Genetic structure is influenced by landscape features: empirical evidence from a roe deer population.

The delimitation of population units is of primary importance in population management and conservation biology. Moreover, when coupled with landscape data, the description of population genetic structure can provide valuable knowledge about the permeability of landscape features, which is often difficult to assess by direct methods (e.g. telemetry). In this study, we investigated the genetic structuring of a roe deer population which recently recolonized a fragmented landscape. We sampled 1148 individuals from a 40 x 55-km area containing several putative barriers to deer movements, and hence to gene flow, namely a highway, rivers and several canals. In order to assess the effect of these landscape features on genetic structure, we implemented a spatial statistical model known as geneland which analyses genetic structure, explicitly taking into account the spatial nature of the problem. Two genetic units were inferred, exhibiting a very low level of differentiation (F(ST) = 0.008). The location of their boundaries suggested that there are no absolute barriers in this study area, but that the combination of several landscape features with low permeability can lead to population differentiation. Our analysis hence suggests that the landscape has a significant influence on the structuring of the population under study. It also illustrates the use of geneland as a powerful method to infer population structure, even in situations of young populations exhibiting low genetic differentiation.

Dispersal is not female biased in a resource-defence mating ungulate, the European roe deer.

Dispersal is frequently more prevalent in one sex compared to the other. Greenwood proposed that patterns of sex-biased dispersal among birds and mammals are linked to their mating strategies. For species where males defend resources rather than females, he predicted female-biased dispersal, because males should remain at their birth site where they are familiar with the distribution of the resources that they must defend. Greenwood's hypothesis has been extensively supported among birds, where most species exhibit a resource-defence mating strategy. However, almost no equivalent information is available for mammals as males generally defend mates in this group. An exception is the European roe deer, a resource-defence mating ungulate. We thus tested Greenwood's hypothesis on this atypical mammalian model, looking for female-biased dispersal using sex-specific inter-individual genetic distances. We conclusively show that gene flow is not higher among females compared to males in the studied roe deer population, and hence that dispersal is not female-biased, suggesting that male mating strategy is not the primary selective force driving the evolution of dispersal in roe deer. We discuss the role of female mate choice and intra-sexual competition as possible alternative selective pressures involved.

Landscape connectivity influences gene flow in a roe deer population inhabiting a fragmented landscape: an individual-based approach.

Changes in agricultural practices and forest fragmentation can have a dramatic effect on landscape connectivity and the dispersal of animals, potentially reducing gene flow within populations. In this study, we assessed the influence of woodland connectivity on gene flow in a traditionally forest-dwelling species--the European roe deer--in a fragmented landscape. From a sample of 648 roe deer spatially referenced within a study area of 55 x 40 km, interindividual genetic distances were calculated from genotypes at 12 polymorphic microsatellite loci. We calculated two geographical distances between each pair of individuals: the Euclidean distance (straight line) and the 'least cost distance' (the trajectory that maximizes the use of wooded corridors). We tested the correlation between genetic pairwise distances and the two types of geographical pairwise distance using Mantel tests. The correlation was better using the least cost distance, which takes into account the distribution of wooded patches, especially for females (the correlation was stronger but not significant for males). These results suggest that in a fragmented woodland area roe deer dispersal is strongly linked to wooded structures and hence that gene flow within the roe deer population is influenced by the connectivity of the landscape.

Spectral acoustic structure of barking in roe deer (Capreolus capreolus). Sex-, age- and individual-related variations.

In roe deer, barking is a loud call commonly given by males and females during inter- or intraspecific interactions. The analysis of a set of 19 spectral variables computed on 560 calls revealed significant variation between sexes, individuals, and probably age classes. Discriminant analysis predicted the sex of an individual with a 93.5% probability from a small portion of the bark frequency range. Among six males, a linear combination of six variables predicted the identity of the barking individual with a 70% probability. These sexual and individual differences provide the potential for social recognition from vocalizations. These results are consistent with the hypothesis that barking in roe deer may allow remote signalling of presence, location and identity, and play an important role in the territorial system of this species.

Artificial neural networks as a classification method in the behavioural sciences.

The classification and recognition of individual characteristics and behaviours constitute a preliminary step and is an important objective in the behavioural sciences. Current statistical methods do not always give satisfactory results. To improve performance in this area, we present a methodology based on one of the principles of artificial neural networks: the backpropagation gradient. After summarizing the theoretical construction of the model, we describe how to parameterize a neural network using the example of the individual recognition of vocalizations of four fallow deer (Dama dama). With 100% recognition and 90% prediction success, the results are very promising.

Bubbleblow in beluga whales (Delphinapterus leucas): a play activity?

In Odontocetes, bubbleblow is reported in several ethograms but its function is poorly understood. For the five captive beluga whales (Delphinapterus leucas) of the Vancouver Public Aquarium, we recorded the occurrence of this behaviour, its diurnal pattern and individual variability. We suggest that bubbleblow can be considered as a solitary play activity.

Microsatellite variation in an introduced mouflon population.

As previous studies of genetic polymorphism in the mouflon (Ovis gmelini) have not provided any valuable markers for population studies, we tested the capacity of microsatellites to index the genetic diversity of a recently introduced mouflon population. Six pairs of bovine primer amplified microsatellites in mouflon, and all six were polymorphic. Furthermore, despite the low number of founders, five loci had a high gene diversity in this introduced population. Unlike other genetic markers, microsatellites could be powerful to study the genetic structure of mouflon populations.

Classifying individuals among infra-specific taxa using microsatellite data and neural networks.

The method of neural networks was tested for its ability to assign individuals on the basis of their multilocus genotypes, using a data collection of 430 honeybees and 8 microsatellite loci. This data set includes various taxonomical levels (populations within the same subspecies, various subspecies belonging to the same evolutionary lineage, and the 3 lineages of the species). Qualitative genotypic data have been submitted to 2 types of transformation (simple coding and coding plus factorial correspondence analysis), and they have been partitioned in 2 sets, a training set of 300 individuals and a testing set of 103 individuals. Two procedures ("leave one out" and "hold out") were applied to evaluate the quality of prediction. Compared to discriminant analysis, neural networks performed better in terms of correctly classified individuals at any taxonomical level. For instance, with the simple coding and the hold out procedure, the proportions of correctly assigned individuals from the testing set were 66.2%, 82.3% and 100% at the populations, subspecies and lineage level, respectively. The potential use of neural networks in populations genetics is discussed.