High prevalence and lineage diversity of avian malaria in wild populations of great tits (Parus major) and mosquitoes (Culex pipiens).

Avian malaria studies have taken a prominent place in different aspects of evolutionary ecology. Despite a recent interest in the role of vectors within the complex interaction system of the malaria parasite, they have largely been ignored in most epidemiological studies. Epidemiology of the disease is however strongly related to the vector's ecology and behaviour, and there is a need for basic investigations to obtain a better picture of the natural associations between Plasmodium lineages, vector species and bird hosts. The aim of the present study was to identify the mosquito species involved in the transmission of the haemosporidian parasites Plasmodium spp. in two wild populations of breeding great tits (Parus major) in western Switzerland. Additionally, we compared Plasmodium lineages, based on mitochondrial DNA cytochrome b sequences, between the vertebrate and dipteran hosts, and evaluated the prevalence of the parasite in the mosquito populations. Plasmodium spp. were detected in Culex pipiens only, with an overall 6.6% prevalence. Among the six cytochrome b lineages of Plasmodium identified in the mosquitoes, three were also present in great tits. The results provide evidence for the first time that C. pipiens can act as a natural vector of avian malaria in Europe and yield baseline data for future research on the epidemiology of avian malaria in European countries.

Morphometric shape analysis using learning vector quantization neural networks - an example distinguishing two microtine vole species

Closely related species may be very difficult to distinguish morphologically, yet sometimes morphology is the only reasonable possibility for taxonomic classification. Here we present learning-vector-quantization artificial neural networks as a powerful tool to classify specimens on the basis of geometric morphometric shape measurements. As an example, we trained a neural network to distinguish between field and root voles from Procrustes transformed landmark coordinates on the dorsal side of the skull, which is so similar in these two species that the human eye cannot make this distinction. Properly trained neural networks misclassified only 3% of specimens. Therefore, we conclude that the capacity of learning vector quantization neural networks to analyse spatial coordinates is a powerful tool among the range of pattern recognition procedures that is available to employ the information content of geometric morphometrics.

Viral-mediated overexpression of mutant huntingtin to model HD in various species.

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder caused by an expansion of CAG repeats in the huntingtin (Htt) gene. Despite intensive efforts devoted to investigating the mechanisms of its pathogenesis, effective treatments for this devastating disease remain unavailable. The lack of suitable models recapitulating the entire spectrum of the degenerative process has severely hindered the identification and validation of therapeutic strategies. The discovery that the degeneration in HD is caused by a mutation in a single gene has offered new opportunities to develop experimental models of HD, ranging from in vitro models to transgenic primates. However, recent advances in viral-vector technology provide promising alternatives based on the direct transfer of genes to selected sub-regions of the brain. Rodent studies have shown that overexpression of mutant human Htt in the striatum using adeno-associated virus or lentivirus vectors induces progressive neurodegeneration, which resembles that seen in HD. This article highlights progress made in modeling HD using viral vector gene transfer. We describe data obtained with of this highly flexible approach for the targeted overexpression of a disease-causing gene. The ability to deliver mutant Htt to specific tissues has opened pathological processes to experimental analysis and allowed targeted therapeutic development in rodent and primate pre-clinical models.

Modelling ecological niches with support vector machines

1. The ecological niche is a fundamental biological concept. Modelling species' niches is central to numerous ecological applications, including predicting species invasions, identifying reservoirs for disease, nature reserve design and forecasting the effects of anthropogenic and natural climate change on species' ranges. 2. A computational analogue of Hutchinson's ecological niche concept (the multidimensional hyperspace of species' environmental requirements) is the support of the distribution of environments in which the species persist. Recently developed machine-learning algorithms can estimate the support of such high-dimensional distributions. We show how support vector machines can be used to map ecological niches using only observations of species presence to train distribution models for 106 species of woody plants and trees in a montane environment using up to nine environmental covariates. 3. We compared the accuracy of three methods that differ in their approaches to reducing model complexity. We tested models with independent observations of both species presence and species absence. We found that the simplest procedure, which uses all available variables and no pre-processing to reduce correlation, was best overall. Ecological niche models based on support vector machines are theoretically superior to models that rely on simulating pseudo-absence data and are comparable in empirical tests. 4. Synthesis and applications. Accurate species distribution models are crucial for effective environmental planning, management and conservation, and for unravelling the role of the environment in human health and welfare. Models based on distribution estimation rather than classification overcome theoretical and practical obstacles that pervade species distribution modelling. In particular, ecological niche models based on machine-learning algorithms for estimating the support of a statistical distribution provide a promising new approach to identifying species' potential distributions and to project changes in these distributions as a result of climate change, land use and landscape alteration.

How a haemosporidian parasite of bats gets around: the genetic structure of a parasite, vector and host compared.

Parasite population structure is often thought to be largely shaped by that of its host. In the case of a parasite with a complex life cycle, two host species, each with their own patterns of demography and migration, spread the parasite. However, the population structure of the parasite is predicted to resemble only that of the most vagile host species. In this study, we tested this prediction in the context of a vector-transmitted parasite. We sampled the haemosporidian parasite Polychromophilus melanipherus across its European range, together with its bat fly vector Nycteribia schmidlii and its host, the bent-winged bat Miniopterus schreibersii. Based on microsatellite analyses, the wingless vector, and not the bat host, was identified as the least structured population and should therefore be considered the most vagile host. Genetic distance matrices were compared for all three species based on a mitochondrial DNA fragment. Both host and vector populations followed an isolation-by-distance pattern across the Mediterranean, but not the parasite. Mantel tests found no correlation between the parasite and either the host or vector populations. We therefore found no support for our hypothesis; the parasite population structure matched neither vector nor host. Instead, we propose a model where the parasite's gene flow is represented by the added effects of host and vector dispersal patterns.