Aerial Ungulate Survey (2014) for

Transcription

1 Aerial Ungulate Survey (2014) for Moose and White-tailed Deer in WMU 515 (Heart Lake), WMU 651 (Lakeland Provincial Park) and WMU 841(Lakeland Provincial Recreation Area) January 7-11, 2014 Grant Chapman, Jordan Besenski, Hannah McKenzie, and Simon Slater Alberta Environment and Sustainable Resource Development Lower Athabasca Region and Joint Oil Sands Monitoring Program Suggested Citation: Chapman, G., Besenski, J., McKenzie, H., and Slater, S Aerial Ungulate Survey (2014), Moose and White-tailed deer in WMU 515 (Heart Lake), WMU 651 (Lakeland Provincial Park) and WMU 841 (Lakeland Provincial Recreation Area). Alberta Environment and Sustainable Resource Development, Government of Alberta. Lower Athabasca Region

2 EXECUTIVE SUMMARY Alberta Environment and Sustainable Resource Development and Joint Oil Sands Monitoring staff surveyed moose and white-tailed deer populations in Wildlife Management Units (WMU) 515, 651, and 841 using a stratified encounter rate distance sampling methodology to estimate population size and structure. The survey was conducted from January 7-11, 2014, during which 133 transects totalling 1,042 km of survey effort was flow for moose. After flying 83 transects in the first 3 days totalling 617 km, white-tailed deer distance estimate collection was discontinued as results were adequate to generate a suitable detection function; groups continued to be recorded during day 4 and 5 to improve encounter rate estimates. A total of 95 moose were observed and the bull:cow:calf ratio for was 29:100:44. The moose population for WMU 515 was estimated to be 375 (90% CI , CV 0.186) with a density of moose/km 2 (90% CI ). Limited moose detections in strata s WMUs 651 & 841, prevented confident estimate of populations in these WMU s. A total of 290 white-tailed deer were observed during the survey on the first three days and 406 were observed during the entire survey period. Deer age and sex classification was not possible due to significant antler drop at the time of the survey. The white-tailed deer population for WMU 515 was estimated to be 2,750 (90% CI 2,201-3,436, CV 0.135) with a density of 0.99 deer/km 2 (90% CI ). White-tailed deer and moose population s estimates in these WMU s indicate that moose population trends are declining and below goal while white-tailed deer are increasing. Future management and harvest recommendations will reflect these trends. Key words: Alberta, aerial survey, ungulates, population estimates, density estimates, age/sex ratios, moose, deer, distance sampling. 1

3 INTRODUCTION Background WMU 515 was last surveyed for ungulates in 2004 (population estimate 641 moose Table 1) using a modified Gasaway methodology (ESRD, 2010) and prior to that in 1999 (population estimate 981 moose) using a transect survey with 50% coverage. A transect survey with 50% coverage was completed in WMU 651 and 841 in 1997 which estimated the moose population of WMU 651 and WMU 841 to be 25 and 80 respectively. These prior survey designs prioritized moose population estimation; however some data were collected on deer populations in WMU 515 (population estimate 1093 deer Table 2). Table 1. Historical data for WMU 515 moose population estimates (* Confidence limit of survey in brackets). Year Population Estimate* Ratio to 100 Cows Bulls Calves Density (moose/km 2 ) (18.4%) (22.8%) (18.6%) Table 2. Historical data for WMU 515 white-tailed deer population estimates (* Confidence limit of survey in brackets). Year Population Ratio to 100 Cows Density Estimate* Bucks Fawns (moose/km 2 ) ,093 (11.6%) N.A. N.A N.A N.A ,750 (13.5%) N.A. N.A Locations of both moose and white-tailed deer in 2015 were found to be distributed throughout the survey area (Figure 2 and 3) with higher densities associated with upland deciduous habitat types. Historically mule deer and elk have not been observed in WMU 515. Other species that were documented during the survey woodland caribou, sharp-tailed grouse and wolves. Objective This survey was designed to estimate the population size and density of moose and deer in WMU s 515, 651 and 841. The survey results were also used to determine age and sex ratios and antler classification of moose and to the extent possible for deer. These WMU s lie within the Joint Oil Sands Monitoring Program (JOSM) area, and JOSM s objective is to increase the 2

4 sampling frequency to five year intervals to better understand ungulate population trends within the oil sands region. METHODS Study area WMU 515 is located north and east of Lac La Biche and WMU s 651 and 841 are located in Lakeland Provincial Park and Lakeland Provincial Recreation Area respectively (Figure 2). This area is within the central mixed wood sub region of the boreal forest natural region of Alberta (Natural Regions Committee 2006), and is bordered to the south by farmland and settled communities. WMU 515 consists of extensive tracts of boreal mixed-wood forest and open muskeg, much of which has been impacted little by industry or human settlement, although Heart Lake First Nation reserve lies within the center of the WMU. The topography is gently rolling, and there are several large lakes throughout the area. There are many large lakes in the survey area and as frozen lakes are non-habitat, we excluded large lakes from the survey area (Table 3). As a result, the population estimate for the study area is based on the estimate of N obtained from the Distance analysis land area, but the final density estimate is based on calculations using the area of the WMUs including lakes so that density estimates are consistent with previous reported methods. Table 3. Areas of each WMU in the study area with and without large lakes (non-habitat). Wildlife Management Unit (WMU) Area including large lakes (km 2 ) Area excluding large lakes (km 2 )

5 Figure 1. Location of the Heart Lake (515), Lakeland Provincial Park (651) and Lakeland Provincial Recreational Area (841) Wildlife Management Units, north and east of Lac La Biche with transects surveyed. 4

6 Survey methods This moose survey was conducted using distance sampling methods (Buckland et al. 2001). Transects were generated using ESRI ArcMap 10.1 by creating a grid of rectangles 1.2 km wide by 10 km long and oriented north-south throughout the WMUs. A random sample of rectangles were chosen and the centre line of each rectangle was used as the transect line. Transect lines shorter than 2 km were not sampled. Each transect was assigned a sequential integer that represented a random seed order that was used to determine what order transects were to be flown. A grand total of 303 transects were generated with a total distance of 2,261 km. Surveys were conducted in a Bell 206 Jet Ranger with rear bubble windows with a survey crew of 3 observers including Grant Chapman (lead) Justin Gilligan (back right) Jordan Besenski (back left) using Star Helicopters in Lac La Biche, with pilot Colin reed. In this survey, the pilot was not permitted to observe and report wildlife sightings until after they had been missed, where the observation was then collected solely for age sex ratio determination and which were not used for population estimation. Transect lines were flown at approximately 300 ft AGL and at a speed of 80 knots. Surveys conditions were recorded including temperature, percentage cloud cover, precipitation and position of observer in the aircraft. During the survey, the front observer restricted their observations to 50 m on either side of the transect line while the back left and back right observers were responsible for all areas from 50 m to as far as they could see on their side of the aircraft. When an animal was detected a waypoint was taken on the transect line and the transect line was continued until the aircraft was perpendicular to the animal or group of animals. Once perpendicular the aircraft left the transect line and collected another waypoint at the location where the animal was first observed. The animal was then classified by sex, age class and antler class if antlers were still present. All animals within 80 m of the original observation were considered to be a part of the same group. Additional covariate measurements were collected at each observed group included crown closure (0-30%, 31-70%, %), activity (bedded, standing, moving), snow cover (bare ground, low vegetation, complete snowcover), light intensity (flat, bright), terrain slope (flat, moderate, steep). Analysis methods Data were analyzed in the program Distance 6.0 (Release 2.0; Thomas et al., 2010). Based on examination of histograms of the distances, data were truncated to improve model fit if necessary (Buckland et al., 2002). Six candidate models were then fit to the data and model fit was assessed using goodness of fit tests, and model selection based on Akaike s Information Criterion (AIC; Buckland et al., 2001, Burnham and Anderson, 2002). When appropriate, we used size-biased regression to estimate cluster size. We considered the effect of including the stratification in the density estimation by estimating strata specific encounter rates. The detection function and cluster size were estimated at the regional level. 5

7 Figure 1. Map of study area, strata (WMUs), transects surveyed and moose detections. 6

8 Figure 2. Map of study area, strata (WMUs), transects surveyed and white-tailed deer detections. Distances and group size were measured for detections on the first 3 days only. On days 4 and 5, only the detections were noted. 7

9 RESULTS Moose One hundred and thirty three transects were surveyed from January 7-11, 2014 for a total survey effort of 1042 km (Figure 1). In total 94 moose were observed from 67 independent groups of moose. The last day of the survey was ended at 1pm due to unsafe flying conditions with heavy snowfall and high winds (70km/hr) and as such the sample sizes for moose was slightly less than desired. Of the 95 moose that were successfully classified, 16 were bulls, 55 were cows, and 24 were calves. Forty percent of the bulls observed were classified as having medium antlers, 7% small antlers and the remainder had shed their antlers already. There were no obvious outliers in the data and neither right-truncation nor binning improved the shape of the distribution, therefore based on the histograms we used the full data set for analysis (n = 67 groups). We fit six candidate models and based on the QQ-plots, histograms and goodness of fit tests, all models fit the data reasonably well and the density estimates were similar across models ( moose/km 2 ; Table 4). Based on AIC, the most supported model was the uniform + cosine. However, the half-normal + cosine model was also highly supported by the data, and overall the differences in AIC were negligible. We choose to use the half-normal + cosine model as it fit the data slightly better in the tail than the uniform + cosine model and had a larger (i.e. more conservative) coefficient of variation (CV). The estimated moose density for WMUs 515/641/851 was moose/km 2 with a 90% confidence interval of ( ) and a CV of The estimated moose abundance was 392 (90% CI ). Table 4. Model selection statistics for candidate models fit to the WMU 515/651/841 moose data. Model Number of Parameters ΔAIC Density Estimate (90% CI) Population Estimate (90% CI) Uniform + Cosine ( ) 388 ( ) Uniform + Simple Polynomial ( ) 384 ( ) Half-Normal + Cosine ( ) 392 ( ) Hazard-Rate + Cosine ( ) 368 ( ) The half-normal + cosine/hermite and the hazard-rate + cosine/hermite models reduced to their key functions as the addition of adjustments terms was not supported by the data. Following estimation of the detection function, encounter rate, and cluster size for the pooled data, results showed that most of the variability in the density estimate is due to variability in encounter rate between transects (74.4%). The encounter rate across all transects was (90% CI , CV 0.147). CV 8

10 Obtaining an accurate density estimate for WMU 515 was the priority, with white-tailed deer secondary, and then parks WMU s a tertiary priority. After examining the spatial distribution of detections, there appeared to be a difference in encounter rate between WMU 515 and WMUs 651/841. To reduce the variability in encounter rate, we stratified the data and estimated encounter rate by strata. Stratification led to an increased encounter rate in WMU (90% CI , CV 0.150). Therefore, we estimate the density for WMU 515 based on the analysis with encounter rate stratified by WMU. The estimated moose density for WMU 515 was (90% CI , CV 0.186). The estimated moose abundance was 375 (90% CI ). Due to the lack of detections of moose in WMUs 651 and 841, it is not possible to estimate the density with acceptable precision in these units using distance methods. Approximated population estimates, needed for moose harvest and licensing, without confidence were estimated to be 15 and 42 moose for WMU s 651 and 841 respectively, which were calculated using the area of the WMU without lakes times the average density of moose found in WMU 515.This is likely higher than the true population in these 2 WMU s as there was noticeably fewer moose observations in these WMU s relative to WMU 515. White-Tailed Deer A total of 290 white-tailed deer were observed and distances recorded during the first three days of surveying a total of 83 transects and 617 km of effort. A total of 406 white-tailed deer were observed over the entire survey period. Deer classification was not conducted due to timing of antler drop, although differentiation between doe and fawn was made. During days 4 and 5 of the survey, detections of deer groups were recorded, but no were distances taken as survey budget and time were limited. Therefore, the entire dataset was used to estimate encounter rate, and only data from days 1-3 were used to fit the detection function. Based on analysis of the distribution of distances in the histograms containing all observations from days 1-3, the data were right truncated at 350 m to remove outliers. Right-truncating was helpful with the half-normal fitting the overall shape. There was a noticeable gap in observations around m. This may be due to the front and back observers not overlapping their search areas with the front observer. Despite the gap, the distribution of distances has a good shoulder, therefore we used the data set truncated at 350 m (n=145 groups). Assessment of the QQ-plots, histograms, and goodness of fit tests for six candidate models indicated that all models fit the data reasonably well (Table 5). AIC values for all models were below 2.0 and the most supported model was the uniform cosine model. The half-normal cosine model was chosen as the best model as it had a larger, more conservative CV associated with the detection function (6.9% compared to 4.4% for the uniform cosine model). 9

11 Similar to the moose data, most of the variability in the density estimate is due to variability in encounter rate between transects (76.8%). We stratified the data to estimate encounter rate by strata, and then also used the detections collected during days 4 and 5 to improve the encounter rate. Table 5. Model selection statistics for the six candidate models fit to the WMU 515, 651, and 841 white-tailed deer data. Model Number of Parameters Goodness of fit Tests (p-value) CvM CvM KS Chi-sq (unif) (cos) Delta AIC CV (%) Uniform + Cosine Uniform + Simple Polynomial Half-Normal + Cosine Hazard-Rate + Cosine The half-normal + cosine/hermite and the hazard-rate + cosine/hermite models reduced to their key functions as the addition of adjustments terms was not supported by the data. The precision of the encounter rate estimates improved when using data from all 5 days as compared to data from the first 3 days. Therefore, we estimated the density in each WMU using the stratified encounter rate estimated from the data from all 5 days. This required calculating density and the variance of the density estimate outside of Distance, as the detection function and cluster size estimates come from a different analysis than the encounter rate estimate (see Buckland et al. 2001, p. 76). Table 6 shows the final white-tailed deer density and abundance estimates, as well as the density estimates after correction for the area of the large lakes. Table 6. Final white-tailed deer density and abundance estimates for WMU 515, 651, and 841, shown with 90% confidence intervals and CVs. Density (calculated based Density (estimated based on Stratum Abundance on abundance and survey area excluding lakes) including area of lakes) WMU ( , CV=13.5%) 2750 ( ) 0.99 ( ) WMU ( , CV=31.8%) 113 (66-194) 0.64 ( ) WMU ( , CV=27.2%) 334 ( ) 0.52 ( ) 10

12 The white-tailed deer buck to doe to fawn sex ratios observed on the first 3 days of the survey was 1:69:83, with another 119 unclassified adult deer (buck or does), and 18 unclassified deer (buck, does, or fawns) observed. These observations produced an adult to fawn ratio of 100:44. The very low buck observations indicated that the majority of the buck cohort had already shed antlers by this time (Table 7) and could also be indicative of high buck harvest in this WMU as many more bucks were expected to be observed. Table 7. Summary of White-tailed Deer Observations and Sex Ratios observed during days 1-3 of survey. Corrected Adult:Fawn Ratio Observed adult to fawns Buck Doe Fawn Unclassified Adults Unclassified White-tailed Deer 100:44 189: No elk or mule deer were observed during this survey and have not been reported in any prior surveys which indicate that they have not been able to establish in these WMU s. There are however established mule deer in adjacent WMU s and establishing elk herds or random sightings in WMU s 517, 503, and 512. Moose and white-tailed deer basic survey info, distance data sheets, and summary of results are enclosed in the Appendix of this report. DISCUSSION This 2014 density estimate for WMU 515 of 0.14 moose/km 2 (90% CI ) is lower than the estimate from the 2004 survey of 0.24 moose/km 2 (90% CI ). The 90% confidence intervals do not overlap, suggesting moose have declined in the area since It is important to note that the 2014 and 2004 surveys use different methodology which prevents is important to consider when interpreting these results, how future distance surveys will enable more detailed comparisons. This survey produced acceptable confidence in our estimates for both moose and deer in WMU 515. Had survey budget and weather conditions permitted, additional survey effort would have been added to improve ability to estimate deer and moose in WMU s 841 and 651. Since 1997 moose abundance has declined in WMU s 651 and 841. If more accurate information is required for these WMUs, future surveys should increase the number of transects flown to increase the number of groups detected, thus providing improved estimates strata detection function and encounter rates (Buckland et al, 2001). 11

13 Moose populations in adjacent WMU s range from moose/km 2 (Table 8). Comparatively, moose density in WMU 515 is at the mid-range and seems to suggest a gradient of lower densities in the eastern units and higher densities in the units to the west which is likely a function of increased habitat quality habitat reduced predation effects. Data was not pooled with the WMU 726 moose survey which was flown (Feb 1-2, 2014) which estimated moose densities to be much lower at 0.05 moose/km 2. Table 8. Comparison of historical moose population parameters in WMUs near 515. WMU Survey Date Population Density Classification (moose/km 2 ) (bull:cow:calf) N.A. 59:100: :100: :100: :100: :100: :100: :100: * * *Calculated by using population estimate divided by area to obtain density. Survey does not have confidence intervals with estimate. Moose harvest goals and success rates of adjacent WMU s are summarized in Table 9. The averaged success rate of antlered moose harvest in WMU 515 is stable at 30% and 19% in the early and late seasons respectively which are consistent with those reported in the adjacent WMU s. Table 9. Bull Moose Special License, harvest goals, and hunter success in adjacent WMU s WMU PreSeason Population Goal 2014 Pre- Hunting Season Population Estimate 2015 Pre- Hunting Season Population Estimate Bulls Cows Season Archery Only General Harvest Goal % Average Hunter Success (%) Bull Hunter Harvest Success Bull Special Licenses issued in draw draw 20 31% % 25% 36% 32% draw draw 15 37% % 37% 42% 31% draw draw 10 45% 43 60% 36% 44% 54% draw draw 15 30% 78 26% 33% 28% 28% draw draw 20 52% % 52% 51% 52% Early unlimited draw 20 42% % 56% 35% 36% Late unlimited draw 20 24% % 20% 37% 16% Early unlimited draw 20 30% 49 36% 38% 22% 31% Late unlimited draw 20 19% 130 9% 14% 31% 11% Early unlimited draw 25 25% % 31% 16% 28% Late unlimited draw 25 10% 100 2% 10% 17% 5% Early unlimited draw 3 0% 5 0% 0% Late unlimited draw 3 10% 5 25% 20% 0% 12

14 White-tailed deer data has been collected in the past in these WMU s, but has not been the primary focus of the ungulate surveys in the area. The estimate of 0.99 deer/km 2 reported in this survey will serve as a baseline for future deer monitoring in WMU 515 and is higher than the estimate provided in 1999 of 0.41 deer/km 2, indicating that deer populations have increased in the last 15 years (Table 10). Unfortunately the timing of this survey did not coincide with whitetailed deer still carrying 100% of antlers and therefore sex ratios are unavailable to inform preseason population models estimates. Future surveys should be planned for early December to enable age:sex determination on white tailed deer. Table 10. Comparison of historical white tailed deer population parameters in WMU s in the North East region. YEAR WMU SPECIES DATE SURVEY TYPE Population Estimate Density (Deer/km 2 ) Confidece Interval +/- (%) Buck Doe Juvenile WTDE 2/1/1995 CLASSIFIED 1205 N/A N/A N/A N/A N/A WTDE 12/15/1995 CLASSIFIED WTDE 2/22/1996 CLASSIFIED N/A N/A N/A N/A WTDE Line (50% cvrg) 50 N/A N/A N/A N/A N/A WTDE Line (50% cvrg) 250 N/A N/A N/A N/A N/A WTDE 1/21/1999 Rand block N/A N/A N/A N/A WTDE 19-Jan-00 Random Block % WTDE Dec-00 Random Block % WTDE Feb.-03 Random Block % WTDE Jan.-08 Random Block % WTDE Jan.-08 Random Block % WTDE Jan.-13 Random Block % WTDE 14-Jan Distance % N/A N/A N/A WTDE 14-Jan Distance % N/A N/A N/A WTDE 14-Jan Distance % N/A N/A N/A WTDE Dec.-13 Age Sex Survey Only N/A N/A N/A WTDE Feb.-13 Random Block % WTDE Dec.-13 Age Sex Survey Only N/A N/A N/A WTDE Dec.-13 Age Sex Survey Only N/A N/A N/A WTDE Feb.-15 Distance % N/A N/A N/A WTDE Jan.-15 Distance % WTDE Feb.-15 Distance % N/A N/A N/A MANAGEMENT RECOMMENDATIONS The 2015 WMU 515 preseason population estimate is 410 moose, which is only 41% of the goal. Moose management recommendations are to reduce bull special licenses to support moose populations to recover toward the goal of 1000 moose in the WMU. It is likely that the moose population will be slow to recover. Current hunting regulations prohibit the harvesting of antlerless moose in WMU 515 and 841, which will support moose recovery. Anecdotal 13

15 knowledge of First Nations moose harvest indicates that harvest is likely high in this WMU and could be a factor that is contributing to low recruitment. White-tailed deer populations are good and appear to be increasing over the long term and general white-tailed deer license hunting opportunities including 2 supplemental antlerless licences will continue to be offered. WMU 515 contains and is adjacent to Cold Lake woodland caribou critical habitat and therefore maintaining liberal hunting seasons for white-tailed deer will support the reduction of this primary prey species for wolves. This will support caribou recovery goals in the area as wolves are known to be contributing to caribou declines. ACKNOWLEDGEMENTS Funding was provided by Environment and Sustainable Resource Development (ESRD), Lower Athabasca Region and through the Joint Oil Sands Monitoring Program (JOSM). The survey was conducted by Grant Chapman, Justin Gilligan and Jordan Besenski. Gratitude is extended to Colin Reed from Star Helicopters for his contributions to the survey. Appreciation is also extended to Cody Clark of ESRD for flight-following from the Lac La Biche Fire Centre. LITERATURE CITED Buckland, S.T., D.R. Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers and L. Thomas Distance sampling: Estimating abundance of biological populations. Oxford University Press, Oxford. 432pp. Burnham K.P, and D.R. Anderson Model selection and multimodel inference: a practical information-theoretic approach, 2nd Edition. Springer, New York Environment and Sustainable Resource Development (ESRD) Aerial Ungulate Survey Protocol Manual. 65pp Natural Regions Committee 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 Distance software: design and analysis of distance sampling surveys for estimating population size. Journal of Applied Ecology 47:

16 APPENDICES A) Basic survey data WMU WMU 515, 651 and 841 Dates of survey Moose January 7-11, 2014 White-tailed deer January 7-9, 2014 Observers Jordan Besenski, Grant Chapman, Justin Gilligan Aircraft Bell 206- Star Helicopters Pilot Colin Reed Cost and time breakdown $34, over 26.7 hours Design Distance sampling design with 133 north-south transect lines Comments Due to low visibility, flight conditions, high winds and snow on the last day the survey was ended early. Survey Weather Data: Date Temp. ( C) Wind Wind Direction Cloud Cover January 7, No wind 100% January 8, km/hr S 100% January 9, Reaching 5 km/hr 100% January 10, Reaching 10 km/hr 5% January 11, to -6 Reaching 30 km/hr 100% Data for individual transects (Moose): Strata ID Strata Area (km 2 ) Transect ID Transect Length (km) Perpendicular Distance from transect (m) Number of animals observed in the group WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU

17 WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU

18 WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU

19 WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU

20 WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU Totals (Moose): Total Survey Area: 3367 km 2 (3141 km 2 without lakes) Total transect length surveyed: 1,045 km Total transects flown: 133 Total number of animals observed: 95 19

21 Data for individual transects (White-Tailed Deer): Strata ID Strata Area (km 2 ) Transect ID Transect Length (km) Perpendicular Distance from transect (m) Number of animals observed in the group WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU Days 20

22 WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU

23 WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU

24 WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU

25 WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU

26 WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU

27 WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU WMU Totals (White-Tailed Deer): Days 1-3 Total Survey Area: 3141 km 2 Total transect length surveyed: 617 km Total transects flown: 83 Total number of animal observed : 290 Day 4-5 Total Survey Area: 3141 km 2 Total transect length surveyed: 425 km Total transects flown: 50 Total number of animal observed :

28 B) Summary of Distance Results Distance Summary For Moose Table1. Areas of each WMU in the study area with and without large water bodies (non-habitat). Wildlife Management Unit (WMU) Area including large lakes (km 2 ) Area excluding large lakes (km 2 ) Data From the survey we have the following data (pooled WMUs 515/651/841): Effort : km # samples : 133 transects # observations : 67 groups Table 2. Number of groups and transects available for estimating the detection function and encounter rate. WMU # Groups # Transects Total Detection Probability Plot (a) All Data (b) Data right-truncated by 5% (c) Data pooled into 75 m bins Figure. Histogram of the WMU 515/641/841 moose data with various choices for right-truncation and cut-points. Neither right-truncation nor binning improved the shape of the distribution over the full data set. 27

29 Expected cluster size Expected cluster size estimated based on regression of: log(s(i)) on g(x(i)) Regression Estimates Slope = E-01 Std error = Intercept = Std error = Correlation= Students-t = E-01 Df = 65 Pr(T < t) = Expected cluster size = Standard error = E-01 Mean cluster size = Standard error = E-01 Estimating encounter rate Following estimation of the detection function, encounter rate, and cluster size for the pooled data, the partitioning of variability in the density estimate was as follows: Component Percentages of Var(D) Detection probability : 17.3 Encounter rate : 74.4 Cluster size : 8.3 Most of the variability in the density estimate is due to variability in encounter rate between transects. The encounter rate across all transects was (90% CI , CV=14.7%). After examining the spatial distribution of detections, there appears to be a difference in encounter rate between WMU 515 and WMUs 651/841. In an effort to reduce the variability in encounter rate, we stratified the data and estimated encounter rate by strata. Stratification led to an increased encounter rate in WMU 515 (Table 4). Unfortunately, due to the low number of observations in WMUs 651 and 841, the encounter rate estimate has very poor precision. Table 3. Estimated encounter rates for the entire study area and by WMU. The parameters are as follows: n=number of groups detected, k=number of transects, L=total transect length, n/l=encounter rate (groups/km). Also shown are 90% confidence intervals and CVs for the encounter rate estimates. Stratum Parameter Estimate Pooled WMU 515 n k L n/l n k L n/l WMU 651 n ( , CV=14.7%) ( , CV=15.0%) 28

30 WMU 841 k L n/l n k L n/l ( , CV=97.5%) ( , CV=68.2%) Density and Abundance Estimate %CV df 90% Confidence Interval Stratum: 1. WMU515 Half-normal/Cosine DS E E D N Stratum: 2. WMU651 Half-normal/Cosine DS E E E-01 D E E N Stratum: 3. WMU841 Half-normal/Cosine DS E E E-01 D E E N Distance Summary For White-Tailed Deer Table 1. Areas of each WMU in the study area with and without large lakes (non-habitat). Wildlife Management Unit (WMU) Area including large lakes (km 2 ) Area excluding large lakes (km 2 ) Data For the entire 5 day survey we have the following data (pooled WMUs 515/651/841): Effort : km # samples : 133 transects # observations : 223 groups During days 4 and 5 of the survey, detections of WTDE groups were recorded, but no distances taken. Therefore, although we use the entire dataset to estimate encounter rate, we only use days 1-3 to fit the detection function. For days 1-3, we have the following data (pooled WMUs 515/651/841). Effort : km # samples : 83 transects 29

31 # observations : 151 groups Detection Probability Plot (a) All Data (b) Data right-truncated at 350 m Figure 1. Histogram of the white-tailed deer data with various choices for right-truncation. Also shown is a half-normal model fitted to these data. Based on the histograms, we proceed using the data truncated at 350 m (n=145 groups). Encounter Rate Component Percentages of Var(D) Detection probability : 16.8 Encounter rate : 76.8 Cluster size : 6.4 Density & Abundance Table 2. Estimates of the various components required to estimate density, showing which components were calculated from which data sets, as well as the calculations of density, its variance, and 90% confidence intervals. Component of Density Estimate Parameter Data and Model Days 1 to 3 f(0) Detection function cv(f(0)) var(f(0)) E-07 Cluster size E(s) All days, stratified by WMU WMU 515 WMU 651 WMU

32 Encounter Rate Density Estimate Confidence Interval Calculation SE(E(s)) var(e(s) cv(e(s)) n L n/l cv(n/l) var(n/l) var(n) cv(n) D var(d) cv(d) df_f(0) 144 df_e(s) 143 df_n/l df tdf(0.05) C D LCL (90%) D UCL (90%) Table 3. Final density and abundance estimates for WMU 515, 651, and 841, shown with 90% confidence intervals and CVs. Stratum Density (calculated based Density (estimated based on abundance and on survey area excluding Abundance including area of lakes) lakes) WMU ( , CV=13.5%) 2750 ( ) 0.99 ( ) WMU ( , CV=31.8%) 113 (66-194) 0.64 ( ) WMU ( , CV=27.2%) 334 ( ) 0.52 ( ) 31