2019 Status of Ameralik caribou population, South region, West Greenland. Technical Report No. 125

1 2019 Status of Ameralik caribou population, South region, West Greenland Technical Report No.125, 2023 Pinngortitaleriffik – Greenland Institute of Natural Resources 2 Title: 2019 Status of Ameralik caribou population, South region, West Greenland Authors: Christine Cuyler1, Tiago A. Marques2, Iúri J.F. Correia3, Aslak Jensen4, Hans Mølgaard5 and Jukka Wagnholt6 1 Pinngortitaleriffik – Greenland Institute of Natural Resources, P.O. Box 570, 3900 Nuuk, Greenland 2 CREEM, University of St Andrews, School of Mathematics and Statistics, Scotland 3 University of Lisbon, Faculty of Sciences, Portugal 4 Solviaq 15, 3900 Nuuk, Greenland 5 P.O. Box 122, 3911 Sisimiut, Greenland 6 Tusass, P.O. Box 1002, 3900 Nuuk, Greenland Series: Technical Report No. 125, 2023 Date of publication: 06 April 2023 Publisher: Pinngortitaleriffik – Greenland Institute of Natural Resources Financial support: Government of Greenland and Pinngortitaleriffik – Greenland Institute of Natural Resources ISBN: 978-87-972977-8-0 ISSN: 1397-3657 EAN: 9788797297780 Cover photo: Christine Cuyler: 11 camouflaged caribou, Ameralik population, near line transect 110, South region. Cited as: Cuyler, C., Marques, T.A., Correia, I.J.F., Jensen, A., Mølgaard, H. & Wagnholt, J. 2023. 2019 Status of Ameralik caribou population, South region, West Greenland. Pinngortitaleriffik – Greenland Institute of Natural Resources. Technical Report No. 125. 109 pp. Contact address: The report is only available in electronic format. PDF-file copies can be downloaded at this homepage: https://natur.gl/forskning/rapporter/ Pinngortitaleriffik – Greenland Institute of Natural Resources P.O. Box 570, 3900 Nuuk, Greenland Phone: +299 36 12 00 E-mail: [email protected] www.natur.gl 3 2019 Status of Ameralik caribou population, South region, West Greenland By Christine Cuyler1, Tiago A. Marques2, Iúri J.F. Correia3, Aslak Jensen4, Hans Mølgaard5 and Jukka Wagnholt6 1 Pinngortitaleriffik –Greenland Institute of Natural Resources, P.O. Box 570, 3900 Nuuk, Greenland 2 CREEM, University of St Andrews, School of Mathematics and Statistics, Scotland 3 University of Lisbon, Faculty of Sciences, Portugal 4 Solviaq 15, 3900 Nuuk, Greenland 5 P.O. Box 122, 3911 Sisimiut, Greenland 6 Tusass, P.O. Box 1002, 3900 Nuuk, Greenland Technical Report No. 125, 2023 Pinngortitaleriffik – Greenland Institute of Natural Resources 4 [Empty page] 5 Table of Contents Summary (English)……………………………………… 8 Eqikkaaneq (kalaallisut) ………………………………. 9 Resumé (dansk)……………………………………….... 11 Introduction ……………………………………………. 13 Methods …………………………………………………. 15 Results…………………………………………………… 23 Discussion ……………………………………………… 43 Acknowledgements……………………………………. 51 Literature cited ………………………………………… 52 Figures 1. Borders of the South region, … Page 13 2. Area covered by 2019 caribou survey of northern portion of South region… Page 17 3. The 25 line transects used in the 2019 survey of South region… Page 19 4. Location and group size of caribou detections (truncated data) … Page 25 5. Exploratory analysis: number of detections by sub-area and line transect… Page 26 6. Exploratory analysis for group size distribution among detections… Page 27 7. Exploratory analysis for caribou encounter rate per line transect… Page 27 8. Observer effect: histograms: detected distances for the three observers … Page 28 9. No. caribou detections per covariates: heli.side, visibility & camouflage … Page 29 10. No. caribou detections per covariates: vegetation/ground or boulders… Page 29 11. No. caribou detections per covariates: solar glare and dead ground… Page 30 12. Summary of the frequency of elevations flown as well as helicopter speed… Page 30 13. Histogram of observed caribou distances for non-truncated and truncated… Page 31 14. Histogram for Half-normal with Visibility as covariate of detected … Page 34 15. Estimated probabilities of detection for each observed group size… Page 34 16. Caribou density (left) and abundance (right) estimates with ... Page 35 17. Observed frequency of cow-calf pairs for 96 groups for which… Page 40 18. Past and present caribou population size estimates with confidence … Page 43 19. Past and present caribou density estimates for the Ameralik population … Page 44 20. Past and present late winter calf (age 10-month) recruitment … Page 46 21. Past and present late winter bull to cow ratios for the Ameralik … Page 47 22. Place names used regarding the northern portion of the South region … Page 55 23. Six caribou, of which five are readily visible. All are within 75 m … Page 56 24. Six camouflaged caribou. Some are visible on a snow patch, but others … Page 56 25. Seven camouflaged caribou. None are readily visible against the mix … Page 57 26. Three camouflaged caribou. Despite full sunshine, none are readily … Page 57 27. 11 camouflaged caribou. Few are readily visible against the mix … Page 58 28. 35 camouflaged caribou. None are readily visible against the mix … Page 58 29. 15 camouflaged caribou. Despite full sunshine, none are readily visible … Page 59 30. 31 camouflaged caribou. None are readily visible against the mix of … Page 59 31. 17 camouflaged caribou. None are readily visible against the mix of … Page 60 6 32. Seven camouflaged caribou. Given the strong sunshine causing deep … Page 60 33. Six camouflaged caribou at the south end of the lake, Isortuarsuk. … Page 61 34. Two groups camouflaged caribou (n= 3+17), south end lake Isortuarsuk… Page 61 35. Five camouflaged caribou, south end of lake, Isortuarsuk. Despite … Page 62 36. Twelve camouflaged caribou, east side of lake, Isortuarsuk. Despite … Page 62 37. Nine camouflaged caribou among dwarf shrub, east side lake … Page 63 38. Five camouflaged caribou, east side of lake, Isortuarsuk. Despite … Page 63 39. Xeric inland sub-area, illustrating vegetation poking through thin snow … Page 64 40. Location of arctic fox (blue phase) indicated by blue circle. Page 64 41. One camouflaged feral sheep in the foreground just left and below … Page 65 42. One feral sheep observed just north of the mouth of Austmannadalen … Page 65 43. Plot sampling grid example of total area A divided into smaller plots… Page 66 44. Example of a patch of tundra with the transect in the middle… Page 69 45. Half-normal (top row) and hazard-rate (bottom row) detection functions… Page 71 46. Possible shapes for the detection function when cosine adjustments are…. Page 72 47. A good model for the detection function should have a shoulder… Page 75 48. Coastal Lowlands sub-area in background beyond mouth of Buksefjord … Page 80 49. Coastal Lowlands sub-area, view east towards bordering mountains. Page 80 50. Coastal Lowlands sub-area, west end line transect 127, view is north. Page 80 51. Glacial Mountains sub-area, Itoqqarmiut Kangerluarsunnguat … Page 81 52. Glacial Mountains sub-area, south of line transect 122 and west of 117 … Page 81 53. Glacial Mountains sub-area, middle portion of line transect 123, view … Page 82 54. Glacial Mountains sub-area, river valley, Qoorusussuaq, which is just … Page 82 55. Glacial Mountains sub-area, east end of line transect 123, illustrating … Page 83 56. Glacial Mountains sub-area, the valley and lake, Eqaluit, an area atypical … Page 83 57. Xeric inland sub-area, line transect 101, view east, note thin snow layer Page 84 58. Xeric inland sub-area, line transect 102, view west, note thin snow layer. Page 84 59. Xeric Inland sub-area, approaching east end line transect 103, view east. Page 85 60. Xeric Inland sub-area, east end line transect 103, view north. Page 85 61. Xeric inland sub-area, line transect 104, south of Kapisillit, view is south. Page 86 62. Xeric Inland sub-area, view north illustrating mouth of broad braided … Page 86 63. Xeric Inland sub-area, east end of line transect 106, view west across … Page 87 64. Xeric Inland sub-area, line transect 107, view east, note thin snow layer … Page 87 65. Xeric Inland sub-area, view northeast over Nunataarsuk and area of line … Page 88 66. Xeric Inland sub-area, line transect 108, Nunataarsuk, view east. Note, … Page 88 67. Xeric Inland sub-area, around the west end of line transect 110, four … Page 89 68. Xeric Inland sub-area, around the east end of line transect 110, two … Page 89 69. Xeric inland sub-area just north of line transect 112 and in proximity to … Page 90 70. Xeric Inland sub-area, view north over the landscape and conditions on … Page 90 71. Xeric Inland sub-area, around the middle of line transect 113, seven … Page 91 72. Xeric Inland sub-area, valley at east end of line transect 113, ten caribou … Page 91 73. Xeric inland sub-area, line transect 114, east of lake, Isortuarsuk, view … Page 92 74. Xeric inland sub-area, east end of line transect 114, view north, illustrating... Page 92 75. Xeric Inland sub-area, while flying line transect 116. Above illustrates … Page 93 76. Xeric inland sub-area, east end line transect 117, at the Greenland Ice … Page 94 77. Xeric inland sub-area, line transect 117, at lake that emptied, view north. Page 94 7 78. Xeric inland sub-area, illustrating condition on the line transect 118, … Page 95 79. ‘Finding Waldo’ camouflage conditions, Xeric inland sub-area, east end … Page 95 80. Xeric inland sub-area, east end line transect 119 at shore of lake, Ilulialik … Page 96 81. Xeric inland sub-area, view to SW end of the lake, Ilulialik and the east … Page 96 82. Histograms for detected distances superimposed with estimated detection... Page 99 83. Locations of the four lakes observed to have recently emptied, before … Page 100 84. Small lake in Central region, Ujarassuit sub-area, that recently emptied in … Page 101 85. Emptied large lake in South region, Xeric Inland sub-area, illustrating … Page 101 86. Recently emptied pond, Austmanntjern, near east end of line transect 112… Page 102 87. Large unnamed lake in South region, Xeric Inland sub-area, on the north … Page 103 88. The three observers, Dr. C. Cuyler… Aslak Jensen… Hans Mølgaard … Page 105 89. AS350 Helicopter viewing windows for the left and right sides, … Page 106 Tables 1. Late winter population parameters, Ameralik caribou population… Page 14 2. Summary of unprocessed results: Survey of Ameralik caribou population… Page 24 3. Model comparison across three Conventional Distance Sampling models… Page 32 4. Encounter rate (ER) estimates per sub-area (stratum) for caribou groups… Page 35 5. Estimates of abundance per sub-area (stratum) for the Ameralik caribou … Page 35 6. Estimates of density per sub-area (stratum) for the Ameralik caribou … Page 35 7. Movement or non-movement of caribou reacting to helicopter fly-by… Page 36 8. Details for movement or non-movement of caribou reacting to helicopter … Page 37 9. Demographics for Ameralik caribou population, South region. March 2019. Page 38 10. Group size relative to composition (demographic dataset), Ameralik… Page 40 11. Approximate elevations for caribou groups observed… Page 41 12. Commonly used key functions and series expansions for detection function. Page 70 13. Detection function parameters’ estimates. Page 99 14. Population caribou population estimates & minimum counts for West … Page 108 Appendices 1. Place names for the South region Page 55 2. Photos of camouflaged caribou (and fox) observed March 2019 Page 56 3. Feral sheep (Ovis aries) near Kuussuaq (Austmannadalen) 14 March 2019 Page 65 4. Statistical methods behind Distance Sampling Page 66 5. Distance Sampling Assumptions – short summary Page 79 6. Photos South region aerial survey conditions, March 2019 Page 80 7. Histograms for detected distances Page 97 8. Glacier bounded lakes that recently emptied, March 2019 Page 100 9. Recommendations for improving future surveys Page 104 10. Recent caribou population estimates & minimum counts for West Greenland Page 108 Raw data may be accessed by contacting Pinngortitaleriffik – Greenland Institute of Natural Resources, Department of Mammals and Birds. 8 Summary This report presents results from the aerial survey carried out by helicopter in early March 2019, for the Ameralik caribou population inhabiting the northern portion of the South region in West Greenland. This population was last surveyed in March 2012, making new estimates of abundance and density necessary. In 2001 and 2006 survey method was strip transect counts. In 2012, methods changed to Distance Sampling. The March 2019 helicopter survey again used Distance Sampling methods. For March 2019, the Ameralik caribou population abundance was estimated at 19,503 caribou (95% CI: 12,404 – 30,665; CV = 0.219; SE = 4268), with density 4.2 ± 0.9 caribou/km2 (95% CI: 2.6–6.6). Overall survey coverage was 9.7% (truncated data), which is a substantial improvement from the 2.15% coverage for 2001 and 2006 strip transect count surveys. Further, it is even improved relative to 8.6% coverage of the 2012 Distance Sampling systematic transects. Despite 18 years of harvest management aimed at controlling caribou abundance and density, the March 2019 Ameralik caribou population size is large and appears to have increased 67% since 2012. The 2012 and 2019 surveys were similar in coverage and method. There is little overlap in the Confidence Intervals for the 2012 and 2019 population estimates. Further, the 2019 CV (0.22) indicates good precision in the 2019 population estimate. The three combined make it reasonable to assume a trend of increasing population size over the 2012-2019 period. The density estimate for the Ameralik population was 4.2 caribou per km2 in area of survey effort. This value is much greater than the management recommended target of 1.2 caribou per km2 (Kingsley & Cuyler 2002, Cuyler et al. 2007). At almost four-fold the target density, Xeric Inland’s 5.8 caribou/km2 may have influenced the observed poor 2019 calf recruitment and sex ratio (below). Exceeding the target caribou density is assumed to raise the risk of overgrazing and thus decline in caribou abundance. Relative to the 2006-2012 period, late-winter calf percentage was similar, however, calf recruitment declined somewhat from previous values. In contrast, the 2019 sex ratio of 22 bulls per 100 cows was poor and considerably lower than previous values, i.e., 83, 81, 62 in 2001, 2006, 2012, respectively, for animals age > 1-year. Population trend beyond 2019 is uncertain, and there is 9 always the possibility of future catastrophic stochastic events, including extreme weather and pathogen outbreaks. Environmental conditions during the 2019 survey provided extraordinary camouflage for caribou. Pooling environmental covariates into a single index for camouflage will improve detection function modelling. Skilled observers and flying helicopter low and slow were critical factors permitting detection of caribou, specifically because 17% of all groups remained stationary. Beyond population parameters, results of interest included relatively high elevations, mean 647 m, used by the Ameralik caribou population in early March. This reflects the relative scarcity of low elevations in the region and likely also avoidance of human disturbance in lowlands. Further, and in contrast to other caribou populations in West Greenland, most (92%) cows possessed antlers in the Ameralik population. Eqikkaaneq (kalaallisut) Uani nalunaarusiami saqqummiunneqarput nunap immikkoortuani tuttassiissutinik aqutsiviusumi qulimiguulik atorlugu martsip aallartilaarnerani 2019-imi tuttunik kisitsinernit inernerit. Tuttut taakku pineqartut nunatta kitaata kujataata avannaatungaani uumasuupput. Tuttutoqatigiiaat kingullermik kisinneqaramik periuseq atorneqartoq tassaavoq, qulimiguulimmit takusat aalajangersimasumik kisinneqartarnerat, taaguuteqartinneqartoq Distance Sampling methods. Periuseq marts 2019-imi kisitsisoqarnerani aamma atorneqarpoq. Ameralimmi tuttut marts 2019 19.503 -nik (95%-konfidensinterval: 12.404 – 30.665; variationskoefficient = 0,219; nalinginnaasumik nikingassutaasartoq standart error = 4268), amerlassuseqarnissaat missiliuunneqarpoq, naatsorsuinertigullu tuttut kvadratkilometer-imut, km2-imut, eqimassuseqarnissaat 4.2 ± 0.9 tuttut/km2 (95% CI: 2,6–6,6) aalajangiunneqarpoq. Nuna kisisitviusoq qulangiuaarneqartorlu 9.7%-iuvoq (trunkerede data), tassa nuna annertunerujussuaq 2001-mi 2006-milu kisitsivigineqartunut sanilliullugu. Taamanikkumut sanilliullugu pitsanngoriaat 8,6 %-iummat, tassa 2012-imut sanilliussilluni. 16 of the year-round ice-free Davis Strait and the low-pressure oceanic storm systems that sweep in from the southwest. However, the climate becomes increasingly dry continental as one moves east towards the Greenland Ice Cap. The inland of the northern portion of the South region is dry owing to the 1500–2000 m elevations associated with the Sermilik fjord, Sermeq glacial tongue and multiple glaciers. See Appendix 1 for place name details. The high elevations physically block the oceanic storm systems, creating a precipitation shadow on the northeastern side making an already dry area truly xeric (Appendix 2, Figs. 25-38; Appendix 6, Figs. 57-79). In addition to caribou, there are just three wild mammals present in the South region: arctic hare (Lepus arcticus Rhoads), arctic fox (Vulpes lagopus Linnaeus), and recently muskoxen (Ovibos moschatus Zimmermann) (Cuyler et al. 2016). The arctic fox is the only terrestrial mammalian predator, as large mammalian predators are absent. In addition to the three wild mammals, feral domestic sheep (Ovis aries), have maintained a small presence in Austmannadalen for several decades (Cuyler et al. 2016) (Appendix 3). These originate from sheep farming attempts near the village of Kapisillit. The recent occurrence of muskoxen was through natural emigration from the population inhabiting the North region (ca. 66°–67°45’N; 49°30’–54°W) of West Greenland. Animals expanded southward, first into the Central region and later into the Nunatarssuaq area of the South region (Cuyler et al. 2016). Since 2012, sporadic sightings of muskoxen have also been reported on the Kangerluat (west of Kapisillit), and even to the south shore of the Ameralia arm of the Ameralik Fjord (Cuyler unpublished). Expansion is possible because the region borders are semi-permeable permitting limited animal movement between adjacent regions. Nevertheless, the borders are assumed effective barriers preventing mass animal movements (Linnell et al. 2000). The South region (ca. 63°30’–64°30’°N; 49°–51°40’W) lies completely within Nuuk Kommunia. Greenland’s capital city, Nuuk, with ca. 18,800 inhabitants, is situated near the mouth of Nuuk fjord in the northwestern tip of this region. About 75 km east of Nuuk in an arm of the Nuuk fjord lies Kapisillit, a village of ca. 52 people. Aside from the crew manning the Buksefjord hydro power station there are no other permanent settlements in the northern portion of the South region, which is seasonally ice-free and covers an area of 7,000–8000 km2, including lakes, rivers, and islands (Cuyler et al. 2003, 2007, 2016). The northern border is provided by the Nuuk fjord. The southern border is delineated by the Sermeq glacial tongue and Sermilik fjord. The 17 western border is the permanently ice-free seacoast of the Davis Strait, and the eastern border is the Greenland Ice Cap. Figure 2. Area covered by 2019 caribou survey of northern portion of South region (4,676 km2), which is inhabited by the Ameralik caribou population. Three different colours illustrate the three sub-areas, designated as Coastal lowland, Glacial mountains, and Xeric inland. Greenland’s capital city, Nuuk, is the red diamond at the tip of the thin long grey peninsula in upper left corner. A comparison of figures 1 and 2, illustrates that the aerial survey effort in 2019 did not cover all the South region’s northern portion. This contrasts with the 2001, 2006 and 2012 surveys. In 2019, the northwest section was omitted owing to recently permitted snowmobile use in grey area (Fig. 2), as disturbance by snowmobile was expected to alter caribou distribution. The 2019 survey concentrated effort to only those areas where snowmobile use continued to be prohibited in the open land. This was an area of 4,676 km2, (excluding lakes, rivers, sand, glaciers, and islands), which also coincided with most caribou detections in the 2012 aerial survey (Cuyler et al. 2016). Aside from a mountainous coast just south of the city of Nuuk, rugged coastal lowlands (< 200 m elevation) prevail between the Ameralik and Sermilik fjords. Moving east the terrain rises to elevations of up to ca. 1000 m and glaciers become common, specifically in the middle and southern portions. Generally, elevations are > 300 m. 18 Field methods Since 2000, early March has been the chosen period for caribou surveys because caribou dispersion is high, group size is small with low variability and daily movement is at the annual minimum (Cuyler et al. 2007, 2011, 2016; Poole et al. 2013). The former two reduce variance among transects, diminish counting error, and maximize precision, while the latter lowers movement between or along transects. The aerial survey period for Ameralik caribou was 08-14 March 2019. The platform for observation was a helicopter AS350. Pilot monitoring of helicopter radar altimeter made maintenance of a constant altitude possible by constantly adjusting for terrain features while flying low (40 m, ca. 120 feet) and slow (ca. 65 km/hour). Participants included three observers, all with previous survey experience: GINR’s senior scientist Christine Cuyler, professional hunter Aslak Jensen (Greenland Association of Professional Hunters (KNAPK)) from Nuuk and Sisimiut hunting officer Hans Mølgaard. Cuyler always sat in front and was the data recorder. Cuyler (Observer 2) focused on detecting caribou directly on track line (center line, 0-line) before animals fled offline owing to approaching helicopter. Jensen and Mølgaard (Observer 1 and 3, respectively) were seated in the rear of the helicopter, on either side. The side they sat on alternated each time the helicopter was refueled, which was usually once daily and sometimes twice. Jensen and Mølgaard could not view the track line but observed animals for all distances beyond. Verbal contact among the observers permitted the digital audio recording of all observations and most importantly, prevented any double counting of groups detected by more than one observer. Two audio devices (SONY IC recorder, ICD-SX712) were used to record separately the observations specific to the left and right side of the line transect. Audio recording devices were on continual recording for each line transect. At the end of each survey day, audio data was downloaded to computer for storage and back-up. Observations were later paired with Global Positioning System (GPS) coordinates of the helicopter at the time of observation. For each detection, the audio recording included distance to (see below) caribou group, as well as group size and behavior and name of the observer. Ground surface and weather conditions were also recorded. Manual click-counters, logging the number of caribou seen by an individual observer, provided low-tech back-up for double-checking the digital audio observations for each line transect. 19 Survey design Aligning line transects perpendicular to known gradients within the surveyed area can maximize precision of the resulting estimate by lowering the encounter rate variance (Buckland et al. 2001). Thus, the transect axis direction (east-west axis) was chosen as perpendicular to previously known animal distribution gradients in March and the west-east climate gradient from wet maritime to dry continental. An initial line transect was computer generated at random in each sub-area (see below), and others followed at 10 or 20 km apart. The line transects flown provide the maximum area coverage possible given the financial resources available. Because some a priori transects were combined during survey, line identification numbers are not consecutive. Figure 3. The 25 line transects used in the 2019 survey of South Region (northern portion), Ameralik caribou population, employing the same three colours as applied to the three sub-areas in above figure 3: Coastal Lowland (orange, 4 lines), Glacial Mountains (blue, 3 lines), and Xeric Inland (red, 18 lines). Transects separated by 10 km, except Glacial Mountains which were separated by 20 km. Line transect numbering is not consecutive, as some a priori lines became amalgamated during survey. The surveyed northern portion of South region area, 4,676 km2, was divided into three sub-areas, named Coastal Lowland (499 km2), Glacial Mountains (1,171 km2), and Xeric Inland (3,006 km2) (Fig. 2). The sampling design for the 20 2019 survey considered 25 systematic parallel line transects of variable length placed over the three sub-areas (Fig. 3). Line transects for Coastal Lowland and Xeric Inland were separated by 10 km and those for Glacial Mountains by 20 km. To avoid possible numbering confusion with the concurrent caribou survey of the Central region, in the South region line transect identification numbers began from 101. Distance to a detected caribou group (object-of-interest) was before caribou movement occurred. Tightly cohesive behavior identified groups of multiple individuals. Excepting groups on the track line, which was distance 0 m, distance was the observer’s instantaneous and subjective estimate of the distance to center of the caribou group. Exact distance measurement from the track line (aka 0-line or center line) to a caribou group was effectively never possible because of practical considerations (details in Cuyler et al. 2021). Therefore, like all previous helicopter caribou surveys in Greenland, for distance measurement perpendicular to the track line, we approximated with rough “distance bins”, i.e., in meters, 0-50, 50-100, 100-200, 200-300, 300-400, 400-500, 500-600, 600-700, 700-800, and 800+. Bin value recorded for a group was always the upper limit of the bin applied. For analysis, we did not correct for the 40m altitude of the helicopter. Instead, we recoded the reported upper distance values to the mid-distance for a specific bin owing to three reasons. First, a caribou group could be at any distance within the bin., e.g., a group recorded in distance bin 300 m, was located somewhere between 200 and 300 meters. Second, placing a caribou group within the correct bin relied heavily on observer ability to estimate distance to the observed animals in rugged terrain. Third, although for level ground (itself rare) the estimated direct line distance from observer (sitting in helicopter at 40 m altitude above ground) to a caribou group would be greater than the perpendicular distance from the track line to that group, those differences were small at 100 m and negligible beyond 200 m (i.e., in meters 8, 4, 3, 2, 2, 2, 1, and 1). Regarding the 0-50 m bin, we assumed observer ability sufficient to compensate for 40 m altitude and assign a perpendicular 50 m distance correctly because immediately adjacent to the helicopter/track line. Further, to aid observer ability to estimate distances, before starting survey the helicopter hovered at 40m altitude while each observer used a “Leica laser range finder 1600” to gauge distances across level airport ground to a priori known perpendicular distances. Then observers marked their window with masking tape delineating the approximate distances for each bin. While on survey, in the absence of caribou and where vertical terrain features occurred, observers used the laser distance 21 finders to test their ability to estimate distance, i.e., to the terrain feature. On rare occasions, observers were able to use the laser range finders for bin distance to a detected stationary group. Once all recorded distances were recoded to mid-distance, to model the detection function all the detections were pooled across observers with the helicopter functioning as a single observer. The pooled data were used to estimate a detection function, then estimate the detection probability and finally to estimate the density of the caribou within the surveyed area (Buckland et al. 2001). The detection function, 𝑔(𝑦), describes the probability of detecting an object-of-interest given that is at a distance 𝑦, from the track line, thus being a non-increasing function of 𝑦 (Buckland et al. 2015). For line transects, 𝑦 is the perpendicular distance from the track line to the detected object. Within DS methods, the probability of detection is explained recurring to these observed distances (Buckland et al. 2001). Distance sampling The caribou group was the selected object-of-interest on which detectability was modelled, i.e., individual caribou within a group were not considered. The individual line transects were the sample unit for design-based conventional DS analysis of the 2019 survey. Details for how this study’s DS analyses were performed are in Appendix 3. Thus, estimated CVs (Coefficients of Variation) from the models are referring to the transects, and total CV estimation is obtained by dividing the estimated standard error by the respective estimate. The estimated standard error is obtained as a pooled estimate for entire region and accounting for transects and their variability, it incorporates the variance from the detection function (Buckland et al. 2001). The recorded distances to the observed caribou groups were used to estimate a detection function. With this, both the caribou detection probability and density within the surveyed area could be estimated (Buckland et al. 2001). The detection function, 𝑔(𝑦), describes the probability of detecting an object of interest (caribou group) given that it is at a distance 𝑦, from the track line, thus being a non-increasing function of 𝑦 (Buckland et al. 2015). For line transects, 𝑦 is the perpendicular distance from the track line to the detected object. Within DS methods, the probability of detection is explained recurring to these observed distances (Buckland et al. 2001). 22 Prior to DS analysis, the raw data was first processed for inconsistencies. Then extensive exploratory data analysis was completed, including evaluation of observed distances, before proceeding to determining the detection function through model fitting and selection (Buckland et al. 2001; Marques et al. 2011; Thomas et al. 2010). To determine the detection function, several models were considered, (Thomas et al. 2010). Typically, the model presenting the lowest AIC value is chosen. Details regarding DS theory, methods and analysis are available in Buckland et al. (2001, 2015), and a briefer summary is provided in Appendix 4, with a summary of DS assumptions in Appendix 5. For analysis, we used R Statistical Software (https://www.r-project.org/). Demographics Sex, age, and late-winter calf recruitment observations were obtained after most of the DS survey was completed. All caribou sighted were sexed and aged following a brief overpass with the helicopter. Sex and age criteria have remained unchanged since 2000 (details in Cuyler et al. 2011, 2016). Briefly, female sex was determined by the presence or absence of a vulva and/or urine patch on the rump of both adults and calves, i.e., antler size, shape, presence, or absence, were not used to determine sex. Two age classes were used, calf (age  10-months) and adult (age > 1-year). Age was determined by body size. 10-month-old calves, male and female, being considerably smaller than all other age classes in March. Calf percentage is given relative to the total number of caribou sexed and aged. Calf recruitment is value for latewinter and provided as the number of calves per 100 cows. Group size was based on proximity and group cohesion during possible flight response. To obtain demographics and recruitment values, on 14 March, large areas of the South region (northern portion) were flown, including the areas surrounding line transects 107, 109 and 110, the length of the Austmannadalen Valley and the highlands between there and the town of Kapisillit, the valley leading from Naujat kûat south to Isortuarssuk Lake and around shores of the latter and in general the mountains between Ameralik Fjord and Nuuk. Elevations where caribou detected Early March elevation use by caribou was approximated using GPS dataset for helicopter elevation/position and matching timestamps with those of the digital audio recording of caribou observations. GPS and digital recorder timestamps were synchronized before survey began. Before analysis, 40 m 23 helicopter altitude was subtracted from all elevations. Thereafter, and lacking a reliable constant correction factor, negative values were deleted. Natural mortality In the past, if locals/hunters observed several caribou carcasses in the terrain or on sea ice, this resulted in alarm about an assumed negative trend for the entire population. To put carcass observations into perspective, since 2000, all technical reports for Greenland caribou surveys have included, for that specific survey year, the expected number of annual adult caribou deaths resulting from natural mortality, i.e., not due to harvest. Age distributions among harvested Greenland caribou populations have suggested a natural mortality of from 8 to 10% per annum (Loison et al. 2000, Cuyler & Østergaard 2005). Meanwhile, natural mortality rates from 4 to 8% were reported for North American populations without predators (Bergerud 1967, 1971, Skoog 1968, Kelsall 1968, Heard & Ouellet 1994), albeit these are now considered low (Bergerud et al. 2008) and density-independent factors, e.g., adverse weather, can increase mortality (Gates et al. 1986). Bergerud (1980) proposed a standard adult mortality rate of 10% for all North American caribou populations, and more recently Bergerud et al. (2008) suggested 7.7% for an increasing population with predators. Large predators are absent in the South region, and inland xeric conditions provide stable weather conditions. Although natural mortality rates vary among years (Bergerud et al. 2008), given the above, an assumed standard natural mortality rate of 8-10% (Kingsley & Cuyler 2002) for Greenland caribou likely yields a reasonable estimate of annual mortality. This rate is applied to the 2019 abundance estimates to provide wildlife managers with a rough number of expected caribou deaths due to natural mortality within the survey year. Results Survey logistics & unprocessed data The aerial survey by helicopter of the Ameralik caribou population occurred within the period 01-14 March, which period was shared with the survey of the Akia-Maniitsoq caribou population. Poor weather made three days nonflyable, as did airport closures on two Sundays. DS data for the Ameralik caribou population was obtained over three days (08, 09 and 12 March). Demographics data was obtained 14 March. Typical of AS350 helicopters carrying three passengers and pilot, refueling was necessary after about 3 hours of flight time, an additional 15-20 minutes were possible when wind 24 conditions and distance to nearest airport permitted. Helicopter flight time totaled 17 hours and 03 minutes. Time flown was divided between line transect DS survey (11 hours; 05 minutes) and the demographics survey (05 hours; 58 minutes). The 2019 survey used 25 line transects for a total distance flown of ca. 453 km, i.e., Coastal Lowland 60 km, Glacial Mountains 48.5 km and Xeric Inland 344.5 km (Table 2). Given the 453 km of line transects flown, an optimistic calculation of survey coverage of the South region’s surveyed Ameralik area (4,676 km2) would be 19-29%, i.e., topography permitting and assuming maximum strip width of 1000-1500 m to either side of the helicopter. However, for analyses (see DS analysis, page 24), the strip width was truncated to 500 m. Thus, coverage averaged 9.2% for the final abundance estimate. The observed raw totals were 231 caribou groups, which included 1,123 caribou. Mean group size was 4.6 ±3.83 caribou, and median group size was 4 caribou. Table 2. Summary of unprocessed results: Survey of Ameralik caribou population by helicopter in the South region, 08-12 March 2019. Parameter South region sub-area Total Coastal Lowland Glacial Mountains Xeric Inland Flight altitude (m) 40 40 40 40 Flight speed (km/hr) 60-70 60-70 60-70 60-70 Sub-area size (km2) 499 1,171 3,006 4,676 Number of lines 4 3 18 25 Distance flown (km) 59.83 48.51 344.37 452.71 Strip width1 (m) 1000-1500 1000-1500 1000-1500 1000-1500 Surveyed area ca. (km2) 120 - 179 97 - 145 689 - 1,033 905 - 1,358 Coverage2 24-36 % 8.3-12.4 % 23-34.4 % 19.4-29.0 % Coverage post-truncation3 12.0 % 4.1 % 11.5 % 9.7 % Total caribou observed 45 23 1055 1123 # Groups observed 5 7 219 231 Mean group size 9.0 3.29 4.82 4.6 Std Deviation group size ± 5.24 ± 3.04 ± 3.64 ± 3.83 Median group size 8 2 4 4 Maximum group size 17 10 23 23 Minimum group size 1 1 1 1 1 Strip width provided is to one side of helicopter only. Must double for total strip width. 2 Coverage prior to truncation of strip width to 500 m. 3 Coverage after truncation of the strip width to 500 m for DS analyses (see page 24). Data processing The raw data set was in Excel format containing the survey variables, including region, sub-area, respective areas (km2), transect identification, recorded distances, group size, and GPS coordinates. Sometimes included 25 with caribou group observations were flight characteristics such as helicopter side and velocity, as well as survey characteristics such as solar glare, visibility, dead ground, snow covering and depth, and surface conditions providing camouflage backgrounds for the caribou. Data pertaining to habitat changes were removed because these concerned habitats exclusively i.e., there were no caribou observations associated. The remaining variables were properly restructured within R Statistical Software. The data set was subject to some prior processing before analysis. Comment fields were deleted. Variable names were recoded to make them sensible in R. All caribou observations were complete, i.e., none were missing their distance or group size component. Thus, no replacements were necessary. Figure 4. Location and group size of caribou detections (truncated data) observed along the line transects flown, 2019 survey of Ameralik caribou population. Preliminary analysis distance sampling For reliable estimates of abundance, Buckland et al. (2001) suggests that sample size is at least 60 to 80 observations and from a minimum of 10 to 20 replicate line transects. The 2019 caribou survey for the South region met these recommendations. For example, regarding observations (detections of groups of one or more caribou), the untruncated sample size was 231, while truncated was 228. Similarly, there were 25 parallel line transects separated by 32 Table 3. Model comparison across the three Conventional Distance Sampling models and models considering different covariates further explaining detection. Key function Formula (variable) 𝒙𝟐 p-value 𝑷 a se (𝑷 a) AIC ∆AIC Hazard-rate Observer 0.037 0.350 0.061 685.042 0.000 Hazard-rate Group size + Observer NA 0.354 0.058 685.178 0.136 Half-normal Observer 0.061 0.326 0.030 689.524 4.482 Half-normal Group size + Observer 0.018 0.326 0.030 691.036 5.994 Half-normal Visibility 0.656 0.548 0.030 777.210 92.168 Half-normal Helicopter side 0.648 0.550 0.031 777.700 92.658 Half-normal 1 0.788 0.554 0.031 778.664 93.622 Uniform with cosine adjustment term of order 1 NA 0.746 0.561 0.022 778.888 93.846 Half-normal Camouflage 0.444 0.549 0.031 779.062 94.020 Half-normal Solar glare 0.446 0.548 0.030 779.062 94.020 Hazard-rate Boulders through snow NA 0.582 0.043 779.360 94.318 Half-normal Dead ground 0.638 0.553 0.031 779.881 94.839 Half-normal Group size 0.634 0.554 0.031 780.582 95.540 Hazard-rate Visibility 0.125 0.603 0.041 781.008 95.966 Uniform with simple polynomial adjustment terms of order 2,4 NA 0.521 0.574 0.035 781.171 96.129 Half-normal Vegetation/Ground through snow 0.205 0.548 0.031 781.194 96.152 Hazard-rate with simple polynomial adjustment term of order 4 1 0.695 0.503 0.073 781.689 96.647 Hazard-rate with cosine adjustment term of order 2 1 0.556 0.357 0.136 782.076 97.034 Hazard-rate Helicopter side 0.142 0.576 0.045 782.219 97.177 Hazard-rate with Hermite polynomial adjustment term of order 4 1 0.521 0.526 0.066 782.268 97.226 Hazard-rate Solar glare 0.040 0.605 0.041 782.960 97.918 Hazard-rate Camouflage 0.048 0.529 0.050 784.079 99.037 Hazard-rate Group size 0.129 0.532 0.051 784.344 99.302 Hazard-rate Dead ground 0.133 0.567 0.047 784.490 99.447 Hazard-rate Vegetation/Ground through snow NA 0.549 0.048 785.649 100.607 Uniform with Hermite polynomial adjustment terms of order 2,4 NA 0.0.043 0.664 0.051 786.815 101.773 Note: Formula = 1 for no covariates. NA is for Uniform Key. Under Chi-square p-value: NA = not enough degrees of freedom for GOF test, (Degrees of freedom calculated considering model parameters, these vary considering which key function used and how many/which explanatory variables considered.) 33 Detection function models were fitted to the truncated data, i.e., strip width 𝑤 = 0. 50 km, to each side of the helicopter. For these models, every combination of key function and adjustment terms was tested (Appendix 7). Additional covariates assessed were Observer, Group size, Visibility, Helicopter side, Vegetation and Ground showing through the snow surface, Boulders showing through the snow surface, Camouflage, Dead ground, and Solar Glare. Group size, as covariate, did little to explain caribou detection. We did not convert Group size into a categorical variable (small, medium, large) because much information is lost to no advantage, and we can estimate a probability of detection for each group size when this covariate is included in model. A summary of the information from each model fitted to the data (Table 3) provides a simple overview of the many models, and includes the respective key functions, adjustment terms, model formula, 𝜒2 Goodness-of-Fit test pvalue, estimates of the detection probability, respective standard error (se (𝑃 a)), AIC, and Δ𝐴𝐼𝐶 comparison between each model and the model with the lowest AIC. The best model fitted to the data possesses the lowest change in AIC value (Δ𝐴𝐼𝐶 = 0). For the 2019 survey data from Ameralik caribou population, this model has the hazard rate function as key function, no adjustment terms added and only Observer as covariate (AIC = 685.042). However, the models with Observer as covariate had very poor fits to the actual data (Appendix 7) and poor, or nonapplicable, p-values. Although those were the models with lowest AIC’s, the plots revealed that the estimated detection functions were nonsense. The next truncated model with the lowest AIC was the Half-normal with Visibility as covariate. The large p-value for 𝜒2 Goodness of Fit Test (null hypothesis is “the model fits well to the data”), means this model fitted the data well (details page 76 this report). Thus, we chose the model ‘Half-normal with Visibility as covariate’ (Fig. 14), which had an estimated probability of detection for the South region of 𝑃 a = 0.548 (se = 0.030, cv = 0.055). It is an averaged estimate since Visibility is included in the model. Consequently, each Visibility level has its separate detection function, corresponding to different estimates for the probability of detection (Fig. 15). When visibility was high, probability of detection was greater than when visibility was medium. Further, large group sizes, e.g., groups ≥ 12 caribou, were only detected when visibility was high. 34 Figure 14. Histogram for Half-normal with Visibility as covariate of detected distances with the estimated detection function overlaid. Figure 15. Estimated probabilities of detection as per Visibility (obtained with the fitted model, truncated data). The estimates for encounter rates indicate that the Xeric Inland sub-area had the most caribou, since its estimate was larger than the other sub-areas (Table 4). Concerning the design-based estimates for caribou abundance and density, Xeric Inland is also the sub-area presenting more caribou (Tables 5, 6, Fig. 16). In March 2019, the Ameralik portion of the South region had an estimated population size of 19,503 caribou (95% CI: 12,404 – 30,665). Sum of abundance estimates for each sub-area equals total estimated abundance for the region. CV of 0.219 (Table 5, Fig. 16) indicates good accuracy for Ameralik abundance estimate. Design-based density estimate for whole survey region was 4.17 caribou/km2, with 95% CI: 2.653 – 6.558 (Table 6, Fig. 16). 35 Table 4. Encounter rate (ER) estimates per sub-area (stratum) for caribou groups of the Ameralik population, considering three strata, six bins, and a detection function fitted with Visibility as covariate. Sub-area Encounter rate Standard Error (se) Coefficient of Variation (cv) Coastal Lowland 0.155 0.021 0.138 Glacial Mountains 0.144 0.052 0.362 Xeric Inland 0.666 0.121 0.182 TOTAL 0.481 0.079 0.164 Table 5. Estimates of abundance per sub-area (stratum) for the Ameralik caribou population in the South region, March 2019, considering three strata, six bins and a Half normal detection function with Visibility as a covariate. Sub-area Abundance Estimate Standard Error (se) Coefficient of Variation (cv) 95% Confidence Interval Lower Upper Coastal Lowland 1221 340 0.278 74 21,127 Glacial Mountains 972 528 0.544 114 8,250 Xeric Inland 17,310 4192 0.242 10,479 28,595 TOTAL 19,503 4268 0.219 12,404 30,665 Table 6. Estimates of density per sub-area (stratum) for the Ameralik caribou population in the South region, March 2019, considering three strata, six bins and a Half normal detection function with Visibility as a covariate. Sub-area Density Estimate Standard Error (se) Coefficient of Variation (cv) 95% Confidence Interval Lower Upper Coastal Lowland 2.447 0.680 0.278 0.148 40.335 Glacial Mountains 0.830 0.451 0.544 0.098 7.045 Xeric Inland 5.758 1.394 0.242 3.486 9.513 TOTAL 4.171 0.913 0.219 2.653 6.558 Figure 16. Estimates for caribou density (left) and abundance (right), with corresponding confidence intervals for three sub-areas, Coastal Lowlands, Glacial Mountains, and Xeric Inland, and finally for the South region (northern portion). 36 Caribou flight reaction or lack thereof Like the North region survey of 2018 (Cuyler et al. 2021), the Ameralik caribou population survey of 2019 used digital audio recorders to collect observation data. The digital recorders permitted including in the dataset what, if any, was the behavioral reaction of the caribou group to the helicopter flying a line transect past or over them. Behavior could then be put in relation to group size and distance from the line transect. Table 7. Movement or non-movement of caribou reacting to helicopter fly-by, March 2019. Dataset that included group size, distance from the line transect, and behavior was n=214. Parameter Ameralik caribou population Movement Non-Movement p – value Number of groups 178 36 % Group Observations 83.2% 16.8% GROUP SIZE Mean group size 5.02 4.31 0.341 Confidence Level (95%) 0.5462 1.4167 Standard Error 0.2768 0.6979 Median 4 3 Mode 2 2 Standard deviation 3.6929 4.1871 Sample Variance 13.6379 17.5325 Maximum 20 23 Minimum 1 1 Number of caribou involved 894 155 DISTANCE 1 Mean distance 193.26 m 320.83 m < 0.0001 Confidence Level (95%) 20.2487 47.3994 Standard Error 10.2605 23.3482 Median 200 300 Mode 50 200 Standard deviation 136.8927 140.0893 Sample Variance 18739.6051 19625.0000 Maximum 800 700 Minimum 50 50 1 Distance from the line transect flown by helicopter. There was no significant difference between the size of caribou groups that exhibited movement and those that did not, mean 5.0 for moving and 4.3 for non-moving (t Stat = - 0.963; two-tailed testing P = 0.341, t = 2.012, df = 47). Non-moving caribou groups were ca. 128 m further away from the line transect flown by the helicopter than those caribou groups showing movement (Table 7). There was a significant difference between the mean 37 distance for groups with movement, 193.26 m, relative to the groups lacking movement, 320.83 m (t Stat = 5.002; two-tailed testing P < 0.0001, t = 2.010, df = 49). Caribou groups reacting to the helicopter fly-by with movement made up 83.2% of all observations for which behavior was reported. Conversely, 16.8% of those groups exhibited little or no movement. The results were similar when considering the absolute number of caribou involved (Table 7). Of 1050 individual caribou for which behavior was reported, 85.2% exhibited movement while 14.8% of lacked movement. Table 8. Details for movement or non-movement of caribou reacting to helicopter fly-by, March 2019. Dataset of observations that included caribou group size, behavior, and distance from line transect, was n= 214 groups, which contained n = 1,050 individual caribou. Ameralik caribou population Category Groups (n = 214) % Individuals (n = 1,050) % Exhibiting Movement Running away 137 64.0 700 66.7 Running away high speed 14 6.5 65 6.2 Walking 11 5.1 50 4.8 Approach* 3 1.4 32 3.0 Confused, circling tightly 6 2.8 18 1.7 Running parallel to line transect 0 0,0 0 0.0 Running, later standing looking 3 1.4 17 1.6 Trotting away 3 1.4 10 1.0 Mixed: some moved, others did not 1 1 0.5 3 0.3 TOTAL 178 83.2 895 85.2 Lacking Movement Standing still 35 16.4 153 14.6 Standing, later walking 0 0.0 0 0.0 Lying down 0 0.0 0 0.0 Lying down, later stood up 1 0.5 2 0.2 Some lying, others standing still 0 0.0 0 0.0 Lying down, later walking 0 0.0 0 0.0 TOTAL 36 16.8 155 14.8 *Approach movement (walking, trotting, running) was towards the helicopter while looking at helicopter. 1Mixed = different behavior by members within same group, e.g., some running towards others, which stood still and looked at the helicopter. Among the 157 ‘running’ groups (Table 8), 151 of those groups exhibited unabated flight, i.e., they never stopped while within view of the helicopter. Group composition (sex, age) was determined for 101 of those groups, of which 88 groups (87.1%) were composed of cows with calves. There were 10 adults only groups (9.9%) and three groups with only juveniles (3%). 38 Considering only the 47 caribou groups whose original position was on or within 50 m of the track line, 42 of those groups (91.3%) never stopped running away, three groups were confused circling tightly about (6.5%), and one group walked (2.2%). Meanwhile, 1 group lacked movement. This was an individual cow on the track line at 930 m elevation. The cow, which possessed both antlers, stood perfectly still facing the approaching helicopter in what appeared to be a defensive posture, even as we flew over her. Demographics & recruitment Sex, age, and late-winter calf recruitment data were collected in separate specific effort that was not part of the line transect DS dataset. On 14 March 2019, using just over 6 hours flight time, we sexed and aged 122 groups of caribou, for a total of 838 animals, in the Ameralik caribou population (Table 9). Cows were almost 58% of the population, bulls (age > 1-year) ca. 13% and calves ca. 30%. Table 9. Demographics for Ameralik caribou population, South region, March 2019. Parameter Ameralik caribou population Number of groups observed 122 GROUP SIZE Mean 6.87 Confidence Interval (95%) 0.8735 Standard Error 0.4412 Standard Deviation 4.8735 Sample Variance 23.7512 Median 5 Mode 3 Maximum 25 Minimum 1 DEMOGRAPHIC Original data Removed 19 orphan calves Total individuals sexed & aged (n) 838 100 % 819 100 % Cow (age > 1 year) 483 57.6 % 483 59.0 % Calves from previous spring 248 29.6 % 229 28.0 % (140 females) 16.71 % - - (106 males) 12.65 % - - (2 unknown sex) 0.24 % - - Bull (age > 1 year) 107 12.8 % 107 13.1 % (30 adults, age > 3) 3.6 % (30 adults, age > 3) 3.7 % (77 juveniles, 1< age < 3) 9.2 % (77 juveniles, 1< age < 3) 9.4 % Recruitment (calves / 100 cows) 51.3 47.4 Sex ratio (Bull age >3 year / Cow) 0.06 0.06 Sex ratio (Bull age >1 year / Cow) 0.22 0.22 39 Calf lacking their dam During demographics data collection a total of 248 calves were observed (Table 9), of which two were ‘true’ orphan calves (0.8%), as they were in a single calf-only group (Table 10) with no cows nearby. The remaining 246 calves (99.2%) were in groups (n=96) that contained cows. However, for 11 of those groups, calf number exceeded the number of cows. In the extreme, one group contained a single cow accompanied by six calves. The five ‘extra’ calves present the possibility of five orphan calves. Those 11 groups contained a total of 65 caribou, whose composition was 18 cows, 37 calves, and 10 bulls (juveniles and/or adults occurred in four of the 10 groups). Among these, twice we observed possible twin calves following one dam/cow, as both calves were inseparable from the cow, i.e., both clung tightly to the cow’s heels. In the nine other groups that contained more cows than calves. The ‘extra’ calf (or calves) was some distance from any cow and her ‘clinging’ calf. This separation was also evident during group flight away from the helicopter. The occurrence of twins aside, within those nine groups there were a possible 17 orphan calves. Adding the two calves in the calf-only group (above), brings the total to 19 orphan calves. Orphan calves (n=19) were 7.7% of all calves observed (n=248). No orphan calves were observed in bull-only groups. If we assume the 19 orphan calves unlikely to survive their first winter, owing to higher mortality than calves with dams, and therefore remove these from the dataset, then the demographic becomes the following: cows 59%, bulls 13% and calves 28%, with a reduced calf recruitment of 47.4 calves per 100 cows (Table 9). Group composition & group size The sex and age composition of caribou groups seems to influence group size. Groups composed of a mix of cows, calves, and bulls (juvenile or adult) were the largest groups observed, mean 10.31 caribou (Table 10). This mix also had the maximum group size of 25 caribou. Groups composed of just cows and calves had the next highest mean, 6 caribou and a maximum group size of 20 caribou. There were only two groups containing just adult bulls (age > 3-years) each with three bulls. There were no groups containing just juvenile bulls. Juvenile bulls were always observed with older animals, which always included cows. Thus, there were no groups composed of a mix of juvenile and adult bulls. There were also no groups of bulls with calves. There was only one calf (age < 1-year) group of two calves, both males. Out of the total 122 groups sexed, and aged, cow-calf pairs occurred in 95 of those groups, which involved 751 caribou (Table 10). Usually there were one or two cow-calf pairs 40 within a group (Fig. 17), with the mean number of cow-calf pairs per group at 2.38 ±1.74 standard deviation. A few groups were notable for the large number of cow-calf pairs. One group of 19 caribou was composed of nine cow-calf pairs and one juvenile bull. A further three groups included seven cow-calf pairs, in addition to the other animals. Table 10. Group size relative to group composition, Ameralik caribou population, South region, March 2019. Parameter Ameralik caribou population, group composition Adult Bull Juvenile Bull Mixed Bull1 Bull1 & Calf Bull1 & Cow Bull1, Cow & Calf Cow Cow & Calf Calf Number of caribou 6 0 0 0 38 433 41 318 2 Number of groups 2 0 0 0 8 42 16 53 1 GROUP SIZE Mean 3.00 - - - 4.75 10.31 2.56 6.00 - CI (95%) - - - - 2.18 1.68 0.61 1.00 - Standard Error - - - - 0.92 0.83 0.29 0.50 - Standard Deviation - - - - 2.60 5.39 1.15 3.65 - Sample Variance - - - - 6.78 29.05 1.33 13.31 - Median 3 - - - 3.5 9.5 2.5 5 2 Mode 3 - - - 3 4 3 7 2 Maximum 3 - - - 9 25 5 20 2 Minimum 3 - - - 2 4 1 2 2 1 Includes both juveniles (1-year < age < 3-year) and adults (age > 3 years). Figure 17. Observed frequency of cow-calf pairs for 95 groups for which demographics information was available. 0 5 10 15 20 25 30 35 40 12345679 Number of observations Number of Cow-Calf pairs 41 Late-winter antler possession The demographics dataset for the 838 sexed and aged caribou (Table 10) included antler possession for most observations. Adult (age > 3-years) bulls lacked antlers and were ca. 28% of all males observed for age > 1-year. Juvenile (age 1½-2½-years) bulls made up 72%. In contrast to adult bulls, 96.1% of juveniles possessed antlers from the previous autumn, two antlers 93.5%: one antler 2.6%), while 3,9% had no visible antlers. Meanwhile, adult cows possessing one or both antlers made up 91.9% of all females (two antlers 79.7%: one antler 12.2%). Polled (no antlers) cows were 8.1%. Antler possession was not recorded for three calves, one female and two males. Regarding female calves (n=139), 66.2% possessed one or both antlers (two antlers 42.4%: one antler 23.7%), while 33.8% of female calves were polled. Regarding male calves (n=104), 87.5% possessed antlers (two antlers 67.3%: one antler 20.2%), while 12.5% were polled. Table 11. Approximate elevations for caribou groups observed: DS survey of the Ameralik caribou population by helicopter in the northern portion of South region, 08-12 March 2019. Parameter South region sub-area Total South region Coastal Lowland Glacial Mountains Xeric Inland Sample size 5 7 219 231 ELEVATION Mean (m) 138 716 656 647 Standard Error (SE) 86.04 131.65 17.65 17.93 Median 80 620 646 633 Mode N/A N/A 898 898 Standard Deviation ± 192.4 ± 348.3 ± 261.1 ± 272.6 Variance 37017.48 121324.67 68193.04 74299.49 Range 465 851 1455 1459 Minimum 12 300 16 12 Maximum 477 1151 1471 1471 Confidence Level (95%) 238.89 322.14 34.78 35.34 Elevations where caribou detected All elevation results for observed caribou indicate only approximate values (Table 11). There were several sources of error on elevation values. The Greenland topography is mountainous and elevation changes can be abrupt, which could place the helicopter at a radically different elevation than the caribou observed. Matching the timestamps could create errors on caribou elevation when the digital recording was made before or after the helicopter passed the caribou location. Even caribou on the track line flown did not necessarily receive correct GPS positions. Owing to flight behavior, these caribou were often digitally recorded while still ahead of the helicopter’s position. Additionally, caribou not on the track line flown could be in terrain at a higher or lower elevation than the 48 mode being 898 m. The relatively high elevations where caribou were observed in 2019 is similar to elevation use observed during the 2012 survey (mean elevation 599 m ±280; Cuyler et al. 2016). It may only be coincidence, but both surveys were preceded by a winter hunting season. In 2012, the season ended the day before, while in 2019 hunting ended 2-weeks prior, i.e., winter 2019 caribou hunting season for the South region was 0115 February, with a quota of 100 Ameralik caribou (Naalakkersuisut 2019). Explanations for the relatively high elevation use in late winter by the Ameralik caribou would include that there are relatively limited lowlands in the South region (Figs. 1, 12) and most caribou observed were inland, where elevations are mostly >200 m. Explanations would also include possible better forage availability at higher elevations in this region. Albeit, given the latitude (ca. 64°N), at the 500-800 m elevations used by the Ameralik caribou population, available winter forage could be assumed inferior and less plentiful than that found in lowland valleys (Körner 2007). Another possibility would be caribou are avoiding hunters present at low elevations. Snowmobile use may also have influenced caribou choice of relatively high elevations in winter (Cuyler et al. 2016). Specifically, the South region is readily accessible by boat and by snowmobile. Use of snowmobiles is permitted over much of the Ameralik caribou population’s range, popularity of recreational use is increasing, and the village of Kapisillit may facilitate snowmobile access even to the Xeric Inland sub-area. Elsewhere, caribou avoid areas of snowmobile use, becoming displaced from, and even abandoning, high-quality habitat (Simpson 1987, Seip et al. 2007). The Ameralik caribou population used elevations that averaged ca. 300 m higher than elevations used by the Akia-Maniitsoq caribou population, which were also surveyed in March 2019 (Cuyler et al. 2023). Explanations would include, most Ameralik caribou were in the Xeric Inland sub-area, which lacks extensive lowland areas. Given current easy access to the region’s lowlands, human disturbance may also be a contributing factor to high elevation use by Ameralik caribou. Almost two decades ago, Ameralik caribou made extensive use of the Coastal Lowland sub-area, which is all <200 m elevation. According to Körner (2007) lowland habitat should be highly preferred by caribou. This supposition was confirmed in March 2001, when 115 caribou were detected on 28 km of lines flown in the Coastal Lowland sub-area (Cuyler et el. 2003). However, thereafter winter hunting was permitted and the number of caribou using the Coastal Lowland sub-area dwindled markedly. In March 2006, just 29 caribou were seen on the same 28 km of lines flown. Further, in the same area, 49 snowmobiles were used (illegally) to hunt down caribou (Cuyler et el. 2007). In March 2012, placement of line transects changed and only one line transected the Coastal Lowland sub-area. It was 13.5 km in length and only seven caribou were seen (Cuyler et el. 2016). In 2019 the number and length of line transects increased. Although now 60 km of lines, we still only detected 45 caribou. Thus, the observed 4.1 caribou per km flown in 2001, dropped 75% to 1 caribou/km in 2006 and decreased further to 0.5 in 2012 and remained low, 0.7, for this study. Late winter use of the Coastal Lowland sub-area by the Ameralik caribou population was high in 2001 and then dropped. Other than the opening of winter hunting season and use of snowmobiles, there has been no change to the subarea in the 2001-2019 period, e.g., no unusual late winter weather events, no new roads or infrastructure, etc. This suggests human disturbance is the important factor influencing late winter elevation use by Ameralik caribou in the South region. While the AkiaManiitsoq caribou had alternate remote lowland areas to use (Cuyler et al. 2023) it appears high elevation was the only option available for Ameralik caribou avoiding human disturbance in lowlands, and specifically the Coastal Lowland sub-area. Late-winter antler possession Bulls As expected for caribou populations, adult (age > 3-years) bulls from the Ameralik population lacked antlers in early March, while most, 96%, juvenile bulls retained one or both their antlers from the previous autumn. Thus, late-winter antler possession among bulls age > 1 year of the Ameralik population is similar to bulls in both the KangerlussuaqSisimiut and Akia-Maniitsoq caribou populations (Cuyler et al. 2021, 2023). Among latewinter male calves (age < 1-year) antler possession varied for those same three populations. Antler possession was common and similar in male calves for the Ameralik and Kangerlussuaq-Sisimiut populations at 87.5% and 86.2%, respectively. In contrast, in the Akia-Maniitsoq population just 40% of male calves possessed antlers (Cuyler et al. 2021, 2023). Cows Although among wild caribou populations in North America, 98% of cows have antlers in late winter (Kelsall 1968, Reimers 1993, Bergerud et al. 2008), antler possession among cows in caribou populations of West Greenland is highly variable and often exhibits a high percentage of polled cows, i.e., no antlers (Thing et al. 1986, Cuyler et al. 2002). In North America, decline in the percentage of antlered cows has been attributed to overgrazed range, because that is a major factor causing poor cow body condition, which precludes 50 antler growth (Gaare & Skogland 1980, Reimers 1983, Thing et al. 1986, Bergerud et al. 2008). In West Greenland, range condition is not the major factor influencing the number of polled cows (Cuyler et al. 2021). In the Ameralik caribou population, 92% of cows had antlers, and most possessed both antlers (80%) rather than just one (12%). This was also reflected in antler possession or absence among female calves, and to a lesser extent even male calves. The 2019 results for antler possession and absence among Ameralik cows is supported by the 2012 survey, which percentage for antlered Ameralik cows was 86% (Cuyler et al. 2016). The greater percentage of antlered cows in 2019 relative to 2012, suggests that summer/autumn 2018 range conditions were not adversely affecting antler growth among Ameralik cows. At 92% antler possession, more Ameralik cows have antlers then cows from either of the other two large caribou populations (Akia-Maniitsoq and Kangerlussuaq-Sisimiut) surveyed in West Greenland. For example, in 2019, just 8% of Ameralik cows were polled, which contrasts sharply with 68% polled Akia-Maniitsoq cows (Cuyler et al. 2023) and the 46% polled Kangerlussuaq-Sisimiut cows in 2018 (Cuyler et al. 2021). Polled cows are common among caribou populations in West Greenland. Specifically, the Ameralik caribou population has a strong semi-domestic reindeer (R. t. tarandus) heritage (Thing et al. 1986, Jepsen et al. 2002), which stems from the Itivnera semi-domestic reindeer herd that went feral in this region during the 1970’s (Cuyler 1999). Since semi-domestic reindeer cows usually have antlers in late winter (Skjenneberg & Slagsvold 1968), we suspect the greater antler possession among cows of the Ameralik, relative to other West Greenland populations, is likely due to this genetic heritage. Other species observed During the early March 2019 survey species other than caribou were observed. These included only ptarmigan, hares, fox, and feral sheep. There were no avian predators observed. Although local knowledge (Cuyler et al. 2016) has reported muskox presence in the South region of West Greenland, like all previous aerial surveys of this region, no muskoxen were observed during the early March 2019 survey. There were two aerial surveys conducted in early March 2019, i.e., this study’s South region and another in the Central region (Cuyler et al. 2023). Although the surveys occurred almost simultaneously, in the South region, excepting feral sheep, there were fewer of each observed species (Cuyler et al. 2023). 51 Still, the South region’s area of survey effort (Table 2) was 2.4 times below that for the Central region (Cuyler et al. 2023). This partially explains the lower number of observations. However, it remains that the South region had fewer ptarmigan and foxes as well as no avian predators. Compared to the Central region, late winter 2019 habitat conditions in the South region appeared unable to support the same abundance of those species. Acknowledgements This project was financed primarily by the Government of Greenland and by Pinngortitaleriffik – Greenland Institute for Natural Resources, Nuuk Greenland. 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Place names used regarding the northern portion of the South region (ca. 63°30’–64°30’°N; 49°–51°40’W), which is inhabited by the Ameralik caribou population. 56 Appendix 2 Photos of c amouflaged caribou (and fox) observed March 2019. Figure 23. Six caribou, of which five are readily visible. All are within 75 m of the helicopter. Photo A. Jensen. Figure 24. Six camouflaged caribou. Some are visible on a snow patch, but others are difficult to spot when among mix of thin snow, bare ground, and flat light (lack of shadows). All are within ca. 100 m of the helicopter. Photo C. Cuyler. 57 C amouflaged caribou Figure 25. Seven camouflaged caribou. None are readily visible against the mix of thin snow, boulders, and bare ground. All are within ca. 100 m of the helicopter. Photo A. Jensen. Figure 26. Three camouflaged caribou. Despite full sunshine, none are readily visible against the mix of patchy snow cover, boulders, bare ground, and willows. All are within 100-150 m of the helicopter. Photo C. Cuyler. 64 C amouflaged arctic fox Figure 39. Xeric inland sub-area, illustrating vegetation poking through thin snow layer. This camouflaged one arctic fox (blue phase) curled in a ball in response to helicopter at 75-100 m. Photo C. Cuyler. Figure 40. Location of arctic fox (blue phase) indicated by blue circle. Photo C. Cuyler. 65 Appendix 3 Feral sheep ( Ovis aries ) near Kuussuaq (Austmannadalen), 14 March 2019 Figure 41. One camouflaged feral sheep in the foreground just left and below center, within 100 m of the helicopter. The mix of bare ground, rocks, and thin snow cover with vegetation poking through the snow surface make detection difficult. This one feral sheep was associated with eleven others (not in photo). Photo C. Cuyler. Figure 42. One feral sheep observed just north of the mouth of the Austmannadalen valley on willow covered peninsula. It was associated with eleven other feral sheep. Photos A. Jensen. 66 Appendix 4 Statistical methods behind Distance Sampling This appendix presents the basic building blocks and reasoning behind Distance Sampling (DS) methods, followed by some details. This summary of statistical methods is from Correia (2020). Fundamental concepts Before entering into the detailed theory behind the DS methodology, we present a simpler design, which is quadrat or plot sampling (Buckland et al. 2001; Marques, 2009). In plot sampling, a region of interest with total area 𝐴, is divided into small plots of area 𝑎 𝑝𝑙𝑜𝑡 (Fig. 43). Some of these small plots are randomly chosen for sampling and the total number of individuals within these, 𝑛𝑝𝑙𝑜𝑡, is recorded. Figure 43. Plot sampling grid example of total area 𝐴 divided into smaller plots of area 𝑎 𝑝𝑙𝑜𝑡 . The density within each plot, 𝐷𝑝𝑙𝑜𝑡, is the number of individuals per unit area for the respective plot so, by definition, it is given by Equation (1) where 𝑎 is the total area sampled within 𝐴 . (i.e., 𝑎 = 4 ⋅ 𝑎 𝑝𝑙𝑜𝑡 = 4𝑘𝑚 2 for Fig. 43) Since a random design was used, the density is a representative estimate, by design, for the total area 𝐴 . Hence, an estimate for the abundance, 𝑁  , can b e obtained b y simply multiplying 𝐷  plot by the total a rea 𝐴 , Equation (2) 67 𝑃 The DS methodology is an extension of quadrat-based sampling methods. The detail that creates the bridge from one methodology to the other is the fact that the method described above assumes that every individual of interest is detected (Miller et al. 2016). Frequently, this assumption cannot be met, specifically if among the individuals of interest there are animals impossible to observe owing to low sightability. Several factors cause low sightability, including topographical barriers, weather conditions, ground surface conditions and many others related to observer training and survey design. The proportion of individuals that were not detected can be estimated using the detection function fitted to the observed distances (Thomas et al. 2002). Once this proportion is estimated, it can be considered to obtain more accurate estimates and then, an extrapolation for a wider region can be done similarly as shown in Equation (2). In DS, this proportion of detected objects in the area 𝑎 is defined as the probability of detection, 𝑃𝑎. Therefore, a density estimate can be obtained as per Equation (1) by adjusting 𝑛𝑝𝑙𝑜𝑡 by 𝑃𝑎, i.e., by correcting the detections for those that were missed. Since the latter cannot be known, in general, an estimate must be also obtained, thus Equation (3) where 𝑃 󰆹 a is an estimate of 𝑃 𝑎 obtained from the distance data, and 𝑎 is the area of the sampled region. Usually 𝑎 = 2𝑤𝐿, with 𝑤 as the truncation distance, for both sides of the track line, and the total transect length 𝐿 = ∑𝑙 𝑗 𝑘 𝑗=1 , where 𝑙 is the length of transect 𝑗. Abundance can be determined using a reasoning analogous to that above (Equation 2). The truncation distance is defined as the distance beyond which distances are not recorded. This can be defined in the field or at the analysis stage. The coefficient of variation of 𝐷 , c 𝑣 (𝐷 ), is related with t w o random comp onen ts referred ab ov e, encoun ter rate ( 𝑛 𝑝𝑙𝑜𝑡 /𝐿 ), and 𝑃 󰆹 a , plus a third one that is the estimate of the exp ected size of detected clusters ( 𝐸 󰆹 ( 𝑠) ). Assuming independence b et w een these, the former is given by Equation (4) An approximation of the standard error of 𝐷 , 𝑠𝑒( 𝐷  ), is defined as Equation (5) 68 Once these are obtained, an approximate 100(1 − 𝛼)% confidence interval (CI) can be determined by Equation (6) Where is the quantile of the N(0,1) distribution 1.96 for a 95% confidence interval). However, the distribution of the 𝐷 is positively skewed, thus an interval assuming that 𝐷  is log-normally distributed has better coverage. According with Buckland et al. (2015), a 100(1-alpha)% confidence interval can be given by Equation (7) where Equation (8) and Equation (9) For further details see Buckland et al. (2001) and Buckland et al. (2015). Probability of detection Given the above, the probability of detecting an object, giving that it is within the area co vered b y the transects, 𝑃  a , needs to b e estimated. F or this pro ject, the ob ject of in terest consists in caribou groups. To illustrate the importance of this probability, consider that an observer walks across a large patch of tundra and detects 8 caribou (Fig. 44). While discussing with the local biologist, and considering the biologist’s experience, he/she will state that, on average, only one third of all caribou present are detected (i.e. , 𝑃  a = 1/3) meaning that probably there were around 24 caribou within that patch of tundra and 16 have been missed. That is where DS is useful, since it allows a rigorous framework for the estimation of P a and then an estimate of abundance can be obtained as shown in Equation (3). 69 Figure 44. Example of a patch of tundra with the transect in the middle. Blue dots represent eight observed caribou, while orange dots represent the 16 undetected ones. The lines perpendicular to the transect represent the recorded distances. Distance Sampling methods The detection function, 𝑔(𝑦), describes the probability of detecting an object of interest given that it is at a distance 𝑦, from the track line (also known as 0-line), thus being a nonincreasing function of 𝑦 (Buckland et al. 2015). For line transects, 𝑦 is the perpendicular distance from the 0-line to the detected object. Within DS methods, the probability of detection is explained recurring to these observed distances (Buckland et al. 2001). Sometimes covariates may be added to explain their relationship with the detection probability. In this situation, we are within the Multiple Covariate Distance Sampling (MCDS) framework (Buckland et al. 2001). Conventional Distance Sampling Conventional Distance Sampling (CDS) occurs when no additional covariates are added to the mo del. Once the detection function is estimated, 𝑃  a can b e obtained via the follo wing equation Equation (10) where 𝜋(𝑦)= 1 𝜔 and, therefore, used to estimate density using Equation (3). For 𝑔(𝑦) it is also specified a flexible semi-parametric model, composed by a key function and some 70 additional series expansions, known as adjustment terms, and their parameters are estimated (Marques et al. 2007). To obtain robust estimates of density, flexible models for 𝑔(𝑦) are needed with the form (Buckland et al. 2001) Equation (11) where 𝑘(𝑦) is the parametric key function and 𝑠(𝑦) represents the additional adjustment terms (Table 12). Table 12. Commonly used key functions and series expansions for the detection function. Adapted from Buckland (2001). The uniform key function has no parameters, while the half-normal and the hazard-rate functions include a scale parameter, 𝜎, which determines the rate at which the function decreases with increasing distance (Fig. 45). Furthermore, the hazard-rate function also includes a shape parameter, 𝑏 , that provides greater flexibility to this function comparing to the others (Buckland et al. 2001). It is not always necessary to include adjustment terms, and, in such cases, these models are referred to as “key only” models. When the key functions are not enough for fitting 𝑔(𝑦) , some series expansions terms may be added to modify its shape (Fig. 46). These terms can be either cosine, simple polynomial or Hermite polynomial (Table 12). It is important to note that these adjustment terms do not depend directly on 𝑦 but on 𝑦𝑠 which is a scaled value of 𝑦 , where 𝑦 𝑠 = 𝑦 𝜔 with 𝜔 being the truncation distance. This allows independence between the shape of the series expansion and the units used for 𝑦 (Marques et al. 2007). 71 Figure 45. Half-normal (top row) and hazard-rate (bottom row) detection functions without adjustments, varying scale (σ) and, only for hazard-rate, shape (b) parameters. Values tested are presented above the plots. On the top row from left to right, the study species becomes more detectable (higher probability of detection at larger distances). The bottom rows show the hazard-rate model’s more pronounced shoulder. Adapted from Buckland et al. (2001). 72 Figure 46. Possible shapes for the detection function when cosine adjustments are included for half-normal and hazard-rate models. Adapted from Buckland et al. (2001). 73 Right truncation of the data, or the removal of the largest distances, is a common procedure that aids model fitting. Some precision might be lost with truncation; however, it is usually slight. On the other hand, precision is increased since the data is easier to model and, consequently, fewer parameters and adjustment terms are required to model the detection function (Couturier et al. 2018). Multiple Covariate Distance Sampling CDS methods can be extended to MCDS, so that 𝑔(𝑦) is modelled as a function not only of distance, but also of a vector of 𝐽 additional covariates for each of the 𝑛 objects of interest, z i = z 𝑖1 , ..., z 𝑖𝐽 , 𝑖 = 1, ..., 𝑛 . Accordingly, the function that describes the probability of detection at a given distance, is represented by 𝑔(𝑦, z ) . These additional covariates can either be discrete or continuous, such as observer and group size, and are assumed to affect only the scale, 𝜎, of the detection function (Marques et al. 2007; Miller et al. 2016). For line transects, 𝑃 ( z i) , i.e., the probability of detecting the 𝑖 -th object of interest given its respective vector of covariates z i can be estimated using the formula presented in Equation (12). Equation (12) with 𝜋(𝑦)= 1 𝜔. Considering the three key functions previously presented, only the uniform key is excluded from MCDS since it does not have a scale parameter. Half-normal and hazard-rate functions can have their scale parameter written as a function of the covariate values as Equation (13) Where 𝛽0and all the 𝛽𝑗’s are the J + 1 coefficients to be estimated with J being the total number of covariates. The estimation of the parameters for both CDS and MCDS is typically done via maximum likelihood (Marques et al. 2007). Once the detection function is estimated, according with (Buckland et al. 2004), density can be estimated as Equation (14) where 𝑎 is the total area survey ed, 𝑃 ( z i ) is the estimated probabilit y of detecting the 𝑖 -th object of interest given its respective vector of covariates zi. 80 Appendix 6 Photos, South region aerial survey conditions for census of the Ameralik caribou population, March 2019 Figure 48. Coastal Lowlands sub-area in background beyond the mouth of Buksefjord, which crosses the middle of this photo, view is south. Line transect 125 parallels Busksefjord. Photo C. Cuyler. Figure 49. Coastal Lowlands sub-area, view east towards bordering mountains. Photo A. Jensen. Figure 50. Coastal Lowlands sub-area, west end line transect 127, view is north. Photo C. Cuyler. 81 South region aerial survey conditions, March 2019 Figure 51. Glacial Mountains sub-area, Itoqqarmiut Kangerluarsunnguat (Buksefjord) and Hydro Power station located at the bottom of fjord. Line transect 121 was flown over the area left of center, view is southwest. Photo A. Jensen. Figure 52. Glacial Mountains sub-area, south of line transect 122 and west of 117, illustrating the mountains and glaciers typical to this sub-area, view is southwest. Photo A. Jensen. 82 South region aerial survey conditions, March 2019 Figure 53. Glacial Mountains sub-area, middle portion of line transect 123, view is northeast up the lake, Sangujaat Tasersuat. Photo A. Jensen. Figure 54. Glacial Mountains sub-area, river valley, Qoorusussuaq, which is just east of line transect 123, view is north. Photo A. Jensen. 83 South region aerial survey conditions, March 2019 Figure 55. Glacial Mountains sub-area, east end of line transect 123, illustrating one of several glaciers in this sub-area, view is south. Photo A. Jensen. Figure 56. Glacial Mountains sub-area, the valley and lake, Eqaluit, an area atypical amongst the mountains and glaciers common to this sub-area, view is southwest. Photo A. Jensen. 84 South region aerial survey conditions, March 2019 Figure 57. Xeric inland sub-area, line transect 101, view east, note thin snow layer. Photo C. Cuyler. Figure 58. Xeric inland sub-area, line transect 102, view west, note thin snow layer. Photo C. Cuyler. 85 South region aerial survey conditions, March 2019 Figure 59. Xeric Inland sub-area, approaching east end line transect 103, view east. Photo C. Cuyler. Figure 60. Xeric Inland sub-area, east end line transect 103, view north. Photo C. Cuyler. 86 South region aerial survey conditions, March 2019 Figure 61. Xeric inland sub-area, line transect 104, south of Kapisillit, view is south. Photo C. Cuyler. Figure 62. Xeric Inland sub-area, view north illustrating mouth of broad braided river valley, Sarqarssuaq, which is sandwiched between Akullersuaq to the north (background) and Nunatarssuaq to the south (foreground), Kangersuneq fjord far left. Line transect 105 ran from inner braided river along far mountainside of Akullersuaq and out to the shores of Kangersuneq. Photos C. Cuyler. 87 South region aerial survey conditions, March 2019 Figure 63. Xeric Inland sub-area, east end of line transect 106, view west across Akullersuaq. Note the amount of bare ground with no snow layer. Photo A Jensen. Figure 64. Xeric Inland sub-area, line transect 107, view east, note thin snow layer and presence of 16 caribou. Photo C. Cuyler. 88 South region aerial survey conditions, March 2019 Figure 65. Xeric Inland sub-area, view northeast over Nunataarsuk and area of line transect 108, Akullersuup Sermia far left. Note, thin snow permits ground to show through. Photo C. Cuyler. Figure 66. Xeric Inland sub-area, line transect 108, Nunataarsuk, view east. Note, thin layer of snow permits ground to show through it. Photo C. Cuyler. 89 South region aerial survey conditions, March 2019 Figure 67. Xeric Inland sub-area, around the west end of line transect 110, four caribou, view NNE. There is a mixture of almost bare ground and windblown snow of varying depth. Photo A. Jensen. Figure 68. Xeric Inland sub-area, around the east end of line transect 110, two groups of caribou, four (upper left) and two (bottom right), view east. The snow layer is generally thin, although thicker patches occur where snow has been windblown into drifts. Photo A. Jensen. 96 South region aerial survey conditions, March 2019 Figure 80. Xeric inland sub-area, east end line transect 119 at shore of lake, Ilulialik, view is northeast from southwest end of the lake. Photo A. Jensen. Figure 81. Xeric inland sub-area, view to SW end of the lake, Ilulialik and the east end point of line transect 119 at lakeshore. Line transect 119 came down off the highlands to the right. Photo A. Jensen. 97 Appendix 7 Histograms for detected distances Histograms for detected distances superimposed with estimated detection functions for all truncated fitted models, presented order as in Table 3 98 99 Figure 82. Histograms for detected distances superimposed with estimated detection functions for all truncated fitted models. The parameter estimates and variability associated with them (Table 13), essentially illustrates that the "Medium" visibility has a lower detection probability estimate than the "High" visibility (as also shown in Fig. 15). Table 13. Detection function parameters’ estimates. Estimate Standard Error Intercept -1.444 0.074 Visibility Medium -0.378 0.156 Note: Estimates are on log scale. 100 Appendix 8 Glacier bounded lakes that recently emptied, March 2019 While flying the 2019 aerial surveys (Central and South regions), we observed four lakes that had emptied recently (Figs. 83-87). All had connections to the Greenland Ice Cap or to glacial tongues. Likely more such lakes exist but were not detected because observations were limited to the apriori line transects flown. Figure 83. Locations of the four lakes observed to have recently emptied, before March 2019. 101 Glacier bounded lakes that recently emptied, March 2019 Figure 84. Small lake in Central region, Ujarassuit sub-area, that recently emptied in what appears to have been an all-at-once event, e.g., surface lake ice remains strewn on mountainside. Highwater line is indicated by thin ice-foot (right of center) running along mountainside. A previous highwater mark is suggested by the light-grey bare rock above the recent highwater ice-foot. This lake borders the north side of Narsap Sermia (Narsap Glacier), and line transect 27. (Fig. 83, Number 1). Photo C. Cuyler. Figure 85. Emptied large lake in South region, Xeric Inland sub-area, illustrating marked highwater line on mountainside. Lake size and height of highwater line compared to current elevation of lake surface suggests an enormous water volume was involved in the emptying event, which may have been prior to freeze up winter 2018/2019, since there is no visible disturbance to surface ice along the lake shore. This lake borders the south side of the Narsap Sermia (Narsap Glacier), and line transect 101. (Fig. 83, Number 2). Photo C. Cuyler. 102 Glacier bounded lakes that recently emptied, March 2019 Figure 86. Recently emptied pond, Austmanntjern, near east end of line transect 112, at Greenland Ice Cap, Xeric Inland sub-area, South region. Google Earth Pro image (taken 2014) shows Austmanntjern much enlarged, while by March 2019 it had emptied. Strewn placement of ice chunks suggests emptying occurred post summer 2018 and all-at-once. Above view is to the north, below is to northwest. (Fig. 83, Number 3). Photos C. Cuyler. 103 Glacier bounded lakes that recently emptied, March 2019 Figure 87. Large unnamed lake in South region, Xeric Inland sub-area, on the north side of line transect 117. This lake borders the north side of a Greenland Ice Cap glacial tongue There were at least five highwater lines on the black mountainside (right side of bottom photo). This suggests the lake has emptied to its present surface level in five stages. While the timing of those events is unknown, the most recent may have been prior to freeze up winter 2018/2019, since there is no visible disturbance to surface ice along the lake shore. Previous lake surface elevation from standard maps was 710 m. Emptying water would pass through the lake, Isortuarsuup Tasia. The Alanngorlia fjord is known for abrupt rises in sea level owing to lake emptying. (Fig. 83, Number 4). Photo A. Jensen. 104 Appendix 9 Recommendations for improving future surveys Aerial survey methods & design The 9.7% survey coverage for the 2019 Distance Sampling (DS) survey of the Ameralik caribou population, promotes accuracy of abundance estimates and should be maintained or improved in the future to permit evaluating population trends. When flying line transects, distance and other factors often make identification of calves impossible, resulting in an underestimate of calf number. Demographic (sex, age, calf recruitment) data must continue to be collected in efforts separate from flying the line transects for DS. Flight altitudes from 30 to 40 m permit scanning the landscape for caribou even out to 1000-1500m from the track line without dead-ground interfering. Just be aware the degree to which the caribou are extremely camouflaged against the typical backgrounds. This can cause observer fatigue, mental exhaustion, even at the relatively slow speeds flown (60-70 km/hour). Any ‘dead’ ground causing caribou detections to be missed, will likely be mitigated by the DS analysis. Training and testing, observer ability to judge correct distance bin is necessary for improvement of this important variable. It is the author’s experience that without practice people commonly misjudge distance. Looking down from above can exacerbate this tendency. Flat terrain may provide a more (normal) horizontal line-of-sight to the animals, which may increase binning accuracy. However, terrain that slopes away, either up or down, confuses observers’ ability to judge distance from track line to animals. The steeper the slope, the greater the errors. The timing for aerial surveys could remain early March because that coincides with annual minimum caribou movement (avoids double counting), and enough day length for flying the pilot maximum of 7-hours per day. Experience from eight surveys since 2000 has illustrated that snow cover and depth is variable regardless of the winter period chosen. In Greenland, helicopters are seldom available at short notice. Book about 3-5 months ahead and reaffirm booking several times thereafter. For estimating the necessary window (dates) that helicopter is booked for survey, first calculate the number of days required for survey. Then, add days to allow for 105 several non-flying days owing to pilot flying hours going over weekly limit, airport closures on Sundays, and poor weather. For the latter a minimum 3-4 days should be allocated. Book survey observers early (Fig. 88), about six months in advance, to ensure the probability of obtaining the observers you require. Attributes would include previous experience detecting superbly camouflaged caribou, and proven lack of nausea, e.g., on ships at sea or in helicopters. Even the usually sedate helicopter maneuvers for line transects can illicit nausea in some. Meanwhile, the non-stop abrupt flying maneuvers required to obtain caribou demographics cause nausea in most persons. Note: Previous helicopter experience, including animal live capture, does not guarantee lack of nausea during sharp the maneuvers specific to caribou demographics work. Figure 88. The three observers, Dr. Christine Cuyler, scientific leader (left), Aslak Jensen, commercial hunter (center), and Hans Mølgaard, Sisimiut hunting officer (right). Standardization of data collection regarding surface conditions Prior to 2019, the covariates (including degree of camouflage, % snow cover, snow depth, icing, visibility, lighting (e.g., flat light, shadow), presence of boulders and their size, vegetation poking through snow layers, etc.) were recorded without standardization and often ad hoc. In contrast the 2019 survey used specific standardized qualitative terms to make the covariates available for analyses. Evaluations for all environmental covariates were standardized to just five easy qualitative terms: Zero, Low, Medium, High, and Extreme. However, there were too many covariates to permit recording each with every detection of the object-of-interest (caribou). If the covariates are to be useful in analyses, an evaluation must be assigned for most caribou detections, ideally for all. However, this is usually