HEALTH RISKS TO HUMANS AND DOMESTIC LIVESTOCK POSED BY FERAL PIGS (Sus scrofa) IN NORTH QUEENSLAND

A sample of 608 adult pigs from Cape York and adjacent islands was examined for parasites and their serum tested for livestock diseases associated with the Queensland tropics. Feral pigs from North Queensland pose a significant health threat to humans with the incidence of Spargana (the plerocercoid of Spirometra erinacei) through the consumption of undercooked pork. Meliodosis (Pseudomonas pseudomalleO. Leptospirosis (L. yar. pomona). and Brucellosis (Brucella suis) are capable of infecting humans directly during unhygienic butchering of infected carcasses. In North Queensland, the widespread intermingled distribution of feral pigs and cattle increases the potential for the transmission of Actinobacillus, Leptospirosis, and Brucellosis from feral pigs to cattle. Both Europeans and Aborigines on Cape York also raise wild-caught feral pigs for meat. It is important to realize that parasites and diseases are present in young pigs and that poor husbandry practices increase the risk of infection from several parasites, i.e., Lungworm (Metastrongylus sp.) Stomach worm (Physocephalus sexalatus. Hvostrongvlus rubidus). Thorny headed worm (Macracanthorrhynchus hirudinaceus) and Kidney worm (Stephanurus dentatus). Heavy infection of these parasites reduce growth rates and cause unthriftiness in infected ani¬mals.

Sapro-Zoonotic Risks Posed by Wild Birds in Agricultural Landscapes

There are over 1400 catalogued human pathogens, with approximately 62% classified as zoonotic (Taylor et al., 2001). Most evidence of direct transmission of pathogens to humans involves domestic and companion animals, whereas the reservoir for most zoonoses is wildlife; yet there are relatively few well-documented cases for the direct involvement of transmission from wildlife to humans (Kruse et al., 2004). In part, this absence of evidence reflects the mobility of wildlife, the difficulty accessing relevant samples, and the smaller number of studies focused on characterizing wildlife pathogens relative to the human and veterinary literature (McDiarmid, 1969; Davis et al., 1971; Hubalek, 2004).

Evaluating Measurement Invariance with Censored Ordinal Data: A Monte Carlo Comparison of Alternative Model Estimators and Scales of Measurement

Evaluations of measurement invariance provide essential construct validity evidence. However, the quality of such evidence is partly dependent upon the validity of the resulting statistical conclusions. The presence of Type I or Type II errors can render measurement invariance conclusions meaningless. The purpose of this study was to determine the effects of categorization and censoring on the behavior of the chi-square/likelihood ratio test statistic and two alternative fit indices (CFI and RMSEA) under the context of evaluating measurement invariance. Monte Carlo simulation was used to examine Type I error and power rates for the (a) overall test statistic/fit indices, and (b) change in test statistic/fit indices. Data were generated according to a multiple-group single-factor CFA model across 40 conditions that varied by sample size, strength of item factor loadings, and categorization thresholds. Seven different combinations of model estimators (ML, Yuan-Bentler scaled ML, and WLSMV) and specified measurement scales (continuous, censored, and categorical) were used to analyze each of the simulation conditions. As hypothesized, non-normality increased Type I error rates for the continuous scale of measurement and did not affect error rates for the categorical scale of measurement. Maximum likelihood estimation combined with a categorical scale of measurement resulted in more correct statistical conclusions than the other analysis combinations. For the continuous and censored scales of measurement, the Yuan-Bentler scaled ML resulted in more correct conclusions than normal-theory ML. The censored measurement scale did not offer any advantages over the continuous measurement scale. Comparing across fit statistics and indices, the chi-square-based test statistics were preferred over the alternative fit indices, and ΔRMSEA was preferred over ΔCFI. Results from this study should be used to inform the modeling decisions of applied researchers. However, no single analysis combination can be recommended for all situations. Therefore, it is essential that researchers consider the context and purpose of their analyses.

Contributions of demography and dispersal parameters to the spatial spread of a stage-structured insect invasion

Stage-structured models that integrate demography and dispersal can be used to identify points in the life cycle with large effects on rates of population spatial spread, information that is vital in the development of containment strategies for invasive species. Current challenges in the application of these tools include: (1) accounting for large uncertainty in model parameters, which may violate assumptions of ‘‘local’’ perturbation metrics such as sensitivities and elasticities, and (2) forecasting not only asymptotic rates of spatial spread, as is usually done, but also transient spatial dynamics in the early stages of invasion. We developed an invasion model for the Diaprepes root weevil (DRW; Diaprepes abbreviatus [Coleoptera: Curculionidae]), a generalist herbivore that has invaded citrus-growing regions of the United States. We synthesized data on DRW demography and dispersal and generated predictions for asymptotic and transient peak invasion speeds, accounting for parameter uncertainty. We quantified the contributions of each parameter toward invasion speed using a ‘‘global’’ perturbation analysis, and we contrasted parameter contributions during the transient and asymptotic phases. We found that the asymptotic invasion speed was 0.02–0.028 km/week, although the transient peak invasion speed (0.03– 0.045 km/week) was significantly greater. Both asymptotic and transient invasions speeds were most responsive to weevil dispersal distances. However, demographic parameters that had large effects on asymptotic speed (e.g., survival of early-instar larvae) had little effect on transient speed. Comparison of the global analysis with lower-level elasticities indicated that local perturbation analysis would have generated unreliable predictions for the responsiveness of invasion speed to underlying parameters. Observed range expansion in southern Florida (1992–2006) was significantly lower than the invasion speed predicted by the model. Possible causes of this mismatch include overestimation of dispersal distances, demographic rates, and spatiotemporal variation in parameter values. This study demonstrates that, when parameter uncertainty is large, as is often the case, global perturbation analyses are needed to identify which points in the life cycle should be targets of management. Our results also suggest that effective strategies for reducing spread during the asymptotic phase may have little effect during the transient phase. Includes Appendix.