Aerial Ungulate Survey (2014) Moose in WMU 511 (Pelican Mountains)

Aerial Ungulate Survey (2014) Moose in WMU 511 (Pelican Mountains) Hanna Neufeld and Jim Castle Environment and Sustainable Resource Development Upper Athabasca Region Suggested Citation: Neufeld, H. and J. Castle. 2014. Aerial Ungulate Survey (2014), Moose in WMU 511 (Pelican Mountains). Environment and Sustainable Resource Development, Government of Alberta. Edmonton, Alberta.

EXECUTIVE SUMMARY An aerial moose survey was conducted in Wildlife Management Unit (WMU) 511 (Pelican Mountains) from February 22 nd -26 th, 2014. The survey was completed by Alberta Environment and Sustainable Resource Development staff using distance sampling methods to estimate a population density for the WMU. In total, 226 transects were surveyed totalling 1588.18 km. The WMU was divided into two strata based on the natural subregions of the area, foothills and boreal. Across both strata, the encounter rate was 0.038 moose/km 2 (90% CI 0.031-0.048), the estimated density was 0.124 moose/km 2 (0.085-0.183) and the estimated population across the WMU was 721 (90% CI 492-1058). Of the 109 moose observed 107 were classified; 24% were bulls, 58% cows, and 18% calves. Key words: Alberta, aerial survey, ungulates, population estimates, density estimates, age/sex ratios, moose, distance sampling.

1 INTRODUCTION 1.1 Background The most recent moose population survey to be completed in WMU 511 occurred in 2003. Prior to that, two other surveys had taken place in 1994 and 1999 (table 1). Table 1 historical data for WMU 511 population estimates. Year Pop. Estimate* Ratio to 100 Cows Density Bulls Calves sq. km 2014 721 (22.9%) 42 31 0.12 2003 2033 (20.2%) 37 56 0.35 1999 1868 (21.3%) 29 50 0.33 1994 1263 (20.9%) 42 50 0.23 *Confidence limit of survey in brackets 1.2 Survey objectives This survey was completed to provide updated moose population estimates in WMU 511. Population parameters included size, density, adult sex ratio and calf ratio. This WMU lies within the Joint Oil Sands Monitoring Program (JOSM) area. Within this program the objective is to increase the sampling frequency to every five years to better understand ungulate population trends. 2 METHODS 2.1 Study area The general boundary of WMU 511 is delineated by the Lesser Slave and Athabasca Rivers in the south and by secondary highways 813 and 754 to Wabasca in the north (Figure 1). WMU 511 is 5794 km 2 and encompasses the boreal (62%) and foothills (38%) natural subregions (Natural Regions Committee 2006). The central portion of the WMU consists largely of the Marten Hills which is a mature spruce/aspen habitat. Both the southern and northeastern portions of the WMU are considered to be a

peatland complex and the southeast part of the WMU is dominated by aspen forest. The southern portion of this study area is also a part of the Slave Lake woodland caribou range. Figure 1 Location of the Pelican Mountains Wildlife Management Unit No. 511, east of Slave Lake, Alberta. The foothills stratum is portrayed in blue while the boreal stratum is in pink. 2.2 Survey methods This aerial survey was completed using distance sampling methods (Buckland et al. 2001). Transects were generated by constructing a grid of rectangles (w=1,200 m, l = 10,000 m) oriented north-south. A random sample of rectangles were selected and the centre line of the rectangle was used as the transect. The length of a transect was a product of the habitat placement and spacing which resulted in transects ranging between 2 and 10 kilometers in length. Transects less than 2 km long were not sampled. The survey design incorporated the two subregions which make up WMU 511, boreal and foothills. Transects were located in both subregions but a single transect would not cross subregional

boundaries; therefore, each transect line was classified as either a foothills or boreal transect. The survey was flown as described in the Distance Sampling chapter of the Aerial Ungulate Survey Protocol Manual (ESRD, 2010) Two Bell 206 Jet Ranger helicopters equipped with bubble windows were used in this survey, with a survey crew of 3 per machine, excluding the pilot. Transects were flown at approximately 300 feet AGL and 80 knots. Survey crew information was reported to the Slave Lake Environment and Sustainable Resource Development Fire Centre daily which included information pertaining to crew positioning in the aircraft, manifest and weight. The observer in the front of the aircraft was responsible for detecting all animals within 50m of the transect. The rear left and right observers were responsible for all areas beyond 50m from the transect. When an animal was detected, a waypoint was collected and the flight path was maintained until the helicopter became perpendicular to the animal(s). At this point, the aircraft would leave the transect and travel to the originally observed location of the animal. Once over the original location of the animal, a waypoint was recorded to document the distance between the transect and the detected animal. Age class, sex and antler class were classified during this time. Animals within 30m of the original observation point were considered to be in the same group. If the group size was larger than 1 animal, the waypoint was taken at the centre point between animals. Exact coordinates for all groups observed were recorded. It was especially important to collect exact waypoints when the animal appeared to be directly on the transect line in order to avoid a spike in the detection function for observations close to the transect line. Other measurements collected for each observation included crown closure (0-30%, 31-70%, 71-100%), light intensity (flat or bright), snow cover (none, some vegetation showing, completely snow covered), terrain (flat, moderate, steep), and activity of the moose when first observed (bedded, standing or moving). Weather conditions were documented daily. Weather parameters recorded included temperature, percentage cloud cover, and precipitation. 2.3 Analysis Techniques The data were analyzed using the program Distance 6.0 (Release 2.0; Thomas et al., 2010). Data were truncated to improve the model fit based on the examination of histograms. Five candidate models were fitted to the data (half-normal + cosine, half-normal + hermite, uniform + cosine, uniform + polynomial, and hazard + cosine) with half-normal key and cosine adjustment terms being the default model. All models were evaluated, however, if models other than the default did not improve the fit or precision of the detection function, or change the estimate, then the preferred default model of halfnormal key and cosine adjustment terms were kept (Buckland et al. 2001). The fit of the model to the data was primarily assessed by observing Akaike Information Criterion (AIC), QQ-plots and Chi-square goodness of fit tests. Right truncation was used when outliers existed or when relatively small frequencies were noted in the chi-square goodness of fit test. Left truncation was used when there was

a prevalent gap in the data within 100m of the transect line. Although left truncation is not optimal, it can be used to resolve a detection error issue to improve the model s fit. Including strata in the chosen model was considered by looking at the effect of strata on density estimates and strata specific encounter rates. The detection function and cluster size remained at the regional level. 3 RESULTS A total of 226 transects were surveyed from February 22-26, totalling 1588 km and 42.5 flying hours Temperatures during this survey ranged from -10 C to -29 C. 109 moose were observed from 80 independent groups. 107 out of the 109 moose were successfully classified, 26 (24%) were bulls, 62 (58%) were cows, and 19 (18%) were calves. The estimated moose density was 0.12 moose/km 2 (CV 0.24, 90% C.I 0.09-0.18) and the total population within WMU 511 was 721 moose (CV 0.24, 90% C.I 492-1058). The detection function with the half normal key and cosine adjustment terms was chosen because of its fit and simplicity. The results from applying stratification (foothills versus boreal) and fitting the detection function with a uniform key and cosine adjustment terms did not improve precision or change the density estimates, therefore strata was not included in the final model. Strata specific data can be viewed in Appendix B. The half normal key with cosine adjustment terms was best supported by truncated data. Data were left truncated at 100 m and right truncated at 500 m for the final model. Analysis results without truncation can be seen in Appendix B and Figure 3. Table 2 Parameter estimates for the leading models. Confidence limits are based on a 90% confidence interval. Density units are reported in moose/km 2. Model names reflect the detection function model keys (HN = half normal, UNI = uniform, HAZ = hazard), adjustment terms (cos = cosine, poly = simple polynomial) and associated levels (T = left truncation at 100m and right truncation at 500m, s = with stratification) for each model. Model N D CV DLCL DUCL NLCL NUCL AIC k HN(cos)T* 721 0.124 0.235 0.085 0.182 492 1058 2.66 1 Boreal 462 0.129 0.304 0.079 0.212 282 758 - - HN(cos)T(s) Foothills 263 0.118 0.369 0.065 0.215 145 478 - - Total 725 0.125 0.236 0.085 0.184 494 1066 4.66 2 UNI(cos)T 641 0.111 0.737 0.037 0.332 214 1926 2.52 3 UNI(poly)T 642 0.111 0.156 0.086 0.143 497 829 0.00 1 HAZ(cos)T 484 0.084 0.155 0.065 0.108 376 625 0.55 2 *model chosen for analysis

Figure 2 Two hundred and twenty six transects were flown in WMU 511 from February 22 nd -26 th, 2014. The foothills stratum is portrayed in blue while the boreal stratum is in pink. 4 DISCUSSION Eleven years had passed since our last moose population survey and we had lost confidence in the huntable population estimate. Therefore a moose population survey was conducted in February 2014 which unfortunately confirmed predictions of a declining moose population of only 721 animals; well below the 2003 survey estimate of 2033 moose and the 1999 survey estimate of 1868 moose. This evidence of a declining moose population warranted a reduction in the tags allocated to the Alberta resident hunters and will later influence the allocation to tourist outfitters in this WMU. According to the survey data, the tag fill rate has continued to fluctuate and does not show a trend over the last decade. The tag fill rate ranged from 27-52% and 15-30% in the early and late season, respectively. Early season permit allocations increased between the years 2003 to 2008 but have since decreased to a low of 132 moose permits in 2012. Allocated permits have steadily decreased in the late season from 485 permits in 2003 to a low of 199 moose permits in 2012. Oddly the online hunter survey data suggests the number of hunter days per animal has remained relatively steady over the last decade with hunters reporting an average of 20 hunter days per animal in

the early season and an average of 34 hunter days per animal in the late season. One might expect the required hunter effort to increase as the moose population decreased; however, it s possible the lower number of allocated tags may have increased a hunter s accessibility to the moose (less hunter interference) or it s possible the rough survey results provided by the sample of hunters (approximately 40% of hunters responded) are not representative of the hunting population. Not only is the moose density in WMU511 much lower than densities reported in previous surveys but there is also evidence of a potential recruitment issue. The observed bull ratio was 42 bulls per 100 cows which is similar to previous surveys in this unit. However, recruitment rate (% calves per total population) was lower than we ve seen over the past 20 years. Calf production equates to only 31 calves per hundred cows which is 38-45% lower than previous surveys conducted in 2003, 1999 and 1994. Given what we know about the continental moose recruitment issues in the southern periphery of their range we believe this data should not be overlooked. These comparisons likely indicate an older mean age of the present population which in turn provides evidence that a population reduction is occurring. However, while it s important to acknowledge the evidence we should wait to see the results of neighbouring units and look for widespread moose recruitment issues across the province that match the declining trends other provinces and states. Aerial Survey Data suggests we did not meet the assumption that all moose on the transect were detected, as observations close to the transect are under-represented (Figure 3). Therefore, the data were analyzed both with and without left truncation at 100m to determine how sensitive the results are to this issue (Buckland, et al. 2001). Figure 3: Detection probability plots with data fitted to a half normal key with cosine adjustments without truncation (left), and data fitted to a half normal key with cosine adjustments with truncation at 100m and 500m (right).

5 MANAGEMENT RECOMMENDATIONS Rather than affecting the hunters with a massive drop in hunting permits in 2014 a prudent response was to step-down the moose allocation over a two-year span. In 2013 we allocated 130 moose permits in the early season and 199 moose permits in the late season. To adjust to the lower population, early and late season allocation for the 2014 hunting season totalled 85 and 179 moose permits respectively with further refinements expected in the 2015 hunting season. While Alberta s moose population and related habitat might be unique we need to continue to monitor our moose populations and be conservative in our allocations until researchers across Canada and the USA pinpoint the cause of the widespread moose recruitment issues. Once further information is gathered we should adjust accordingly. 6 ACKNOWLEDGEMENTS Surveys were conducted by the following Environment and Sustainable Resource employees: Jim Castle, Kevin Downing, Mike Banko, Jordan Besenski, Justin Gilligan, Hanna Neufeld and Scott Donker. The pilots for this survey were Rod Drake (Highland Helicopters) and Evan Cole (Delta). Krista Josey (ESRD) from the Slave Lake Fire Centre provided flight following. Finally we would like to thank Hannah McKenzie for all her insight into the finer points of Distance. 7 LITERATURE CITED Buckland, S. T., Anderson, D. R., Burnham, K.P., Laake, J.L. 2012. Distance Sampling: Estimating Abundance of Biological Populations. Environment and Sustainable Resource Development. 2010. Aerial Ungulate Survey Protocol Manual. Natural Regions Committee 2006. Natural Regions and Subregions of Alberta. Compiled by D.J. Downing and W.W. Pettapiece. Government of Alberta. Pub. No. T/852. Thomas, L., S.T. Buckland, E.A. Rexstad, J.L Laake, S. Strindberg, S.L Hedley, J. R, B. Bishop, T. A. Marques, and K.P Burnham. 2010. Distance software: design and analysis of distance sampling surveys for estimating population size. Journal of Applied Ecology 47: 5-14. DOI: 10.1111/j.1365-2664.2009.01737.x

8 APPENDICES A) Basic survey data presented in an abbreviated listing WMU 511(Pelican Mountains) Dates of survey February 22 nd 26 th 2014 Observers Jim Castle, Kevin Downing, Mike Banko, Jordan Besenski, Justin Gilligan, Hanna Neufeld, Scott Donker Aircraft 206 Bell Jet Ranger (Delta and Highland) Pilot Evan Cole and Rod Drake Cost and time breakdown Total hours flown: 42.5 Total cost: $45,038.30 Design Distance sampling design with 568 North-South transects generated 1.2km apart. These transects were identified as either being within the foothills or boreal subregion. Data for individual transects Strat_ID Area (km2) TransID TransLength PerpDist (m) ClusterSize (km) Boreal 3572.30 B-002 3.24 Boreal 3572.30 B-005 8.13 Boreal 3572.30 B-006 8.41 330.61 2 Boreal 3572.30 B-009 8.06 212.92 2 Boreal 3572.30 B-009 8.06 119.99 1 Boreal 3572.30 B-009 8.06 46.02 1 Boreal 3572.30 B-009 8.06 46.02 1 Boreal 3572.30 B-009 8.06 234.92 2 Boreal 3572.30 B-010 7.96 275.98 1 Boreal 3572.30 B-012 8.17 135.55 1 Boreal 3572.30 B-020 4.35 1 Boreal 3572.30 B-020 4.35 163.84 1 Boreal 3572.30 B-021 4.32 283.71 1 Boreal 3572.30 B-021 4.32 387.52 3 Boreal 3572.30 B-022 4.30 Boreal 3572.30 B-024 4.25 Boreal 3572.30 B-027 4.17 Boreal 3572.30 B-028 4.14 Boreal 3572.30 B-029 4.12 Boreal 3572.30 B-030 4.09 Boreal 3572.30 B-033 2.72 374.33 1 Boreal 3572.30 B-037 7.12

Boreal 3572.30 B-038 6.54 Boreal 3572.30 B-039 5.89 Boreal 3572.30 B-040 4.89 Boreal 3572.30 B-045 5.18 202.72 1 Boreal 3572.30 B-047 6.35 Boreal 3572.30 B-048 6.76 Boreal 3572.30 B-051 7.34 155.21 1 Boreal 3572.30 B-053 8.21 Boreal 3572.30 B-054 8.19 Boreal 3572.30 B-058 8.57 Boreal 3572.30 B-058 8.57 Boreal 3572.30 B-059 8.73 192.23 1 Boreal 3572.30 B-060 9.36 298.48 3 Boreal 3572.30 B-063 10.00 255.87 1 Boreal 3572.30 B-064 10.00 Boreal 3572.30 B-074 10.00 Boreal 3572.30 B-077 10.00 302.13 1 Boreal 3572.30 B-077 10.00 657.93 2 Boreal 3572.30 B-079 10.00 Boreal 3572.30 B-080 10.00 151.27 1 Boreal 3572.30 B-082 10.00 Boreal 3572.30 B-083 10.00 Boreal 3572.30 B-086 10.00 Boreal 3572.30 B-093 10.00 Boreal 3572.30 B-096 10.00 98.28 1 Boreal 3572.30 B-097 10.00 Boreal 3572.30 B-098 10.00 312.42 2 Boreal 3572.30 B-100 10.00 Boreal 3572.30 B-101 10.00 76.77 2 Boreal 3572.30 B-101 10.00 182.67 1 Boreal 3572.30 B-109 4.82 Boreal 3572.30 B-110 4.42 404.55 1 Boreal 3572.30 B-111 4.25 Boreal 3572.30 B-112 3.08 Boreal 3572.30 B-113 2.37 Boreal 3572.30 B-114 2.84 Boreal 3572.30 B-118 7.85 Boreal 3572.30 B-120 5.39 Boreal 3572.30 B-126 5.76 Boreal 3572.30 B-128 6.15 615.81 3 Boreal 3572.30 B-128 6.15 262.38 2 Boreal 3572.30 B-132 6.85 Boreal 3572.30 B-160 5.65 14.28 2

Boreal 3572.30 B-169 9.21 Boreal 3572.30 B-171 8.99 592.86 1 Boreal 3572.30 B-172 9.84 Boreal 3572.30 B-175 5.90 12.67 1 Boreal 3572.30 B-175 5.90 304.37 2 Boreal 3572.30 B-177 6.07 114.42 2 Boreal 3572.30 B-183 7.01 Boreal 3572.30 B-199 6.85 382.94 1 Boreal 3572.30 B-203 10.00 140.33 2 Boreal 3572.30 B-204 10.00 Boreal 3572.30 B-210 10.00 Boreal 3572.30 B-217 10.00 Boreal 3572.30 B-220 10.00 Boreal 3572.30 B-223 10.00 Boreal 3572.30 B-227 3.46 Boreal 3572.30 B-301 3.22 Boreal 3572.30 B-305 6.47 563.01 2 Boreal 3572.30 B-305 6.47 127.42 1 Boreal 3572.30 B-307 4.28 Boreal 3572.30 B-309 6.47 Boreal 3572.30 B-317 5.52 Boreal 3572.30 B-317 5.52 Boreal 3572.30 B-318 2.44 Boreal 3572.30 B-392 2.58 Boreal 3572.30 B-393 2.61 Boreal 3572.30 B-394 2.32 Boreal 3572.30 B-395 2.55 Boreal 3572.30 B-400 6.58 42.45 1 Boreal 3572.30 B-402 6.64 Boreal 3572.30 B-404 7.28 310.35 1 Boreal 3572.30 B-408 7.17 168.51 1 Boreal 3572.30 B-416 7.74 Boreal 3572.30 B-418 7.80 Boreal 3572.30 B-423 8.24 Boreal 3572.30 B-428 7.81 Boreal 3572.30 B-431 7.84 Boreal 3572.30 B-448 6.70 Boreal 3572.30 B-450 6.72 Boreal 3572.30 B-456 5.96 Boreal 3572.30 B-460 6.14 Boreal 3572.30 B-462 6.64 Boreal 3572.30 B-466 5.98 Boreal 3572.30 B-468 5.90

Boreal 3572.30 B-470 5.95 Boreal 3572.30 B-472 6.18 Boreal 3572.30 B-476 7.00 Boreal 3572.30 B-484 6.86 Boreal 3572.30 B-488 8.06 Boreal 3572.30 B-491 7.23 Boreal 3572.30 B-495 4.97 Boreal 3572.30 B-498 8.61 721.05 2 Boreal 3572.30 B-499 9.67 348.25 1 Boreal 3572.30 B-502 10.00 Boreal 3572.30 B-504 10.00 Boreal 3572.30 B-506 10.00 Boreal 3572.30 B-513 10.00 Boreal 3572.30 B-515 10.00 Boreal 3572.30 B-516 10.00 161.37 1 Boreal 3572.30 B-517 10.00 Boreal 3572.30 B-523 10.00 225.56 2 Boreal 3572.30 B-525 10.00 Boreal 3572.30 B-526 10.00 Boreal 3572.30 B-530 10.00 Boreal 3572.30 B-534 10.00 Boreal 3572.30 B-535 10.00 Boreal 3572.30 B-537 2.25 203.53 3 Boreal 3572.30 B-538 3.37 Boreal 3572.30 B-546 6.96 242.62 2 Boreal 3572.30 B-546 6.96 481.89 1 Boreal 3572.30 B-547 7.48 Boreal 3572.30 B-548 9.01 Boreal 3572.30 B-554 3.67 Boreal 3572.30 B-554 3.67 Boreal 3572.30 B-555 2.35 Boreal 3572.30 B-557 3.17 Boreal 3572.30 B-563 6.50 Boreal 3572.30 B-567 6.60 Foothills 2226.17 F-123 4.79 Foothills 2226.17 F-125 5.36 8.49 1 Foothills 2226.17 F-131 2.35 Foothills 2226.17 F-139 3.47 Foothills 2226.17 F-141 4.54 258.18 2 Foothills 2226.17 F-143 5.03 Foothills 2226.17 F-147 4.77 Foothills 2226.17 F-149 6.08 148.98 2 Foothills 2226.17 F-151 6.57

Foothills 2226.17 F-159 4.20 Foothills 2226.17 F-176 3.13 Foothills 2226.17 F-180 4.33 Foothills 2226.17 F-182 3.44 199.47 1 Foothills 2226.17 F-182 3.44 58.56 1 Foothills 2226.17 F-182 3.44 12.45 1 Foothills 2226.17 F-190 4.73 380.48 2 Foothills 2226.17 F-192 4.50 Foothills 2226.17 F-196 4.19 Foothills 2226.17 F-230 2.52 Foothills 2226.17 F-232 5.69 79.93 1 Foothills 2226.17 F-233 7.77 145.68 1 Foothills 2226.17 F-235 10.00 Foothills 2226.17 F-238 10.00 Foothills 2226.17 F-239 10.00 Foothills 2226.17 F-242 10.00 140.08 2 Foothills 2226.17 F-244 10.00 Foothills 2226.17 F-248 10.00 Foothills 2226.17 F-251 10.00 Foothills 2226.17 F-254 10.00 123.56 1 Foothills 2226.17 F-265 10.00 Foothills 2226.17 F-266 10.00 368.84 1 Foothills 2226.17 F-273 9.55 Foothills 2226.17 F-274 10.00 Foothills 2226.17 F-275 10.00 Foothills 2226.17 F-279 10.00 Foothills 2226.17 F-285 10.00 Foothills 2226.17 F-289 7.33 Foothills 2226.17 F-293 6.42 Foothills 2226.17 F-295 8.95 Foothills 2226.17 F-296 9.29 Foothills 2226.17 F-297 9.57 Foothills 2226.17 F-298 8.73 378.59 1 Foothills 2226.17 F-298 8.73 344.78 1 Foothills 2226.17 F-298 8.73 410.09 1 Foothills 2226.17 F-298 8.73 226.75 1 Foothills 2226.17 F-299 8.73 Foothills 2226.17 F-300 8.58 Foothills 2226.17 F-302 5.36 Foothills 2226.17 F-304 4.89 Foothills 2226.17 F-314 2.37 Foothills 2226.17 F-316 2.49 Foothills 2226.17 F-319 3.29

Foothills 2226.17 F-320 4.46 261.63 1 Foothills 2226.17 F-321 5.37 Foothills 2226.17 F-322 5.86 395.79 1 Foothills 2226.17 F-326 9.56 Foothills 2226.17 F-332 9.99 277.48 1 Foothills 2226.17 F-333 10.00 Foothills 2226.17 F-334 10.00 Foothills 2226.17 F-337 10.00 257.90 2 Foothills 2226.17 F-339 10.00 52.02 1 Foothills 2226.17 F-341 10.00 Foothills 2226.17 F-342 10.00 Foothills 2226.17 F-343 10.00 222.32 1 Foothills 2226.17 F-347 10.00 116.79 1 Foothills 2226.17 F-347 10.00 300.63 1 Foothills 2226.17 F-348 10.00 Foothills 2226.17 F-349 10.00 Foothills 2226.17 F-352 10.00 Foothills 2226.17 F-353 10.00 Foothills 2226.17 F-354 10.00 Foothills 2226.17 F-357 10.00 Foothills 2226.17 F-358 10.00 307.97 1 Foothills 2226.17 F-360 10.00 Foothills 2226.17 F-362 10.00 309.89 1 Foothills 2226.17 F-366 10.00 Foothills 2226.17 F-367 10.00 38.11 2 Foothills 2226.17 F-368 10.00 Foothills 2226.17 F-371 10.00 122.30 1 Foothills 2226.17 F-373 10.00 Foothills 2226.17 F-374 10.00 292.41 1 Foothills 2226.17 F-374 10.00 273.29 1 Foothills 2226.17 F-376 10.00 Foothills 2226.17 F-379 10.00 Foothills 2226.17 F-380 10.00 Foothills 2226.17 F-384 10.00 Foothills 2226.17 F-389 6.73 Foothills 2226.17 F-397 2.03 Foothills 2226.17 F-399 2.81 Foothills 2226.17 F-407 2.53 Foothills 2226.17 F-411 2.44 Foothills 2226.17 F-421 2.44 Foothills 2226.17 F-427 2.25 Foothills 2226.17 F-434 2.33 Foothills 2226.17 F-437 3.04

Foothills 2226.17 F-439 3.36 128.75 1 Foothills 2226.17 F-441 2.59 Foothills 2226.17 F-445 2.74 156.39 1 Foothills 2226.17 F-447 3.08 Foothills 2226.17 F-459 3.85 Foothills 2226.17 F-477 2.48 215.79 1 Foothills 2226.17 F-481 3.43 Foothills 2226.17 F-483 3.69 Foothills 2226.17 F-487 3.11 Total Survey Area (km 2 ) Total transect length surveyed (km) Total number of groups observed 5794.05 1588.18 80 109 Total number of animals observed B) Density Estimation Results Table: Analysis results without truncation Model N D CV DLCL DUCL NLCL NUCL AIC k HN(cos) 447 0.077 0.235 0.053 0.113 305 656 0.00 2 HN(cos)(s) Boreal 288 0.081 0.219 0.056 0.115 202 412 - Foothills 187 0.084 0.485 0.039 0.182 86 404 - Total 475 0.082 0.233 0.056 0.120 324 695 0.00 4 UNI(cos) 463 0.080 0.146 0.063 0.101 364 588 2.39 1 UNI(poly) 464 0.080 0.158 0.062 0.104 358 601 3.5 2 HAZ(cos) 445 0.077 0.163 0.059 0.100 341 582 0.00 2 Table: Strata specific results with truncated data Stratum Total area (km 2 ) Total transect length surveyed (km) Number of groups detected Encounter rate Detection Probability Boreal 3572.31 905.02 34 33.2 60.2 Foothills 2226.17 683.16 27 35.9 61.5 Total 5794.07 1588.18 61 - Survey Summary Effort : 1588.179 # samples : 226

Width : 500.0000 Left : 100.0000 # observations: 61 Model selection results Model 1 Half-normal key, k(y) = Exp(-y**2/(2*A(1)**2)) Component Percentages of Var(D) ------------------------------- Detection probability : 60.9 Encounter rate : 34.6 Cluster size : 4.5 Estimation Summary Encounter Rate Estimate %CV df 90% Confidence Interval ------------------------------------------------------ n 61.000 k 226.00 L 1588.2 n/l 0.38409E-01 13.80 225.00 0.30613E-01 0.48190E-01 Left 100.00 Width 500.00 Estimation Summary Expected cluster size Estimate %CV df 90% Confidence Interval ------------------------------------------------------ Average cluster size 1.3443 5.47 60.00 1.2270 1.4727 Half-normal/Cosine r -0.20700E-01 r-p 0.43709 E(S) 1.3246 4.98 59.00 1.2189 1.4395 Estimation Summary Density and Abundance Half-normal/Cosine Estimate %CV df 90% Confidence Interval ------------------------------------------------------ DS 0.093929 22.92 135.90 0.064570 0.13664 D 0.12442 23.46 148.27 0.084825 0.18250 N 721.00 23.46 148.27 492.00 1058.0

Chi-squared goodness of fit test. This test provides a measure of how adequately the detection function fits the distance data. Kolmogorov-Smirnov test ----------------------- D_n = 0.0989 p = 0.5899 Cramer-von Mises family tests ----------------------------- W-sq (uniform weighting) = 0.1176 0.500 < p <= 0.600 Relevant critical values: W-sq crit(alpha=0.600) = 0.0972 W-sq crit(alpha=0.500) = 0.1191 C-sq (cosine weighting) = 0.0769 0.500 < p <= 0.600 Relevant critical values: C-sq crit(alpha=0.600) = 0.0624 C-sq crit(alpha=0.500) = 0.0772 Expected cluster size estimation table. This compares the mean cluster size with the expected cluster size calculated using regression. It provides a measure of how strongly cluster size biases the results. Expected cluster size estimated based on regression of: log(s(i)) on g(x(i)) Regression Estimates -------------------- Slope = -0.362689E-01 Std error = 0.228053 Intercept = 0.246784 Std error = 0.147746 Correlation= -0.0207 Students-t = -0.159037 Df = 59 Pr(T < t) = 0.437091 Expected cluster size = 1.3246 Standard error = 0.65948E-01 Mean cluster size = 1.3443 Standard error = 0.73497E-01