Sequential sampling: a novel method in farm animal welfare assessment

Lameness in dairy cows is an important welfare issue. As part of a welfare assessment, herd level lameness prevalence can be estimated from scoring a sample of animals, where higher levels of accuracy are associated with larger sample sizes. As the financial cost is related to the number of cows sampled, smaller samples are preferred. Sequential sampling schemes have been used for informing decision making in clinical trials. Sequential sampling involves taking samples in stages, where sampling can stop early depending on the estimated lameness prevalence. When welfare assessment is used for a pass/fail decision, a similar approach could be applied to reduce the overall sample size. The sampling schemes proposed here apply the principles of sequential sampling within a diagnostic testing framework. This study develops three sequential sampling schemes of increasing complexity to classify 80 fully assessed UK dairy farms, each with known lameness prevalence. Using the Welfare Quality herd-size-based sampling scheme, the first ‘basic’ scheme involves two sampling events. At the first sampling event half the Welfare Quality sample size is drawn, and then depending on the outcome, sampling either stops or is continued and the same number of animals is sampled again. In the second ‘cautious’ scheme, an adaptation is made to ensure that correctly classifying a farm as ‘bad’ is done with greater certainty. The third scheme is the only scheme to go beyond lameness as a binary measure and investigates the potential for increasing accuracy by incorporating the number of severely lame cows into the decision. The three schemes are evaluated with respect to accuracy and average sample size by running 100 000 simulations for each scheme, and a comparison is made with the fixed size Welfare Quality herd-size-based sampling scheme. All three schemes performed almost as well as the fixed size scheme but with much smaller average sample sizes. For the third scheme, an overall association between lameness prevalence and the proportion of lame cows that were severely lame on a farm was found. However, as this association was found to not be consistent across all farms, the sampling scheme did not prove to be as useful as expected. The preferred scheme was therefore the ‘cautious’ scheme for which a sampling protocol has also been developed.     

Estimating Neospora caninum prevalence in wildlife populations using Bayesian inference

Prevalence of disease in wildlife populations, which is necessary for developing disease models and conducting epidemiologic analyses, is often understudied. Laboratory tests used to screen for diseases in wildlife populations often are validated only for domestic animals. Consequently, the use of these tests for wildlife populations may lead to inaccurate estimates of disease prevalence. We demonstrate the use of Bayesian latent class analysis (LCA) in determining the specificity and sensitivity of a competitive enzyme‐linked immunosorbent assay (cELISA; VMRD^®, Inc.) serologic test used to identify exposure to Neospora caninum (hereafter N. caninum) in three wildlife populations in southeastern Ohio, USA. True prevalence of N. caninum exposure in these populations was estimated to range from 0.1% to 3.1% in American bison (Bison bison), 51.0% to 53.8% in Père David's deer (Elaphurus davidianus), and 40.0% to 45.9% in white‐tailed deer (Odocoileus virginianus). The accuracy of the cELISA in American bison and Père David's deer was estimated to be close to the 96% sensitivity and 99% specificity reported by the manufacturer. Sensitivity in white‐tailed deer, however, ranged from 78.9% to 99.9%. Apparent prevalence of N. caninum from the test results is not equal to the true prevalence in white‐tailed deer and Père David's deer populations. Even when these species inhabit the same community, the true prevalence in the two deer populations differed from the true prevalence in the American bison population. Variances in prevalence for some species suggest differences in the epidemiology of N. caninum for these colocated populations. Bayesian LCA methods could be used as in this example to overcome some of the constraints on validating tests in wildlife species. The ability to accurately evaluate disease status and prevalence in a population improves our understanding of the epidemiology of multihost pathogen systems at the community level.     

On the selection of thresholds for predicting species occurrence with presence‐only data

Presence‐only data present challenges for selecting thresholds to transform species distribution modeling results into binary outputs. In this article, we compare two recently published threshold selection methods (maxSSS and maxF_pb) and examine the effectiveness of the threshold‐based prevalence estimation approach. Six virtual species with varying prevalence were simulated within a real landscape in southeastern Australia. Presence‐only models were built with DOMAIN, generalized linear model, Maxent, and Random Forest. Thresholds were selected with two methods maxSSS and maxF_pb with four presence‐only datasets with different ratios of the number of known presences to the number of random points (KP–RP_ratio). Sensitivity, specificity, true skill statistic, and F measure were used to evaluate the performance of the results. Species prevalence was estimated as the ratio of the number of predicted presences to the total number of points in the evaluation dataset. Thresholds selected with maxF_pb varied as the KP–RP_ratio of the threshold selection datasets changed. Datasets with the KP–RP_ratio around 1 generally produced better results than scores distant from 1. Results produced by We conclude that maxF_pb had specificity too low for very common species using Random Forest and Maxent models. In contrast, maxSSS produced consistent results whichever dataset was used. The estimation of prevalence was almost always biased, and the bias was very large for DOMAIN and Random Forest predictions. We conclude that maxF_pb is affected by the KP–RP_ratio of the threshold selection datasets, but maxSSS is almost unaffected by this ratio. Unbiased estimations of prevalence are difficult to be determined using the threshold‐based approach.     

Models of Eucalypt phenology predict bat population flux

Fruit bats (Pteropodidae) have received increased attention after the recent emergence of notable viral pathogens of bat origin. Their vagility hinders data collection on abundance and distribution, which constrains modeling efforts and our understanding of bat ecology, viral dynamics, and spillover. We addressed this knowledge gap with models and data on the occurrence and abundance of nectarivorous fruit bat populations at 3 day roosts in southeast Queensland. We used environmental drivers of nectar production as predictors and explored relationships between bat abundance and virus spillover. Specifically, we developed several novel modeling tools motivated by complexities of fruit bat foraging ecology, including: (1) a dataset of spatial variables comprising Eucalypt‐focused vegetation indices, cumulative precipitation, and temperature anomaly; (2) an algorithm that associated bat population response with spatial covariates in a spatially and temporally relevant way given our current understanding of bat foraging behavior; and (3) a thorough statistical learning approach to finding optimal covariate combinations. We identified covariates that classify fruit bat occupancy at each of our three study roosts with 86–93% accuracy. Negative binomial models explained 43–53% of the variation in observed abundance across roosts. Our models suggest that spatiotemporal heterogeneity in Eucalypt‐based food resources could drive at least 50% of bat population behavior at the landscape scale. We found that 13 spillover events were observed within the foraging range of our study roosts, and they occurred during times when models predicted low population abundance. Our results suggest that, in southeast Queensland, spillover may not be driven by large aggregations of fruit bats attracted by nectar‐based resources, but rather by behavior of smaller resident subpopulations. Our models and data integrated remote sensing and statistical learning to make inferences on bat ecology and disease dynamics. This work provides a foundation for further studies on landscape‐scale population movement and spatiotemporal disease dynamics.     

The effects of nicotine on human fetal development

Maternal smoking during pregnancy continues to represent a major public health concern. Nicotine is extremely harmful to the developing fetus through many different mechanisms, and the harms increase with later gestational age at exposure. Pregnancies complicated by maternal nicotine use are more likely to have significant adverse outcomes. Nicotine‐exposed children tend to have several health problems throughout their lives, including impaired function of the endocrine, reproductive, respiratory, cardiovascular, and neurologic systems. Poor academic performance and significant behavioral disruptions are also common, including ADHD, aggressive behaviors, and future substance abuse. To diminish the adverse effects from cigarette smoking, some women are turning to electronic cigarettes, a new trend that is increasing in popularity worldwide. They are largely perceived as being safer to use in pregnancy than traditional cigarettes, although there is not adequate evidence to support this claim. At this time, electronic cigarette use during pregnancy cannot be recommended. Birth Defects Research (Part C) 108:181–192, 2016. © 2016 Wiley Periodicals, Inc.     

Cannabinoid receptor 1 (CNR1) gene variant moderates neural index of cognitive disruption during nicotine withdrawal

Nicotine withdrawal‐related disruption of cognitive control may contribute to the reinforcement of tobacco use. Identification of gene variants that predict this withdrawal phenotype may lead to tailored pharmacotherapy for smoking cessation. Variation on the cannabinoid receptor 1 gene (CNR1) has been related to nicotine dependence, and CNR1 antagonists may increase attention and memory functioning. We targeted CNR1 variants as moderators of a validated neural marker of nicotine withdrawal‐related cognitive disruption. CNR1 polymorphisms comprising the ‘TAG’ haplotype (rs806379, rs1535255 and rs2023239) were tested independently, as no participants in this sample possessed this haplotype. Nicotine withdrawal‐related cognitive disruption was indexed as increased resting electroencephalogram (EEG) alpha‐1 power density across 17 electrodes. Seventy‐three Caucasian Non‐Hispanic smokers (≥15 cigarettes per day) visited the laboratory on two occasions following overnight smoking/nicotine deprivation. Either two nicotine or two placebo cigarettes were smoked prior to collecting EEG data at each session. Analyses showed that rs806379 moderated the effects of nicotine deprivation increasing slow wave EEG (P = 0.004). Smokers homozygous for the major allele exhibited greater nicotine withdrawal‐related cognitive disruption. The current findings suggest potential efficacy of cannabinoid receptor antagonism as a pharmacotherapy approach for smoking cessation among individuals who exhibit greater nicotine withdrawal‐related cognitive disruption.