Wild Boar and Red Deer Display High Prevalences of Tuberculosis-Like Lesions in Spain

We describe the distribution of tuberculosis-like lesions (TBL) in wild boar (Sus scrofa) and red deer (Cervus elaphus) in Spain. Animals with TBL were confirmed in 84.21% of mixed populations (n = 57) of red deer and wild boar and in 75% of populations of wild boar alone (n = 8) in central and southern Spain (core area). The prevalence of TBL declined towards the periphery of this region. In the core area, the prevalence ranged up to 100% in local populations of wild boar (mean estate prevalence 42.51%) and up to 50% in red deer (mean estate prevalence 13.70%). We carried out exploratory statistical analyses to describe the epidemiology of TBL in both species throughout the core area. Prevalence of TBL increased with age in both species. Wild boar and red deer mean TBL prevalence at the estate level were positively associated, and lesion scores were consistently higher in wild boars than in red deer. The wild boar prevalence of TBL in wild boar did not differ between populations that were or were not cohabiting with red deer. Amongst the wild boars with TBL, 61.19% presented generalized lesions, and the proportion of generalized cases was similar between sex and age classes. In red deer, 57.14% of TBL-positive individuals presented generalized lesions, and the percentage of generalized cases increased with age class, but did not differ between the sexes. These results highlight the potential importance of wild boar and red deer in the maintenance of tuberculosis in south central Spain.

Review of Applied Spatial Data Analysis with R by R. S. Bivand, E. J. Pebesma, and V. Gomez-Rubio

Most authors struggle to pick a title that adequately conveys all of the material covered in a book. When I first saw Applied Spatial Data Analysis with R, I expected a review of spatial statistical models and their applications in packages (libraries) from the CRAN site of R. The authors’ title is not misleading, but I was very pleasantly surprised by how deep the word “applied” is here. The first half of the book essentially covers how R handles spatial data. To some statisticians this may be boring. Do you want, or need, to know the difference between S3 and S4 classes, how spatial objects in R are organized, and how various methods work on the spatial objects? A few years ago I would have said “no,” especially to the “want” part. Just let me slap my EXCEL spreadsheet into R and run some spatial functions on it. Unfortunately, the world is not so simple, and ultimately we want to minimize effort to get all of our spatial analyses accomplished. The first half of this book certainly convinced me that some extra effort in organizing my data into certain spatial class structures makes the analysis easier and less subject to mistakes. I also admit that I found it very interesting and I learned a lot.