change occurs.

Our specific objectives therefore were to: 1. Establish and complete winter snow-track transects in four caribou UWR polygons and four moose UWR polygons per First Nations community during two field sampling sessions. 2. Complete accurate weather and snow observations during two field sessions. 3. Complete habitat plots for each significant change in forest cover within each transect and, time permitting, use this information to describe the habitat found in UWR polygons.

My hypothesis was: 1. There is a significant difference in wildlife species use between caribou UWR and moose UWR.

STUDY AREA

The study area was the low elevation (<1200m) area of the Mackenzie Defined Forest Area in the Mackenzie Timber Supply Area. The track surveys were restricted to areas within reasonable proximity to the communities of Tsay Keh and Kwadacha. A rough boundary of this area included the area of the Rocky Mountain Trench extending north from the Ingenika Arm of Williston Lake to Carcajou Creek north of Ft. Ware. The East and West boundaries were defined by the area below 1200 meters elevation on either side of the Finlay and Fox Rivers (Figure 1). Legally designated caribou and moose UWRs were used as samples, but were supplemented with other modeled winter range results, the methods for which are described later in this report.

The Communities

Kwadacha First Nation

The Kwadacha First Nation is located at the community of Ft. Ware; approximately 570 kilometres north of Prince George, BC. The community is the home of the Tsek’ene people and is situated in the Rocky Mountain Trench at the meeting of the Fox, Kwadacha, and Finlay Rivers (Kwadacha Nation 2010).

The Kwadacha First Nation is a part of the Kaska Dena Council which is comprised of the Dease River First Nation at Good Hope Lake, BC, the Daylu Dena at Lower Post, BC, the Liard First Nation at Watson Lake, YT and the Ross River Dena at Ross River, YT (Kaska Dena Council 2010). Traditionally, the territory of the Kaska Dena encompassed 240,000 km2 of north-western BC, south-east Yukon and the southern Northwest Territories.

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Figure 1. Wildlife snow-track study area including caribou and moose winter range polygons, North-central British Columbia, January-March 2010.

Tsay Keh Dene

The community of Tsay Keh is located where the Finlay River flows into the northern tip of the Williston Reservoir in north-central BC and in approximately 495 kilometers north of Prince George, BC. The traditional territory of the Tsay Keh Dene encompasses an area bounded by Mt. Trace to the North, South Pass Peak to the West, the Nation River to the South and by Mt. Laurier to the East (BC Treaty Commission 2010).

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Ungulate Winter Range

Caribou

Caribou UWR was mainly composed of even-aged pyro-climax lodgepole pine stands. These stands were generally located at valley bottom on flat terrain between 680 and 880 meters elevation. Most stands were very open and uniform, with species composition being >90% lodgepole pine aged between 60 and 140 years. MPB attack ranged from 0 to 50% and was mostly first year attack, with some second year attack. No lodgepole pine trees in their third year of MPB attack were noted. Other tree species found were Engelmann spruce (Piecea engelmannii) and trembling aspen (Populus temuloides). Accurate determination of understory vegetation was difficult in the winter, but it was generally sparse. Understory trees were composed of young spruce and pine trees. Understory shrubs included soopalalie (Shepherdia canadensis), rose spp. (Rosa spp), blueberry and huckleberry (Vaccinium spp.) and Labrador tea (Ledum groenlandicum).

Moose

Species composition of moose UWR was more diverse and dense than caribou UWR. Two general habitat types were noted in moose UWR polygons: spruce dominated (>50%) mixed species stands and black spruce wetlands. Moose UWR polygons were located between 680 and 1075 meters elevation. Other species encountered in the spruce dominated stands included lodgepole pine, trembling aspen, black spruce (Picea mariana), black cottonwood (Populus trichocarpa), mountain alder (Alnus tenuifolia), and sub-alpine fir (Abies lasiocarpa). On average, species composition was 70% spruce, 25% lodgepole pine and 5% trembling aspen or black cottonwood. Spruce stands were multi aged and formed a dense canopy. In general, spruce trees were aged between 80-160 years, lodgepole pine between 50 and 100 years, trembling aspen between 40 and 80 year and black cottonwood between 60 and 120 years. Understory trees included: spruce, alder, willow (Salix spp), and lodgepole pine. A complete inventory of understory shrubs and plants was not possible due to snow cover, but several species were observed including willow, alder, scrub (dwarf) birch (Betula glandulosa), cottonwood, rose, and Labrador tea.

Black spruce wetlands were characterised by large, flat and mostly open areas occupied by low density stunted black spruce less than 18 meters in height. Tree species composition was essentially 100% black spruce and ages ranged from 30 to 140 years. These sites were also characterised by a dense shrub layer that was generally composed of 50% willow spp. and 50% scrub (dwarf) birch. Labrador tea was present at many of these sites.

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METHODS

Ungulate Winter Range Determination

Caribou

There were sufficient samples of legally designated caribou UWR located around Tsay Keh but not around Kwadacha (Figure 1). In Kwadacha therefore, additional sample units were derived based on modeled Pine Lichen Winter Range (PLWR) from the Caribou Habitat Assessment and Supply Estimator (McNay et al. 2006). This was the same model used to derive the legally designated UWR. PLWR in the Chase model is defined as: • Forest stands composed of greater than 90 percent Lodgepole pine • Site index of less than 14.5 • Age classes were defined as: o <40 o 40-70 o 70-140 (preferred) o >140

The Chase model output was displayed in Arcmap 9.0 as a three class raster where: • 1 is non-PLWR • 2 is unequivocal • 3 is preferred

Only polygons displaying class three were displayed and added to existing UWR polygons to form possible sampling areas. In order to smooth the polygons the displayed cells were expanded by 1 cell using the generalisation tools. These polygons were then converted from raster to shapefile and are displayed in Figure 1.

Moose

Approved moose UWR polygons were not abundant within sampling proximity to either community, therefore the moose output of the Mountain Pine Beetle (MPB) Habitat Supply Model (HSM) was used to create additional sampling polygons (McNay et al. 2006).

The model defines moose habitat as: • BEC site series greater than or equal to 01 • Stand age less then 40 years • Less than 1200 meters in elevation

The output was a raster that gave the probability of seeing moose in a certain area. The data management tools in Arcmap 9.0 were used to narrow down potential moose UWR polygons to sample from. First, the raster calculator was used to find all cells greater than 225 which classified the cells as either 1’s or 0’s. The 1’s were cells that were greater than 225 and the 0’s were cells less than 225. The “SetNull” option of the raster

Snow-tracks survey in MPB 2009-10 6 MACDONALD AND MCNAY WILDLIFE INFOMETRICS INC. calculator was used to delete all cells with a 0 value. This gave all cells where there was a higher probability of seeing moose. At this point the cells formed a fragmented patchwork on the landscape that was not easily sampled in the field. Therefore, the ArcView Spatial Analyst Region Group tool was used to group neighbouring cells. Then the raster calculator was used to eliminate groups of cells that were less than 100ha in area. In order to smooth the remaining polygons, the groupings of cells were expanded by 1 cell using the Spatial Analyst Expand tool. These polygons were then converted from raster to shapefile. Smoothing of the polygons was completed by the Smooth tool on ArcView’s Advanced Editing Toolbar. Using this tool, polygons were smoothed by allowing vertices to be shifted by up to 50m during the application of the smoothing algorithm. The resultant polygons are displayed in Figure 1.

One additional moose UWR polygon (ALAL-54) was added to the sample while in the field. Originally, a UWR polygon on the west side of the Fox River was to be sampled, but due to poor ice conditions it was determined that the Fox River could not be crossed safely. The location of ALAL-54 was determined by local knowledge of current and historical moose activity.

Field Sampling

Once the caribou and moose UWR polygons were determined, those polygons that were accessible by either truck or snowmobile were selected to form possible sampling areas. Very large accessible UWR polygons were split to make sampling achievable in the field. Those that were not accessible were discarded.

For each community four caribou and four moose UWR polygons were randomly selected using a random number table generated with Microsoft Excel (Table 1, Figure 2)

Table 1. Randomly-selected caribou and moose UWR polygons by study area. Study Area Caribou UWR Moose UWR Polygons Polygons Tsay Keh RATA 04 ALAL 17 RATA 06 ALAL 22 RATA 07 ALAL 25 RATA 15 ALAL 33 Kwadacha RATA 03 ALAL 10 RATA 25 ALAL 11 RATA 26 ALAL 15 RATA 28 ALAL 54

The track survey crews were comprised of four people and a crew leader. Depending on the proximity of the UWR polygons being sampled, crews either worked together on one transect, with two people completing the track transect and the other two carrying out the habitat plots and snow stations. However, if two UWR polygons were in close proximity such that crews could stay within reliable radio contact, then crews could split up forming two crews of two people. Each crew would then do the track survey, snow stations, and habitat plots within their respective polygon.

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Snow Track Surveys

For each of the randomly selected UWR polygons a snow-track transect was completed within its boundaries. Transects were sampled twice during the duration of the project. Snowmobiles were used to access the Point of Commencement (POC) and the actual transects were completed by crews of 2-4 persons travelling on snowshoes. Transect POC’s were determined by access to the polygon and placed in a position to maximize the coverage of the selected polygon (Figure 3). Continuous transects were run perpendicular to slopes whenever present. If a corner was needed in a transect line, it was positioned at an angle of 90 degrees or greater to the existing line, thus reducing the possibility of double counting tracks (ABMI 2009). Transect length was kept to a maximum of two kilometres. If two kilometres of transect could not be achieved within a given transect because of time restraints, then as much transect as possible was completed.

To allow enough opportunity to encounter tracks and to properly age the tracks encountered, transects were only attempted a minimum of 12 hours after a snowfall. The remoteness of the study area did not allow for much flexibility around snowfall events. In order for a track to be included as part of the transect it had to bisect the transect line. Once a track or other wildlife sign, such as a bed or scat, was found, it was then identified to species (species codes are displayed in Appendix A), aged (hour, day, week, month or year), the number of track sets were counted, the UTM location was recorded, habitat identified, and a picture of the track was taken. If the number of track sets in a track observation were to numerous to accurately count then the observation was recorded as a trail. If pellet groups were encountered on a transect then the relative number of pellet groups within eyeshot were recorded.

Snow Measurements

Snow measurements were comprised of six measurements of snow depth, sinking depth and the depths of present crusts. A measurement was completed at the POC of each transect. Additional measurements were done at least every 250 meters thereafter (Figure 3). Further measurements were completed when differences in forest cover and snow conditions were encountered. To capture the influence of the forest canopy on snow conditions, comparable measurements were done in open areas outside of the transect.

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A. B.

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Habitat Plots

Detailed winter habitat plots were systematically completed within each of the track transects. Habitat plot locations were first determined by examining the Vegetation Resource Inventory (VRI) data for the area. A habitat plot was placed within each significant change in habitat encountered along a transect (Figure 3).

Actual habitat plots were comprised of 3.99m or 5.64m radius plots to achieve a plot size of either 50m2 or 100m2 respectively. The standard size was the 3.99m radius plot; however in low density stands where a 3.99m radius only captured one or two trees a 5.64m radius plot was used.

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Information gathered at the habitat plots included:

Physical Characteristics • UTM Location • Slope • General Location • Slope Position Macro • Elevation • Slope Position Meso • Aspect • Pictures

Current Wildlife Sign • Species • Abundance • Sign Type • Age • Activities • Comments

Canopy Closure At the plot center and four meters off the plot center in the 8 cardinal directions the following was recorded: • Moosehorn Measurement • Total Sinking Depth • Total Snow Depth • Snow Crusts Depths

Overstory Vegetation • Species • Stage of Mountain Pine Beetle • Diameter at Breast Height Attack • Vegetation Strata • Height • Wildlife Tree Class • Age

Understory Vegetation • Species • Percent Cover • Vegetation Strata • Percent Browse

Weather

Weather observations were gathered several times a day during sampling sessions. The time, cloud cover, wind, temperature, precipitation, snow depth, snow cover, days since snowfall, and days since five centimetres of snow were recorded.

Data Analysis

To determine if there was a significant difference in species use between caribou UWR and moose UWR the data collected had to be managed in a manner that would allow for proper analysis. Data management included the following: • If any track observations were greater than a week old (month or year) they were not included in the analysis. The number of days since 5 cm of snow was used to standardize the track count. • All observations of ruffed grouse (Bonasa umbellus) and spruce grouse (Falcipennis canadensis) were lumped together into a common category called RUGR, because their tracks were indistinguishable from each other in the snow. • Due to the difficulty in identification and the low numbers observed, all observations of weasel (Mustela nivalis) and ermine (Mustela erminea) were lumped together into a small mustelid category.

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• Species that were observed less than five times during the whole survey were removed from the analysis. These species were: o Rocky mountain elk (Cervus elaphus) o Caribou o Wolverine (Gulo gulo) • Any unknown species were grouped into an unknown category.

Standardization of the data was required because the number of days since snow and the length of transects varied. In order to standardize the data, a calculation of the number of tracks per day per 100 meters was used. For the purposes of this report this calculation was presented as NT/D100m. The calculation represents the number of tracks that were likely to be seen on a given day within 100 metres of transect. Data analysis was completed using SAS (SAS Institute, Cary, North Carolina, USA). The data was tested for normality using the Kolmogorov-Smirnov test. Data found to be not normally distributed was transformed and tested for normality using the Kolmogorov- Smirnov test. The transformations were the square root, log and inverse of NT/D100m.

Possible contributing factors of the variance in NT/D100m were tested using an unbalanced Analysis of Variance (ANOVA). An unbalanced ANOVA was utilised because the same number of species were not likely to be found on each transect. The factors assessed were: • Session (2) o 1: January 15th to 24th o 2: February 14th and 23rd • Study Area (2) o Kwadacha o Tsay Keh • Strata (2) o Caribou UWR o Moose UWR • Species

Factors that were found to be significant were tested for interactions using the Waller- Duncan K-Ratio t test. The Waller-Duncan test was a multiple range test that tested for a difference in means.

Variances were tested for homogeneity using Levene’s Test for Homogeneity.

RESULTS

Snow and Weather Observations

In general most snow depths in session one were between 38 and 50 centimetres. Overall, snowfall was abnormally low and temperatures were relatively high for January and February. Session one started on January 15th, the day after a five centimetre snowfall, during that same day temperatures rose to +4°C. Subsequently, temperatures stayed below zero causing a light crust to form on the snow surface. Two light snowfalls

Snow-tracks survey in MPB 2009-10 12 MACDONALD AND MCNAY WILDLIFE INFOMETRICS INC. on the 17thand 18th, totalling between 0.5 and 1.0 cm each, improved tracking conditions. It did not snow again during the first session. Daily temperatures averaged between - 1°C and -5°C until the 22nd when observed daily temperatures dropped between -5°C and -18°C.

Generally, snow depths in session two ranged between 45 and 60 centimetres. Session two commenced on February 14th, two to four days after the last snowfall event. Approximately 15cm of snow had fallen since session one, this accumulation of new snow had consolidated greatly due to warm weather. Only one significant snowfall of approximately one centimetre on February 16th occurred during session two. Temperatures remained warm for most of the session. Between February 14th and February 19th daily temperatures ranged from -8°C to +6°C. Temperatures cooled off on the 20th, ranging between -1°C and -15°C for the duration of the session.

Data Analysis

Analysis of collected data and presentation of results was restricted to the snow-tracking data due to insufficient resources for a full analysis of the collected forest inventory. During the two sampling sessions a total of 1,466 individual tracks were observed in 794 track observations on 16 snow-track transects. Tracks of the 15 following species were observed: red squirrel (Tamiasciurus hudsonicus), snowshoe hare (Lepus americanus), marten (Martes americana), fisher (Martes pennanti), lynx (Lynx canadensis), ermine (Mustela erminea), least weasel (Mustela nivalis), wolf (Canis lupus), wolverine (Gulu gulo), deer mouse (Peromyscus maniculatus), spruce grouse (Falcipennis canadensis), ruffed grouse (Bonasa umbellus), rocky mountain elk (Cervus elaphus), caribou, and moose.

The Kolmogorov-Smirnov test determined that the data for the NT/D100m was not normally distributed (p< 0.0001) and although the inverse transformation was still not statistically normal (p < 0.01), it proved to be close to normal and was much better than other tested transformations. The inverse of NT/DT100m was therefore used for the rest of the analysis.

Unbalanced ANOVA results determined that the factors of session (F1,166=0.91, P=0.341) and strata (F1,166=1.99, P=0.160) were not significant factors in the NT/DT100m observed. Since strata was not a significant contributing factor to the number of tracks observed, the hypothesis that there was a difference in wildlife species use between caribou UWR and moose UWR was rejected. The factors of species (F1,166=3.41, P=0.000) and study area (F1,166=6.53, P=0.011) were significant (Table 2). There was no significant interaction between study area and species (F9,166= 0.65, P=0.748).

The Waller-Duncan K-Ratio t Test determined that there were significant differences in the rates that different species tracks were observed. Red squirrel (0.364 NT/D100m) and snowshoe hare (.317 NT/D100m) tracks were observed at significantly (p<0.05) greater rates than other species, while deer mouse (0.0597 NT/D100m), fisher (0.0584 NT/D100m), lynx (0.0542 NT/D100m) and small Mustelid (0.065 NT/D100m) were observed at significantly (p<0.05) lower rates than other species (Figure 4). The remaining species were observed at the following rates: Moose (0.172 NT/D100m), wolf

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(0.105 NT/D100m), pine marten (0.140 NT/D100m), grouse (0.158 NT/D100m) and other species (0.094 NT/D100m).

Table 2. The significance of species and study area (r-square = 0.207660) as factors of the number of tracks observed per day per 100 meters of snow-track transect. Tsay Keh and Kwadacha, North-central BC, January-March 2010. Factor Degrees Sum of Mean Square F-Value P-Value of Squares Freedom Model 11 0.0028714 0.00002610 3.69 0.000 Error 155 0.00109560 0.00000707 NA NA Corrected Total 166 0.00138273 NA NA NA Species 10 0.00024095 0.00002409 3.41 0.000 Study Area 1 0.00004619 0.00004619 6.53 0.011

TAHU

SMMU

RUGR

PEMA

OTHR

MAPE

MAAM

LYCA

LEAM

CALU

ALAL

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 Number of tracks / day * 100m

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DISCUSSION

Results of the analysis demonstrated that UWR type did not have a significant influence on the number of tracks observed. Therefore our hypothesis that there would be a significant difference in wildlife species use between caribou UWR and moose UWR was rejected. A possible explanation for this result is that the tracks of certain species were observed at high rates in both strata because those species were habitat generalists. Red squirrel and snowshoe hare tracks, for example, were observed at a significantly higher rate than other specie’s tracks. In the case of both red squirrel and snowshoe hare this interpretation of the analysis is open to debate. The mature trees found within both UWR types offer suitable habitat for red squirrel, however the seeds of mature spruce trees found in moose UWR are considered to be a preferred food source (Environment Yukon 2009). Snowshoe hare have been shown to use all forest types but also prefer the more dense forest associated with moose UWR than the relatively open forest that was usually found in caribou UWR polygons (Hodges 2002, Litvaitis et al. 1985).

The lack of a significant influence regarding the factor of session on the NT/D100m was not unexpected as snow and weather conditions were relatively similar throughout both sampling periods. However, the result that study area was a significant factor in the NT/D100m was unexpected as in both study areas similar habitat was sampled under similar weather conditions. There were two transects within the Kwadacha study area that produced a relatively low number of track observations and as such likely were the contributing factor to this unexpected result.

The influence of species as a factor of NT/D100m was significant. Reasons for this result are intuitive as different species occupy the landscape at varying densities and distribution than other species. The Waller-Duncan K Ration t Test confirmed the significance by revealing that red squirrel and snowshoe hare tracks were observed at a higher rate than other species and that deer mice, lynx, fisher, and other small mustelid tracks were observed at lower rates than other species track observed. This result is supported by the fact that deer mice, while relatively abundant, leave small tracks that are easily erased. Whereas, lynx, fisher and other small mustelids are found at relative low densities compared to red squirrel and snowshoe hare. The high rate of observed snowshoe hare tracks along with the low rate of lynx tracks may possibly support the association between snowshoe hare and lynx populations documented elsewhere (Keith and Windberg 1978)

The remaining species tracks that were seen at an average rate included moose, wolf, marten and grouse. All of these species are of special concern to the local aboriginal communities. Moose is perhaps the single most important food source for both communities and there has been increasing concern expressed within both Kwadacha and Tsay Keh about increasing wolf populations. The principal concern is that increasing wolf populations could possibly cause a dramatic decline in the local moose population. Marten has traditionally been a mainstay of local fur trappers and grouse, along with snowshoe hare, is a primary source of Ghudi (edible game).

The winter of 2009/2010 was not an average winter for north-central BC. Snowfall was abnormally low and temperatures were relatively high. The warm temperatures posed certain logistical problems with accessing sites. Proper ice development on many local rivers had not occurred. Many of the sampling sites were located on the west side of the

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Fox River and during a normal winter, crossing the river on snowmobiles could be safely done. However, during January and February of 2010 the integrity of the ice was unknown and as such crossings were not attempted.

Due to limited access and the extensive travel time to the study area, the exact snow- track protocols suggested by ABMI (2009) could not be adopted. These protocols included: the use of snowmobiles instead of snowshoes for traveling on the track transects (ABMI-1 2009) and only performing surveys between three and six days after a track obliterating event (snowfall >1cm or daily average wind >30km/hr (ABMI-1 2009)). Rather we adopted the majority of the principals suggested by D’Eon (2006) and several of the other protocols suggested by ABMI (2009) to form a method that was conducive to our study area. However, there were several recommendations for improvement that could be implemented for future years. • When unknown tracks are encountered, measurements of track length and width should be recorded along with pictures (ABMI-1 2009). This would help with the identification of unknown tracks in the office. • Determine the parameters around a track obliterating event that would better encompass all specie’s tracks. ABMI (2009) uses snowfall >1 centimetre or daily average wind > 30 km/hr but what is a track obliterating event for one species is not necessarily so for another species. • Record the number of days since a track obliterating event when completing a snow-track transects (ABMI-1 2009). • Develop an alpha and beta species richness index for the number of species observed. Alpha species richness index would distinguish the species richness within particular strata (i.e., caribou UWR and moose UWR), by comparing the number of species observed in a single transect to the total observed within the strata. Beta species richness would compare the difference in species richness between strata types (i.e., caribou UWR species richness vs. moose UWR species richness (Proulx 1998)).

Owing to the relative isolation of the communities, it was difficult for band members living in Kwadacha and Tsay Keh to obtain the proper 1st aid training that was required for the snow-track project and this severely restricted the number of available workers. In future years it would be worthwhile to host a course prior to project commencement for members who are to be hired. By providing a course it would not only ensure that all workers have valid and adequate first aid training for the duration of the project but it would also allow for more involvement from community members.

The opportunity to use the wildlife snow-track project as a tool for monitoring ecosystems should not be overlooked. Some of these opportunities are as follows: • Assessing ecosystem change as the MPB epidemic progresses; • Building First Nations technical capacity for the management of natural resources (i.e., knowledge of modern-day resource values, using existing traditional knowledge and activities as the foundation for information collection techniques); • Employing aspects of the species accounting system developed by the University of British Columbia (UBC) (Bunnell et al. 2009) to monitor terrestrial vertebrate species. The species accounting system (SAS) is designed to allocate all terrestrial vertebrate species found within an area into distinct groups of relative monitoring efficiencies. There are six groups that are defined by their increasing reliance on a particular habitat type. Group 1 being the least dependant and

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containing species that are considered to be generalists, while group 5 contains species that are specialists that require specific habitats and for which connectivity is important. Group 6 species are an exception and include those species that are known to occur in an area, but do not depend on forested ecosystems and are therefore not monitored. The snow-track survey offers opportunities to estimate species distribution and relative abundance and therefore a statistical basis for assignment of species into specific monitoring groups for the SAS. • Promote sustainable forest management through the establishment of permanent long-term biodiversity monitoring plots within the Mackenzie Timber Supply Area (TSA). The standard monitoring plots would not only involve wildlife and avian monitoring techniques (such as the snow-track survey conducted here), but would also include detailed habitat monitoring methods. Several of the methods previously suggested by Proulx (1999), for a similar monitoring program (never implemented) within the Mackenzie TSA, could be integrated into this monitoring program. Plots could be established by using the 19 distinct Biogeoclimatic (BEC) Zone sub zones found within the MacKenzie TSA as the basis for plot determination. Industrial areas (roads, cutblocks, and human occupation) could be contrasted with remote areas within each BEC sub zone to form a total 38 permanent sampling plots (i.e., one permanent plot would be located within an industrial area of a particular subzone and another permanent plot would be located within a more remote area of the same subzone). Due to the expansive size of the Mackenzie TSA and its’ geographical position in the province, there would be enough remote area to make this goal achievable. Sites could be visited twice a year, once in winter conditions and once in summer conditions. Sites could be visited using existing roads, trails, and water routes along with the appropriate method of travel that could include: 4x4 trucks, ATV’s, boats, snowmobiles, snowshoes, skiing and hiking. Several of the remote areas would likely need to be accessed using a combination of both rotary-wing and fixed- wing aircraft (equipped with wheels, floats or skis as seasonally required). The combination of rotary-wing and fixed-wing aircraft allows for a relatively cost effective approach to transporting personnel and supplies into remote areas. Each field sampling session would roughly involve four to six weeks of field work for an experienced two person crew. The Alberta Biodiversity Monitoring Institute (ABMI) has been able to create a comparable biodiversity monitoring program that encompasses the entire province of Alberta. The ABMI program also involves the repeated sampling of permanent sites, as the repeated sampling of permanent sites can reduce the amount of spatial variance in a sample (Larsen et al. 2001). ABMI has been able to demonstrate relatively high statistical power by implementing consistent data collection methods, choosing indicators with low measurement error and surveying chosen indicators in consistent seasonal periods (ABMI-2 2009). • The recent re-development of the Aatse Davie Trail, along with functional well- built cabins, could provide the access necessary to expand the survey area in future years (BC Ministry of Forests and Range (MOFR) and BC Ministry of Community Development (MOCD) 2009). The Aatse Davie trail is a 300 km long traditional trail that follows the Rocky Mountain Trench north from Ft. Ware along the Fox River over Sifton Pass and then down the Kechika River to Lower Post near the Yukon border (Muskwa-Kechika Management Area (MKMA) 2008). Much of this trail has been opened up to summer and winter travel. With the proper logistical planning the Aatse Davie trail, along with the existing network of

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other traditional trails and cabins, could be used to access new sampling sites for the snow-tracking survey or for the implementation of a broader monitoring of biodiversity.

Despite the potential improvements and/or advancements that we recommend above, the snow-track survey provided the opportunity to improve the involvement of aboriginal communities in the collection of resource information. The data that were collected stand as an example of a baseline for future monitoring and for comparing and contrasting the eminent effect of the MPB in pine-dominated forests.

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LITERATURE CITED

Alberta Biodiversity Monitoring Institute (ABMI-1). 2009. Terrestrial field data collection protocols (10001), version 2009-04-20: winter terrestrial protocols: snow tracking. Alberta Biodiversity Monitoring Institute, Alberta, Canada. Report available at abmi.ca. Accessed 04 January 2010.

Alberta Biodiversity Monitoring Institute (ABMI-2). 2009. Alberta Biodiversity Monitoring Insitute: the science of monitoring: statistical power. http://www.abmi.ca/abmi/aboutabmi/aboutabmi.jsp?categoryId=2&subCategoryId=43 4&pageCategoryId=60&refresh1=t. Accessed 16 March 2010

BC Ministry of Forests and Range (MOFR). 2009. Provincial-level projection of the current mountain pine beetle outbreak. http://www.for.gov.bc.ca/hre/bcmpb/. Accessed 08 March 2010.

BC Ministry of Forests and Range and BC Ministry of Community Development (BC MOFR and BC MOCD). 2009. News release: community trust restoring historic Kwadacha trail. http://www2.news.gov.bc.ca/news_releases_2005- 2009/2009FOR0073-000817.htm#. Accessed 11 March 2010.

BC Treaty Commission. 2010. First Nations and negotiations: Tsay Keh Dene Band. http://www.bctreaty.net/nations/tsaykeh.php. Accessed 10 March 2010.

Bunnell, F.L., L.L. Kremsater, A. Moy, and P. Vernier. 2009. Conservation framework for Canadian Forest Products tenures in northeastern British Columbia. Centre for Applied Conservation Research, University of British Columbia, Vancouver, British Columbia.

D’Eon, R.G. 2001. Using snow-track surveys to determine deer winter distribution and habitat. Wildl. Soc. Bull. 29:879-887.

D’Eon, R. G., S.F. Wilson, and D. Hamilton. 2006. Ground-based inventory methods for ungulate snow-track surveys. Standards for components of British Columbia’s biodiversity No. 33a. Resource Information Standards Committee, British Columbia Min. of Environment, Victoria, BC.

Environment Yukon. 2009. Red squirrel. http://www.environmentyukon.gov.yk.ca/wildlifebiodiversity/mammals/redsquirrel.php Accessed 12 March 2010.

Hodges, K.A. 2002. Hinterland who’s who: mammal fact sheets: snowshoe hare. http://www.hww.ca/hww2.asp?id=103. Accessed 12 March 2010.

Kaska Dena Council. 2010. Kaska Dena Council homepage. http://www.kaskadenacouncil.com/. Accessed 10 March 2010.

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Keith, L.B and L.A. Windberg. 1978. A demographic analysis of the snowshoe hare cycle. Wildl. Mongr. 58(1).

Kwadacha Nation. 2010. Kwadacha Nation homepage. http://www.kwadacha.com/nation. Accessed 10 March 2010.

Larsen, D.P., T.M. Kincaid, S.E. Jacobs, and N.S. Urquhart. 2001. Designs for evaluating local and regional scale trends. BioScience 51:1069–1078

Litvaitis, J.A., J.A. Sherbourne and J.A. Bissonette. 1985. Influence of understory characteristics on snowshoe hare habitat use and density. J. Wildl. Manage. 49(4):866-873.

McNay, S., D. Heard, R. Sulyma, and R. Ellis. 2008. A recovery action plan for northern caribou herds in north-central British Columbia. Forrex Forest Research Extension Partnership, Kamloops, B.C. Forrex Series 22. url: http://www.forrex.org/publications/other/forrexseries/fs22.pdf

McNay, R.S., B.G. Marcot, V. Brumovsky, and R. Ellis. 2006. A bayesian approach to evaluating habitat for woodland caribou in north-central British Columbia. Can. J. of For. Res. 36:3117-3133.

Muskwa-Kechika Management Area (MKMA). 2008. History and settlement: pre European contact. http://www.muskwa-kechika.com/management-area/history.asp. Accessed 11 March 2010.

Proulx, G. 1998. Plan of research to monitor biodiversity in the managed forests of Finlay Forest Industries. Alpha Wildlife Research and Management Ltd. Sherwood Park, Alberta, Canada.

Proulx, G. 1999. Biodiversity monitoring program in Donohue Forest Products Inc. timber supply area – MacKenzie Region. Alpha Wildlife Research and Management Ltd. Sherwood Park, Alberta, Canada.

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APPENDIX A. SPECIES CODES

Species Code 1 Scientific Name Common Name

RATA Rangifer tarandus Caribou ALAL Alces alces Moose CEEN Cervus elaphus Elk CALU Canis lupus Wolf VUVU Vulpes vulpes Fox LYCA Lynx canadensis Lynx GUGU Gulo gulo Wolverine MAPE Martes pennanti Fisher MAAM Martes americana Pine marten MUVI Mustela vison Mink MUNI Mustela nivalis Least weasel MUER Mustela erminea Ermine LEAM Lepus americanus Snowshoe hare TAHU Tamiasciurus hudsonicus Red squirrel PEMA Peromyscus maniculatus Deer mouse UTAM Tamias spp. Unknown chipmunk USOR Sorex spp. Unknown shrew RUGR Bonasa umbellus Ruffed grouse SPGR Falcipennis canadensis Spruce grouse SMMU NA Small Mustelid UPRD NA Unknown predator UUNG NA Unknown ungulate UMST NA Unknown mustelid UNK NA Unknown OTHR NA Other species 1 Four letter codes derived from the scientific names; the first 2 letters of both words of the species scientific names (e.g., Alces alces = ALAL for moose).

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