Jennifer I. Schmidt

Population Structure and Genetic Diversity of Moose in Alaska

Moose (Alces alces) are highly mobile mammals that occur across arboreal regions of North America, Europe, and Asia. Alaskan moose (Alces alces gigas) range across much of Alaska and are primary herbivore consumers, exerting a prominent influence on ecosystem structure and functioning. Increased knowledge gained from population genetics provides insights into their population dynamics, history, and dispersal of these unique large herbivores and can aid in conservation efforts. We examined the genetic diversity and population structure of moose (n = 141) with 8 polymorphic microsatellites from 6 regions spanning much of Alaska. Expected heterozygosity was moderate (H(E) = 0.483-0.612), and private alleles ranged from 0 to 6. Both F(ST) and R(ST) indicated significant population structure (P < 0.001) with F(ST) < 0.109 and R(ST) < 0.125. Results of analyses from STRUCTURE indicated 2 prominent population groups, a mix of moose from the Yakutat and Tetlin regions versus all other moose, with slight substructure observed among the second population. Estimates of dispersal differed between analytical approaches, indicating a high level of historical or current gene flow. Mantel tests indicated that isolation-by-distance partially explained observed structure among moose populations (R(2) = 0.45, P < 0.01). Finally, there was no evidence of bottlenecks either at the population level or overall. We conclude that weak population structure occurs among moose in Alaska with population expansion from interior Alaska westward toward the coast.

Antler Size of Alaskan Moose Alces Alces Gigas: Effects of Population Density, Hunter Harvest and Use of Guides

Abstract Moose Alces alces gigas in Alaska, USA, exhibit extreme sexual dimorphism, with adult males possessing large, elaborate antlers. Antler size and conformation are influenced by age, nutrition and genetics, and these bony structures serve to establish social rank and affect mating success. Population density, combined with anthropogenic effects such as harvest, is thought to influence antler size. Antler size increased as densities of moose decreased, ostensibly a density-dependent response related to enhanced nutrition at low densities. The vegetation type where moose were harvested also affected antler size, with the largest-antlered males occupying more open habitats. Hunts with guides occurred in areas with low moose density, minimized hunter interference and increased rates of success. Such hunts harvested moose with larger antler spreads than did non-guided hunts. Knowledge and abilities allowed guides to satisfy demands of trophy hunters, who are an integral part of the Alaskan economy. Heavy harvest by humans was also associated with decreased antler size of moose, probably via a downward shift in the age structure of the population resulting in younger males with smaller antlers. Nevertheless, density-dependence was more influential than effects of harvest on age structure in determining antler size of male moose. Indeed, antlers are likely under strong sexual selection, but we demonstrate that resource availability influenced the distribution of these sexually selected characters across the landscape. We argue that understanding population density in relation to carrying capacity (K) and the age structure of males is necessary to interpret potential consequences of harvest on the genetics of moose and other large herbivores. Our results provide researchers and managers with a better understanding of variables that affect the physical condition, antler size, and perhaps the genetic composition of populations, which may be useful in managing and modelling moose populations.

CATCH PER UNIT EFFORT FOR MOOSE: A NEW APPROACH USING WEIBULL REGRESSION

Abstract The relationship between hunters and their environment is a key component in managing wildlife populations. Identifying hunters characteristics, motivations, and efforts is crucial to understanding if a hunt will be successful. We predicted that landscape characteristics and moose (Alces alces) densities would affect success of hunts. As in wildlife management programs elsewhere, moose hunters in interior Alaska, USA, must return harvest tickets to the Alaska Department of Fish and Game. These tickets provide location of hunts (Uniform Coding Units) and other details. Our modeling of responses (1997–2001) from harvest tickets indicated that location of hunts, mode of transportation, hunting regulations, use of commercial services, year, density of roads, hunter-to-moose ratio, moose density, and residency of hunters were important predictors of success. In addition, we documented that the linear-regression approach to measuring catch per unit effort (CPUE) was inappropriate because it produced an inverse, but not significant, relationship between hunting effort and success. This outcome occurred because most hunts, particularly for large mammals, ended with the harvesting of an animal. Likewise, modeling of hunter success with logistic regression was similarly biased by measures of hunter effort. We established that a time-to-event Weibull regression provided substantial improvement over standard models of CPUE. Weibull regression accurately represented the positive relationship between effort and success, and it can be used to model length of hunt and other covariates related to hunters and landscape characteristics for predicting success.

Climate-driven effects of fire on winter habitat for caribou in the Alaskan-Yukon Arctic.

Climatic warming has direct implications for fire-dominated disturbance patterns in northern ecosystems. A transforming wildfire regime is altering plant composition and successional patterns, thus affecting the distribution and potentially the abundance of large herbivores. Caribou (Rangifer tarandus) are an important subsistence resource for communities throughout the north and a species that depends on terrestrial lichen in late-successional forests and tundra systems. Projected increases in area burned and reductions in stand ages may reduce lichen availability within caribou winter ranges. Sufficient reductions in lichen abundance could alter the capacity of these areas to support caribou populations. To assess the potential role of a changing fire regime on winter habitat for caribou, we used a simulation modeling platform, two global circulation models (GCMs), and a moderate emissions scenario to project annual fire characteristics and the resulting abundance of lichen-producing vegetation types (i.e., spruce forests and tundra >60 years old) across a modeling domain that encompassed the winter ranges of the Central Arctic and Porcupine caribou herds in the Alaskan-Yukon Arctic. Fires were less numerous and smaller in tundra compared to spruce habitats throughout the 90-year projection for both GCMs. Given the more likely climate trajectory, we projected that the Porcupine caribou herd, which winters primarily in the boreal forest, could be expected to experience a greater reduction in lichen-producing winter habitats (−21%) than the Central Arctic herd that wintered primarily in the arctic tundra (−11%). Our results suggest that caribou herds wintering in boreal forest will undergo fire-driven reductions in lichen-producing habitats that will, at a minimum, alter their distribution. Range shifts of caribou resulting from fire-driven changes to winter habitat may diminish access to caribou for rural communities that reside in fire-prone areas.

Relationship of Community Characteristics to Harvest Reporting: Comparative Study of Household Surveys and Harvest Tickets in Alaska

Accurate harvest reporting is critical for wildlife management. Rural Alaskan communities reported a median of 42% of moose harvested via traditional harvest tickets compared to those reported in household surveys. This harvest-report ratio did not change over time. Twice as many moose were reported harvested during subsistence household surveys (n = 8,039) than on hunter harvest tickets (n = 3,557). Percentage of the community that was indigenous, used and shared moose, and absence of a wildlife biologist or road access were associated with low harvest-report ratios. Analysis revealed that household surveys provide important information about moose harvest rates and their use should be expanded. Reporting rates might be improved by building trust through respectful dialogue between hunters and managers and by placing more emphasis on the benefits of reporting harvests and less emphasis on enforcement.

Measuring and Correcting Response Heaping Arising From the Use of Prototypes

Imprecision in respondent recall can cause response heaping in frequency data for particular values (e.g., 5, 10, 15). In human dimensions research, heaping can occur for variables such as days of participation (e.g., hunting, fishing), animals/fish harvested, or money spent on licenses. Distributions with heaps can bias population estimates because the means and totals can be inflated or deflated. Because bias can result in poor management decisions, determining if the bias is large enough to matter is important. This note introduces the logic and flow of a deheaping program that estimates bias in means and totals when people use approximate responses (i.e., prototypes). The program can make estimates even when spikes occur due to bag limits. The program is available online, and smooths heaps at multiples of 5 (numbers ending in 5 and 0) and 7 (e.g., 7, 14, 21), and produces standard deviations in estimates.

Demographic and economic disparities among Arctic regions

We use demographic and economic indicators to analyze spatial differences and temporal trends across 18 regions surrounding the Arctic Ocean. Multifactor and cluster analysis were used on 10 indicators reflecting income, employment and demography from 1995 to 2008. The main difference is between regions with high population densities, low natural growth rate, and low unemployment (Russia, Norway and Iceland) and regions with high unemployment rate and high natural growth rate (mainly North American regions). However, once those parameters were accounted for sub-regional differences start to emerge. Variation among the regions was a result of national policies and regional differences such as access and presence of natural resources (i.e. oil, gas, mining, etc.). We found only weak temporal trends, but regions with resource extraction show some signs of higher volatility. Overall, the Arctic has experienced out-migration with only Iceland and two regions in Canada experiencing in-migration.