The population genetic structure of clonal organisms generated by exponentially bounded and fat-tailed dispersal

Long distance dispersal (LDD) plays an important role in many population processes like colonization, range expansion, and epidemics. LDD of small particles like fungal spores is often a result of turbulent wind dispersal and is best described by functions with power-law behavior in the tails ("fat tailed"). The influence of fat-tailed LDD on population genetic structure is reported in this article. In computer simulations, the population structure generated by power-law dispersal with exponents in the range of -2 to -1, in distinct contrast to that generated by exponential dispersal, has a fractal structure. As the power-law exponent becomes smaller, the distribution of individual genotypes becomes more self-similar at different scales. Common statistics like G(ST) are not well suited to summarizing differences between the population genetic structures. Instead, fractal and self-similarity statistics demonstrated differences in structure arising from fat-tailed and exponential dispersal. When dispersal is fat tailed, a log-log plot of the Simpson index against distance between subpopulations has an approximately constant gradient over a large range of spatial scales. The fractal dimension D-2 is linearly inversely related to the power-law exponent, with a slope of similar to -2. In a large simulation arena, fat-tailed LDD allows colonization of the entire space by all genotypes whereas exponentially bounded dispersal eventually confines all descendants of a single clonal lineage to a relatively small area.

Identification of Bartonella species in rodents, shrews and cats in Denmark: detection of two B-henselae variants, one in cats and the other in the long-tailed field mouse

Small mammals and stray cats were trapped in two areas of North Zealand, Denmark, and their blood cultured for hemotrophic bacteria. Bacterial isolates were recovered in pure culture and subjected to 16S rDNA gene sequencing. Bartonella species were isolated from five mammalian species: B. grahamii from Microtus agrestis (field vole) and Apodemus flavicollis (yellow-necked field mouse); B. taylorii from M. agrestis, A. flavicollis and A. sylvaticus (long-tailed field mouse); B. tribocorum from A. flavicollis; R vinsonii subsp. vinsonii from M. agrestis and A. sylvaticus; and B. birtlesii from Sorex vulgaris (common shrew). In addition, two variant types of B. henselae were identified: variant I was recovered from three specimens of A. sylvaticus, and B. henselae variant 11 from I I cats; in each case this was the only B. henselae variant found. No Bartonella species was isolated from Clethrionomys glareolus (bank vole) or Micromys minutus (harvest mouse). These results suggest that B. henselae occurs in two animal reservoirs in this region, one of variant I in A. sylvaticus, which may be transmitted between mice by the tick Ixodes ricinus, and another of variant 11 in cats, which may be transmitted by the cat flea (Ctenocephalides felis). To our knowledge, this is the first report of the occurrence of B. henselae and B. tribocorum in Apodemus mice.

Resolution of sharp fronts in the presence of model error in variational data assimilation

We show that the four-dimensional variational data assimilation method (4DVar) can be interpreted as a form of Tikhonov regularization, a very familiar method for solving ill-posed inverse problems. It is known from image restoration problems that L1-norm penalty regularization recovers sharp edges in the image more accurately than Tikhonov, or L2-norm, penalty regularization. We apply this idea from stationary inverse problems to 4DVar, a dynamical inverse problem, and give examples for an L1-norm penalty approach and a mixed total variation (TV) L1–L2-norm penalty approach. For problems with model error where sharp fronts are present and the background and observation error covariances are known, the mixed TV L1–L2-norm penalty performs better than either the L1-norm method or the strong constraint 4DVar (L2-norm)method. A strength of the mixed TV L1–L2-norm regularization is that in the case where a simplified form of the background error covariance matrix is used it produces a much more accurate analysis than 4DVar. The method thus has the potential in numerical weather prediction to overcome operational problems with poorly tuned background error covariance matrices.