MOOSE DENSITY AND COMPOSITION IN THE PARSNIP RIVER WATERSHED, , DECEMBER 2005.

ANDREW B. D. WALKER, 5657 Ave., Prince George, B.C., Canada, V2N 2C4

DOUGLAS C. HEARD, British Columbia Ministry of Environment, 4051 – 18th Ave., Prince George, B.C., Canada, V2N 1B3

VOLKER MICHELFELDER, British Columbia Ministry of Environment, 4051 – 18th Ave., Prince George, B.C., Canada, V2N 1B3

GLEN S. WATTS, British Columbia Ministry of Environment, 4051 – 18th Ave., Prince George, B.C., Canada, V2N 1B3

2006 Final report for the Ministry of Environment. Project No. 2914568 ABSTRACT In order to better understand the effects of hunting, changing landscapes, new management programs and predator-prey relationships involving moose in the Parsnip River watershed, we carried out a stratified random block survey in December 2005 (Gasaway et al. 1986). The early winter use of forest cover types by moose in the Parsnip River study area was used to delineate 2 strata. Estimates of moose numbers were determined by incorporating sightability bias from vegetation cover around each moose. Our total population estimate for the 2,501 km2 area was 3,000 ± 440 moose ( x ± SE). We counted 270 moose in 41 sample units (SUs) and surveyed a total of 181 km2. We observed an overall density of 1.18 moose/km2. The number of bulls per 100 cows was only slightly greater for the observed ratio than what was estimated after correcting for sightability (63 ± 10.9 bulls per 100 cows versus 59 ± 10.6 bulls per 100 cows, respectively), while the average number of calves per 100 cows was slightly lower for observed (26 ± 5.1 moose) than estimated ratios (30 ± 7.1 moose). We believe the moose population in the Parsnip River has changed little since the previous estimate of 2,600 ± 600 moose in 1998, which used a similar sampling and statistical design.

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TABLE OF CONTENTS

Table of Contents ...... ii List of Tables ...... iii List of Figures...... iv Introduction...... 1 Study area ...... 1 Methods...... 2 Sampling Strategy...... 2 Data Analysis ...... 4 Results ...... 5 Search effort and conditions ...... 5 Population size and density ...... 5 Composition ...... 5 Distribution ...... 6 Discussion ...... 6 Census methods ...... 6 Population size, composition and distribution ...... 8 Acknowledgements ...... 10 Literature Cited ...... 10 Appendix A. Itinerary and personnel involved in the Parsnip River moose census, December 2005...... 18 Appendix B. Moose observations, vegetation cover, snow depth and search effort in each sample unit during the Parsnip River moose census, December 2005...... 19 iii

LIST OF TABLES

Table 1. Vegetation cover classes, range of vegetation cover (%), detection probability and sightability correction factor, that was used to extrapolate population estimates of moose in the Parsnip River watershed, December 2005 (adapted from Heard et al. 1999a; Quayle et al. 2001)...... 15 Table 2. Observed and estimated number of moose by stratum in the Parsnip River watershed (December 2005)...... 16 Table 3. The number and percentage of groups by vegetation cover class and mean vegetation cover ( x ± SE) that bull moose, barren cows without calves and maternal cows were observed using in the Parsnip River study area, December 2005. Sample size (n) indicates the number of groups in each vegetation cover class. The amount of

vegetation cover did not differ among groups (F2, 170 = 1.52, P = 0.221) using a one- way analysis of variance (ANOVA)...... 17

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LIST OF FIGURES

Fig. 1. The delineation of sample units, distribution of high density moose stratum (stratum 1) and study area boundary of the Parsnip River moose inventory, December 2005...... 13 Fig. 2. The randomly selected sample units that were surveyed during the Parsnip River moose inventory, December 2005...... 14 1

INTRODUCTION

Moose (Alces alces) are the most abundant big game animal in the Omineca region of northern British Columbia. They are hunted by First Nation and licensed hunters for sustenance and trophies. In order to better understand the effects of hunting, changing landscapes, new management programs and predator-prey relationships, estimates on the rate of population change are crucial (Gasaway et al. 1986). For these reasons we carried out a stratified random block survey (Gasaway et al. 1986) in December 2005 to estimate the population and composition of moose wintering in the Parsnip River watershed. We interpreted the results in relation to previous estimates from the Parsnip and Omineca regions in order to determine population trends that will aid future management decisions. The previous population was estimated at 1,400 ± 600 ( x ± 90 % CI) moose based on counts within 95 randomly selected survey plots (Heard et al. 1999a). However, using Anderson and Lindzey’s (1996) relationship between sightability and vegetation cover, the population was estimated at 2,600 ± 1000 moose. Calf recruitment was low at 23 ± 5.3 calves per 100 cows ( x ± SE) and sex ratio was high (112 ± 22 bulls per 100 cows). Quayle et al. (2001) followed Anderson and Lindzey’s (1996) approach to quantify moose numbers, using sightability data from moose in British Columbia. This model was subsequently adapted and included moose sightability data from the Ingenika River. Following this approach we believe population and composition estimates should be similar to those documented by Heard et al.’s (1999a) Anderson and Lindzey (1996) estimate, considering the lack of regulation changes regarding the harvest of moose or their predators.

STUDY AREA

The Parsnip River drainage falls within the Omineca region and Ministry of Environment’s wildlife management units 7-16 and 7-23 (Fig.1). The study area covered 2,501 km2 and was delineated by the 1200 m contour line to the east, the headwaters of 2 the Parsnip River to the south and the height of land between the Parsnip and Crooked River watershed to the west. The northern border runs south of Kenny Siding from the 1200 m contour and west along the CN rail line to highway 97. Radio-collared moose along the Parsnip River never traveled outside of these extents during previous years (D. Heard, unpubl. data). The area consists primarily of the wet cool variant of the sub-boreal spruce (SBSwk1) biogeoclimatic zone with some wet cool variant of the Engelmann spruce subalpine fir (ESSFwk2) and the very wet cool variant of the sub-boreal spruce (SBS vk) zones (Meidinger and Pojar 1991). The SBS contains hybrid white-Engelmann spruce (Picea glauca × engelmanni) and subalpine fir (Abies lasiocarpa) with extensive successional stands of lodgepole pine (Pinus contorta) and trembling aspen (Populus tremuloides) caused by recurrent disturbances. Occurring at higher elevations, the ESSF is dominated by Engelmann spruce and subalpine fir with lodgepole pine widespread throughout as the seral species. Mean annual precipitation ranges from 42 - 1700 cm, with 25 - 50 % as snow. An average annual temperature of 2.5 °C is typical for these biogeoclimatic zones. Snow persists from November through April with precipitation evenly distributed throughout the year (Meidinger and Pojar 1991).

METHODS

Sampling Strategy We divided the study area into 2 strata based on the use of different forest cover classes by moose in the Parsnip River study area (Heard et al. 1999a). Data was acquired using Land and Resource Data Warehouse’s (LRDW’s) Vegetation Resources Inventory (VRI) data. In order to remain consistent with Heard et al.’s (1999a) delineation of strata, we selected young forests and shrubby areas, irrespective of the nature of disturbance (human or “natural”), as stratum 1 (S1). This stratum generally contains the highest densities of moose (Heard et al. 1999a; Heard et al. 1999b; Heard et al. 2001). Young forests were defined as forests ≤ 40 years of age (projected age in VRI). We used the following VRI descriptors to select shrubby, open areas: M 3

(meadow), OR (open range), NPBR (non productive brush), NCBR (non commercial brush) and NSR (not sufficiently restocked). For the remainder of the study area we used shrub crown closure as a surrogate to identify shrubby, open areas. We included all areas with shrub crown closure ≥ 60 %. Powerline right-of-ways were also included because recurrent brushing provides early seral vegetation and palatable moose forage (Rea and Gillingham 2001). Stratum 2 (S2) consisted of the remaining forest cover types but was dominated by forests > 40 years old and small amounts of gravel bars, swamps, muskeg, roads and recently logged areas that had yet to be entered into the LRDW’s VRI database. We divided the census zone into a predetermined grid of 9 km2 (3.2 × 2.8 km) blocks. Adjacent blocks were arbitrarily joined so that ≥ 4 km2 of S1 was present in each sample unit (SU). This was an attempt to ensure moose would be observed in each SU (Heard et al. 1999a). SUs were therefore made up of between 1 and 6 blocks for a total of 149 (Fig. 1). We randomly selected 41 of the 149 SUs to census (Fig. 2) and surveyed only the S1 portion of most of those SUs. Of those 41, however, 6 were randomly selected and the entire area was surveyed and observed moose were recorded as being in S1 or S2. Between the 12th and 16th of December 2005, a crew consisting of 2 observers, a navigator (who recorded the data) and a pilot (Appendix A) surveyed SUs from a Bell 206B Jet Ranger Helicopter, 30 – 50 m above the ground. SU boundaries were located using the helicopter’s Global Positioning System (GPS) and a search pattern consisting of transects 200 – 400 m apart, depending on vegetation cover, were flown to cover the SU. We circled each moose and recorded its age and sex as a calf (~ 8 months old), cow or bull, based on the presence of a white vulva patch, the bell length and shape and facial colouration and morphology (Heard et al. 1999a). Vegetation cover, to the nearest 5 %, was recorded within 9 m of where the moose was first seen according to the standards developed by Unsworth et al. (1998). The positions of all groups were recorded with a GPS location using a Garmin GPSMAP 76s (Garmin International, Inc. 2006). The recorded locations were subsequently plotted to determine the position of moose groups relative to stratum and SU boundaries.

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Data Analysis Vegetation cover estimates were used to correct for sightability bias to determine stratum specific density and population estimates. Vegetation cover estimates were grouped into 5 classes each with a specific detection probability (DP) and sightability correction factor (SCF), as determined by Quayle et al. (2001), following the approach of Anderson and Lindzey (1996) (Table 1). The DP included sightability data from moose near the Ingenika River (D. Heard, unpubl. data). For each stratum, a naïve population estimate and sampling variance for unequal sized SUs were calculated using Jolly (1969). Sightability and model variance were calculated using the program Aerial Survey (Unsworth et al. 1998) but modified with data from Heard et al. (1999a) and Quayle et al. (2001). Aerial Survey calculates a population estimate using a sampling fraction based on the number of surveyed SUs divided by the total number of SUs in the study area (Unsworth et al. 1998). Our analysis used a sampling fraction equal to the censused area divided by the total stratum area. In this approach, we are not limited to SUs of equal size. We calculated the final population estimate as the product of the naïve population estimate for both stratums and their SCF. The variance for the final population estimate was the sum of the sampling, sightability and model variances for both strata. The population composition for the observed and estimated (corrected for sightability) number of calves and bulls per 100 cows were calculated using a jackknife estimator (Efron 1982). The number of cows, calves and bulls were summed for each SU to determine the mean and variance of calf:cow and bull:cow ratios. Ratios were calculated across both strata due to the small number of S2 SUs surveyed. We only measured the search effort for S1 SUs because search time was not measured independently for SUs where S1 and S2 were both censused. We compared vegetation cover estimates between cows with calves (maternal cows), cows without calves (barren cows) and bulls using a one-way analysis of variance (ANOVA) (Zar 1999) to see if segregation by sex or maternal status may have biased our observed calf:cow and bull:cow ratios. Values were log-transformed after examining assumptions of normality and homogeneity of variance (Levene’s test) (Zar 1999). We assumed 5 statistical significance at α ≤ 0.10 for all tests and all statistical procedures were conducted using StataTM (Release 9.0, StataCorp LP 2005).

RESULTS

Search effort and conditions Temperatures during the census ranged from -3° to -14 °C with clear to overcast conditions. The search effort during the census was 8.4 ± 0.38 min/km2 ( x ± SE; n = 28). Snow cover was 100 % throughout the study area and snow depth averaged 17.1 ± 2.41 (n = 38) cm with a minimum of 3 and maximum of 61 cm.

Population size and density Our total population estimate for the 2,501 km2 Parsnip River study area was 3,000 ± 440 moose (Table 2). We counted 270 moose in 41 SUs and observed an average group size of 1.80 ± 0.088 moose (Table 2; Appendix B). In S1, the open shrub-dominated vegetation and young regenerating forests, 259 moose from 35 SUs were counted and in S2, the older forest stands, 11 moose from 6 SUs were counted. The mean SCF for both strata (S1 = 1.18 and S2 = 2.98) was 1.25. With a total of 181 km2 surveyed we observed an overall density of 1.18 moose/km2. Corrected moose density in S1 (2.12 moose/km2) was over twice as high as in S2 (0.88 moose/km2). The population estimate, however was slightly larger in S2 (1,700 ± 410 moose) compared to S1 (1,280 ± 163 moose) because of the much greater area of S2. The coefficient of variation (CV) for S2 was also greater than S1.

Composition The number of calves and bulls per 100 cows was similar between observed ratios and estimated ratios corrected for sightability. No bull moose were observed in S2 and the number of bulls per 100 cows was only slightly greater for the observed ratio than what was estimated after correcting for sightability (63 ± 10.9 bulls per 100 cows versus 59 ± 10.6 bulls per 100 cows, respectively). The average number of calves per 6

100 cows was slightly lower for observed (26 ± 5.1 moose) than estimated ratios (30 ± 7.1 moose).

Distribution Most moose were observed in VCC 1 and the number decreased with increasing amounts of cover (Table 3). Cows without calves were almost evenly distributed between VCC 1 and 2. Bulls were observed least often in S3 and the only observation exceeding VCC 3 was of a cow and calf in mature spruce forest. The differences in mean vegetation cover were not statistically significant (F2, 170 = 1.52, P = 0.221) and segregation of moose by sex or maternal status relative to vegetation cover was not apparent (Table 2; Appendix B).

DISCUSSION

Census methods High and low moose density strata, based on previously observed habitat use patterns of moose in the Parsnip River study area (Heard et al. 1999a), were defined by a priori stratification of the census zone using GIS and VRI forest cover data. With a coefficient of variation of 15 % for the total population estimate, we were satisfied the sampling strategy allowed for a reasonable number of surveyed SUs in order to describe the variation among SUs and between strata. We observed considerably greater variation in estimates for S2 relative to S1. Some variation was attributed to the cow and calf observed in mature spruce at 65 % cover. If these individuals were observed in VCC 1 (vegetation cover between 0 and 20 %), the sightability and model variance would be lowered by ~ 2 orders of magnitude (sightability variance of 20,273 would be lowered to ~200 – 300; current model variance of 4,862 would be lowered to ~3 - 5). Further research involving radio-collared moose in low density strata would improve the relationship between vegetation cover and moose sightability, especially at higher VCC. Most of the variation, however, was a result of sampling and, with a lowered S2 population estimate, the coefficient of variation would 7 only be reduced to ~22 - 23 %. Discrepancies between GIS data and observed forest attributes (dated maps) likely contributed additional variation in the S2 population estimate. Improperly defined stratification as a result of new logging, silviculture treatments or successional growth affects moose forage and availability (Eschholz et al. 1996; Thompson and Stewart 1998; Rea and Gillingham 2001) and may influence their distribution and abundance (Nielsen et al. 2005). Improved stratification and mapping of forest cover with remote sensing and ground truthed assessment or updated VRI data would help reduce variation in population estimates of moose in the Parsnip River study area. Snow influences the energetic expenditure (Parker et al. 1984; Fancy and White 1987; Dailey and Hobbs 1989) and distribution of temperate ungulates (Seip and Bunnell 1985; Hjeljord 2001) including moose (Demarchi 2003; Dussault et al. 2005). Considering the low snow depths (17.1 ± 2.41 cm [ x ± SE]) during the 2005 census, moose distributions may be more variable than previous years. Mean snow depth during the 1998 census was 65 ± 2.9 cm from 50 measurements taken throughout the census area (Heard et al. 1999a). Increasing snow depths can be responsible for seasonal movements to and from winter ranges (Demarchi 2003; Dussault et al. 2005). If moose did not move to wintering areas below 1200 m, we would have underestimated the population in the Parsnip River watershed. Our resulting mean search effort (8.4 ± 0.38 min/km2) was substantially greater than the previous Parnsip census (6.1 ± 0.42 min/km2 [Heard et al. 1999a]) and other moose inventories conducted under similar sampling designs and forest cover types (e.g. 3.3 min/km2 [Heard et al. 2001]). Search effort is a function of transect spacing, flight speed and, because we circle each moose, the number of animals observed (Heard et al. 2001). These are all related to vegetation cover. Regardless of transect spacing and navigation difficulties, we suspect the primary reason for the high survey rate was attributed to slightly higher moose densities (i.e., 1.18 moose/km2) and relatively high vegetation cover where we saw moose (i.e., SCF = 1.25). The previous Parsnip River inventory observed 1.11 moose/km2 (Heard et al. 1999a) using Anderson and Lindzey’s (1996) approach while the Lower McGregor/Herrick River study area observed 0.45 moose/km2 with an average SCF of 1.16. Although the SCF for the 8

Lower McGregor/Herrick River was lower, the area lies within the same biogeoclimatic zones and has similar moose habitat and forest cover. The 2005 study area contained slightly less high quality moose habitat (S1) than in 1998 (603 km2 vs 646 km2), even though the study area was larger (2,501 km2 versus 2,287 km2). We attributed the difference to changes in the methods used to delineate strata and amendments in the study area boundary. The data used to derive the 1998 strata boundaries was not available during the 2005 census and, although we were able to use similar stratification criteria for a portion of the study area (regarding non- productive and non-forest descriptors), discrepancies persisted. We extended the eastern portion of the study area to the height of land between the Parsnip and Crooked River watersheds and the northern portion was extended to the CN rail line and highway 97.

Population size, composition and distribution A previous population estimates in the Parsnip River watershed using Anderson and Lindzey’s (1996) model was 2,600 ± 1000 moose ( x ± 90 % CI) (Heard et al. 1999a). Although there was considerable variation in the 1998 estimate we believe the moose population (i.e., 3,000 ± 110 moose [90 % CI]) in the Parsnip River has changed little over the past 7 years. Although both the observed and estimated number of bulls was less than the ratio of 112 ± 21.5 bulls per 100 cows observed in the 1998 census (Heard et al. 1999a), the estimates were well above the threshold of 30 bulls per 100 cows proposed for moose management in northern BC (Ministry of Environment, Lands and Parks, 1996). Mean recruitment in 2006, at 26 to 30 calves per 100 cows, was slightly higher than in 1998 when it was 23 ± 5.3 calves per 100 cows. The absence of sexual segregation by cover and the similarity between sightability corrected and uncorrected composition estimates was consistent with past surveys in the Prince George/Omineca region (Heard et al. 1999a; Heard et al. 1999b; Heard et al. 2001). Our observed ratios of calves and bulls per 100 cows would be biased if moose segregated by cover. Moose tend to segregate most during winter (Miquelle et al. 1992) and have been observed segregating relative to cover during this period of time (Bowyer et al. 2001). Cows and cows with calves have been observed 9 using areas of greater cover than bulls during winter. Bowyer et al.’s (2001) assessment, however, did not quantify the amount or juxtaposition of cover around a moose, but qualitatively assessed sites occupied by moose in winter. They hypothesized that female moose were more vulnerable to predation and that the risk of predation limited the use of open areas by females as the lack of concealment cover increased their exposure to cursorial predators (i.e., wolves). Miquelle et al. (1992) suggested that segregation in winter results from dimorphisms in body size and seasonal patterns of energy expenditure between male and female moose. Males are generally in extremely poor condition during winter and try to maximize energy intake by situating themselves in areas where forage biomass is high. Although we did not observe the segregation of moose by vegetation cover our results do not conflict with Bowyer et al. (2001) and Miquelle et al. (1992) because techniques differed; our assessment (i.e., aerial estimate of cover within a 9 m radius) was on a much finer scale then that of Bowyer et al. (2001) and did not address issues associated with forage biomass. Even though our findings do not provide evidence for the segregation of moose by cover, differences in the use of strata by cows and bulls were observed (i.e., stratum). Differences in habitat use by a sex or age group would influence composition estimates if the sightability or chance of observing a group was affected. No bulls were ever observed in S2 and additional variation in the population estimate was attributed to a cow and calf moose using mature spruce with dense cover. These findings contrast those of Quayle et al. (2001), who documented that cow groups used non-conifer habitats significantly more than bull groups. Balsom et al. (1996) also concluded that mature forests do not provide critical moose habitat. No decrease in recruitment or increase in mortality has been observed with a reduction in mature forest cover (Balsom et al. 1996). We do not believe our findings provide enough evidence to conclude that moose in the Parsnip River watershed segregated by stratum or that the use of S2 habitats reduced the probability of being observed. We suspect that stratum differences were an artifact of small sample size, considering only 6 cow groups in 3 S2 SUs were observed over the duration of the census.

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ACKNOWLEDGEMENTS

We are thankful for the safe and skilful flying of Greg Altoft. Doug Wilson helped observe and provided GPS support over the duration of the census. Kim Poole provided statistical advice regarding model assumptions and sensitivity. Funding for this project was provided by the Ministry of Environment.

LITERATURE CITED

Anderson, C.R., and Lindzey, F.G. 1996. Moose sightability model developed from helicopter surveys. Wildlife Society Bulletin 24: 247-259. Balsom, S., Ballard, W.B., and Whitlaw, H.A. 1996. Mature coniferous forest as critical moose habitat. Alces 32: 131-140. Bowyer, R.T., Pierce, B.M., Duffy, L.K., and Haggstrom, D.A. 2001. Sexual segregation in moose: effects of habitat manipulation. Alces 37: 109-122. Dailey, T.V., and Hobbs, N.T. 1989. Travel in alpine terrain: Energy expenditures for locomotion by mountain goats and bighorn sheep. Canadian Journal of Zoology 67: 2368-2375. Demarchi, M.W. 2003. Migratory patterns and home range size of moose in the central Nass Valley, British Columbia. Northwestern Naturalist 84: 135-141. Dussault, C., Ouellet, J., Courtois, R., Huot, J., Breton, L., and Jolicoeur, H. 2005. Linking moose habitat selection to limiting factors. Ecography 28: 619-628. Efron, B. 1982. The jackknife, the bootstrap, and other resampling plans. Society for Industrial and Applied Mathematics, Philadelphia, Pennsylvania, USA. Eschholz, W.E., Servello, F.A., Griffith, B., Raymond, K.S., and Krohn, W.B. 1996. Winter use of glyphosate-treated clearcuts by moose in Maine. Journal of Wildlife Management 60: 764-769. Fancy, S.G., and White, R.G. 1987. Energy expenditures for locomotion by barren- ground caribou. Canadian Journal of Zoology 65: 122-128. Gasaway, W.C., Stephen, D.D., Daniel, J.R., and Samuel, J.H. 1986. Estimating moose 11

population parameters from aerial surveys. University of Alaska-Fairbanks, Fairbanks, Alaska, USA. Heard, D.G., Watts, G.S., and Smith, R. 2001. Moose density and composition in the lower McGregor River and Herrick Creek watersheds, British Columbia, January 2001. Final Report for Common Land Information Base. Project No. 01028 Heard, D.G., Zimmerman, K.L., Yaremko, L.L., and Watts, G.S. 1999a. Moose population estimate for the Parsnip River drainage, January 1998. Final report for Forest Renewal British Columbia. Project No. OP96004. Heard, D.G., Zimmerman, K.L., Watts, G.S., and Barry S.P. 1999b. Moose density and composition around Prince George, British Columbia, December 1998. Final Report for Common Land Information Base. Project No. 99004. Hjeljord, O. 2001. Dispersal and migration in northern forest deer - Are there unifying concepts? Alces 37: 353-370. Jolly, G.M. 1969. Sampling methods for aerial censuses of wildlife populations. East African Agriculture and Forestry Journal 34: 46-49. Meidinger, D., and Pojar, J. 1991. Ecosystems of British Columbia: Special report series 6. British Columbia Ministry of Forests, Crown Publications Inc., Victoria, British Columbia, Canada. Ministry of Environment, Lands and Parks. 1996. Wildlife Harvest Strategy: Improving British Columbia’s wildlife harvest regulations. Province of British Columbia Ministry of Environment, Lands and Parks, Victoria, British Columbia, Canada. Miquelle, D.G., Peek, J.M., and Van Ballenberghe, V. 1992. Sexual segregation in Alaskan moose. Wildlife Monographs 122: 1-57. Nielsen, S.E., Johnson, C.J., Heard, D.C., and Boyce, M.S. 2005. Can models of presence-absence be used to scale abundance? - Two case studies considering extremes in life history. Ecography 28: 197-208. Parker, K.L., Robbins, C.T., and Hanley, T.A. 1984. Energy expenditures for locomotion by mule deer and elk. Journal of Wildlife Management 48: 474-488. Quayle, J.F., MacHutchon, A.G., and Jury, D.N. 2001. Modeling moose sightability in south-central British Columbia. Alces 37: 43-54. Rea, R.V., and Gillingham, M.P. 2001. The impact of the timing of brush management 12

on the nutritional value of woody browse for moose Alces alces. Journal of Applied Ecology 38: 710-719. Seip, D.R., and Bunnell, F.L. 1985. Foraging behaviour and food habits of Stone's sheep. Canadian Journal of Zoology 63: 1638-1646. StataCorp LP. 2005. Intercooled Stata 9.0 for Windows. StataCorp, College Station, Texas, USA. Thompson, I.D. and R.W. Stewart. 1998. Management of moose habitats. In Ecology and management of the North American moose. Edited by A.W. Franzmann and C.C. Schwartz. Smithsonian Institution Press, Washington, USA. pp. 377-401 Unsworth, J.W., Leban, F.A., Garton, E.O., D. J. Leptich, and Zager, P. 1998. Aerial Survey: User's manual. Electronic edition. Idaho Department of Fish and Game, Boise, Idaho, USA. Zar, J.H. 1999. Biostatistical analysis. 4th edition Prentice-Hall, Upper Saddle River, New Jersey, USA.

Fig. 1. The delineation of sample units and distribution of high (stratum 1) and low (stratum 2) density moose strata in the Parsnip River study area, December 2005.

Sample Units Stratum 1 Study Area

McLeod Lk. N

7-23

7-16

[[ [

05 10203040 Kilometers

13

14

Kilometers 6 8 7 21 Study Area Stratum 1Sample Units Stratum 2Sample Units 10 42 9 N 35 24 9 25 29 32 48 during the Parsnip River moose inventory, December 5 72 0 10203040 68 71 89 74 83 84 87 96 98 79 107 136 77 88 110 109 108 127 114 144 Lk. 149 McLeod The randomly selected sample units that were surveyed 2005. Fig. 2.

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Table 1. Vegetation cover classes, range of vegetation cover (%), detection probability and sightability correction factor, that were used to extrapolate population estimates of moose in the Parsnip River watershed, December 2005 (adapted from Heard et al. 1999a; Quayle et al. 2001).

Vegetation Cover Vegetation Detection Probability Sightability Class (VCC) Cover (%) (DP)a Correction Factor (SCF)b 1 0 - 20 0.958 1.044 2 21 - 40 0.781 1.280 3 41 - 60 0.361 2.770 4 61 - 80 0.082 12.183 5 81 - 100 0.014 71.676 aDP = 1/SCF bSCF = 1/((exp(4.9604-1.8437×VCC))/(1+exp(4.9604-1.8437×VCC)))

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Table 2. Observed and estimated number of moose by stratum in the Parsnip River watershed (December 2005).

Stratum 1 Stratum 2 Total Moose Observed 259 11 270 Mean Sightability Correction Factor (SCF) 1.18 2.98 1.25 Corrected Number of Moose 304 33 337 Area of Surveyed Sample Units (km2) 143 37 181 Corrected Density (moose/km2) 2.12 0.88 1.18 Total Stratum Area (km2) 603 1,898 2,501 No. of Sample Units Surveyed 35 6 41 No. of Sample Units in Stratum 149 149 298 Corrected Population Estimate 1,280 1,700 3,000 Sampling Variance 26,133 143,548 169,681 Sightability Variance 574 20,273 20,847 Model Variance 13 4,862 4,875 Total Variance of Population Estimate 26,720 168,683 195,403 Standard Error of Population Estimate 163 410 440 Coefficient of Variation of Population Estimate (%) 13 25 15

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Table 3. The number and percentage of groups by vegetation cover class and mean vegetation cover ( x ± SE) that bull moose, barren cows without calves and maternal cows were observed using in the Parsnip River study area, December 2005. Sample size (n) indicates the number of groups in each vegetation cover class. The amount of vegetation cover did not differ among groups (F2, 170 = 1.52, P = 0.221) using a one-way analysis of variance (ANOVA).

Vegetation Vegetation Cover Class (VCC) Cover (%) 1 2 3 4 Total x ± SE n (%) n (%) n (%) n (%) n (%) Bulls 42 (65 %) 21 (32 %) 2 (3 %) 0 (0 %) 65 (100 %) 18.1 ± 1.15 Barren cows 38 (51 %) 36 (48 %) 1 (1 %) 0 (0 %) 75 (100 %) 20.8 ± 1.10 Maternal cows 19 (58 %) 13 (39 %) 0 (0 %) 1 (3 %) 33 (100 %) 21.1 ± 2.15 173 (100 Total 86 (57 %) 60 (40 %) 3 (2 %) 1 (1 %) 19.9 ± 0.85 %)

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APPENDIX A. Itinerary and personnel involved in the Parsnip River moose census, December 2005.

Date Navigator Observers Pilot 12-Dec-05 Doug Heard Andrew Walker, Doug Wilson Greg Altoft 13-Dec-05 Glen Watts Andrew Walker, Volker Michelfelder Greg Altoft 14-Dec-05 Glen Watts Andrew Walker, Volker Michelfelder Greg Altoft 15-Dec-05 Glen Watts Andrew Walker, Doug Heard Greg Altoft 16-Dec-05 Glen Watts Andrew Walker, Volker Michelfelder Greg Altoft

APPENDIX B. Moose observations, vegetation cover, snow depth and search effort in each sample unit during the Parsnip River moose census, December 2005.

Sample Waypoints Date Stratum Total Cows Calves Bulls Veg. Snow Search Area Search unit cover depth time (km2) effort 2 ( x ) (cm) (min) (min/km ) 6 127-133a 15-Dec-05 1 14 11 2 1 15.7 10 19 2.67 7.1 7 126 15-Dec-05 1 2 1 0 1 15.0 10 14 2.77 5.1 8 15-Dec-05 1 0 0 0 0 12 19 2.62 7.3 9 122 15-Dec-05 1 1 0 0 1 25.0 10 17 3.19 5.3 10 123-125 15-Dec-05 1 4 2 1 1 15.0 10 26 3.52 7.4 21 16-Dec-05 1 0 0 0 0 4 30 3.15 9.5 24 134 16-Dec-05 1 2 1 1 0 25.0 9 24 2.84 8.5 25 153,155- 16-Dec-05 1 7 3 2 2 11.7 5 23 2.42 9.5 156 29 135-136 16-Dec-05 1 2 1 0 1 35.0 55 41 4.07 10.1 32 137,139- 16-Dec-05 1 21 13 3 5 19.2 3 39 4.69 8.3 142,144, 146-147, 149-152 35 114-121 15-Dec-05 1 15 6 3 6 22.5 8 55 5.80 9.5 42 14-Dec-05 1 0 0 0 0 42 25 3.05 8.2 48 14-Dec-05 1 0 0 0 0 20 22 4.33 5.1 68 157-164 16-Dec-05 1 18 10 2 6 26.3 8 38 5.67 6.7 71 79,81-85, 14-Dec-05 1 27 16 2 9 21.0 8 40 6.67 6.0 88-91a 72 103-105 15-Dec-05 1 11 7 0 4 8.3 17 NA 2.85 NA 74 91b-92,96, 15-Dec-05 1 9 4 1 4 25.6 15 39 4.54 8.6 98-102 77 108-113 15-Dec-05 1 13 2 0 11 14.2 10 35 3.06 11.4 79 166-169 16-Dec-05 1 10 8 0 2 22.5 11 32 3.87 8.3

83 14-Dec-05 1 0 0 0 0 12 24 4.03 6.0 19

Appendix B Continued

Sample Waypoints Date Stratum Total Cows Calves Bulls Veg. Snow Search Area Search unit cover depth time (km2) effort 2 ( x ) (cm) (min) (min/km ) 84 14-Dec-05 1 0 0 0 0 NA NA 3.79 NA 87 75-77 14-Dec-05 1 3 3 0 0 21.7 8 26 3.58 7.3 88 63-66,68- 14-Dec-05 1 13 6 1 6 20.0 10 48 4.55 10.6 72 89 49-55 13-Dec-05 1 15 7 0 8 12.2 35 55 4.95 11.1 96 10-18 12-Dec-05 1 17 8 2 7 22.5 12 49 5.07 9.7 98 44-45,47 13-Dec-05 1 5 0 0 5 16.7 61 NA 5.23 NA 107 40-43 13-Dec-05 1 5 2 1 2 13.8 15 NA 7.70 NA 108 7-8 12-Dec-05 1 6 5 1 0 24.2 10 NA 4.42 NA 109 26-28 13-Dec-05 1 10 4 5 1 15.0 10 49 4.17 11.8 110 20-21 12-Dec-05 1 3 2 0 1 17.5 12 28 3.42 8.2 114 56-62 14-Dec-05 1 13 6 4 3 11.4 20 40 4.21 9.5 127 5 12-Dec-05 1 2 1 0 1 40.0 15 NA 3.36 NA 136 29-33 13-Dec-05 1 7 5 1 1 25.0 35 47 6.05 7.8 144 12-Dec-05 1 0 0 0 0 NA NA 2.51 NA 149 2-3 12-Dec-05 1 4 2 1 1 16.7 10 56 4.58 12.2 72.2 106 15-Dec-05 2 2 1 1 0 65.0 17 NA 7.69 NA 98.2 46,48 13-Dec-05 2 3 3 0 0 10.0 61 NA 7.33 NA 107.2 34-36 13-Dec-05 2 6 3 3 0 31.7 15 NA 7.02 NA 108.2 12-Dec-05 2 0 0 0 0 10 NA 4.50 NA 127.2 12-Dec-05 2 0 0 0 0 15 NA 5.54 NA 144.2 12-Dec-05 2 0 0 0 0 NA NA 4.98 NA

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