Bloodmeal analysis in Culicoides midges collected near horses,donkeys and zebras in the Eastern Cape,South Africa

An upsurge in African horse sickness (AHS) in the Eastern Cape, South Africa, from 2006 led to an epidemiological reassessment of the disease there. Light trapping surveys carried out near horses, donkeys and zebras in 2014–2016 collected 39 species of Culicoides midge (Diptera: Ceratopogonidae) that are potential vectors of AHS. To establish if these midges fed on equids, DNA sequences were obtained from the gut contents of 52 female midges (35 freshly blood‐fed, 13 gravid and four parous), representing 11 species collected across 11 sites. Culicoides leucostictus fed on all three equids. Culicoides bolitinos, Culicoides imicola and Culicoides magnus fed on both horses and donkeys. Culicoides onderstepoortensis fed on donkeys, and Culicoides similis and Culicoides pycnostictus fed on zebras. Bloodmeals from cows, pigs, warthogs, impalas and a domestic dog were also identified in various species, but none of the midges tested had fed on birds. These results contribute to knowledge of the vectorial capacity of several species of Culicoides with regard to AHS in the Eastern Cape and point to potential reservoir hosts, of which donkeys, zebras and domestic dogs have previously been found to harbour AHS. Blood‐fed midges were also obtained throughout winter, indicating the potential for endemic AHS in the province.     

Intraspecific genetic variation of a Fagus sylvatica population in a temperate forest derived from airborne imaging spectroscopy time series

1. The growing pace of environmental change has increased the need for large‐scale monitoring of biodiversity. Declining intraspecific genetic variation is likely a critical factor in biodiversity loss, but is especially difficult to monitor: assessments of genetic variation are commonly based on measuring allele pools, which requires sampling of individuals and extensive sample processing, limiting spatial coverage. Alternatively, imaging spectroscopy data from remote platforms may hold the potential to reveal genetic structure of populations. In this study, we investigated how differences detected in an airborne imaging spectroscopy time series correspond to genetic variation within a population of Fagus sylvatica under natural conditions.
2. We used multi‐annual APEX (Airborne Prism Experiment) imaging spectrometer data from a temperate forest located in the Swiss midlands (Laegern, 47°28'N, 8°21'E), along with microsatellite data from F. sylvatica individuals collected at the site. We identified variation in foliar reflectance independent of annual and seasonal changes which we hypothesize is more likely to correspond to stable genetic differences. We established a direct connection between the spectroscopy and genetics data by using partial least squares (PLS) regression to predict the probability of belonging to a genetic cluster from spectral data.
3. We achieved the best genetic structure prediction by using derivatives of reflectance and a subset of wavebands rather than full‐analyzed spectra. Our model indicates that spectral regions related to leaf water content, phenols, pigments, and wax composition contribute most to the ability of this approach to predict genetic structure of F. sylvatica population in natural conditions.
4. This study advances the use of airborne imaging spectroscopy to assess tree genetic diversity at canopy level under natural conditions, which could overcome current spatiotemporal limitations on monitoring, understanding, and preventing genetic biodiversity loss imposed by requirements for extensive in situ sampling.

     

Semiparametric Regression in Size-Biased Sampling

Summary .  Size-biased sampling arises when a positive-valued outcome variable is sampled with selection probability proportional to its size. In this article, we propose a semiparametric linear regression model to analyze size-biased outcomes. In our proposed model, the regression parameters of covariates are of major interest, while the distribution of random errors is unspecified. Under the proposed model, we discover that regression parameters are invariant regardless of size-biased sampling. Following this invariance property, we develop a simple estimation procedure for inferences. Our proposed methods are evaluated in simulation studies and applied to two real data analyses.     

Analysis of Multiply Matched Retrospective Studies When Risk Factor Under Study is Discrete,With Two or More Levels

The odds ratio is known to closely approximate the relative risk when the disease is rare. Logistic regression models are often used to estimate such odds ratios, but here a different model is used which avoids the assumptions implicit in logistic modelling; it also has the advantage of providing a test of homogeneity for odds rat os in situations where the logistic model cannot.     

Does re-creation of extensive pasture-forest mosaics provide an economically sustainable way of nature conservation in Sweden''s forest dominated regions?

There is a major risk that many of the remaining semi-natural pastures in Swedish forest dominated regions will lose their grazing in the near future with lost biodiversity as a result. The reason is the high costs of grazing small pastures with cattle from generally small herds. The approaching decoupling of the present EU income support per head of cattle will increase the risk. Calculations based on economies of scale in beef production and opportunity cost of forest and arable land suggest that re-creating extensive pasture-forest mosaics consisting of existing semi-natural pastures and adjacent arable fields and forests can secure economically sustainable grazing. The risk of local extinction of grassland species due to habitat isolation is also lower in large mosaics than in small, scattered pastures.     

Portrait of the Patient as a Young Man: An Exploration of the Use of Photographs in Hospital

The display of personal photographs in hospital is a common practice that has yet to be rigorously examined. The photographs displayed are subject to interpretation by the viewer and may lead to misunderstandings or miscommunication if clarification of meaning is not sought. This paper explores a range of possible meanings that the display of photographs in hospital may hold, based on a case study of a 15 year old boy hospitalised with a life threatening illness. Further research is needed into the actual meanings attributed to the display of photographs in hospital by patients and family members.     

Joint multiple quantitative trait loci mapping for allometries of body compositions and metabolic traits to body weights in broiler

In order to map quantitative trait loci (QTLs) for allometries of body compositions and metabolic traits in chicken, we phenotypically characterize the allometric growths of multiple body components and metabolic traits relative to BWs using joint allometric scaling models and then establish random regression models (RRMs) to fit genetic effects of markers and minor polygenes derived from the pedigree on the allometric scalings. Prior to statistically inferring the QTLs for the allometric scalings by solving the RRMs, the LASSO technique is adopted to rapidly shrink most of marker genetic effects to zero. Computer simulation analysis confirms the reliability and adaptability of the so-called LASSO-RRM mapping method. In the F₂ population constructed by multiple families, we formulate two joint allometric scaling models of body compositions and metabolic traits, in which six of nine body compositions are tested as significant, while six of eight metabolic traits are as significant. For body compositions, a total of 14 QTLs, of which 9 dominant, were detected to be associated with the allometric scalings of drumstick, fat, heart, shank, liver and spleen to BWs; while for metabolic traits, a total of 19 QTLs also including 9 dominant be responsible for the allometries of T4, IGFI, IGFII, GLC, INS, IGR to BWs. The detectable QTLs or highly linked markers can be used to regulate relative growths of the body components and metabolic traits to BWs in marker-assisted breeding of chickens.     

Body size and area‐incidence relationships: is there a general pattern?

Aim This paper tests firstly for the existence of a general relationship between body size of terrestrial animals and their incidence across habitat patches of increasing size, and secondly for differences in this relationship between insects and vertebrates. Location The analysis was based on the occupancy pattern of 50 species from 15 different landscapes in a variety of ecosystems ranging from Central European grassland to Asian tropical forest. Methods The area‐occupancy relationship was described by incidence functions that were calculated using logistic regression. A correlation analysis between body size of the species and the patch area referring to the two given points of the incidence function was performed. In order to test for an effect of taxon (insects vs. vertebrates), an analysis of covariance was conducted. Results In all species, the incidence was found to increase with increasing patch area. The macroecological analysis showed a significant relationship between the incidence in habitat patches and the body size of terrestrial animals. The area requirement was found to increase linearly with increasing body size on a log‐log scale. This relationship did not differ significantly between insects and vertebrates. Conclusions The approach highlighted in this paper is to associate incidence functions with body size. The results suggest that body size is a general but rather rough predictor for the area requirements of animals. The relationship seems valid for a wide range of body sizes of terrestrial animals. However, further studies including isolation of habitats as well as additional species traits into the macroecological analysis of incidence functions are needed.     

Comparing Stochastically Restricted Linear Estimators in a Regression Model

Conditions for superiority of the minimum dispersion estimator over another with respect to the covariance matrix are derived when the vector parameter of a regression model is subject to competing stochastic restrictions. The restrictions may also consist both of a deterministic part and a stochastic part.