Abstract. Wildlife managers are often asking for reliable information of population density across larger spatial scales. In this study, we examined the spatiotemporal relationships between moose density as estimated by cohort analysis and the density indices (1) harvest density (HD; hunter kills per km 2 ), (2) moose seen per unit effort (SPUE), seen moose density (SMD; seen moose per km 2 ), and density of moosevehicle accidents (MVA density; e.g., traffic kills per km 2 ) in 16 areas in Norway with 13-42 years of data. HD showed a close positive relationship with moose density both within and between regions. However, the temporal variation in HD was best explained as a delayed reflection of moose density and tended to overestimate its growth and decline. Conversely, SMD and SPUE were unable to predict the spatial variation in moose density with high precision, though both indices were relatively precise temporal reflectors of moose density. However, the SPUE tended to underestimate population growth, probably because of a decrease in searching efficiency with increasing moose density. Compared to the other indices, MVA density performed poor as an index of moose density within regions, and not at all among regions, but may, because of its independent source of data, be used to cross-check population trends suggested by other indices. Our study shows that the temporal trends in moose density can be surveyed over large areas by the use of cheap indices based on data collected by hunters and local managers, and supports the general assumption that the number of moose killed per km 2 provides a precise and isometric index of the variation in moose density at the spatial scale of our study.

Modern breeding schemes for livestock species accumulate a large amount of genotype and phenotype data which can be used for genome-wide association studies (GWAS). Many chromosomal regions harboring effects on quantitative traits have been reported from these studies, but the underlying causative mutations remain mostly undetected. In this study, we combine large genotype and phenotype data available from a commercial pig breeding scheme for three different breeds (Duroc, Landrace, and Large White) to pinpoint functional variation for a region on porcine chromosome 7 affecting number of teats (NTE). Our results show that refining trait definition by counting number of vertebrae (NVE) and ribs (RIB) helps to reduce noise from other genetic variation and increases heritability from 0.28 up to 0.62 NVE and 0.78 RIB in Duroc. However, in Landrace, the effect of the same QTL on NTE mainly affects NVE and not RIB, which is reflected in reduced heritability for RIB (0.24) compared to NVE (0.59). Further, differences in allele frequencies and accuracy of rib counting influence genetic parameters. Correction for the top SNP does not detect any other QTL effect on NTE, NVE, or RIB in Landrace or Duroc. At the molecular level, haplotypes derived from 660K SNP data detects a core haplotype of seven SNPs in Duroc. Sequence analysis of 16 Duroc animals shows that two functional mutations of the Vertnin ( VRTN ) gene known to increase number of thoracic vertebrae (ribs) reside on this haplotype. In Landrace, the linkage disequilibrium (LD) extends over a region of more than 3 Mb also containing both VRTN mutations. Here, other modifying loci are expected to cause the breed-specific effect. Additional variants found on the wildtype haplotype surrounding the VRTN region in all sequenced Landrace animals point toward breed specific differences which are expected to be present also across the whole genome. This Landrace specific haplotype contains two missense mutations in the ABCD4 gene, one of which is expected to have a negative effect on the protein function. Together, the integration of largescale genotype, phenotype and sequence data shows exemplarily how population parameters are influenced by underlying variation at the molecular level.

The plant stress hypothesis states that plant stress factors other than herbivory improve herbivore performance due to changes in the content of nutritive or defensive compounds in the plants. In Norway, the bilberry (Vaccinium myrtillus) is important forage for the bank vole (Myodes glareolus) in winter and for the moose (Alces alces) in summer and autumn. The observed peaks in bank vole numbers after years with high production of bilberries are suggested to be caused by increased winter survival of bank voles due to improved forage quality. High production of bilberries should also lead to higher recruitment rates in moose in the following year. We predict, however, that there is an increasing tendency for a 1-year delay of moose indices relative to vole indices with decreasing summer temperatures, because low temperatures prolong the period needed by plants to recover in the vole peak year, and thus positively affect moose reproduction also in the succeeding year. In eight out of nine counties in south-eastern Norway, there was a positive relationship between the number of calves observed per female moose during hunting and a bilberry seed production index or an autumn bank vole population index. When dividing the study area into regions, there was a negative relationship between a moose-vole time-lag index and the mean summer temperature of the region. These patterns Communicated by C. Gortázar suggest that annual fluctuations in the production of bilberries affect forage quality, but that the effect on moose reproduction also depends on summer temperatures.

Shoulder lesions and body condition of sows at weaning have both environmental and genetic causes. The traits can be scored at farm level, and following recording, the traits can be included in the breeding goal and directional selection can be applied. However, to further increase the genetic progress of these traits, it is advantageous to develop indicator traits on the selection candidates (test boars or gilts, not yet exhibiting the phenotype themselves). It has previously been suggested that the scapula morphology and the spine of scapula might be a key factor for the sow to develop shoulder lesions. In this study, we developed 11 novel traits describing the morphology of the shoulder blade based on computed tomography images from scanned test boars. These traits include the area, length, width, height, and volume of the shoulder blade as well as 6 traits obtained from principal component analysis, describing 80% of the variation observed for the scapula spine profile. The analyzed traits have moderate to high heritability (h2 from 0.29 to 0.78, SE = 0.06), low to medium genetic correlations with shoulder lesions (up to 0.4, SE = 0.1), and body condition scoring at weaning (up to 0.25, SE = 0.1). These novel phenotypes can now be recorded automatically and accurately prior to selection of the AI boars. If such recordings are included in multivariate genomic selection models, it is expected to improve the genetic progress of shoulder lesions and body condition score by weaning.