Management Unit 4-34 Moose Inventory

Transcription

1 Management Unit 4-34 Moose Inventory Prepared For: John Krebs Fish & Wildlife Compensation Program (Columbia Basin) Victoria St Nelson, BC V1L 4K3 Prepared By: Patrick Stent Ministry of Environment Environmental Stewardship Division February 2009

2

3 Executive Summary Moose (Alces alces) are an important big game species in the Kootenay region of British Columbia (BC), valued for both hunting and wildlife viewing. Aerial inventories are used to collect moose population and composition data, which is needed for managers to set appropriate harvest levels. To provide a current estimate of moose numbers in the Spillimacheen drainage and surrounding watersheds (Management Unit [MU] 4-34) we conducted a Stratified Random Block survey from January 14 th to 23 rd, Objectives of the survey were to collect moose population and composition data so we could estimate population size and assess the status of the population. The survey followed standard survey protocols outlined by the Resource Information Standards Committee (RISC 2002) and the AERIAL SURVEY User s Manual (Unsworth et al. 1999). We delineated 54 sample units (i.e., blocks) within the study area. A pre-stratification fixed-wing flight using a Cessna 206 airplane was used to rank each block (nil, low or high) according to the number of moose expected to be within the block during the survey. Blocks to be inventoried were randomly selected and surveyed in a Bell 206B Jet Ranger helicopter. Moose were classified following criteria outlined in Timmerman and Buss (1997). We estimated moose population size using the program MOOSEPOP and corrected for incomplete sightability of moose groups using a BC sightability model in the program AERIAL SURVEY. The final estimate was calculated in the spreadsheet program HEARDPOP using population estimates from MOOSEPOP and sightability correction factors from AERIAL SURVEY. We surveyed 20 of 42 low blocks and 6 of the 12 high stratum blocks. Overall survey intensity was 2.7 minutes/km 2 and 554 km 2 of the 1160 km 2 study area was surveyed. We counted 33 moose in 24 groups, including 16 cows, 6 calves and 11 bulls (5 of which had shed their antlers). The population estimate for MU 4-34 was 88 moose (± 34 moose or 39% [90% C.I]; moose; CV = 0.23). The overall correction factor was 1.27 and the corrected density was 0.06 moose per km 2, the lowest density reported in the Kootenay region to date. We estimated calf:cow ratios of 38:100 (20-56 [90% C.I]) and bull:cow ratios of 95:100 ( [90% C.I]). Snowpack was slightly below average for the month of January and moose were observed over a wide elevation band ( m), with 61% of moose observed above 1100 m. Moose densities were twice as high (0.08 moose per km 2 ) north of Bobbie Burns Creek than in the southern portion of the MU (0.04 moose per km 2 ). The majority of moose (67%) were observed in regenerating clearcuts, while 23% were observed in riparian habitats. Only one moose was detected in >50% vegetative cover. Our data suggest the moose population in MU 4-34 is depressed, although suitable moose habitat was not limited throughout the study area. Hunter harvest data show a decline in harvest and hunter success over the past 15 years, suggesting moose numbers have declined. Local knowledge supports this finding and the population decline has been attributed to ingrowth of highly valuable habitats (i.e., burns) and increased predation rates. Wolves are the primary predator of moose in the Kootenays and are believed to be increasing throughout the region. The extent to which wolves are limiting moose populations in the Kootenays is undetermined; however research has shown that low density moose populations in multi-predator ecosystems will remain depressed unless predator numbers are reduced. Ministry of Environment, February 2009 i

4 Much of the study area was heavily forested and we spent substantial time surveying areas of >70% cover that were of little value to moose. Stratification accuracy was poor, which we attribute to the low densities of moose and excessive speed of the fixed-wing aircraft. Stratification using habitat attribute data may achieve greater accuracy (Heard et al. 2008) and minimize time spent surveying areas that support insignificant numbers of moose. We also stratified the study area for elk (Cervus elaphus nelsoni) and observed a total of 272 elk during the inventory. Using the Hiller 12-e elk model in AERIAL SURVEY, we estimated a population of 594 elk ± 434 (90% C.I) for MU 4-34 and 1190 elk ± 1091 (90% C.I) for the Columbia Wetlands (both sides of the Columbia River from just north of Radium to Kinbasket Lake). The movement of elk herds between survey blocks and across the Columbia River resulted in our population estimate being bounded by a wide confidence interval. We recommend that future elk surveys in the area cover both sides of the Columbia River and estimate population size for the entire wetland complex. The Columbia River is an insignificant boundary to elk and a population estimate that includes elk wintering on both sides of the river would be more practical for management purposes. Ministry of Environment, February 2009 ii

5 Table of Contents Executive Summary... i Table of Contents... iii List of Figures... iii List of Tables... iii Introduction... 2 Study Area... 3 Methods... 5 Sampling Strategy... 5 Stratification... 5 Surveying... 5 Data Analysis... 6 Elk... 7 Results... 8 Other Species Discussion Elk Survey Methods Acknowledgements Literature Cited List of Figures Figure 1: Moose harvest statistics for Management Unit 4-34 (Spillimacheen) from 1976 to 2008, calculated from hunter survey data... 2 Figure 2: Map showing moose survey blocks and stratification ratings... 4 Figure 3: Map showing block stratification ratings, blocks surveyed and moose observations scaled to group size for the MU 4-34 study area, surveyed January 14 th 23rd, Figure 4: Elevation range of moose observed during the MU 4-34 moose inventory Figure 5: Bull moose observed during the MU 4-34 moose inventory t List of Tables Table 1: Vegetation cover classes and their associated detection probability and sightability correction factors... 7 Table 2: Moose population statistics for the MU 4-34, Surveyed January 14 th -23 rd, Table 3: Observed and estimated sex and age classification data from programs Moosepop and AERIAL SURVEY Table 4: Observed and estimated sex and age classification data from program AERIAL SURVEY for elk in MU 4-34, East Kootenay, surveyed January rd, Ministry of Environment, February 2009 iii

6 Introduction Moose (Alces alces) are an important big game species in the Kootenay region of British Columbia (BC), valued for both hunting and wildlife viewing. Construction of hydroelectric dams throughout the Columbia Basin in the early 1970 s resulted in flooding of many lowland areas which were likely valuable moose habitats, although moose populations are generally considered healthy in the Kootenay region today (Poole 2007). Since 1991, moose harvest in the Kootenay region has been regulated with limited entry hunting (LEH) seasons, in which a limited number of permits to hunt bull moose are allocated to specific management units (MUs) each year, and are issued to resident hunters using a lottery. Moose are also harvested by guide outfitters in the Kootenay region, with harvests being regulated by a quota system. Quantitative data on moose population numbers is needed for wildlife managers to set appropriate harvest levels. In the East Kootenay, aerial surveys have been used to assess the relative health of moose populations, although data are deficient for MU 4-34 and permit allocation has been based on best guesses of population size, estimated using a combination of hunter harvest data and anecdotal sighting reports (Poole 2007). Hunter harvest data show a decline in hunter success in MU 4-34 since the 1990 s, with only 6 moose being harvested in 2008, despite 25 LEH and 4 guide outfitter permits being issued to the MU (Figure 1). Figure 1: Moose harvest statistics for Management Unit 4-34 (Spillimacheen) from 1976 to 2008, calculated from hunter survey data. Total moose harvested in 2008 was the number of compulsory inspection moose tooth samples submitted for MU 4-34 and we assumed that 100% of drawn hunters participated in the hunt. Ministry of Environment, February

7 We conducted a Stratified Random Block survey (Gasaway 1986) of MU 4-34 to assess the distribution of moose, provide a current estimate of moose populations in this area and estimate age and sex ratios. Our secondary objective was to estimate elk (Cervus elaphus nelsoni) numbers in MU Data will be used as a basis for future LEH permit allocation, to ensure effective management of moose and elk populations. Study Area The study area lies in MU 4-34, located on the west side of the Columbia River, extending from Horsethief Creek to the Beaver River. Biogeoclimatic (BGC) zones within the study area include the Interior Cedar Hemlock (ICH), Montane Spruce (MS) and Interior Douglas Fir (IDF). The ICH zone occurs at the northern end of the study area at low elevations with climax stands consisting mainly of western hemlock (Tsuga heterophylla) and western red cedar (Thuja plicata) (Braumandl and Curran 2002). The IDF zone occurs at lower elevations in the southern part of the study area and is characterised by mixed stands of Douglas-fir (Pseudotsuga menziesii), western larch (Larix occidentalis) and lodgepole pine (Pinus contorta). The MS zone typically occurrs above the IDF and is dominated by stands of spruce (Picea spp.) and subalpine fir (Abies lasiocarpa) (Parish et al. 1996). The Engelmann Spruce Subalpine Fir (ESSF) zone and Interior Mountain-heather Alpine (IMA) zones occur at higher elevations (D.Mackillop, MoFR, pers. comm.) throughout the study area but did not extend into areas surveyed. Past flooding events created expansive networks of marshes and riparian habitats in low lying areas along the Columbia River. Mosaics of willows (Salix spp.), black cottonwood (Populus balsamifera) and cattails (Typha spp.) were common in these areas. Regenerating clearcuts and avalanche chutes were early seral habitats commonly encountered above valley bottom. The southern portion of the study area is considered within the dry climatic region while the northern third of the MU (predominantly north of the Spillimacheen River) is within the wet climatic region. The range in climatic conditions between the north and south portions of the study area is reflected in historic snowfall data, with mean snowfall averaging 124 cm in Revelstoke (130 km west of Spillimacheen) and 33 cm in Cranbrook (160 km south of Spillimacheen) for the month of January ( ; Environment Canada 2009). Average accumulated snow for January was 20 cm in Cranbrook and 51 cm in Revelstoke over this time period. Snow water equivalents recorded at East Creek (2030 m) in the Duncan River drainage indicated snow levels were around 90% of the average for the month of January (MoE, Water Stewardship Division, 2009). Potential predators of moose within the study area included wolves (Canis lupus), black bears (Ursus americanus), grizzly bears (U. arctos), and cougars (Felis concolor). Elk, mule deer (Odocoileus hemionus), and white-tailed deer (O. virginianus) were present and often abundant in some areas. Mountain goats (Oreamnos americanus) occur at high elevations in some portions of the study area. Ministry of Environment, February

8 MU 4-34 Moose Inventory Figure 2: Map showing moose survey blocks and stratification ratings for MU 4-34, surveyed January 14th-23rd, Ministry of Environment, February

9 Methods We used a stratified random block design (Gasaway 1986) and followed methods for aerial surveys outlined in the Resource Information Standards Committee (RISC) manual for aerial ungulate inventories (2002) and the AERIAL SURVEY user s manual (Unsworth et al. 1999). Sampling Strategy We gathered background information on moose distributions from Conservation Officers and a guide outfitter who was knowledgeable with the area. Survey units (blocks) were delineated throughout all known moose winter ranges with the 1700 m elevation contour being used as the upper block boundary on south aspects and the 1400 m elevation contour being used on north, east and west aspects. Lower block boundaries typically followed roads, and to a lesser extent, streams. We attempted to make blocks close to 20 km 2 so they could be surveyed in about an hour. Stratification We conducted a fixed wing flight in a Cessna 206 airplane 1 day prior to the inventory to stratify blocks based on expected moose numbers. Flight speed averaged 160 km/h at an altitude 90 to 150 m above ground level; one to three passes were made over each subunit with larger units receiving greater survey effort. During the flight a pilot, navigator and two backseat observers were present. A laptop with Arcpad (Version U; Environmental Systems Research Institute) was used for navigation. This program was connected to a Garmin 76 Global Positioning System (GPS), which allowed surveyors to track the position of the airplane relative to block boundaries. During the flight, observers noted moose observations, evidence of use and relative habitat suitability within blocks. We rated habitat suitability based on availability of early seral habitat (i.e., cutblocks, avalanche chutes and riparian areas). Blocks were rated as either nil, low or high based on the number of moose expected to be in the block during the time of the survey. High blocks were assumed to hold more moose than low blocks, although we did not establish strata cut offs based on expected moose numbers. Blocks rated nil were assumed to hold no moose and were not surveyed. We initially planned to randomly sample 75% of the high blocks and 50% of low blocks but later adjusted sampling effort so that more low blocks would be sampled as there was greater variation in moose numbers in low blocks. Surveying We used a Bell 206B helicopter, equipped with wedge windows to survey selected blocks. The survey team consisted of one primary observer, situated in the front of the helicopter and 2 secondary observers situated in the rear. The primary observer was responsible for navigating through survey blocks using Arcpad on a laptop computer. Flight paths were recorded using the tracklog function in Arcpad to ensure complete coverage of each survey block. The secondary observer on the right side of the helicopter was responsible for recording data for animal observations. Two members of the survey crew were present for 5 of the 6 days. We used 4 Ministry of Environment, February

10 different secondary observers throughout the survey period, all of whom had experience participating in moose surveys. Blocks were flown in transects m apart as dictated by topography and vegetative cover. Flights were conducted m above the treetops in forested habitats and up to 130 m above ground level in open habitats. Flight speed ranged from 55 to 95 km/hour in most habitat types; agricultural fields and other open habitats were flown at up to 130 km/hour. We surveyed blocks by following fixed bearings in relatively flat terrain and elevation contours in steep terrain. When moose were sighted, the pilot circled the group and observers attempted to classify animals. Antlerless moose were classified as cows, calves or antlerless bulls. Antlerless bulls were identified by the absence of a white vulval patch and the presence of pedicel scars. All antlerless bulls were recorded as unclassified bulls. Antlered bulls were classified as teens (spike-forks), subprime bulls (antlers not extending beyond ears) and prime bulls (antlers extending beyond ears and at least one brow tine; Timmerman and Buss 1997). Moose that could not be definitively classified were recorded as unclassified. We photographed moose using a Nikon D80 camera with a 300 mm lens to verify classification of antlerless moose. We also recorded relative snow cover where moose were observed and oblique vegetative cover around the first animal seen in the group (Unsworth et al. 1999, Quayle et al. 2001). Percent cover of vegetation was discussed among surveyors to standardize the estimates once animals were observed. We also recorded habitat feature codes (MoE & MoF 1998) for habitat types that moose were spotted in. We recorded a waypoint for each moose group that was spotted using a Garmin 76 GPS and recorded observation data on the AERIAL SURVEY data form. Elevational distribution of moose was determined by subtracting the approximate above-ground height of the helicopter (40 m) from elevations of GPS waypoints, taken from the helicopter at initial locations of moose observations. Data Analysis We determined correction factors and associated variances for moose observed using the program AERIAL SURVEY (Unsworth et al. 1999) with sightability correction applied to each moose group and averaged over respective strata. Detection probabilities were determined using sightability data from a BC model (Quayle et al. 2001), which was updated with data compiled from an additional 20 sightability trials conducted in Prince George in 2001 (J. Quayle, MoE, unpublished data; Table 1). This model corrects the count of individual groups of observed moose by multiplying the number counted by the inverse of the probability that the group would have been seen. The probability that a group will be seen depends on group size (larger groups are more visible), vegetation class (moose in open vegetation are more visible) and snow (moose are more visible with greater snow cover). The BC model uses 5 cover classes, separated at the 20%, 40%, etc. boundaries of percent vegetation cover. We generated uncorrected population estimates (not corrected for incomplete sightability) for each stratum using the program MOOSEPOP (Version 2.0; R.A. DeLong and D.J. Reed, Alaska Department of Fish and Game, Fairbanks, Alaska, USA). This program accommodates blocks of unequal size by using density as the survey result while AERIAL SURVEY assumes all blocks are of equal size. The final population estimate was generated by multiplying Ministry of Environment, February

11 uncorrected population estimates for each stratum (calculated in MOOSEPOP) by their respective correction factors (calculated in AERIAL SURVEY) in a spreadsheet program developed by MoE (HEARDPOP; J. Quayle, MoE, unpublished data). The variance for the population estimate was calculated as the sum of the sightability and model variance from AERIAL SURVEY and the sampling variance from the program MOOSEPOP. All estimated parameters are presented with associated 90% confidence intervals. Moose and elk observation data were entered in a Species Inventory Data System (SPI) compatible database and will be imported to the provincial database. Table 1: Vegetation cover classes and their associated detection probability and sightability correction factors (program AERIAL SURVEY, Unsworth et al. 1998, as modified using Quayle et al and updated with data from Prince George sightability trials, 2001 [J. Quayle, MoE, unpublished data]). Vegetation class Percent vegetation cover Detection probability Sightability correction factor Class Class Class Class Class Elk Moose blocks were also rated for expected elk numbers (nil [0 elk per block], low [<35 elk per block] or high [>35 elk per block]) and all relevant data, including groups size, vegetative cover, snow cover and animal activity (standing, bedded or moving; Unsworth et al. 1999) were recorded during the survey so an elk estimate could be generated for MU 4-34 in AERIAL SURVEY using the Hiller 12-e elk sightability model. Bull elk were classified as yearlings (spikes), raghorns (3 to 5-points with small, thin antlers) and adult bulls (large, massive antlers with 5 or more points; RISC 2002). Cows (females > 1-year-old) and calf elk were also classified. We took photographs of elk when we encountered groups of >15 animals to verify counts and classifications recorded from the air. We used a Nikon D80 digital camera with a 300 mm telephoto lens to photograph elk, with picture size set to 18 megapixels. We attempted to photograph elk when they were broadside so we could compare rostrum lengths of antlerless elk to help identify calves. We relied on bull classification data recorded from the air as antlers were difficult to detect in photographs when animals were tightly grouped together. We verified aerial counts of elk by opening full sized digital photographs in Microsoft Paint and marking each elk in red once it was counted. Deer Mule and white-tailed deer observations were tallied within each block but we did not classify animals to sex or age. No snow cover or vegetative cover data were recorded for deer observations. Ministry of Environment, February

12 Results The pre-stratification flight was conducted on January 13 th and took 5.5 hours to complete. Skies were clear and there was no snowfall recorded in the southern end of the study area since January 10th, although we noticed higher elevation areas and the northern extreme of the MU had a recent dusting of snow. Snowfall accumulation measured at the Revelstoke airport shows the most recent snowfalls occurred on January 10 th and 11 th, although combined snowfall was less than a centimetre. A total of 16 moose and approximately 382 elk and 150 white-tailed deer were observed during the fixed wing flight. Moose tracks were difficult to distinguish from elk and we had to rely more on our subjective assessments of habitat suitability to rate blocks where the 2 species overlapped. The helicopter survey ran from January 14 th to 23 rd, although we were unable to fly for 4 days due to dense fog in the Columbia Valley. Flying conditions were less than ideal during the survey due to flat light at low elevations caused by a dense cloud layer persisting around 1700 m elevation. Conditions were sunny when block 67 was surveyed as it occurred above the cloud layer. Snow cover was generally complete, although low elevation, south-facing slopes in the southern end of the MU were showing bare patches at the base of trees. Blocks varied in size from 13 to 30 km 2 ( x = 21 km 2 ) and required 27 to 120 minutes to survey ( x = 58 minutes, SD = 20 minutes). Overall survey intensity was 2.7 minutes/km 2. We surveyed 554 km 2 of the 1160 km 2 study area. Total survey time was 24 hours and 59 minutes. We surveyed 20 of 42 low blocks (48%) and 6 of 12 high blocks (50%). Overall sampling effort was 48% (Table 1). We observed 33 moose in 24 groups during the inventory. We classified 16 cows, 6 calves, 5 spike/fork bulls, 1 prime bull and 5 unclassified bulls (i.e., bulls that had shed their antlers). Stratification accuracy was variable with 0 to 5 moose being observed in low blocks ( x = 1.1; SD = 1.7) and 0 to 5 moose being observed in high blocks ( x = 2.1; SD = 2.2). There were 11 low blocks and 2 high blocks where we observed no moose. Ministry of Environment, February

13 Figure 3: Map showing block stratification ratings, blocks surveyed and moose observations scaled to group size for the MU 4-34 study area, surveyed January 14 th 23 rd, Ministry of Environment, February

14 The sightability corrected estimate for MU 4-34 was 88 moose (CI: moose; CV = 0.23) after correcting for incomplete sightability and extrapolating to blocks not surveyed. The overall sightability correction factor was 1.27 (Table 1) and 91% of moose were observed in the 2 most open vegetation cover classes. Estimated moose density was 0.06 moose per km 2 with a low stratum density of 0.05 moose per km 2 and a high stratum density of 0.10 moose per km 2 (Table 2). Table 2: Moose population statistics for the MU 4-34, Surveyed January 14 th -23 rd, 2009 Parameter Stratum 1 Stratum 2 (low) (high) Total No. of SU 1 in stratum No. of SU surveyed Total stratum area (km 2 ) Area of surveyed SU's Moose observed Uncorrected estimate (observed density extrapolated to unsurveyed portions of the study area) Sightability correction factor Corrected population estimate Coefficient of variation Corrected density (moose/km 2 ) SU = Survey Unit or block We estimated 37 cows, 14 calves and 35 bulls for MU 4-34, with estimated ratios of 38 calves:100 cows (CI: 20-56) and 95 bulls:100 cows (CI: ). Table 3: Observed and estimated sex and age classification data and 90% confidence intervals from AERIAL SURVEY for moose in MU 4-34, East Kootenay, surveyed January 14 th -23 rd, Calves:100 Bulls:100 Cows Calves Bulls Unclassified Cows Cows Observed Estimated (AERIAL SURVEY) 90 % Confidence Interval The greatest number of moose (67%) were observed in the Herbaceous Cutblock habitat type while the second most occupied habitat was the River type, where we observed 24% of the total moose. We observed 1 moose in the Young Forest habitat type and another in the Mature Forest type. One moose was also observed in an avalanche chute. Moose were observed between 821 and 1567 m elevation with 20 moose (61%) observed above 1100 m. Six moose (3 cows and 3 bulls) were observed above 1500 m elevation (Figure 4). Ministry of Environment, February

15 Figure 4: Elevation range of moose observed during the MU 4-34 moose inventory, January 14 th - 23 rd, Elevation was determined by subtracting the average above ground height of the helicopter (40 m) from the elevation recorded on the GPS. Other Species We observed 272 elk, 325 white-tailed deer and 32 mule deer during the inventory. We surveyed all blocks rated low for elk and 7 of the 15 blocks rated high. The remaining 34 blocks stratified were assumed to hold no elk and were rated nil. Elk numbers ranged from 0 to 166 in high blocks ( x = 38.9; SD = 65.3), while no elk were detected in low blocks. Fourteen blocks rated nil for elk (that were low or high moose blocks selected for sampling) were surveyed and contained no elk. Our elk population estimate for the MU was 595 ± 434 (90% C.I) or 73 % (Table 4). We classified 159 elk as cows, 34 calves, 17 spikes and 14 raghorns (Table 5). We were unable to classify 48 elk observed during the inventory. We estimated calf:cow ratios of 21:100 (CI: 0-42) and bull:cow ratios of 20:100 (CI: 0-40). We photo-verified counts and classifications for 5 groups of elk; 2 large groups of elk (80 and 93 animals) were not classified from the air and we relied on photographs for all classification. Two groups of elk containing 14 and 17 animals (counted from the air) were undercounted by 2 and 3 animals, respectively. Photo-verified counts were consistent with aerial counts for a group of 21 elk observed. Ministry of Environment, February

16 Table 4: Elk population statistics for MU 4-34, surveyed January 14 th -23 rd, Parameter Stratum 1 (low) Stratum 2 (high) Total No. of SU 1 in stratum No. of SU surveyed Total stratum area (km 2 ) Area of surveyed SU's Elk Observed Uncorrected estimate (Observed density extrapolated to unsurveyed portions of the study area) Sightability correction factor SU = Survey Unit or block Corrected population estimate Corrected density (elk/km 2 ) Table 5: Observed and estimated sex and age classification data and 90% confidence intervals from program AERIAL SURVEY for elk in MU 4-34, surveyed January 14 th -23 rd, Cows Calves Spikes Raghorns Adult Bulls Unclass. Calves:100 Cows Bulls:100 Cows Observed Estimated (AERIAL SURVEY) 90 % Confidence Interval Discussion This was the first time MU 4-34 has been surveyed for moose, although moose observations were recorded during ungulate monitoring censuses conducted from (Tinker et al. 1997). Comparing data between these surveys is difficult as the early surveys overlapped MUs and blocks did not extend far up drainages. No obvious population trends can be inferred from the data; however a surprising number of moose (38) were observed in the North Columbia Marshes block (Castledale to Donald) in 1996, which is likely a function of the extreme snowpack that winter. Our population estimate of 88 moose seems relatively low for the size of MU 4-34 and the availability of early seral habitat we observed. Harvest data support our finding of a depressed population as moose harvest and hunter success have declined dramatically since the late 1980s and early 1990s (Figure 1). Local knowledge attributes the decline in moose number to ingrowth Ministry of Environment, February

17 of high value habitats (mainly burns) and increasing numbers of wolves (W. Cibulka, Conservation Officer Service; Don Fowler, guide outfitter, pers comm.). Snowpack was slightly below average for the month of January and moose were distributed over a wide elevation band, with a surprising number of animals being observed above 1500 m (Figure 4). We were diligent at surveying above the block boundaries if snow depths were <1m and we encountered 3 moose above mapped boundaries. We were unable to survey above block 64 due to dense fog and may have missed a group of moose above our last transect as there were 2 sets of tracks leading upslope. Detecting 2 moose above this block would have increased our estimate for the low stratum but the overall change to the population estimate would be small. Track occurrences above other blocks surveyed suggested that the majority of moose were wintering within block boundaries. I calculated moose densities for the northern and southern halves of MU 4-34 using Bobbie Burns Creek as the boundary. Moose densities were twice as high (0.08 moose per km 2 ) in surveyed blocks to the north of Bobbie Burns Creek than in the southern half of the MU (0.04 moose per km 2 ), suggesting that the majority of the moose population resides in the northern half of the MU. We observed no obvious differences in habitat suitability between the northern and southern halves of the MU; however moose seemed to be exploiting low elevation riparian areas to a greater extent in the northern part of the MU where elk were less common. Local knowledge suggests that a wolf pack travels through the Columbia River and preys on elk wintering in the Columbia Wetlands (W. Cibulka, Conservation Officer Service, pers. comm.). Moose may avoid wintering close to elk herds due to the increased risk of predation or competition for food. The estimated density for MU 4-34 (0.06 moose per km 2 ) was the lowest moose density estimate reported in the Kootenay Region to date. Moose inventories west of MU 4-34 in the South Selkirk Region (MUs 4-17, 4-19, 4-27 to 4-31 and 4-33) estimated moose densities of 0.12 moose per km 2 (Stent et al. 2008), while inventories southeast of our study area in the Lodgepole (northern portion of MU 4-02), Flathead (northern portion of MU 4-01) and Elk (MU 4-23) drainages estimated moose densities to be 1.37, 1.15 and 0.60 moose per km 2 for the respective study areas (Poole et al. 2008). Winter densities provide only a rough comparison of animal abundance among areas since the extent of winter range is dictated by snow levels rather than year-round resource availability. The bull:cow estimate of 95 bulls:100 cows (CI: ) suggests that hunting levels are low in MU Unhunted populations generally have bull ratios of <90 bulls:100 cows, hence the true ratio for this population is likely near the lower confidence limit. We feel that predation is the main factor limiting this population as suitable habitat was not limited in the study area. Research suggests that low density moose populations in multi-predator ecosystems will not recover unless there is a reduction in the number of predators in the population (Gasaway et al. 1992; Bergerud 1983). Wolves are thought to be the main predator of moose in the Kootenays (Mowat 2007) and typically prey on calves and old adults. It is suggested that wolf numbers have increased throughout the Kootenays in the recent past (Gaynor et al. 2007) and may be limiting moose populations in parts of the region. Wolf hunting and trapping seasons are quite liberal in the Kootenays, but it is unlikely that human harvest is limiting wolf population growth. Research suggests that moose numbers will decline when ratios of 20 moose per wolf exist (Gasaway et al. 1983). Black and grizzly bears are also predators of moose calves (Crete and Jolicoeur 1987; Ballard et al. 1991) and are common throughout the study area. Ministry of Environment, February

18 Our estimated calf:cow ratio (38 calves:100 cows [CI: 20-56]) was not significantly different than ratios calculated for other Kootenay moose populations (Stent et al. 2009; Poole 2008; Serrouya and Poole 2007; Poole et al. 2008). The calf:cow ratio required to maintain a stable population in the absence of hunting is suggested to be approximately 25 calves:100 cows (at 6-9 months of age; Begerud, 1992), but may rise to calves:100 cows in hunted populations depending on the adult harvest and natural mortality rates (Hatter and Bergerud, 1991). Inferring population growth from age ratios is weak inference, but given females have not been hunted since 1997 it would appear that calf survival to mid-winter is not a major limiting factor in this population. Compulsory inspection data (MoE, unpublished data) show 6 bull moose were harvested in MU 4-34 in The pre-harvest moose estimate for the MU would be 96 moose if the number harvested was added to our population estimate. Current harvest rates would account for 17.1% harvest of the bull population and 6.3% of the overall moose population, assuming our preharvest estimate is accurate. The number of harvest permits allocated to MU 4-34 in 2008 (29) seems high relative to our bull estimate; however high bull to cow ratios from our survey show that bull numbers are not likely limited by hunter harvest. The availability of remote habitats and wide dispersal of bulls throughout the MU are factors that may contribute to low harvest levels and poor hunter success (Figure 1), which likely prevent overharvest of bulls. Elk Movement of elk herds between survey blocks and across the Columbia River resulted in our population estimate being bounded by a wide confidence interval (595 ± 434). We observed herds of elk just outside boundaries of 2 high blocks that had no elk within them during the survey. Two other high blocks where we observed no elk showed evidence of recent use. No elk were observed more than 200 m above valley bottom, although local knowledge suggests a greater number of elk winter above valley bottom on the east side of the Columbia River (Walter Cibulka, former Conservation Officer, pers. comm.). I ran a second analysis for the entire Columbia Wetlands, assuming there were another 15 high blocks east of the Columbia River. The estimate came out to be 1190 elk (CI: ). To provide a more accurate estimate of elk wintering in the Columbia Wetlands, researchers should survey both sides of the Columbia River as the river is not a barrier to elk and frequent movement occurs between the MUs. This is a more practical approach than surveying one side of the river exclusively and would provide managers with a total estimate of elk wintering in the Columbia Wetlands. Habitat is relatively open along the river and could be surveyed quickly. Deer We observed 296 of 325 white-tailed deer within or immediately adjacent to the Columbia Wetlands. White-tailed deer were only detected in 4 blocks above valley bottom and we observed no deer north of Golden. The greatest number of white-tailed deer was observed in block 1, where we counted 145 deer. Mule deer distribution was patchy throughout the study area and most animals were observed along Steamboat Mountain. The most mule deer were observed in block 7 where we counted 15 deer. It is expected that greater numbers of mule deer Ministry of Environment, February

19 winter on hillsides east of the Columbia River, which receive greater solar radiation in the winter. Survey Methods Our survey intensity (2.7 minutes/km 2 ) was similar to survey intensity of past Kootenay moose inventories ( minutes/km 2 ; Poole and Serroya 2003; Serrouya and Poole 2007; Poole et al. 2008). Moose were observed in relatively open terrain and we were able to classify all animals to sex and age class from the air. Digital photographs were used to verify classification of antlerless moose and helped identify distinguishing features (i.e., vulval patches and pedicel scars; Figure 5). For accurate classification from photographs, we recommend using a relatively high power lens (>200 mm) and large megapixel photographs (>10 megapixel). Figure 5: Bull moose observed during the MU 4-34 moose inventory (January 14 th -23 rd, 2009), showing pedicel scars. Antlerless moose classification was verified using photographs taken with a Nikon D80 digital camera with a 300 mm telephoto lens. Pedicel scars and vulval patches could be easily identified when photographs were blown up, providing animals were in relatively open habitat. Low moose densities made stratification of the study area difficult and the number of moose detected in low blocks was not statistically different than high blocks (t=0.26, df=25, 2-sided p = 0.25). Deciding if a block should be rated low or high became tedious when blocks contained high value habitat but showed no obvious moose sign. We recommend using an aircraft with a slower cruise speed (i.e., Cessna 185 or 182) for future stratification flights as this would improve the crew s ability to detect moose and tracks from the machine. As the majority of moose were observed in early seral habitats, stratifying the study area based on habitat attribute data may be a more desirable approach. Heard et al. (2008) stratified inventory areas using vegetation data for 6 separate surveys in central BC and found moose density estimates Ministry of Environment, February

20 averaged 3.5 times higher in blocks mapped as high density areas than those mapped as low density areas, although there were discrepancies between habitat types identified in the GIS and those seen from the air. The majority of MU 4-34 was dominated by dense lodgepole pine, Douglas-fir and spruce forests, interspersed with cutblocks. Moose were nearly impossible to detect in densely forested areas and we observed no animals in >50% vegetative cover, although moose tracks were occasionally spotted through the canopy. Judging by the number of fresh tracks seen in these habitat types, we felt densely forested habitats supported an insignificant number of moose, likely less than 1 moose per 20km 2. Surveying heavily forested areas that were of little value to moose seemed like wasted effort and we recommend classifying these habitat types as nil for future surveys. Acknowledgements The Ministry of Environment would like to thank everyone who helped make the 2009 MU 4-34 moose inventory a success. Foremost we would like to thank John Christensen of Airspan Helicopters and Calvin Nickel of Babin Air for their exceptional piloting skills. Participants in moose surveys included Walter Cibulka, Lawrence Umsonst, Becky Phillips, Garth Mowat and Tara Szkorupa. These people also assisted in planning of the survey and reviewing draft versions of this report. Paul Frasca and Kari Stuart-Smith of Tembec Industries generously provided us accommodations in Parson and allowed us to use the heli-pad at the Tembec office. Background information on moose distributions and population trends were provided by Don Fowler and Walter Cibulka. A portion of the Discussion and Data Analysis section was written by Kim Poole. This project was funded by the Fish and Wildlife Compensation Program Large Mammal Monitoring project. Literature Cited Ballard, W.B., Whitman, J.Sx and D.J. Reed Population dynamics of moose in southcentral Alaska. Wildl. Monogr pp. BC Ministry of Forests and BC Ministry of Environment Lands and Parks Field manual for describing terrestrial ecosystems. Province of British Columbia, Victoria, BC. Bergerud, A.T Rareness as an antipredator strategy to reduce predation risk for moose and caribou. Pages in D. R. McCullough and R. B. Barrett, editors. Wildlife 2001: populations. Elsevier Science Publishing Ltd., London, United Kingdom. Bergerud, A.T., Wyett, W. and J.B Snyder The role of wolf predation in limiting a moose population. J. Wildl. Manage. 47: Braumandl, T.F., and M.P. Curran A field guide for the site identification for the Nelson Forest Region. British Columbia Ministry of Forests, Victoria, British Columbia, Canada. Ministry of Environment, February

21 Gasaway, W. C., DuBois, S.D., Reed, D.J, and S. J. Harbo Estimating moose population parameters from aerial surveys. Biological Papers No. 22, University of Alaska Fairbanks, Alaska, USA. Gasaway, W.C., Boertje, R.D., Grandgard, D.V., Kellyhouse, K.V., Stephenson, R.O. and D.G. Larsen The role of predation in limiting moose at low densities in Alaska and Yukon and implications for conservation. Wildl. Monogr pp. Gaynor, C., van Oort, H., Mowat, G. and L. DeGroot Predator surveys within Kootenay region mountain caribou recovery areas: Data summary report. Ministry of Environment, Nelson, BC. Heard, D., Walker, A., Ayotte, J., and G. Watts Using GIS to modify a stratified random blocks survey design for moose. Alces 44: Ministry of Environment. Water Stewardship Division Columbia snow pillow data. URL: Accessed on February 10, Parish, R. R. Coupe and D. Lloyd Plants of Southern Interior British Columbia and the Inland Northwest. Lone Pine Publishing. Poole, K.G A population review of moose in the Kootenay Region. Unpublished report for British Columbia Ministry of Environment, Cranbrook, British Columbia. Poole, K.G Moose inventory of Management Units 4-22 (Bull River) and 4-20 (St. Mary River), East Kootenay, January Unpublished report prepared for British Columbia Ministry of Environment, Cranbrook, British Columbia. Poole, K. and R. Serrouya Moose population monitoring in the Lake Revelstoke valley, Prepared for: Downie Street Sawmills, Wood River Forest Inc., Joe Kozek Sawmills Ltd., Revelstoke Community Forest Corporation and Mount Revelstoke and Glacier National Parks, Revelstoke, BC. Quayle, J. F., MacHutchon, A.G, and D. N. Jury Modeling moose sightability in southcentral British Columbia. Alces 37: RISC (Resources Information Standards Committee) Aerial-based inventory methods for selected ungulates: bison, mountain goat, mountain sheep, moose, elk, deer and caribou. Standards for components of British Columbia s biodiversity No. 32. Version 2.0. Resources Inventory Committee, B.C. Ministry of Sustainable Resource Management, Victoria, British Columbia. Robinson, H Ungulate aerial survey analysis and summary 2000, 2004 and 2007 in the South Selkirk Mountains of Southeastern British Columbia. Profile Environmental Services, Calgary, Alberta. Ministry of Environment, February

22 Serrouya, R, and K. G. Poole Moose population monitoring in the Lake Revelstoke (Management Units 4-38 and 4-39) and North Thompson (MUs 3-43 and 3-44) valleys, January 2006 and Unpublished report for HCTF, Victoria, British Columbia. Stent, P., Gaynor, C., and G. Mowat Central Selkirk moose population inventory. Ministry of Environment, Nelson, BC. Timmermann, H. R., and M. E. Buss Population and harvest management. Pages in A. W.Franzmann and C. C. Schwartz, editors. Ecology and management of the North American moose. Smithsonian Institute Press, Washington, D.C., USA. Tinker, T., Heavean, P., and L. Ingham Columbia Basin, Large Mammal Monitoring: AERIAL SURVEYs. Prepared for Columbia Basin Fish and Wildlife Compensation Program. Prepared by Glenside Ecological Services, Victoria, BC. Unsworth, J. W., F. A. Leban, E. O. Garton, D. J. Leptich, and P. Zager AERIAL SURVEY: User s Manual. Electronic Edition. Idaho Department of Fish & Game, Boise, Idaho, USA. Ministry of Environment, February