The influence of sampling scheme and interpolation method on the power to detect spatial effects of forest birds in Ontario (Canada)

Spatial ecology is becoming an increasingly important component of resource management, and the general monitoring of how human activities affect the distribution and abundance of wildlife. Yet most work on the reliability of sampling strategies is based on a non-spatial analysis of variance paradigm, and little work has been done assessing the power of alternative spatial methods for creating reliable maps of animal abundance. Such a map forms a critical response variable for multiple scale studies relating landscape structure to biotic function. The power to reconstruct patterns of distribution and abundance is influenced by sample placement strategy and density, the nature of spatial auto-correlation among points, and by the technique used to extrapolate points into an animal abundance map. Faced with uncertainty concerning the influence of these factors, we chose to first synthesize a model reference system of known properties and then evaluate the relative performance of alternative sampling and mapping procedures using it. We used published habitat associations of tree nesting boreal neo-tropical birds, a classified habitat map from the Manitou Lakes area of northwestern Ontario, and point count means and variances determined from field studies in boreal Canada to create 4 simulated models of avian abundance to function as reference maps. Four point sampling strategies were evaluated by 4 spatial mapping methods. We found mixed-cluster sampling to be an effective point sampling strategy, particularly when high habitat fragmentation was avoided by restricting samples to habitat patches >10 ha in size. We also found that of the 4 mapping methods, only stratified ordinary point kriging (OPK) was able to generate maps that reproduced an embedded landscape-scale spatial effect that reduced nesting bird abundance in areas of higher forest age-class fragmentation. Global OPK was effective only for detecting broader, regional-scale differences. This revised version was published online in July 2006 with corrections to the Cover Date.     

Porcine malignant hyperthermia susceptibility: erythrocytic osmotic fragility

Erythrocyte osmotic fragility was determined in 27 Pietrain swine which were susceptible to malignant hyperthermia (MH), 29 Yorkshire swine which were resistant to MH (controls), and 50 crossbred swine (Pietrain x Yorkshire), half of which were MH susceptible. Halothane challenge tests and blood creatine kinase activity were used as criteria for determining MH susceptibility. Mean values for osmotic fragility of erythrocytes in concentrations of NaCl between 60 and 120 mM were significantly different for the 3 groups (P less than 0.001). Hemolysis (50%) of erythrocytes occurred at NaCl concentrations of 90 mM for Pietrains, 85 mM for crossbreds, and 78 mM for controls. Increased fragility values occurred in 96% of the Pietrains, 3% of the controls, and 42% of crossbred swine that were halothane test-positive, and 58% of halothane test-negative crossbreds (P less than 0.05). The mean time of onset of signs of MH in response to halothane challenge testing was twice as long in the crossbreds as in Pietrains (P less than 0.01). Reticulocyte counts were moderately high in blood samples from both the Pietrains (P less than 0.001) and the crossbreds (P less than 0.05). Of the swine which were tested for erythrocyte selenium-dependent glutathione peroxidase activity, values were within acceptable laboratory limits in 18 of 20 Pietrains, 14 of 14 halothane test-negative crossbreds, and 8 of 8 halothane test-positive crossbreds. In 2 of 20 Pietrains, a 35% deficiency of this enzyme was found. Heinz bodies were not detected in erythrocytes examined from 21 Pietrains, 20 crossbred swine (8 halothane test positives), and 12 controls.(ABSTRACT TRUNCATED AT 250 WORDS)     

Genetic parameter estimates among scale activity score and farrowing disposition with reproductive traits in swine

The primary objective of this study was to determine if certain behavior traits were genetically correlated with reproduction. If 1 or both of the behavior traits were found to be correlated, a secondary objective was to determine if the behavior traits could be useful in selecting for more productive females. A scale activity score taken at 5 mo of age and a farrowing disposition score taken at farrowing were selected as the behavioral traits. Scale activity score ranged from 1 to 5 and farrowing disposition ranged from 1 to 3. Reproductive traits included age at puberty, number born alive, number born dead, litter birth weight, average piglet birth weight, number weaned, litter weaning weight, average weaning weight, wean-to-estrus interval, ovulation rate including gilts, and postweaning ovulation rate. Genetic correlations between scale activity score and reproduction ranged from -0.79 to 0.61. Three of the correlations, number born alive (P < 0.01), average piglet birth weight (P < 0.001), and wean-to-estrus interval (P = 0.014), were statistically significant but included both favorable and antagonistic correlations. In contrast, all but 1 of the farrowing disposition correlations was favorable and ranged from -0.66 to 0.67. Although only the correlation with litter birth weight was significant (P = 0.018), the consistent favorable direction of all farrowing disposition correlations, except average weaning weight, shows a potential for inclusion of farrowing disposition into a selection program.     

Genetic analysis of behavior traits in swine production

Estimates of genetic parameters related to pig behavior under stressful situations are required before selection programs can be designed to produce more docile pigs. Pig behavior was evaluated in a pedigreed Landrace–Duroc–Yorkshire composite population. Piglets were evaluated for their response to handling at 1 d of age (n=11069), being placed on their back for 60 s at ~24 d of age (n=975), and being confined in a scale while backfat measurements were being collected (n=9035). Feeding behavior was monitored in a growing–finishing facility (n=1162) including preferences for feeding positions. Feeders were placed along a fence with one end adjacent to a gate (gate-end) and the other end open. An animal model was fitted to the data using WOMBAT where litter was included for d 1 activity scores and backtest traits. Fixed effects of sex, pen/year-season/date of collection in all analyses along with scorer (d 1 activity score) and a covariate of age (d 154 weight and backfat). Multiple trait models were fit to estimate genetic covariances among traits. All estimates of heritability were significantly different than zero. Activity scores and backtest traits had the lowest estimates of heritability (0.15–0.19), measures of feeding behavior were more variable (0.16–0.60) while production data had high heritabilities (>0.5). Genomic heritability estimates were similar to standard heritability estimates for most traits, except traits measured at a young age. All traits measured during the backtest had strong genetic correlations and similar estimated heritability. Among feeding behavior traits, number of meals/d and average meal length were highly correlated with total daily meal time. In addition, animals that preferred to eat alone avoided the open-end position at the feeder. The only behavioral traits with genetic correlations significantly different from zero with production traits were associated with feeding behavior where animals that ate longer meals and spent more time at the feeder/d tended to be heavier and fatter at 154 d. In addition, animals that ate more meals/d were fatter and animals that preferred the gate-end position of the feeder were heavier. Pigs with more reactive personalities tended to eat fewer meals/d, each longer in duration, and they preferred the gate-end feeder position. The measures of pig behavior studied were heritable and selection for more docile pigs should not have large detrimental effects on performance.     

Genome-wide association study of swine farrowing traits. Part II: Bayesian analysis of marker data

Reproductive efficiency has a great impact on the economic success of pork (sus scrofa) production. Number born alive (NBA) and average piglet birth weight (ABW) contribute greatly to reproductive efficiency. To better understand the underlying genetics of birth traits, a genome-wide association study (GWAS) was undertaken. Samples of DNA were collected and tested using the Illumina PorcineSNP60 BeadChip from 1,152 first parity gilts. Traits included total number born (TNB), NBA, number born dead (NBD), number stillborn (NSB), number of mummies (MUM), total litter birth weight (LBW), and ABW. A total of 41,151 SNP were tested using a Bayesian approach. Beginning with the first 5 SNP on SSC1 and ending with the last 5 SNP on the SSCX, SNP were assigned to groups of 5 consecutive SNP by chromosome-position order and analyzed again using a Bayesian approach. From that analysis, 5-SNP groups were selected having no overlap with another 5-SNP groups and no overlap across chromosomes. These selected 5-SNP non-overlapping groups were defined as QTL. Of the available 8,814 QTL, 124 were found to be statistically significant (P < 0.01). Multiple testing was considered using the probability of false positives. Eleven QTL were found for TNB, 3 on SSC1, 3 on SSC4, 1 on SSC13, 1 on SSC14, 2 on SSC15, and 1 on SSC17. Statistical testing for NBA identified 14 QTL, 4 on SSC1, 1 on SSC4, 1 on SSC6, 1 on SSC10, 1on SSC13, 3 on SSC15, and 3 on SSC17. A single NBD QTL was found on SSC11. No QTL were identified for NSB or MUM. Thirty-three QTL were found for LBW, 3 on SSC1, 1 on SSC2, 1 on SSC3, 5 on SSC4, 2 on SSC5, 5 on SSC6, 3 on SSC7, 2 on SSC9, 1 on SSC10, 2 on SSC14, 6 on SSC15, and 2 on SSC17. A total of 65 QTL were found for ABW, 9 on SSC1, 3 on SSC2, 9 on SSC5, 5 on SSC6, 1 on SSC7, 2 on SSC8, 2 on SSC9, 3 on SSC10, 1 on SSC11, 3 on SSC12, 2 on SSC13, 8 on SSC14, 8 on SSC15, 1 on SSC17, and 8 on SSC18. Several candidate genes have been identified that overlap QTL locations among TNB, NBA, NBD, and ABW. These QTL when combined with information on genes found in the same regions should provide useful information that could be used for marker assisted selection, marker assisted management, or genomic selection applications in commercial pig populations.