Description of collared peccary (Tayassu tajacu) milk composition

Composition of collared peccary (Tayassu tajacu) milk was described for samples obtained from a group of four captive females maintained on a high quality diet and four wild-caught females inhabiting semiarid rangelands in southern Texas. The gross composition of peccary and porcine milks are fairly similar, with some differences in mineral composition. Results suggest that bottle formulations developed for domestic swine piglets would provide adequate nutrition for hand-rearing nursling peccaries.     

Intraspecific aggression results in death of a collared peccary

An adult male collared peccary (Dicotyles tajacu) caught from the wild was released into an outdoor enclosure containing 12 other wild adult peccaries. The introduced male died of severe infection as a result of scrotal wounds inflicted by resident peccaries.     

Importance of rare habitats and riparian zones in a tropical forest fragment: preferential use by Tayassu pecari, a wide-ranging frugivore

In forest fragments, rare habitats contribute to heterogeneity and may provide unique resources for frugivorous species like peccaries with spatially and temporally complex patterns of range use. This study examined seasonal habitat use by two sympatric peccary species ( Tayassu pecari and Tayassu tajacu ) in an Atlantic forest fragment on the plateau region of São Paulo state, Brazil. Previous studies showed that range use by T. pecari was highly nonrandom, and that both species persisted at population densities typical of larger forest fragments. To explain this, we quantified the use and availability of habitats and riparian zones with compositional analysis. Use by T. pecari was nonrandom with respect to availability. Habitat preferences were different during dry and wet seasons and corresponded closely with seasonal movements, core range and fruit use. Although aquatic habitats made up a small proportion of the herd home range, they were most preferred by T. pecari during both seasons. Headwater palmito Euterpe edulis swamps were most favored in the dry season, whereas swamps and marshes near larger streams were preferred in the wet season. Tayassu pecari preferred riparian zones <50 m from streams over drier zones. These habitats were important sources of fruits, travel routes and corridors between forest patches in the agricultural matrix. The least-preferred habitat of T. pecari during both seasons was disturbed forest edge dominated by bamboo. Habitat and riparian zone use were herd-specific for T. tajacu and related to habitat quality and composition where stable home ranges had been established. The persistence of viable peccary populations after 75 years of fragmentation-associated pressures is related to preservation of rare habitats and overall habitat diversity. Thus, T. pecari is an indicator of high habitat diversity in forest fragments and will function as an umbrella species when targeted for conservation.     

Spotlight surveys for white-tailed deer: Monitoring panacea or exercise in futility?

Many monitoring programs for white-tailed deer (Odocoileus virginianus) on both private and public lands across the United States have long relied on the use of road-based spotlight surveys for monitoring population size and trends. Research has suggested spotlight surveys are ineffective and that road-based surveys for deer are biased because of highly variable detection rates. To evaluate variability in detection rates relative to the assumption that repeated surveys along roads will provide reliable trend data for use in calculating deer density estimates, we collected 5 years of thermal-imager and spotlight survey data using a multiple-observer, closed-capture approach. Using a Huggin's closed capture model, data bootstrapping, and variance components analyses, our results suggest that density estimates for white-tailed deer generated from data collected during road-based spotlight surveys are likely not reflective of the standing deer population. Detection probabilities during individual spotlight surveys ranged from 0.00 to 0.80 (median = 0.45) across all surveys, and differed by observer, survey, management unit, and survey transect replicate. Mean spotlight detection probability (0.41) and process standard deviation (0.12) estimates indicated considerable variability across surveys, observers, transects, and years, which precludes the generation of a correction factor or use of spotlight data to evaluate long-term trends at any scale. Although recommended by many state, federal, and non-governmental agencies, our results suggest that the benefit of spotlight survey data for monitoring deer populations is limited and likely represents a waste of resources with no appreciable management information gained. © 2012 The Wildlife Society.     

Data reduction prior to inference: Are there consequences of comparing groups using a t-test based on principal component scores?

Researchers often use a two-step process to analyze multivariate data. First, dimensionality is reduced using a technique such as principal component analysis, followed by a group comparison using a -test or analysis of variance. Although this practice is often discouraged, the statistical properties of this procedure are not well understood, starting with the hypothesis being tested. We suggest that this approach might be considering two distinct hypotheses, one of which is a global test of no differences in the mean vectors, and the other being a focused test of a specific linear combination where the coefficients have been estimated from the data. We study the asymptotic properties of the two-sample -statistic for these two scenarios, assuming a nonsparse setting. We show that the size of the global test agrees with the presumed level but that the test has poor power. In contrast, the size of the focused test can be arbitrarily distorted with certain mean and covariance structures. A simple method is provided to correct the size of the focused test. Data analyses and simulations are used to illustrate the results. Recommendations on the use of this two-step method and the related use of principal components for prediction are provided.