Summary 1.Spatially explicit Bayesian clustering techniques offer a powerful tool for ecology and wildlife management, as genetic divisions can be correlated with landscape features. We used these methods to analyse the genetic structure of a population of European wild boar Sus scrofa with the aim of identifying effective barriers for disease management units. However, it has been suggested that the methods could produce biased results when faced with deviations from random mating not caused by genetic discontinuities, such as isolation by distance (IBD). 2. We analysed a data set consisting of 697 wild boar multilocus genotypes using spatially explicit ( baps, geneland ) and non-explicit ( structure ) Bayesian methods. We also simulated and analysed data sets characterized by different degrees of IBD, with and without genetic discontinuities. 3. When analysing the empirical data set, different programs did not converge on the same clustering solution and some clusters were difficult to explain biologically. Results from the simulated data showed that IBD, also present in the empirical data set, could cause the Bayesian methods to overestimate genetic structure. Simulated barriers were identified correctly, but the programs superimposed further clusters at higher IBD levels. 4. It was not possible to ascertain with confidence whether the clustering solutions offered by the various programs were an accurate reflection of population genetic structure in our empirical data set or were artefacts created by the underlying IBD pattern. 5. Synthesis and applications : We show that Bayesian clustering methods can overestimate genetic structure when analysing an individual-based data set characterized by isolation by distance. This bias could lead to the erroneous delimitation of management or conservation units. Investigators should be critical and suspicious of clusters that cannot be explained biologically. Data sets should be tested for isolation by distance and conclusions should not be based on the output from just one method.

The joint analysis of spatial and genetic data is rapidly becoming the norm in population genetics. More and more studies explicitly describe and quantify the spatial organization of genetic variation and try to relate it to underlying ecological processes. As it has become increasingly difficult to keep abreast with the latest methodological developments, we review the statistical toolbox available to analyse population genetic data in a spatially explicit framework. We mostly focus on statistical concepts but also discuss practical aspects of the analytical methods, highlighting not only the potential of various approaches but also methodological pitfalls.

The potential link between badgers and bovine tuberculosis has made it vital to develop accurate techniques to census badgers. Here we investigate the potential of using genetic profiles obtained from faecal DNA as a basis for population size estimation. After trialling several methods we obtained a high amplification success rate (89%) by storing faeces in 70% ethanol and using the guanidine thiocyanate / silica method for extraction. Using 70% ethanol as a storage agent had the advantage of it being an antiseptic. In order to obtain reliable genotypes with fewer amplification reactions than the standard multiple-tubes approach, we devised a comparative approach in which genetic profiles were compared and replication directed at similar, but not identical, genotypes. This modified method achieved a reduction in polymerase chain reactions comparable with the maximumlikelihood model when just using reliability criteria, and was slightly better when using reliability criteria with the additional proviso that alleles must be observed twice to be considered reliable. Our comparative approach would be best suited for studies that include multiple faeces from each individual. We utilized our approach in a well-studied population of badgers from which individuals had been sampled and reliable genotypes obtained. In a study of 53 faeces sampled from three social groups over 10 days, we found that direct enumeration could not be used to estimate population size, but that the application of mark-recapture models has the potential to provide more accurate results.

In many European countries, the wild boar (Sus scrofa) is often associated with crop damage. In this study, we analyse data relating to 13,276 cases of wild boar damage to agricultural crops over a 10-year period in Luxembourg (an area of 2,586 km 2 in Western Europe). Results show that (1) damage is more severe in this area than in others; (2) damage to permanent grassland is far more frequent and more severe than damage to annual crops; (3) trichomatous crops such as barley are avoided; (4) damage is seasonally distributed according to type of crop; (5) damage is distributed spatially in a non-uniform manner; (6) damage intensity is significantly correlated with wild boar hunting bags, both over time and space. We suggest that wild boar management strategy should always take into account the issue of damage to agricultural crops. Our results imply that measures for preventing or reducing damage should be more targeted in time and space and that adjustments to cropping patterns should contribute towards a reduction of wild boar damage.

While motorways are often assumed to influence the movement behaviour of large mammals, there are surprisingly few studies that show an influence of these linear structures on the genetic make-up of wild ungulate populations. Here, we analyse the spatial genetic structure of red deer (Cervus elaphus) and wild boars (Sus scrofa) along a stretch of motorway in the Walloon part of Belgium. Altogether, 876 red deer were genotyped at 13 microsatellite loci, and 325 wild boars at 14 loci. In the case of the red deer, different genetic clustering tools identified two genetic subpopulations whose borders matched the motorway well. Conversely, no genetic structure was identified in the case of the wild boar. Analysis of isolation-by-distance patterns of pairs of individuals on the same side and on different sides of the motorway also suggested that the road was a barrier to red deer, but not to wild boar movement. While telemetry studies seem to confirm that red deer are more affected by motorways than wild boar, the red deer sample size was also much larger than that of the wild boars. We therefore repeated the analysis of genetic structure in the red deer with randomly sub-sampled data sets of decreasing size. The power to detect the genetic structure using clustering methods decreased with decreasing sample size.

Urban areas are expanding rapidly, but a few native species have successfully colonized them. The processes underlying such colonization events are poorly understood. Using the blackbird Turdus merula, a former forest specialist that is now one of the most common urban birds in its range, we provide the first assessment of two contrasting urban colonization models. First, that urbanization occurred independently. Second, that following initial urbanization, urban-adapted individuals colonized other urban areas in a leapfrog manner. Previous analyses of spatial patterns in the timing of blackbird urbanization, and experimental introductions of urban and rural blackbirds to uncolonized cities, suggest that the leapfrog model is likely to apply. We found that, across the western Palaearctic, urban blackbird populations contain less genetic diversity than rural ones, urban populations are more strongly differentiated from each other than from rural populations and assignment tests support a rural source population for most urban individuals. In combination, these results provide much stronger support for the independent urbanization model than the leapfrog one. If the former model predominates, colonization of multiple urban centres will be particularly difficult when urbanization requires genetic adaptations, having implications for urban species diversity.

Molecular forensic methods are being increasingly used to help enforce wildlife conservation laws. Using multilocus genotyping, illegal translocation of an animal can be demonstrated by excluding all potential source populations as an individual's population of origin. Here, we illustrate how this approach can be applied to a large continuous population by defining the population genetic structure and excluding suspect animals from each identified cluster. We aimed to test the hypothesis that recreational hunters had illegally introduced a group of red deer into a hunting area in Luxembourg. Reference samples were collected over a large area in order to test the possibility that the suspect individuals might be recent immigrants. Due to isolation-by-distance relationships in the data set, inferring the number of genetic clusters using Bayesian methods was not straightforward. Biologically meaningful clusters were only obtained by simultaneously analysing spatial and genetic information using the program baps 4.1. We inferred the presence of three genetic clusters in the study region. Using partial Mantel tests, we detected barriers to gene flow other than distance, probably created by a combination of urban areas, motorways and a river valley used for viticulture. The four focal animals could be excluded with a high certainty from the three genetic subpopulations and it was therefore likely that they had been released illegally.

Sampling DNA noninvasively has advantages for identifying animals for uses such as mark-recapture modeling that require unique identification of animals in samples. Although it is possible to generate large amounts of data from noninvasive sources of DNA, a challenge is overcoming genotyping errors that can lead to incorrect identification of individuals. A major source of error is allelic dropout, which is failure of DNA amplification at one or more loci. This has the effect of heterozygous individuals being scored as homozygotes at those loci as only one allele is detected. If errors go undetected and the genotypes are naively used in mark-recapture models, significant overestimates of population size can occur. To avoid this it is common to reject low-quality samples but this may lead to the elimination of large amounts of data. It is preferable to retain these low-quality samples as they still contain usable information in the form of partial genotypes. Rather than trying to minimize error or discarding error-prone samples we model dropout in our analysis. We describe a method based on data augmentation that allows us to model data from samples that include uncertain genotypes. Application is illustrated using data from the European badger (Meles meles).