National Park Service 

U.S. Department of the Interior

Natural Resource Stewardship and Science

Moose (Alces alces) population survey in YukonCharley Rivers National Preserve, November 2019

Natural Resource Report NPS/YUCH/NRR—2020/2127 
ON THE COVER

Bull moose bedded down in Yukon-Charley Rivers National Preserve, November 2019.

Photograph by: Matt Cameron
Moose (Alces alces) population survey in YukonCharley Rivers National Preserve, November 2019

Natural Resource Report NPS/YUCH/NRR—2020/2127 

Matthew D. Cameron and Dylan T. Schertz

Yukon-Charley Rivers National Preserve 

4175 Geist Road 

Fairbanks, AK 99709

May 2020

U.S. Department of the Interior 

National Park Service

Natural Resource Stewardship and Science 

Fort Collins, Colorado
ii

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Please cite this publication as:

Cameron, M. D., and Schertz, D. T. 2020. Moose (Alces alces) population survey in Yukon-Charley 

Rivers National Preserve, November 2019. Natural Resource Report NPS/YUCH/NRR—2020/2127. 

National Park Service, Fort Collins, Colorado. 

NPS 191/168887, May 2020
iii

Contents

Page

Figures ................................................................................................................................................... iv

Tables..................................................................................................................................................... v

Executive Summary .............................................................................................................................. vi

Acknowledgments ................................................................................................................................. vi

Introduction ............................................................................................................................................ 1

Study Area ............................................................................................................................................. 3

Methods .................................................................................................................................................. 4

Results.................................................................................................................................................... 6

Weather and Snow Conditions ....................................................................................................... 6

Stratification and Survey Details.................................................................................................... 6

Population Estimate........................................................................................................................ 9

Sex and Age Composition ............................................................................................................ 10

Discussion ............................................................................................................................................ 13

Literature Cited .................................................................................................................................... 15

Appendix A. Survey Logistics ............................................................................................................. 17

Appendix B. Comparing 2 Stratifications ............................................................................................ 18
iv

Figures 

Page

Figure 1. Location of moose survey units within and around Yukon-Charley Rivers 

National Preserve (YUCH), Alaska. ...................................................................................................... 2

Figure 2. Typical topography and vegetation of the survey area. ......................................................... 3

Figure 3. Stratification (indicated by color of survey unit) and numbers of moose counted 

in each unit in Yukon-Charley Rivers National Preserve, November 2019. ......................................... 7

Figure 4. Estimated observable fall moose total (with 90% confidence intervals) for 

Yukon-Charley Rivers National Preserve from GSPE surveys from 2003–2019, Alaska. ................. 10
v 

Tables 

Page

Table 1. Summary of stratification and sampled units from moose population surveys in 

Yukon-Charley Rivers National Preserve, 1987 to 2019. ...................................................................... 8

Table 2. Summary of survey statistics and density estimates for moose population 

surveys in Yukon-Charley Rivers National Preserve, 1987 to 2019. .................................................... 9

Table 3. Summary of observed moose during the surveys in Yukon-Charley Rivers 

National Preserve, 1987 to 2019. ......................................................................................................... 11

Table 4. Number of sets of twins seen during moose surveys in Yukon-Charley Rivers 

National Preserve, 1987 to 2019. ......................................................................................................... 12
vi 

Executive Summary

● Overall survey dates: November 10–23, 2019 (10 days of survey, 4 weather days) 

○ Stratification dates: November 11–17, 2019 (5 days of survey, 3 weather days) 

○ Survey dates: November 18–23, 2019 (5 days of survey, 1 weather day) 

● Total survey area: 3,096 mi2 (8,018 km2), 555 survey units 

● Area surveyed: 670 mi2 (1,735 km2), 120 survey units 

● Total moose observed: 249 (142 cows, 37 calves [1 set of twins], 70 bulls [3 spike-fork bulls]) 

● Average search effort: 5.7 minutes/mi2 (2.2 minutes/km2) 

● Population estimate: 873 moose (90% CI: 730–1,016; +/−16%) 

● Estimated density: 0.28 moose/mi2 (0.11 moose/km2) 

● Estimated age/sex ratios: 26 calves:100 cows, 2 yearlings bulls:100 cows, 49 bulls:100 cows 

Acknowledgments 

This survey was funded by U.S. National Park Service, Central Alaska Network Vital Signs 

Monitoring Program, and Yukon-Charley Rivers National Preserve, Alaska. Many safe flight hours 

and aircraft support were provided during the survey by pilots Andy Greenblatt, Brett Nigus, Jesse 

Cummings, and Zack Knaebel. Jordan Pruszenski, Maxwell Newton, Hilary Hilmer, Dylan Schertz, 

and Matt Cameron served as observers in aircraft, and we thank all observers for their help and 

dedication to the survey. We thank Kyle Joly, Mat Sorum, Jeff Wells, and Graham Frye for 

providing invaluable input on earlier versions of this report. We are especially grateful to Mat Sorum, 

Kyle Joly, and Jeff Wells for their help, advice, and coordination in the planning and implementation 

of this survey.
1 

Introduction 

Moose (Alces alces) are an integral component of the boreal ecosystem in Yukon-Charley Rivers 

National Preserve. Moose are considered good indicators of long-term habitat change within the 

Preserve ecosystem because they require large quantities of resources from their habitat year-round, 

and populations have the potential to respond to long-term changes in resource conditions. In 

addition, moose are an important subsistence and sport hunting resource, and are named specifically 

in the founding legislation for the Preserve (ANILCA, 1980). The Central Alaska Network (CAKN)

of the National Park Service (NPS) designated monitoring fauna distribution and abundance as one 

of the top 3 Vital Signs for the Network. Moose were identified as one such species to monitor as 

they are a common herbivore present in all 3 park units and indicate the health of lower elevational 

ecosystems (relative to the elevations within the Network). The CAKN is monitoring animal species 

across an elevational gradient as part of an effort to track long-term changes in ecosystems across all 

3 Alaskan park units. Moose surveys are scheduled for each park unit every 3 years. 

The NPS and CAKN conducted an aerial moose survey in partnership during November 10–23, 

2019, in and around Yukon-Charley Rivers National Preserve (YUCH), Alaska (Figure 1). Moose 

surveys have been conducted within YUCH for over 30 years, with 8 surveys being conducted within 

the current survey area over the last 22 years.

The objectives of the 2019 moose survey were to: 1) estimate numbers of total moose in a 3,096 mi2

survey area of the preserve, 2) estimate sex and age ratios, and 3) improve the precision of the 

population estimate by conducting an aerial stratification prior to the survey. 
2 

Figure 1. Location of moose survey units within and around Yukon-Charley Rivers National Preserve 

(YUCH), Alaska. State of Alaska Game Management Unit (GMU) boundaries are indicated in maroon.
3 

Study Area

The moose survey was conducted along a 30–40 mile- (48–64 km) wide corridor of the Yukon River 

drainage within and around YUCH, between Eagle and Circle, Alaska (Figure 1). The topography of 

the area consists mainly of rolling hills and river bluffs (Figure 2). Isolated rugged terrain occurs on 

several eroded mountains, with peaks generally under 6000 feet (1800 meters). Vegetation is 

dominated by black spruce (Picea mariana), and several species of deciduous hardwoods including 

aspen (Populus tremuloides) and birch (Betula papyrifera). Ponds, sloughs and large areas of tussock 

tundra are common in the flats along the Yukon River and lower parts of large tributaries such as the 

Charley and Kandik rivers. Wildfire burns of varying sizes and ages are present throughout the study 

area including the more recent large fires from the summers of 1999 and 2004 along the Yukon, 

Nation and Kandik rivers. 

Figure 2. Typical topography and vegetation of the survey area. Foothills of the confluence of the Charley 

River and Yukon River, photo by Matt Cameron.
4 

Methods 

Moose population surveys were conducted using the GeoSpatial Population Estimator (GSPE) 

method (Ver Hoef 2008), a modification of a technique initially developed by Gasaway et al. (1986). 

The GSPE method is widely used in Alaska, which allows comparison between survey areas. The 

publications, “GeoSpatial Survey Operations Manual” (Kellie and DeLong 2006) and “GeoSpatial 

Population Estimator Software User’s Guide” (DeLong 2006) provide guidelines for sample unit 

design and selection, navigation, and data analysis. 

We initially planned to conduct this survey in November of 2018 to maintain the scheduled 3-year 

interval between surveys. However, temperatures were warmer than normal and adequate snowfall 

for reliable survey conditions did not accumulate until late in the month (Sousanes and Hill, 2018). 

Daylight hours are greatly diminished this late in the fall season and surveying at this remote site 

becomes increasingly difficult and inefficient with such shortened days, so we rescheduled the 

survey for 2019. 

In November 2019, we performed the first aerial stratification of the entire study area prior to 

conducting the survey, as recommended by Sorum and Joly (2016). We followed the protocol 

described in Kellie and DeLong (2006) and flew centerline transects through all the survey units at 

speeds ranging from 100–140 knots and at an altitude of 800–1500 ft above ground level. We flew in 

a Cessna 185 and then a Found Bush Hawk, placing 3 observers with the pilot. For each unit we 

recorded the number of moose detected, presence of moose tracks, and predominant habitat. If any 

moose were detected, a unit was classified as a high stratum. If no moose were detected, presence of 

tracks and predominant habitat were used to determine strata, with deciduous shrub habitat (such as 

burned areas, high elevation shrub lines, and riparian corridors) characterizing high stratum 

(Schwartz and Franzmann, 1989, MacCracken et al. 1997). Since the first half of the stratification 

effort occurred when snow and light conditions were poor for easily detecting tracks, we relied 

primarily on habitat classifications. For units which were of questionable stratum and were surveyed 

previously, we used information from the last 3 surveys to help inform stratum classification (Kellie 

and DeLong 2006). Because the Yukon River corridor represented a large area of high-quality habitat 

but virtually no moose were detected within it during the stratification, we flew the river corridor on 

the last stratification day when tracking conditions were excellent and re-classified units as low strata 

unless a moose was detected in or near the unit. 

Sample units to survey were randomly selected from each density strata following Kellie and 

DeLong (2006). Approximately 10 – 20% of the units were withheld from the random selection and 

subjectively used to fill in-between blocks of units because the GSPE has a spatial component whose 

results are improved if there are no large gaps (> 50 km) among surveyed units. Because 

observations are more variable in the high stratum than the low stratum, we allocated greater sample 

effort to the high-density units to reduce overall variance (Kellie and DeLong 2006). We selected 

120 units to survey out of the 555 total units, with a goal to sample approximately 40% and 12% of 

the high and low stratum units, respectively. 
5 

For the survey, four tandem seat fixed-winged aircraft (Piper PA-18 and Top Cub CC-18) were used 

to survey ~ 5.6 mi2

 units that were delineated by 2 minutes of latitude by 5 minutes of longitude. 

Aircrafts used Global Positioning System (GPS) to navigate to and within assigned units and search 

intensity varied with habitat. The survey protocol required high search intensity in forested habitats 

(8–10 minutes per square mile) and lower intensity in open habitats or areas with significant water. 

Survey patterns varied according to terrain. Within lowland area, survey aircrafts generally flew 

transects about 300 to 800 feet AGL at 70 knots. Within mountainous terrain, survey aircrafts 

followed the contour of the mountains. 

An observer was paired with each pilot, and the observer’s job was to record data on an iPad using 

the NPS application, “Park Observer,” (developed by the Alaska Regional GIS Team). The iPads 

were loaded with base maps, the survey grid, and the remaining units to be surveyed each day. Using 

the Park Observer “Fall Moose Comp” (v1.0) protocol, the observer recorded the track log as well as 

moose group location and composition which were detected during the survey. Numbers of moose in 

each group were recorded and the sex and age classification of each moose was determined. Moose 

were classified as: cow, calf, yearling bull (spike or forked antlers), medium bull (antler spread > 

spike/fork, but < 50 inches [127 cm]), and large bull (antler spread ≥ 50 inches [127 cm]). Total 

moose, moose density and sex/age ratios were calculated using the GeoSpatial Population Estimator 

software (DeLong 2006, Kellie and Delong 2006). 
6 

Results

Weather and Snow Conditions

The majority of the weather conditions for flying the stratification and survey were fair to excellent. 

At the beginning of the stratification, Coal Creek weather station recorded 11” of snow depth with 

3.5” falling in a storm 4 days prior (MesoWest 2020). Snow cover in the study area was complete 

except for the Yukon River corridor, which mostly had vegetation showing due to high winds along 

the river. Tracking conditions were difficult in the first half of the stratification but improved 

throughout as snow accumulated. These multiple snow events during the stratification resulted in a 

snow depth at Coal Creek of approximately 15” at the beginning of the survey portion (MesoWest

2020). Snow cover was complete throughout the study area with fresh snow at the onset of the 

survey, and snow conditions and sightability were good to excellent throughout the survey. 

Temperatures ranged from −16°F to 34°F at the Coal Creek weather station during the project

(MesoWest 2020). The Yukon River was partially frozen-over during the survey, with the ice edge 

initially beginning approximately halfway across the study area and building upstream throughout 

the stratification and survey. 

Stratification and Survey Details

The combined stratification and survey was conducted between 10–23 November. The stratification 

was completed in 5 days of flying between 10–17 November, in which all units of the study area 

were stratified. Of the 555 total units, 202 (36%) were classified as high strata and 353 (64%) as low 

strata (Figure 3). 

We conducted the survey between 19–23 November, completing 120 of 555 survey units which 

covered 22% of the survey area (Figure 3, Table 1). Eighty (39%) of the high-density units and 40 

(11%) of the low-density units were surveyed (Table 1). A total of 64.0 hours (3,839 minutes) of 

flight time was spent searching for moose for an average of 32.0 minutes per survey unit. Search 

intensity averaged 5.7 minutes per mi2

 (2.2 minutes/km2, Table 2). All selected units were completed 

during the survey. 

Aviation and project costs for the 2019 Yukon-Charley Rivers NP fall moose survey are found in 

Appendix A. Complete survey results for the entire 2019 survey are archived in, and can be retrieved 

from, the ADFG WINFONET database under the survey name “YUKON-CHARLEY, Fall, 2019” 

(http://winfonet.alaska.gov/; accessed 20 December 2019) as well as the NPS repository IRMA 

(“Moose (Alces alces) population survey in Yukon-Charley Rivers National Preserve, November 

2019”; Code: 2272089). 
7

Figure 3. Stratification (indicated by color of survey unit) and numbers of moose counted in each unit in Yukon-Charley Rivers National Preserve, 

November 2019. 
8

Table 1. Summary of stratification and sampled units from moose population surveys in Yukon-Charley Rivers National Preserve, 1987 to 2019. 

Areas are presented as square miles and number of units (for stratified random and GSPE surveys) are in parentheses. 

Survey 

Year Survey Area Sample Area

% 

Sampled

Stratification Survey

High Med Low High Med Low

1987a 3556 240 6.7 – – – – – –

1994a 2790 1844 66.1 – – – – – –

1997 2758 (201) 1358 (98) 49.2 303 (22) 788 (57) 1667 (122) 238 (21) 537 (38) 537 (39)

1999 2745 (200) 1389 (100) 50.6 333 (24) 622 (45) 1790 (131) 333 (24) 576 (42) 480 (34)

2003 3157 (566) 591 (106) 18.7 1049 (188) – 2108 (378) 340 (61) – 251 (45)

2006 3096 (555) 841 (151) 27.2 899 (161) – 2197 (394) 540 (97) – 301 (54)

2009 3096 (555) 618 (111) 20.0 899 (161) – 2197 (394) 329 (59) – 289 (52)

2012 3096 (555) 664 (119) 21.4 899 (161) – 2197 (394) 362 (65) – 301 (54)

2015 3096 (555) 714 (128) 23.0 949 (170) – 2147 (385) 458 (82) – 256 (46)

2019 3096 (555) 670 (120) 21.6 1127 (202) – 1969 (353) 447 (80) – 223 (40)

a Not directly comparable with later surveys.
9 

Population Estimate

The GSPE population estimate for observable moose in YUCH was 873 moose (90% confidence 

interval [CI] = 730–1,016 [16% relative precision]) which yielded a density of 0.28 moose/mi2

 (0.11 

moose/km2

; Table 2, Figure 4). 

Table 2. Summary of survey statistics and density estimates for moose population surveys in YukonCharley Rivers National Preserve, 1987 to 2019. 

Year Method 

Survey 

Area mi2

(# units)

Sampled 

Area mi2 

(# units)

Search 

Intensity

(min/mi2) 

Population 

Estimate 

Relative 

Precision

Density 

(moose/mi2) 

Sightability 

Correction 

Factor

1987a Gasaway 3556 240 4.4 1116 – 0.31 –

1994a Gasaway 2790 (245) 1844 (162) 1.0 551 0.23 0.20 1.34

1997 Stratified 

Random

2758 (201) 1358 (98) 3.9 602 0.20 0.22 1.22

1999 Stratified 

Random

2745 (200) 1389 (100) 4.0 830 0.19 0.30 1.18

2003 GSPE 3157 (566) 591 (106) 6.6 696 0.24 0.22 –

2006 GSPE 3096 (555) 841 (151) 5.4 605 0.19 0.20 – 

2009 GSPE 3096 (555) 618 (111) 6.7 1109 0.19 0.36 –

2012 GSPE 3096 (555) 664 (119) 6.0 780 0.21 0.25 –

2015 GSPE 3096 (555) 714 (128) 5.0 1138 0.18 0.37 –

2019 GSPE 3096 (555) 670 (120) 5.7 873 0.16 0.28 –

a Not directly comparable with later surveys.
10 

Figure 4. Estimated observable fall moose total (with 90% confidence intervals) for Yukon-Charley Rivers 

National Preserve from GSPE surveys from 2003–2019, Alaska.

Sex and Age Composition

The sex and age composition of the 249 observed moose were as follows: 142 cows, 70 bulls, and 37 

calves (Table 3). Composition of the observed bulls included 3 yearling bulls (spiked or forked 

antlers), 32 medium bulls, and 35 large bulls (Table 3). No single-antlered bulls were observed, 

suggesting that antler shedding was not an issue for the survey. The estimated sex and age ratios of 

the population were 28 calves:100 cows (90% CI = 19-36), 2 yearling bulls (spike/fork):100 cows 

(90% CI = 0-3), and 49 bulls:100 cows (90% CI = 34-65; Table 3). One set of twins was observed in 

the survey (Table 4). 
11

Table 3. Summary of observed moose during the surveys in Yukon-Charley Rivers National Preserve, 1987 to 2019. Sex and age ratios were 

calculated in WinfoNet for GSPE surveys and 90% confidence intervals for these are reported in parentheses.

Survey 

Year

Area

(mi2) 

Total 

Bulls

Total 

Cows

Total 

Calves

Total 

Moose

Bulls/ 100 

Cows

YrlBulls/

100 Cows

Calves/ 

100 Cows % Bulls % Cows % Calves

1987a 3556 88 73 7 169 121 14 10 52 43 4

1994a 2790 147 176 37 364 84 7 21 40 48 10

1997 1358 136 197 51 384 60 8 28 35 51 13

1999 1389 169 266 77 513 51 5 36 33 52 15

2003 591 55 87 17 159 60 (38–82) 6 (2–11) 25 (13–37) 35 55 11

2006 841 63 89 28 180 73 (53–94) 7 (2–11) 33 (21–46) 35 49 16

2009 618 102 164 42 308 60 (40–79) 13 (8–18) 27 (17–37) 33 53 14

2012 664 80 118 25 223 68 (46–89) 11 (5–17) 24 (15–33) 36 53 11

2015 718 105 165 44 314 72 (50–94) 4 (2–6) 25 (17–32) 33 53 14

2019 670 70 142 37 249 49 (34–65) 2 (0–3) 28 (19–36) 28 57 15

a Not directly comparable with later surveys
12 

Table 4. Number of sets of twins seen during moose surveys in Yukon-Charley Rivers National Preserve, 

1987 to 2019.

Survey Year Sets of twins seen

1987 0

1994 1

1997 3 

1999 6

2003 1

2006 5 

2009 4

2012 0

2015 4 

2019 1

Average 3
13 

Discussion

Moose in Yukon-Charley National Preserve (Alaska GMU 20E, 25B, 25C, and 25D) continue to 

occur at low densities. The fall count of 873 observable moose resulted in a moose density of 0.28 

moose /mi2

, or 0.11 moose/km2. Continued conservative management of harvest is recommended. 

This report continues the documentation of the Yukon-Charley moose population existing at low 

densities for over 30 years (Sorum and Joly 2016). 

The population estimate of observable moose during the 2019 survey represents a 23% decrease from 

the previous point estimate of 2015, however, this difference is not statistically significant as the 90% 

confidence intervals overlap. As such, we interpret these new results as normal fluctuations of a 

population that is relatively stable at the decadal scale. We observed the greatest proportion of cows 

ever in this survey, while calf metrics are within previously observed ranges. Notably, we observed 

the lowest ever percentage of bulls and yearling bulls in this survey (49:100 and 2:100, respectively) 

and may indicate increased harvest pressure in the Preserve. We interpret these results cautiously, 

however, since the bulls:100 cows 90% confidence interval overlaps all previous GSPE ratio 

estimates and the yearling bulls ratio was distinct from only 2 other prior years. Additionally, 

yearling bull classification can exhibit greater variability between observers and antler development 

rates may be influenced by range conditions, potentially biasing this metric low (Young and Boertje 

2018). For comparison, the 2015 estimate for 25D to the north was 35 bulls:100 cows (Caikoski, 

2018) and the Tok Central area of 20E, which has the most similar moose density to YUCH of the 

GMU, has fluctuated between 54:100 and 67:100 between 2010–2012 (Wells, 2018). 

The management goal of the Preserve is for natural and healthy populations (ANILCA 1980), and we 

interpret these decreased metrics of bulls in the Preserve as cautionary indicators to be monitored 

further. Low yearling bull recruitment into the population for extended periods could further skew 

the composition of the population away from natural levels and be an indicator of potential future 

population decline. Yet without understanding annual harvest within the Preserve, we lack the ability 

to identify causes of such trends and make sound management decisions. We recommend continued 

monitoring of these metrics and incorporating measures of harvest within the Preserve in future 

reports. While the long-term trend (since 1997) of the moose population is likely positive, a rigorous 

analysis of the population trajectory was outside the scope of this project. With the accumulation of 

now 8 comparable surveys, we look forward to such an analysis of moose population trends and 

associated factors. 

We made two major changes to the survey this year that we felt improved the overall survey. The 

first was to conduct the first aerial stratification of the entire study area, and we credit this with 

resulting in the most precise population estimate of any survey to date (Appendix B). While this 

addition increased the financial cost and duration of the survey, we believe it will be applicable for 

future surveys because we based the stratification classification primarily on habitat, which generally 

changes slowly over time. We recommend further refinement of the stratification classification in 

future surveys by adjusting strata classes with survey data from the previous decade for those units 

which have been surveyed (as described in Kellie and DeLong 2006), by re-flying a subset of units 
14 

prior to the survey, or by incorporating information on moose habitat use gained from concurrent 

studies in the Preserve. The second change we implemented was to collect data entirely on iPads 

using the application Park Observer. While we had initial concerns with battery life in the cold 

conditions and with potential user error, we had no major issues with using the iPads during the 

survey and external battery packs were adequate to power the iPads all day. Data analysis was greatly 

expedited since the data was already stored as spatial data and backing up data in the field was easy. 

An added benefit was that by updating survey maps every day with the remaining units to be 

surveyed, observers could help pilots plan the most efficient route and we had no units which were 

surveyed twice. We recommend the continued use of iPads for moose survey data collection. 

We see one future improvement that can be made to this survey. Sightability trials were completed 

for this population in 1997 and 1999 and we recommend re-estimating them when feasible. We did 

not include sightability corrected population estimates in this report because we felt that the 

correction factors were outdated and we lacked the detailed information to incorporate the variability

of sightability into the population estimate. With the planned deployment of GPS collars on moose in 

YUCH between this and the next survey, the conditions will be ideal to measure sightability. 

Additionally, we expect that insights from moose space use at the time of the survey will help inform 

and refine the stratification classification.

This moose survey was notable for both being delayed by a year due to weather (originally scheduled 

to occur in 2018) as well as being the longest survey on record for the Preserve. Rescheduling the 

2018 survey was due to the later-than-normal arrival of snow conditions and the lack of a clear 

weather window to initiate the survey. While the long duration of the 2019 survey was principally 

due to the addition of an aerial stratification, the high degree of weather variability experienced 

during the survey was notable. Even in late November, we experienced a warming event with rainon-snow at Coal Creek. Our survey was characterized by working around inclement weather, and in 

hindsight we never had an obviously clear weather window in which to conduct the survey. While we 

expect that the next moose survey in Yukon-Charley can be conducted in the fall, given the strong 

trend of increasingly warm climate conditions in the Arctic (Box et al 2019), we are concerned for 

the long-term feasibility of continuing these surveys as-is at this time of year. Delaying the survey is 

constrained by increasingly short days in December, which limit daily flight time to inefficient 

schedules in this remote location as well as adequate light conditions for surveying. As climate 

warming continues, managers may need to consider alternative survey methods, such as estimating 

sightability each year to account for different conditions, emerging technologies such as thermal 

detectors (McCafferty 2007), or switching to spring population surveys for the Preserve, such as 

those performed in more western and northern regions (e.g., Sorum et al. 2015). 
15 

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Schwartz, Charles C., and Albert W. Franzmann. 1989. Bears, wolves, moose, and forest succession, 

some management considerations on the Kenai Peninsula, Alaska. Alces 25: 1–10. 

Sorum, M. S., Joly, K., Cameron, M. D. 2015. Aerial moose survey within and around Gates of the 

Arctic National Park and Preserve, March 2015. Natural Resource Report NPS/GAAR/NRR— 

2015/967. National Park Service, Fort Collins, Colorado. 
16 

Sorum, M. S. and K. Joly. 2016. Moose (Alces alces) population survey in Yukon-Charley Rivers 

National Preserve, November 2015. Natural Resource Report NPS/YUCH/NRR—2016/1150. 

National Park Service, Fort Collins, Colorado. 

Sousanes, P. and K. Hill. 2018. Yukon-Charley Rivers Fall 2018 Weather Summary. Central Alaska 

Inventory and Monitoring Network. Available at 

https://irma.nps.gov/DataStore/Reference/Profile/2259248 (accessed 21 January 2020). 

Ver Hoef, J. M. 2008. Spatial methods for plot-based sampling of wildlife populations. 

Environmental and Ecological Statistics 15, 3–13. 

Wells, J. J. 2018. Moose management report and plan, Game Management Unit 20E: Report period 1 

July 2010–30 June 2015, and plan period 1 July 2015–30 June 2020. Alaska Department of Fish 

and Game, Species Management Report and Plan ADF&G/DWC/SMR&P-2018-18, Juneau. 

Young, D.D., and R. D. Boertje. 2018. Age-related antler characteristics in an intensively managed 

and nutritionally stressed moose population. Alces 54: 37–44. 
17 

Appendix A. Survey Logistics

Table A-1. Aviation usage and cost during the 2019 spring moose survey. Survey and stratification hours 

include ferry flights.

Planes Pilots Description Hours Total

C-185, 

Found

Andy Greenblatt, Brett Nigus Shuttle 11.47 $4,234

C-185, 

Found

Andy Greenblatt, Brett Nigus Stratification 22.4 $8,014

PA-18,

Top Cub

Jesse Cummings, Andy Greenblatt, 

Zack Knaebel, Brett Nigus

Survey 112.3 $31,494

Total $43,742

Table A-2. Overall project cost during 2019 spring moose survey . 

Category Description Quantity Cost/Unit Total

Food – – – $2,340

Fuel CCC/Eagle 100LL 1,310 gal $6.15 $7,472

– Ferry 100LL 380 gal – $2,064

Housing NPS housing – – $645

Aviation Detailed in Table A – – $43,742

Total Project Cost $56,848
18 

Appendix B. Comparing 2 Stratifications

We were interested in the effect that the new aerial stratification had on the population estimate for 

Yukon-Charley Rivers Preserve in comparison to the previous desktop stratification. To better 

understand how both stratifications were performing, we assigned surveyed units in the 2015 and the 

2019 surveys with both stratification classifications and compared moose counts in the high and low 

stratums Tables B-1 and B-2). The goal of the stratification is to partition the variability in the study 

area (Kellie and DeLong 2006) and since the low strata comprise a greater proportion of the area 

being estimated, reducing the variability in the low strata has a greater effect on reducing the overall 

estimate variability. For the 2015 survey, the aerial stratification resulted in a greater spread of 

average moose counts between the low and high strata (less moose in the low and more in the high 

strata) as well as reducing the standard deviation of the low strata (Table B-1). This change was 

primarily due to having more 0 moose counts in the low strata and fewer 0 moose counts in the high 

strata with the aerial stratification (Figure B-1). 

Table B-1. Summaries of count data from GSPE units by stratum for the 2015 YUCH moose survey, for 

both the desktop stratification used in the survey and the aerial stratification from 2019.

Method Stratification 

Average # of 

moose

Standard 

Deviation

# of 

Units

Desktop 

Stratification

High 3.0 3.7 82

Low 1.5 2.5 46

Aerial 

Stratification

High 3.9 3.9 59

Low 1.2 2.2 69
19 

Figure B-1. Counts of moose in GSPE units from the 2015 YUCH survey by strata, separated by both 

stratification methods. Green indicates the desktop stratification used in the 2015 survey and yellow 

indicates the aerial stratification flown in 2019.

For the 2019 survey data, the pattern was similar. The aerial stratification resulted in a lower average 

number of moose in the low strata and a higher average in the high strata compared to the previous 

desktop stratification (Table B-2). The aerial stratification also resulted in a reduced standard 

deviation for the low strata, which was likely due to having fewer high counts of moose in the low 

strata as compared to the desktop stratification (Figure B-2). Interestingly, the aerial stratification 

included more 0 counts in the high strata than in the desktop stratification, and less 0 counts in the 

low strata (Figure B-2). 

Table B-2. Summaries of count data from GSPE units by stratum for the 2019 YUCH moose survey, for 

both the desktop stratification used in the survey and the aerial stratification from 2019.. 

Method Stratification 

Average # of 

moose

Standard 

Deviation

# of 

Units

Desktop 

Stratification

High 2.5 2.8 53

Low 1.7 2.6 67

Aerial 

Stratification

High 2.7 3.0 80

Low 0.9 1.4 40
20 

Figure B-2. Counts of moose in GSPE units from the 2019 YUCH survey by strata, separated by both 

stratification methods. Green indicates the desktop stratification used in the 2015 survey and yellow 

indicates the aerial stratification flown in 2019.

We do not compare estimates for the study population between the 2 stratification classifications for 

2015 and 2019 because the reassignment to different stratification classes invalidates the sample unit 

selection process as outlined in Kellie and DeLong (2006) and results in undesirable allocations of 

sample units to the 2 stratums (more low units than high units; Tables B-1 and B-2). But, by 

examining the effect of stratification classifications on moose counts for these 2 years of surveying, 

we gained confidence that the new stratification is achieving a better estimate by more effectively 

partitioning the low-density units into the low stratum. Observed variability was indeed lower for the 

low strata in both years using the aerial stratification and owing to the greater number of low strata 

units than high, we expect this to have resulted in the lower overall variability in the 2019 estimate.

A potential improvement we see for future surveys would be to reduce the number of units with 0 

counts in the high strata, which could help to reduce the observed variability in that category. 
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