![]() ![]() We ranked each population using 2 years of twinning data to reduce the effects of a year of unusual weather we found no evidence that a multiyear ranking resulted largely from unusual weather. Results largely supported the twinning- based nutritional ranking and an inverse relationship between twinning rates and browse removal rates (Table 2). Using additional data on reproduction and short- yearling mass in 6 of these 15 areas, we highlighted consistencies and inconsistencies in the twinning-based ranking (Table 2). We summarized multiyear twinning data from 15 moose populations or subpopulations in Alaska to rank the respective populations’ apparent nutritional status (Table 1 Fig. We did not test for differences among other moose population statistics because of differences in years sampled and sample sizes. We calculated 95 % confidence intervals for age- specific parturition and twinning rates using binomial confidence intervals (Cochran 1977). We used a z -test to test for differences in twinning rates before and after the median calving date (Remington and Schork 1970). We used a paired 2-tailed t -test to test for subpopulation differences in parturition rates, and a 2-sample t -test with equal variances to test for subpopulation differences in the mean mass of female short-yearlings (Zar 1999). We tested for subpopulation differences in GMU 20A. Alaska Department of Fish and Game (2002, 2004, 2006) moose management reports describe specific methodology for estimating moose numbers in the respective GMUs. 1986) of 1.21 derived from proportions of radiocollared moose observed during 2003– 2006 surveys. During 1999– 2005, we used geospatial survey methodology (DeLong 2006, Kellie and DeLong 2006) and a composite sightability correction factor (Gasaway et al. During 1978–1998, we used stratified–random methodology and sightability correction factors derived from intensive searches (Gasaway et al. Moose density estimates in GMU 20A during 1960–1994 are from Gasaway et al. ![]() We based standard errors for percent removal per plant on the binomial distribution (Cochran 1977). We derived browse removal estimates using one winter of data in each study area, except in GMU 20D (2 winters). We estimated browse removal only on paper birch ( Betula papyrifera ), quaking aspen and balsam poplar ( Populus spp.), and willow ( Salix spp.) that exhibited current annual growth between 0.5 m and 3.0 m above the ground. Seaton, Alaska Department of Fish and Game, unpublished data). We derived unique regression coefficients for each browse species and study area, and selected browse plots based on systematic sampling (C. twigs throughout the winter to estimate regression coefficients relating twig diameter to dry mass (Telfer 1969, Seaton 2002). ![]()
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