Given recent actions to increase sustained yield of moose (Alces alces) in Alaska, USA, we examined factors affecting yield and moose demographics and discussed related management. Prior studies concluded that yield and density of moose remain low in much of Interior Alaska and Yukon, Canada, despite high moose reproductive rates, because of predation from lightly harvested grizzly (Ursus arctos) and black bear (U. americanus) and wolf (Canis lupus) populations. Our study area, Game Management Unit (GMU) 20A, was also in Interior Alaska, but we describe elevated yield and density of moose. Prior to our study, a wolf control program (1976–1982) helped reverse a decline in the moose population. Subsequent to 1975, moose numbers continued a 28‐year, 7‐fold increase through the initial 8 years of our study (λB1 = 1.05 during 1996–2004, peak density = 1,299 moose/1,000 km2). During these initial 8 hunting seasons, reported harvest was composed primarily of males ( = 88%). Total harvest averaged 5% of the prehunt population and 57 moose/1,000 km2, the highest sustained harvest‐density recorded in Interior Alaska for similar‐sized areas. In contrast, sustained total harvests of <10 moose/1,000 km2 existed among low‐density, predator‐limited moose populations in Interior Alaska (≤417 moose/1,000 km2). During the final 3 years of our study (2004–2006), moose numbers declined (λB2 = 0.96) as intended using liberal harvests of female and male moose ( = 47%) that averaged 7% of the prehunt population and 97 moose/1,000 km2. We intentionally reduced high densities in the central half of GMU 20A (up to 1,741 moose/1,000 km2 in Nov) because moose were reproducing at the lowest rate measured among wild, noninsular North American populations. Calf survival was uniquely high in GMU 20A compared with 7 similar radiocollaring studies in Alaska and Yukon. Low predation was the proximate factor that allowed moose in GMU 20A to increase in density and sustain elevated yields. Bears killed only 9% of the modeled postcalving moose population annually in GMU 20A during 1996–2004, in contrast to 18–27% in 3 studies of low‐density moose populations. Thus, outside GMU 20A, higher bear predation rates can create challenges for those desiring rapid increases in sustained yield of moose. Wolves killed 8–15% of the 4 postcalving moose populations annually (10% in GMU 20A), hunters killed 2–6%, and other factors killed 1–6%. Annually during the increase phase in GMU 20A, calf moose constituted 75% of the predator‐killed moose and predators killed 4 times more moose than hunters killed. Wolf predation on calves remained largely additive at the high moose densities studied in GMU 20A. Sustainable harvest‐densities of moose can be increased several‐fold in most areas of Interior Alaska where moose density and moose: predator ratios are lower than in GMU 20A and nutritional status is higher. Steps include 1) reducing predation sufficient to allow the moose population to grow, and 2) initiating harvest of female moose to halt population grow...

We focused on describing low nutritional status in an increasing moose (Alces alces gigas) population with reduced predation in Game Management Unit (GMU) 20A near Fairbanks, Alaska, USA. A skeptical public disallowed liberal antlerless harvests of this moose population until we provided convincing data on low nutritional status. We ranked nutritional status in 15 Alaska moose populations (in boreal forests and coastal tundra) based on multiyear twinning rates. Data on age‐of‐first‐reproduction and parturition rates provided a ranking consistent with twinning rates in the 6 areas where comparative data were available. Also, short‐yearling mass provided a ranking consistent with twinning rates in 5 of the 6 areas where data were available. Data from 5 areas implied an inverse relationship between twinning rate and browse removal rate. Only in GMU 20A did nutritional indices reach low levels where justification for halting population growth was apparent, which supports prior findings that nutrition is a minor factor limiting most Alaska moose populations compared to predation. With predator reductions, the GMU 20A moose population increased from 1976 until liberal antlerless harvests in 2004. During 1997‐2005, GMU 20A moose exhibited the lowest nutritional status reported to date for wild, noninsular, North American populations, including 1) delayed reproduction until moose reached 36 months of age and the lowest parturition rate among 36‐month‐old moose (29%, n = 147); 2) the lowest average multiyear twinning rates from late‐May aerial surveys (x = 7%, SE = 0.9%, n = 9 yr, range = 3‐10%) and delayed twinning until moose reached 60 months of age; 3) the lowest average mass of female short‐yearlings in Alaska (x̄ = 155 ± 1.6 [SE] kg in the Tanana Flats subpopulation, up to 58 kg below average masses found elsewhere); and 4) high removal (42%) of current annual browse biomass compared to 9‐26% elsewhere in boreal forests. When average multiyear twinning rates in GMU 20A (sampled during 1960‐2005) declined to <10% in the mid‐ to late 1990s, we began encouraging liberal antlerless harvests, but only conservative annual harvests of 61‐76 antlerless moose were achieved during 1996‐2001. Using data in the context of our broader ranking system, we convinced skeptical citizen advisory committees to allow liberal antlerless harvests of 600‐690 moose in 2004 and 2005, with the objective of halting population growth of the 16,000‐17,000 moose; total harvests were 7‐8% of total prehunt numbers. The resulting liberal antlerless harvests served to protect the moose population's health and habitat and to fulfill a mandate for elevated yield. Liberal antlerless harvests appear justified to halt population growth when multiyear twinning rates average ≤10% and ≥1 of the following signals substantiate low nutritional status: <50% of 36‐month‐old moose are parturient, average multiyear short‐yearling mass is <175 kg, or >35% of annual browse biomass is removed by moose.

We determined wolverine (Gulo gulo) distribution and occurrence probabilities using aerial surveys and hierarchical spatial modeling in a 180,000-km 2 portion of Interior Alaska, USA. During 8 February-12 March 2006, we surveyed 149 of 180 1,000-km 2 sample units for wolverine tracks. We observed wolverine tracks in 99 (66.4%) sample units. Wolverine detection probability was L 69% throughout the survey period. Posterior occurrence probabilities of whether a wolverine track occurred in a sample unit was dependent on survey timing, number of transects flown, number of neighboring sample units with detected tracks, percentage of the sample unit with elevation M 305 m, and human influences. Our model indicated strong evidence of occurrence (.0.80) in 72% of the 180 survey units, strong evidence of absence (,0.20) in 12%, and weak evidence of occurrence or absence (0.20-0.80) in 16%. Wolverine area of occupancy made up 83% of the study area. Simulations illustrated that 2-4 survey routes were necessary for the survey technique to provide strong evidence of wolverine presence or absence in Interior Alaska if a track was not identified along the first route. The necessary number of survey routes depends on the occurrence probability in a sample unit. We provided managers with a map of wolverine distribution in Interior Alaska and an efficient and lower-cost method to detect coarse-scale changes in wolverine distribution. Our technique was effective in both Interior Alaska and Ontario, Canada, suggesting it would be effective throughout most of the boreal forest range of wolverines where tracks can be readily observed from the air. The technique requires a certain skill level in recognizing tracks; it is essential that tracks are identified correctly and training may be necessary depending on surveyor experience.

We sent 76 canines and 77 incisors (I1) from 84 known-age moose (Alces alces) !2 years old sampled from near Fairbanks, Alaska, USA (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)) to Matson's Laboratory (Milltown, MT) to test G. Matson's accuracy rate in estimating moose ages. To estimate ages, G. Matson counted annuli in the cementum of root tips using a Giemsa-staining technique and assumed a birth date of 1 June. We originally radiocollared moose at 9 months of age, and we extracted teeth upon death. Estimated moose ages averaged 7.0 years using canines and 6.9 using I1 teeth (range ¼ 2-16 year), and known ages of each sample averaged 7.1 years. The accuracy rate among 76 canines was 74% and improved to 95% when ignoring errors within 1 year of the known age; comparative results among 77 I1 teeth were 66% and 94%. By far the most frequent error was a 1-year underestimate in age, particularly for moose that died in July and August, which included the seasonal transition period associated with completing peripheral annuli formation. After controlling for À1-year errors associated with the seasonal transition period, we found evidence for errors accumulating with age. We found no significant difference in accuracy based on which tooth was sectioned. However, G. Matson observed more individually distinct annuli and regular deposition patterns in canines, compared with incisors. Thus, we recommend the more easily extracted canine for estimating moose age. Ó 2015 The Wildlife Society.

Hunting is important to many people because it provides food, recreation, and cultural identity, so proper management of wildlife is necessary. Wildlife agencies and researchers often rely on harvest data supplied by hunters, but interpretation of these data can be misleading when biases are not acknowledged, assessed, and corrected. We use harvest information collected by the Alaska Department of Fish and Game (ADF&G) from moose (Alces alces gigas) hunters to examine and correct 3 common biases in harvest data: heaping in responses of estimated effort (i.e., rounding), changes in report design, and nonreporting. We found that bias due to heaping was limited (2.8%). A large increase in special permits in 2004 (6.1% in 2003 and 40.1% in 2004) corresponded with increases in individuals with multiple permits (8.6% and 17.3%), which biased estimates of hunt participation calculated from permit data. Failure to correct for multiple reports per hunter also resulted in an artificial decline in success over time. Road access influenced reporting rates; rural Alaska residents without a road had the lowest reporting rate (67%) and rural with a road the greatest (82%). A statewide trend of 663 additional hunters per year calculated from raw permit data was eliminated once data were corrected for both multiple permits and nonreporting. Late reporters were also less likely to hunt (11.8%) than all reporters. Our research shows that survey data bias can significantly influence data interpretation, and wildlife managers must balance information needs, time constraints, and financial resources when determining which biases to correct. © 2015 The Wildlife Society.