South Selkirk Ungulate Survey: 2011 Survey Report

Transcription

1 South Selkirk Ungulate Survey: 2011 Survey Report Prepared For: Fish and Wildlife Compensation Program Columbia Basin Nelson, BC Prepared By: Patrick Stent 1 and Ross Clarke 2 1 Mackenzie Wildlife Consulting: th Street, Nelson BC, V1L 3A4; (250) ; mackenziewildlife@gmail.com 2 Fish and Wildlife Compensation Program: Victoria Street, Nelson BC, V1L 4K3; (250) ; Ross.Clarke@bchydro.com

2 Executive Summary The Fish and Wildlife Compensation Program (FWCP) has been conducting repeated winter aerial surveys every 3-4 years since 2000 to monitor mule deer (Odocoileus hemionus) and white-tailed deer (Odocoileus virginianus) populations in Management Units 4-07 and 4-08 (South Selkirks). We analyzed data from the 2011 South Selkirk survey to update mule deer and white-tailed deer population estimates and to assess population trends for both species. We also estimated population size of elk (Cervus canadensis) and moose (Alces americanus) for the South Selkirks based on observation data from this survey. All population estimates are corrected for sightability models used logistic regressions in the program Aerial Survey. No sightability model exists for white-tailed deer so we used a mule deer model to correct white-tailed deer data for sightability bias. There were 349 mule deer, 504 white-tailed deer, 525 elk and 87 moose observed over 26 hours of survey time. We estimated populations of 841 mule deer (90% CI: ), 1212 white-tailed deer (CI: ), 858 elk (CI: ) and 153 moose (CI: ). There was no significant change in mule and white-tailed deer population estimates from 2007, although mule deer counts were higher in Mule deer show an increasing trend since 2000, while white-tailed deer appear more stable. Elk increased significantly each survey year since 2000; however the low calf ratio (21:100 cows) suggests populations may be approaching ecological carrying capacity. Moose estimates decreased significantly from 2007, when the population appears to have peaked. Cougar numbers are suggested to be increasing in the Kootenays and we predict food limitation and cougar predation could be limiting the white-tailed deer population. Predation of mule deer by cougars should also become more frequent if cougar numbers continue to increase, which will likely be driven by white-tailed deer population cycles. White-tailed deer trend data from this survey do not correspond with spring spotlight count data in the Pend d Oreille Valley, which suggest an increase in white-tailed deer numbers since the 2007 Pend d Oreille fire. We question the accuracy of white-tailed deer population estimates generated using the mule deer sightability model because it has never been tested on white-tailed deer. Stent and Clarke 2011 ii

3 Table of Contents Executive Summary... ii Table of Contents...iii Introduction... 1 Project Area... 1 Methods... 3 Survey Unit Definition... 3 Surveying... 4 Data Analysis... 5 Results... 6 Mule Deer... 6 White-tailed Deer... 7 Elk... 9 Moose Discussion Mule Deer and White-tailed Deer Elk Moose Survey Methods Recommendations Literature Cited List of Figures Figure 1: Ungulate survey blocks in Management Units 4-07 and 4-08, West Kootenay Figure 2: Winter snowfall (November-April) measured at the Castlegar airport, Figure 3: Mule deer raw counts, population estimates and growth rates for the South Selkirks... 7 Figure 4: White-tailed deer raw counts, population estimates and growth rates for the South Selkirks s. 8 Figure 5: Elk raw counts, population estimates and growth rates for the South Selkirks Figure 6: Elk bull:cow and calf:cow ratios for the South Selkirks from winter aerial surveys, Error bars are 90% confidence intervals Figure 7: Moose raw counts, population estimates and growth rates for the South Selkirks Figure 8: Cougar control kills and hunter kills in the Kootenay Region, Stent and Clarke 2011 iii

4 List of Tables Table 1: Mule deer population statistics for the South Selkirks Table 2: White-tailed deer population statistics for the South Selkirks Table 3: Elk population statistics for the South Selkirks Table 4: Moose population statistics for the South Selkirks List of Appendices Appendix 1: Mule deer and white-tailed deer block stratification ratings and number observed during winter surveys, Appendix 2: Elk and moose block stratification ratings and number observed during winter surveys, Appendix 3: Elevation distribution of white-tailed deer, mule deer, moose and elk groups in the South Selkirk project area Stent and Clarke 2011 iv

5 Introduction The Fish and Wildlife Compensation Program (FWCP) have been conducting repeated winter aerial surveys every 3-4 years since 2000 to monitor mule deer (Odocoileus hemionus) and white-tailed deer (Odocoileus virginianus) populations in Management Units 4-07 and 4-08 (South Selkirks). Surveys were conducted in January and February 2011 to update population estimates for mule deer and white-tailed deer and assess population trends for both species. Elk (Cervus canadensis) and moose (Alces americanus) data were also recorded during surveys to provide population data for these species to regional wildlife managers. Aerial surveys are used by the FWCP to monitor ungulate trends in populations that have been impacted by hydroelectric development. This is especially pertinent to the MU 4-08 portion of the project area, where a large area of low elevation winter range was lost in the Pend d Oreille Valley after the Seven Mile Dam was constructed in 1979 and the Pend d Oreille River was flooded. Aerial survey data are also needed to assess the response of ungulates to the 2007 Pend d Oreille fire, which affected a large proportion of valuable winter range in the valley. This report summarizes data from the 2011 South Selkirk ungulate survey, which is compared to past survey data to assess population trends for mule deer, white-tailed deer, elk and moose. Surveys followed general standards for aerial inventories (RISC 2002; Unsworth et al. 1999) Project Area The project area lies in the South Selkirk and Bonnington Mountain Ranges, within Management Units (MUs) 4-07 and 4-08 (Figure 1). The Salmo, Pend d Oreille, Kootenay and Columbia Rivers form the major drainages within the study area. The Nature Conservancy of Canada - Darkwoods private property encompasses a large area of ungulate summer range in MU Winter snowfall (November-April) measured at the Castlegar Airport (495 m ASL) has averaged close to 200 cm per season since Extreme snowfall occurred during the 1996/1997 winter (Figure 2). All survey units (i.e., blocks) occurred in the Interior Cedar Hemlock (ICH) biogeoclimatic (BGC) zone. BGC subzones surveyed include the ICH very warm (xw) and ICH dry warm (dw; Braumandl and Curran 2002). The ICH xw occurs at low elevations ( m) on warm aspects and is characterized by mixed seral stands of Douglas fir (Pseudotsuga menziesii) and Ponderosa pine (Pinus ponderosa) and climax stands of Western red cedar (Thuja plicata) and Western hemlock (Tsuga heterophylla). The ICH dw subzone also occurs at low elevations ( m) in the area but on cooler sites than the ICH xw and stands typically includes a greater diversity of tree species in mixed seral stands (Douglas fir, Paper birch [Betula papyrifera], Western larch (Larix occidentalis) and Western white pine [P. monticule]). The ICH mw2 subzone occurs above the ICH dw ( m) on cooler and moister sites, where climax stands typically contain Western hemlock and Western red cedar, while mixed-seral stands typically contain these species as well as Douglas fir, Western larch and hybrid white spruce (Picea glauca x englemanii). Stent and Clarke

6 The Engelmann Spruce Subalpine Fir (ESSF) zone and Interior Mountain-heather Alpine (IMA) zones occur at higher elevations throughout the study area but did not extend into the areas surveyed. Figure 1: Ungulate survey blocks in Management Units 4-07 and 4-08, West Kootenay. Blocks highlighted in green were surveyed in January and February Stent and Clarke

7 Snowfall (cm) South Selkirk Ungulate Survey Analysis Total Snowfall Average Snowfall Figure 2: Winter snowfall (November-April) measured at the Castlegar airport, Much of the low elevation winter range along the major drainages is classified as Natural Disturbance Type 4 (NDT4), suggesting frequent, stand-maintaining fires occurred prior to the era of fire suppression. Shrub-dominated hillsides created from past fire events were common on south-facing hillsides throughout the study area. The most notable fire event recently was the 2007 Pend d Oreille fire (3969 ha), which affected a large proportion of ungulate winter range on both sides of the Pend d Oreille River. Significant wildfires also occurred at higher elevations in 2003 throughout MU Other ungulates occurring in survey areas include bighorn sheep (Ovis canadensis), mountain goats (Oreamnos americanus) and mountain caribou (Rangifer tarandus caribou). Common predators include cougars (Felis concolor), wolves (Canis lupus), coyotes (C. latrans), grizzly bears (Ursus arctos) and black bears (U. americanus). Methods We used a stratified random block (SRB) survey design (Gasaway et al. 1986). Survey procedures followed methodology outlined in the RISC (Resource Inventory Standards Committee) manual (2002) and the Aerial Survey User s Manual (Unsworth et al. 1999). Survey Unit Definition and Stratification Columbia Basin Ungulate Database shapefiles (Heaven et al. 1999) were used to separate the study area into survey units (i.e., blocks), using the 1200 m elevation contour as the upper boundary for all blocks. Survey blocks were pre-stratified (low, medium or high) for each species based on number of animals Stent and Clarke

8 expected to be within blocks during the time of the survey. We used results from past surveys (Robinson and Clarke 2007) as well as biologists knowledge of ungulate distribution to assign blocks strata ratings for all 4 ungulate species. All of the blocks rated high and medium for mule deer and white-tailed deer were surveyed, while 6 of the 30 blocks rated low for mule deer and 5 of the 29 blocks rated low for white-tailed deer were surveyed (Appendices 1 & 2). In total there were 44 survey blocks, of which 20 blocks were selected for the 2011 survey (15 blocks in MU 4-08 and 5 blocks in MU 4-07). Block size ranged from 15 to 53 km 2 (mean= 31.1 km 2 ). Surveying All surveys were conducted in a Bell 206B Jet Ranger with 3 observers (plus the pilot). The same survey crew was used each day of the survey and all crew members participated in the earlier South Selkirk surveys. We flew contours (i.e., transects) across hillsides, m above tree tops at speeds of approximately km/hr. The first transect was spaced approximately 200 m upslope from the block boundary and then transects were repeated across the hillside, working in an upslope direction. Transects were spaced m apart, as dictated by forest cover within the block. The observer in the front of the machine was responsible for navigating, which was assisted using ArcPad mobile GPS (Version 10; Environmental Systems Research Institute) on a laptop computer. The ArcPad program was connected to a Garmin 60csx Global Positioning System (GPS) so the position of the helicopter could be tracked in real-time. Flight paths were recorded on the digital map using the tracklog function to ensure block coverage was complete and transect spacing was suitable. All ungulate classification followed criteria outlined in the Aerial Based Methods for Selected Ungulates (RISC 2002). Mule deer and white-tailed deer were not classified as surveys occurred after most bucks had shed their antlers. Bull elk were classified as yearlings (spikes), raghorns (3-5 points with small, thin antlers) and adult bulls ( 6-points on at least 1 antler). Cows (females > 1-year-old) and calf elk were also classified. The majority of moose had shed their antlers prior to the survey so we used vulva patches, and to a lesser extent, pedicel scars and facial coloration to distinguish bulls from cows. Calf moose were identified based on short rostrum length and small body size. All moose and elk that could not be definitively classified were recorded as unclassified. Variables recorded for ungulate observations differed for each species; snow cover and oblique vegetation cover was recorded for all ungulates observed, although vegetation cover was estimated around the first animal observed in the group for moose, mule deer and white-tailed deer, but averaged over a 10 m radius around all elk in the group (Unsworth et al. 1999). We used sketches showing examples of animals in different cover classes (5%-90%) to help assign vegetation cover for each observation. Animal activity was recorded using 3 classes (standing, bedded or moving) for mule deer, white-tailed deer and elk. The mule deer model used habitat class variables that were recorded for all observations. Stent and Clarke

9 Data Analysis Sightability models were used to correct for incomplete sightability of ungulates in the program Aerial Survey (Unsworth et al. 1999). The Hiller 12-e winter elk model was used for elk, the Hiller 12-e winter mule deer model for mule deer and white-tailed deer and the BC model for moose (Quayle et al. 2001). Sightability models correct for the proportion of animals within survey blocks that went undetected during surveys. Logistic regressions used in sightability models incorporate a combination of variables known to affect the probability of animal detection from the air. Variables affecting detection probability differ between models but generally include a combination of group size, animal activity, snow cover on the ground, oblique vegetation cover or habitat type surrounding the animal(s). In general, sightability models predict highest detection probability for large groups of moving animals in open habitat with complete snow cover. The mule deer model was developed in southern Idaho, while the elk model was developed in northcentral Idaho and the moose model was developed in central BC. The elk and mule deer models have not been tested in BC but have been used exclusively in past surveys in the Kootenays (Phillips et al. 2008; Stent 2010). Past moose data for the South Selkirks have been analysed with the Wyoming moose model (Unsworth et al. 1999), which was developed in relatively open habitat, with very few observations in >40% vegetation cover. We re-analysed the past data using the BC model as this model is based on more sightability trial data from densely vegetated habitats and is more applicable to the South Selkirks project area. The BC moose model has been used for all recent moose inventories in the Kootenays (e.g., Poole et al. 2008; Stent et al. 2009; Serrouya and Poole 2007). Past mule deer and white-tailed deer data from the South Selkirks was re-analysed using the mountain brush/aspen vegetation cover type instead of juniper/mountain mahogany as the latter vegetation type had been used by surveyors to describe shrub-dominated habitats with good sightability, while the model predicts lowest sightability in this cover type and assigns high sightability correction for deer groups that were assigned this cover type variable. A Garmin 60CSx GPS was used to take waypoints at the initial location of ungulate observations for all species. When possible, elk were photographed using a Nikon D-80 camera with a mm Vibration Resistant lens. Photographs were used to verify bull elk classifications, calf numbers and total counts. There were several groups of >10 elk encountered during the 2004, 2007 and 2011 surveys that were not classified, except for raghorn and larger bulls, which were recorded whenever detected. As a result, bull ratios were skewed unrealistically high in these surveys because a large proportion of cows were not included in the analysis. Because surveyors were confident that the large unclassified elk groups contained only cows, calves and spike bulls, we used calf and spike ratios from the classified population for each survey to classify these groups and hence derive overall population ratios. We determined elevation of ungulates by uploading and plotting UTM coordinates in ArcGIS and joining points to nearest 20 m contour lines. All population ratio data are expressed with 90% confidence intervals, generated in the program Aerial Survey. Mean annual population growth rates were calculated between the 2007 and 2011 survey years and compared to growth rates calculated between the earlier Stent and Clarke

10 South Selkirk surveys (Robinson and Clarke 2007). We used a chi-square test of homogeneity to test for differences in population estimates between surveys (Unsworth et al. 1999). Results Surveys occurred from January 22 nd to February 21 st, 2011; however inclement weather and aircraft booking conflicts resulted in the survey being delayed from January 24 th to February 19 th. All MU 4-07 blocks (and 1 block in MU 4-08) were completed January 22 nd -23 rd, while the remaining MU 4-08 blocks were surveyed in February. Total survey time was approximately 26 hours, excluding ferry time. Snow cover was generally complete in MU 4-07 but patchy below 1000 m on warm aspects in the Pend d Oreille and Salmo River drainages. Snow pillow data recorded at Redfish Creek (north of study area; 2085 m) suggested the high elevation snowpack was close to seasonal norms (Ministry of Environment, River Forecast Centre: Snowpack measured at the Castlegar airport (495 m) was 26 cm on January 22 nd and 15 cm on February 19 th (Environment Canada: Mule Deer There were 349 mule deer observed during surveys. More than half the deer (52%) were observed in the 5 blocks surveyed in MU Mule deer counts were similar to 2007 counts in MU 4-08 (n = 192) but 96% higher than 2007 counts (n = 92) in MU The sightability-corrected estimate was 841 deer (90% CI: ) for the South Selkirks (Table 1), which did not differ significantly from the 2007 estimate (X 2 = 2.59, df = 1, P = 0.11) (Figure 3). The highest mule deer counts came from the Midge Creek (n=64), Corn Creek (n=51) and Porcupine Creek (n=45) blocks (Appendix 1). Mule deer density was similar in high and medium blocks, but substantially lower for low stratum blocks. Mule deer were encountered between 480 and 1380 m (Appendix 3) and occurred at higher average elevations (949 m) than other ungulates. Raw counts of mule deer (i.e., no sightability correction) increased each survey year since 2000, although population estimates only increased significantly between 2000 and 2004 (X 2 = 4.70, df = 1, P = 0.03). Stent and Clarke

11 Table 1: Mule deer population statistics for the South Selkirks, surveyed January 22 nd to February 21 st, Sightability-corrected estimates were calculated in the program Aerial Survey. Stratum Rating Parameter Low Medium High Total Number of Blocks Number of Blocks Surveyed Deer Observed Naïve Extrapolated Sightability-Corrected Estimate % Confidence Interval Density (deer per km 2 ) Sightability-Correction Factor Density extrapolated to blocks not surveyed but no sightability correction applied 1200 Mule Deer estimated Mule Deer counted Linear (Mule Deer estimated) y = x Figure 3: Mule deer raw counts, population estimates and mean annual growth rates* for the South Selkirks from winter aerial surveys, Estimated values are corrected for incomplete sightability in the program Aerial Survey. Large triangles are estimates from survey years and small diamonds are estimates based on mean annual growth rates. Error bars are 90% confidence intervals. * N t N 1/ t 0 White-tailed Deer There were 504 white-tailed deer observed during surveys with 46% occurring in 3 blocks in the Pend d Oreille Valley. White-tailed deer counts were also high in the Midge Creek (n = 69) block as well as the block above Waneta and Montrose (n = 59). White-tailed deer counts increased by 323% in MU 4-07 Stent and Clarke

12 from the 2007 survey, while the MU 4-08 count increased by 47%. White-tailed deer counts within the 3 Pend d Oreille blocks affected by the fire were higher in 2011 (n = 232) than 2007 (n = 145) but less than half the 2004 count (n = 675) for these 3 blocks (Appendix 1). White-tailed deer were encountered between 460 and 1060 m (mean = 694 m) and occurred at lower elevations than other ungulates (Appendix 3). We estimated a population of 1212 white-tailed deer (CI: ), which did not differ significantly from the 2007 estimate (X 2 = 1.78, df = 1, P = 0.18), despite the higher raw count in 2011 (Table 2). White-tailed deer increased significantly between 2000 and 2004 surveys (X 2 =20.87, df = 1, P <0.01), but decreased significantly between the 2004 and 2007 surveys (X 2 = 12.87, df = 1, P <0.01) (Figure 4). Table 2: White-tailed deer population statistics for the South Selkirks, surveyed January 22 nd to February 21 st, Sightability-corrected estimates were calculated in the program Aerial Survey. Stratum Rating Parameter Low Medium High Total Number of Blocks Number of Blocks Surveyed Deer Observed Naïve Extrapolated Sightability-Corrected Estimate % Confidence Interval Density (deer per km 2 ) Sightability-Correction Factor White-tailed deer estimated White-tailed deer counted Figure 4: White-tailed deer raw counts, population estimates and growth rates for the South Selkirks from winter aerial surveys, Estimated values are corrected for incomplete sightability in the program Aerial Survey. Large triangles are estimates from survey years and small diamonds are estimates based on mean annual growth rates. Error bars are 90% confidence intervals. Stent and Clarke

13 Elk There were 525 elk observed during the survey (295 cows, 61 calves, 30 spikes, 78 raghorn bulls, 60 adult bulls and 1 unclassified). Elk counts in MU 4-08 (n = 215) were similar to 2007 counts (n = 231); however elk counts were 213% higher in MU 4-07 in 2011 (n = 269) than 2007 (n = 126), despite the fewer blocks surveyed in 2011 (Appendix 2). We estimated 858 elk (CI: ) for the South Selkirks, which represented a significant increase in the elk population from 2007 (X 2 = 12.50, df = 1, P <0.01) (Table 3). Elk were the only species to increase significantly each survey year after 2000 (Figure 5). Elk counts within the 3 Pend d Oreille blocks affected by the fire were similar between 2011 (n = 11) and 2007 surveys (n = 18). Table 3: Elk population statistics for the South Selkirks, surveyed January 22 nd to February 21 st, Sightability-corrected estimates were calculated in the program Aerial Survey. Stratum Rating Parameter Low Medium High Total Number of Blocks Number of Blocks Surveyed Elk Observed Naïve Extrapolated Sightability-Corrected Estimate % Confidence Interval Density (elk per km 2 ) Sightability-Correction Factor Elk estimated Elk counted Linear (Elk estimated) y = x Figure 5: Elk raw counts, population estimates and mean annual growth rates for the South Selkirks from winter aerial surveys, Estimated values are corrected for incomplete sightability in the program Aerial Survey. Large triangles are estimates from survey years and small diamonds are estimates based on mean annual growth rates. Error bars are 90% confidence intervals. Stent and Clarke

14 Calves or Bulls per 100 Cows South Selkirk Ungulate Survey Analysis Elk calf ratios decreased from 51:100 cows (CI: 42-60) in 2007 to 22:100 cows (CI: 17-27) in 2011 (Figure 6). Bull ratios were similar in 2011 (72:100 cows [CI: 51-92]) and 2007 (79:100 cows [CI: ]), although there appears to be a declining trend in this demographic since 2004 (Figure 6). Elk were encountered between 560 and 1460 m (mean = 910 m) (Appendix 3). Calf:Cow Bull:Cow Figure 6: Elk bull:cow and calf:cow ratios for the South Selkirks from winter aerial surveys, Error bars are 90% confidence intervals. Moose There were 87 moose observed during the survey, including 32 cows, 8 calves, 18 bulls and 29 moose that could not be classified to sex or age class. Moose densities were low throughout the study area, with highest counts occurring in the Corn Creek (n = 15), Dodge Creek (n = 11) and Sheep Creek (n = 10) blocks. We originally used 3 strata for moose blocks but combined the medium and high strata because moose counts were similar between the strata (medium =3.1 moose/block, SD = 4.0; high = 8.8 moose/block, SD = 5.1). No moose were observed in low blocks during the survey (Table 4). We estimated a population of 153 moose (CI: ) for the South Selkirks (Table 4), which was significantly lower than the 2007 estimate (X 2 = 4.43, df = 1, P = 0.03). Prior to the 2007 survey, the moose population increased between the 2000 and 2004 surveys (X 2 = 22.7, df = 1, P < 0.01) and appears to have peaked in 2007, although the increase from 2004 to 2007 was not significant (X 2 = 3.75, df = 1, P = 0.05) (Figure 7). Estimated bull ratios were 66:100 cows (CI: 35-97) and calf ratios were 28:100 cows (CI: 17-45). Moose were not classified in the earlier surveys and hence, we are unable to compare population ratios. Moose were encountered between 540 and 1260 m (mean = 940) (Appendix 3). Stent and Clarke

15 Table 4: Moose population statistics for the South Selkirks, surveyed January 22 nd to February 21 st, Sightability-corrected estimates were calculated in the program Aerial Survey. Stratum Rating Parameter Low High Total Number of Blocks Number of Blocks Surveyed Moose Observed Naïve Extrapolated Sightability-Corrected Estimate % Confidence Interval Density (moose per km 2 ) Sightability-Correction Factor Moose estimated Moose counted Figure 7: Moose raw counts, population estimates and mean annual growth rates for the South Selkirks from winter aerial surveys, Estimated values are corrected for incomplete sightability in the program Aerial Survey. Large triangles are estimates from survey years and small diamonds are estimates based on mean annual growth rates. Error bars are 90% confidence intervals. Discussion Mule Deer and White-tailed Deer Our data suggest an increasing trend in the South Selkirk mule deer population since 2000, although population estimates only increased significantly between 2000 and 2004 (Figure 3). Mule deer and Stent and Clarke

16 South Selkirk Ungulate Survey Analysis white-tailed deer were thought to be at lowest population levels in the late 1990 s as a result of significant winterkill in 1996/97 and high cougar abundance and intense predation afterwards (Mowat 2009; Robinson et al. 2002). The 2000 white-tailed deer estimate was similar to 2007 and 2011 estimates and suggest the white-tailed deer population may have equilibrated by 2000 or earlier, while mule deer recovered more slowly. It is important to note that the mule deer sightability was used to correct white-tailed deer data; hence these results may not accurately reflect trend changes in the white-tailed deer population. The index of cougar abundance suggests a decline in the cougar population after 1998, although the cougar population appears to now be recovering (Figure 8). There has been no significant change in mule deer estimates between 2004 and 2011; however the raw counts suggest mule deer are increasing in MU 4-07 but are stable in MU Total hunter kills Problem kills Smoothed problem kill_3yr Smoothed hunter kill_3yr Figure 8: Cougar control kills and hunter kills in the Kootenay Region, Data originate from compulsory inspection reports. It is difficult to predict how mule deer and white-tailed deer populations will respond to an increasing cougar population. Robinson et al. (2002) found cougar predation to be the leading cause of mortality for radio-collared mule deer and white-tailed deer in the South Selkirks; however this study occurred over a time period when cougar abundance was very high. Cougar predation may be exerting a more limiting pressure on white-tailed deer than mule deer as the latter species appears to be increasing with the increasing cougar population (i.e., post 2004), while white-tailed deer appear relatively stable. Alternatively, cougar numbers may still be so low that predation is not a significant limiting factor for Stent and Clarke

17 Peak Count South Selkirk Ungulate Survey Analysis either deer species. Predation of mule deer by cougars should also become more frequent if cougar numbers continue to increase (i.e., apparent competition hypothesis; Holt 1977) and future cougar population trends will ultimately depend on white-tailed deer population cycles in the South Selkirks (Robinson et al. 2002). Despite the liberalization of white-tailed deer hunting regulations recently to promote greater harvest of females, it is unlikely that harvest is sufficient to limit population growth of this species and we suspect white-tailed deer are food-limited in this population. This is supported by spring spotlight count data, which show an increasing trend in white-tailed deer counts in the Pend d Oreille after hunting seasons were liberalized in 2007 (Figure 9). There is no hunting harvest of female mule deer and hence, hunting likely has a negligible impact on growth of this species within the study area Figure 9: Peak counts of white-tailed deer from spring spotlight counts in the Pend d Oreille Valley, The high count of white-tailed deer in the 2004 aerial survey (Figure 4) occurred when snow cover was most incomplete at low elevations and although complete snow cover generally increases sightability of ungulates, shallow snow conditions may encourage deer to use open habitats where they may be more easily detected. Conversely, heavy snow likely forces deer into mature coniferous forests, where they have lower sightability (Mowat and Kuzyk 2009). Data from the 2004 survey show 76% of deer were observed in mountain brush/aspen or agriculture/open vegetation classes when snow cover averaged 61%, while 47% of deer were observed in these habitats in 2011, when snow cover was most complete at lower elevations (mean: 79% snow cover for white-tailed deer observations). Although sightability models are intended to account for differences in habitat use between surveys, the Idaho model is suggested to be weak in forested winter ranges and has not been tested on white-tailed deer Stent and Clarke

18 Estimate South Selkirk Ungulate Survey Analysis (Unsworth et al. 1999). Overall we suspect the high count in 2004 reflects a shift in habitat use and not an increase in abundance. Mule and white-tailed deer population estimates decreased for the 2000, 2004 and 2007 survey years after observations in the juniper/mountain mahogany class were re-analysed using the mountain brush/aspen class (Figure 10). Both mule deer and white-tailed deer estimates changed most substantially in 2007 when the juniper/mountain mahogany class was used more than other survey years. This vegetation class was used for deer observed in shrub-dominated habitat where sightability was expected to be excellent. However use of the juniper/mountain mahogany class caused estimates to be skewed high as greater sightability correction is applied to this class than the conifer class, which we feel is unrealistic for this study area. Overall, mule deer estimates should be more accurate using the mountain brush/aspen class as sightability correction is lower for this vegetation type than the conifer class. Because the sightability model was developed in southern Idaho, habitat type variables may not fit well in the West Kootenay, where coniferous forests comprise a greater proportion of winter ranges. Thus, estimates presented for mule deer may not accurately reflect true population numbers but should provide indications of population trend. Again, we question the accuracy of the presented white-tailed deer population trends, given the sightability model has never been tested on this species Mule Deer (former estimate) White-tailed Deer (former estimate) Mule Deer (new estimate) White-tailed Deer (new estimate) Figure 10: Mule deer and white-tailed deer population estimated for the South Selkirks ( ) using the program Aerial Survey. The dashed lines illustrate how the inclusion of the juniper/mountain mahogany vegetation class in the sightability model skewed the estimates high. The new estimates used the mountain brush/aspen vegetation class instead of the juniper/mountain mahogany class, which likely assigns more realistic sightability correction in shrub-dominated habitats encountered in the South Selkirk study area. Stent and Clarke

19 The 2011 survey was the first to occur since the 2007 Pend d Oreille fire. Track occurrences suggested a substantial number of white-tailed deer (and elk) were using the regenerating fire, although white-tailed deer counts within the Pend d Oreille blocks (n = 232) were similar to 2007 counts (n = 235) but lower than 2000 (n = 402) and 2004 counts (n = 675; Appendix 1). Conversely, spring spotlight counts conducted at low elevations in the Pend d Oreille Valley suggest an increase in white-tailed deer numbers after 2007 (Figure 9). White-tailed deer have likely benefited from the forage production generated by the fire but may not use the area as intensively in the late winter when snow accumulation forces deer into coniferous forests where they are harder to enumerate from the air. However the high white-tailed deer counts from post-2007 spring spotlight surveys likely reflect an increasing trend in the Pend d Oreille white-tailed deer population, which could be related to the 2007 fire. Elk Elk were the only species to increase significantly each year of the survey. The increasing trend in elk numbers is consistent with other populations in the Kootenays (Szkorupa and Mowat 2010) and Idaho (IDFG 2010). The expanding trend is also supported by elk densities increasing in the medium and high strata from 2007 and the greater number of medium and high strata blocks in the study area compared to earlier surveys (Appendix 2). The decline in elk calf ratio suggests this population could be approaching ecological carrying capacity. Research suggests calves are more sensitive to nutritional stress than cows, and are expected to decline first in response to limited food (Sauer and Boyce 1983). Bull elk ratios suggest a declining trend since 2000, although the 2004 bull ratio exceeded 100:100 cows, which is unlikely in a population where more bulls than cows are harvested. We suspect large cow/calf groups wintering on the Creston flats were missed in this survey, which would cause bull ratios to be skewed high. Past inventories have shown large cows and calf groups winter at these locations (Stent and Mowat 2008) as well as outside of the MU. A decline in the MU 4-08 bull ratio from 2007 is suggested by Stent (2011) and attributed to this unit changing hunting regimes from limited entry to a 6-point or greater general open season in 2010 (Stent 2011). Moose Survey data suggest moose populations increased in the South Selkirks between 2000 and 2007 but have since declined (Figure 7), although the 2000 and 2004 estimates could be biased low as moose were a low priority during these surveys. Moose population estimates decreased each survey year after re-analysing the data with the BC sightability model, which is likely due to the BC model assigning slightly lower sightability correction for moose observed in high vegetation classes (i.e., >30%) than the former model. The BC model is likely more applicable to survey areas that involve a significant component of densely vegetated habitat (Quayle et al. 2001). Portions of the Corn and Topaz Creek blocks were surveyed for mule deer in December 2010 (Stent 2011) and surveyors counted more moose in these areas than the 2011 survey, despite expending less survey effort here. Moose were detected at substantially higher elevations in the December survey (mean: 1336 m), which is expected given the shallow mid-elevation snowpack in December; however we Stent and Clarke

20 suspect January snow levels were not sufficient to push moose to lower elevations and there were likely animals wintering in habitat above block boundaries that was not visited in the latter survey. Hence, our 2011 moose estimate is probably conservative. All bull moose had shed their antlers by the time of the survey, which made classification of adult moose more time consuming and in many cases adult moose could not be classified. Despite the limited classification data, bull ratios appear relatively high in the South Selkirks and similar to bull ratios reported in other West Kootenay populations (Reid et al. 2011; Serrouya and Poole 2007). Survey Methods Inclement weather and aircraft booking conflicts forced us to delay the survey for 3 weeks shortly after the survey began. It is undesirable to have a long time gap in the middle of a survey because animals could move between blocks; however this was not a concern in our survey as we were able to complete the MU 4-07 portion of the survey area before the weather delay and ungulate movement between MU 4-07 and MU 4-08 is unlikely in the late winter. Stratification accuracy has been variable for both deer species, despite the FWCP having block counts from previous surveys available to assign block ratings. All blocks rated medium and high for mule and white-tailed deer were surveyed in the past 3 surveys, which left no sampling variance in estimates for these strata. Deer numbers have been highly variable in the medium and high strata blocks year to year and population estimates would undoubtedly be imprecise if the FWCP did not sample all blocks within these strata, which would make it more difficult to detect changes in populations between surveys. We recommend all medium and high stratum blocks continue to be censused in future surveys to maximize precision in population estimates. Because the low stratum comprises such a large proportion of the study area and few low blocks are sampled each survey year, there can be high sampling variance in this stratum if mule deer counts are variable (i.e., 2004; Appendix 1). In such circumstances it may be desirable to sample additional low blocks to reduce sampling variance and reduce the risk of bias. Elk numbers have also been most variable in the medium and high strata and fortunately an adequate number of these blocks have been sampled to achieve relatively precise elk estimates as medium and high deer blocks were typically medium or high strata elk blocks (Appendix 1 & 2). No moose or elk have been observed in low stratum blocks, which results in population estimates of zero for the entire stratum. Very few low blocks were sampled for both species (Tables 3 and 4) to support these estimates and we predict some of the low blocks that were not surveyed will hold small numbers of moose and elk; hence the estimates of zero are unrealistic for the low stratum. Considering the moose population estimate was only 153 animals and the low stratum comprises such a large proportion of the study area (23 blocks), a density of 1 moose per low block would increase the estimate by roughly 15%. The low stratum also comprised a large proportion of the study area for elk (17 blocks); however an estimate of 858 elk (65% observed in high blocks) would not likely change significantly if small numbers of elk (i.e., 1-5 elk per block) were observed in low stratum blocks. If moose and elk become a higher priority in future surveys, then the FWCP may wish to sample a greater proportion of low blocks so stratum estimates are based on greater sampling effort. Stent and Clarke

21 Stratification ratings should be more accurate for blocks that have been surveyed once or more since 2000 than blocks that have never been surveyed. Low density areas have been identified with good accuracy for all species; however ratings for these areas may need to be revisited as the landscapes change with logging, fire activity and succession. Low density areas may be used to a greater extent by elk and mule deer, which appear to be increasing in the study area. A winter fixed-wing flight could be used to re-assess strata ratings for blocks that have not been surveyed recently and/or areas where habitats may have changed significantly (i.e., south side of the Pend d Oreille River). Using 2 strata for moose blocks seems most appropriate for this low-density population as moose counts have never exceed 15 per survey block and counts have been highly variable among the 3 strata used previously (Appendix 2). A GIS-based habitat query could also be used to improve stratification accuracy in the study area (Reid et al. 2011; Heard et al. 2008). Using this method, high and low value habitat is mapped within each survey block and moose estimates are calculated by multiplying the density of moose in the area of high and low value habitat surveyed by the area of each habitat class in the study area. This approach has been used in populations similar to the South Selkirks where the landscape is a mosaic of densely forested and logged habitat. In such areas, the majority of survey blocks include a mixture of high and low value habitat, which makes it difficult to separate high density blocks from low density blocks and often results in poor stratification accuracy. Recommendations Continue to sample all medium and high stratum mule deer and white-tailed deer blocks, but include a greater proportion of low blocks if funding permits or if deer counts are variable within the sampled low blocks. Low blocks that have never been surveyed should be given priority over blocks that have been recently surveyed. Use the mountain brush/aspen vegetation class instead of the juniper/mountain mahogany class for deer observed in shrub-dominated habitat. Conduct fixed-wing flight in the winter to stratify blocks that have never been surveyed and blocks that have not been surveyed since Use 2 strata (low and high) for moose blocks. Vegetation queries could also be used to improve stratification accuracy for moose. Visit blocks and on the Creston flats in future surveys and classify elk observed within the blocks to supplement data for the study area (if funding permits). Both blocks are relatively open and could be surveyed in a short period of time. This should reduce the chance that the cow and calf component of the population will not be underrepresented as large cow and calf groups typically winter in this area. Stent and Clarke

22 Literature Cited Braumandl, T. F., and M. P. Curran A field guide for site identification for the Nelson Forest Region. British Columbia Ministry of Forests, Victoria, British Columbia, Canada. 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. Heard, D., Walker, A., Ayotte, J., and G. Watts Using GIS to modify a stratified random blocks survey design for moose. Alces 44: Heaven, P. C., M. T. Tinker, and I. Adams Columbia Basin Fish and Wildlife Compensation Program: Ungulate Monitoring Plan. Glenside Ecological Services. Canal Flats, B.C. Holt, R.D Predation, apparent competition, and the structure of prey communities. Theor. Popul. Biol. 12: Idaho Department of Fish and Game Progress report: Elk. Report prepared for the Idaho Department of Fish and Game, Boise, Idaho. Mowat, G. and G. Kuzyk Mule deer and white-tailed deer population review for the Kootenay region of British Columbia. Unpublished report for Ministry of Environment, Nelson BC. Phillips, B., T. Szkorupa, G. Mowat and P. Stent East Kootenay Trench Elk Inventory. Ministry of Environment, Cranbrook, British Columbia. Poole, K., T. Kinley, and R. Klafki Moose inventory of the Lodgepole (MU 4-02), Upper Flathead (MU 4-01), and Lower Elk (MU 4-23B), East Kootenay, December 2007 and January Unpublished report prepared for the Ministry of Environment, Cranbrook, BC. Poole, K A Population Review of Moose in the Kootenay Region. Prepared for British Columbia Ministry of Environment, Kootenay Region. Aurora Wildlife Research, Nelson, British Columbia. Quayle, J. F., MacHutchon, A.G, and D. N. Jury Modeling moose sightability in southcentral British Columbia. Alces 37: Reid, A., G.Mowat, and P.Stent South Monashee Moose Inventory. Report prepared for Ministry of Natural Resource Operations, Nelson BC. RISC (Resources Information Standards Committee) Aerial-based inventory methods for selected ungulates: bison, mountain goat, mountain sheep, moose, elk, deer and caribou. Standards for Stent and Clarke

23 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.S., R.B. Wielgus and J.C. Gwilliam Cougar predation and population growth of sympatric mule deer and white-tailed deer. Can. Jour. Zool 50: Robinson, H. and R. Clarke Ungulate aerial survey analysis and summary 200, 2004 and 2007 in the South Selkirk Mountains of southeastern British Columbia. Report for Fish and Wildlife Compensation Program, Nelson, BC. Sauer, J. R. and M. S. Boyce Density Dependence and Survival of Elk in Northwestern Wyoming. J. Wild. Manag 47: Serrouya, R. and K. 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 BC. Stent, P West Kootenay elk composition surveys: January and February Report prepared for the Ministry of Natural Resource Operations, Nelson, BC. Stent, P., G. Gaynor and G. Mowat, Central Selkirk Moose Population Inventory. Unpublished report for the Ministry of Environment, Environmental Stewardship Branch, Kootenay Region, Cranbrook, BC. Stent, P. and G. Mowat Creston Elk Population Inventory Ministry of Environment, Kootenay Region, Nelson, British Columbia. Szkorupa, T. and G. Mowat A population review for elk in the Kootenay Region. Report prepared for the Ministry of Environment, Environmental Stewardship Division, Cranbrook BC. Unsworth, J. W., F. A. Leboan, E. O. Garton, D. J. Leptich, and P. Zager Aerial survey: user s manual. Electronic edition. Idaho Department of Fish and Game, Boise, Idaho. Stent and Clarke

24 Appendix 1: Mule deer and white-tailed deer block stratification ratings and number observed during winter surveys, Survey Mule Deer White-tailed Deer Unit L - L - L - L - L - L - L - L M 0 L 0 L - L - L 2 L 0 L - L M 0 L - L - L - L 0 L - L - L H 17 H 29 H 4 M 64 M 7 M 25 H 16 H M 0 L - L - L - L 0 L - L - L M - M 0 L 0 L - M - M 4 M 1 L L - L - L 0 L - L - L - L 2 L M 4 M 5 M 30 H 7 M 7 M 36 H 8 M H 10 H 11 H 3 M 24 L 14 H 15 H 3 M L - L 23 H 8 M 34 M M 0 L 11 M H 44 H 61 H 47 H 51 M 4 M 5 M 3 M L - L - L - L - L - L - L - L L - L - L - L 0 L - L - L - L M - M 3 M 9 M 15 L - L 9 M 13 M L - L - L - L - L - L - L - L L - L - L - L - L - L - L - L L - L - L - L - L - L - L - L L 0 L - L - L - H 0 L - L - L L - L - L - L - L - L - L - L M - M 0 L - L - M - M 0 L - L L - L - L - L - L - L - L - L H 32 H 37 H 57 H 16 L 0 L 3 M 9 M H 1 M 6 M 64 H 23 L 0 L 0 L 18 H M 11 H 9 H 20 H 45 L 0 L 0 L 0 L L - L - L - L - L - L - L - L L - L - L 0 L 0 L - L - L 16 H L 0 L - L - L - M 0 L - L - L L - L - L - L - L - L - L - L L 0 L 12 H 9 M 1 M 9 M 19 H 24 H L - L - L - L - L - L - L - L L - L - L - L - L - L - L - L L 0 L - L - L - M 0 L - L - L L 0 L 0 L 1 L 0 H 20 H 46 H 9 M L 0 L 0 L 2 L 10 H 208 H 231 H 43 H L 11 H 10 H 2 M 5 H 174 H 398 H 93 H L - L 6 M 10 M 13 M - M 0 L 0 L M 7 M 32 H 16 H 30 H 29 H 17 H 11 M M - M 5 M 2 M 4 L - H 2 M 0 L L - L - L - L - L - L - L - L L 0 L - L - L - M 0 L - L - L L - L 3 M 0 L - M - M 10 M 11 M L 0 L - L - L - M 0 L - L - L L - L - L - L - L - L - L - L L - L - L - L 7 L - L - L - L 11 Total Low Total Med Total High Stent and Clarke