Aurélie Coulon

Costs of dispersal

Dispersal costs can be classified into energetic, time, risk and opportunity costs and may be levied directly or deferred during departure, transfer and settlement. They may equally be incurred during life stages before the actual dispersal event through investments in special morphologies. Because costs will eventually determine the performance of dispersing individuals and the evolution of dispersal, we here provide an extensive review on the different cost types that occur during dispersal in a wide array of organisms, ranging from micro‐organisms to plants, invertebrates and vertebrates. In general, costs of transfer have been more widely documented in actively dispersing organisms, in contrast to a greater focus on costs during departure and settlement in plants and animals with a passive transfer phase. Costs related to the development of specific dispersal attributes appear to be much more prominent than previously accepted. Because costs induce trade‐offs, they give rise to covariation between dispersal and other life‐history traits at different scales of organismal organisation. The consequences of (i) the presence and magnitude of different costs during different phases of the dispersal process, and (ii) their internal organisation through covariation with other life‐history traits, are synthesised with respect to potential consequences for species conservation and the need for development of a new generation of spatial simulation models.

Statistical methods in spatial genetics

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.

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 × 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.

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 × 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 (FST = 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.

Identifying future research needs in landscape genetics: Where to from here?

Landscape genetics is an emerging interdisciplinary field that combines methods and concepts from population genetics, landscape ecology, and spatial statistics. The interest in landscape genetics is steadily increasing, and the field is evolving rapidly. We here outline four major challenges for future landscape genetic research that were identified during an international landscape genetics workshop. These challenges include (1) the identification of appropriate spatial and temporal scales; (2) current analytical limitations; (3) the expansion of the current focus in landscape genetics; and (4) interdisciplinary communication and education. Addressing these research challenges will greatly improve landscape genetic applications, and positively contribute to the future growth of this promising field.

Congruent population structure inferred from dispersal behaviour and intensive genetic surveys of the threatened Florida scrub‐jay (Aphelocoma cœrulescens)

The delimitation of populations, defined as groups of individuals linked by gene flow, is possible by the analysis of genetic markers and also by spatial models based on dispersal probabilities across a landscape. We combined these two complimentary methods to define the spatial pattern of genetic structure among remaining populations of the threatened Florida scrub‐jay, a species for which dispersal ability is unusually well‐characterized. The range‐wide population was intensively censused in the 1990s, and a metapopulation model defined population boundaries based on predicted dispersal‐mediated demographic connectivity. We subjected genotypes from more than 1000 individual jays screened at 20 microsatellite loci to two Bayesian clustering methods. We describe a consensus method for identifying common features across many replicated clustering runs. Ten genetically differentiated groups exist across the present‐day range of the Florida scrub‐jay. These groups are largely consistent with the dispersal‐defined metapopulations, which assume very limited dispersal ability. Some genetic groups comprise more than one metapopulation, likely because these genetically similar metapopulations were sundered only recently by habitat alteration. The combined reconstructions of population structure based on genetics and dispersal‐mediated demographic connectivity provide a robust depiction of the current genetic and demographic organization of this species, reflecting past and present levels of dispersal among occupied habitat patches. The differentiation of populations into 10 genetic groups adds urgency to management efforts aimed at preserving what remains of genetic variation in this dwindling species, by maintaining viable populations of all genetically differentiated and geographically isolated populations.

Permanent Genetic Resources added to Molecular Ecology Resources Database 1 October 2009–30 November 2009

This article documents the addition of 411 microsatellite marker loci and 15 pairs of Single Nucleotide Polymorphism (SNP) sequencing primers to the Molecular Ecology Resources Database. Loci were developed for the following species: Acanthopagrus schlegeli, Anopheles lesteri, Aspergillus clavatus, Aspergillus flavus, Aspergillus fumigatus, Aspergillus oryzae, Aspergillus terreus, Branchiostoma japonicum, Branchiostoma belcheri, Colias behrii, Coryphopterus personatus, Cynogolssus semilaevis, Cynoglossus semilaevis, Dendrobium officinale, Dendrobium officinale, Dysoxylum malabaricum, Metrioptera roeselii, Myrmeciza exsul, Ochotona thibetana, Neosartorya fischeri, Nothofagus pumilio, Onychodactylus fischeri, Phoenicopterus roseus, Salvia officinalis L., Scylla paramamosain, Silene latifo, Sula sula, and Vulpes vulpes. These loci were cross‐tested on the following species: Aspergillus giganteus, Colias pelidne, Colias interior, Colias meadii, Colias eurytheme, Coryphopterus lipernes, Coryphopterus glaucofrenum, Coryphopterus eidolon, Gnatholepis thompsoni, Elacatinus evelynae, Dendrobium loddigesii Dendrobium devonianum, Dysoxylum binectariferum, Nothofagus antarctica, Nothofagus dombeyii, Nothofagus nervosa, Nothofagus obliqua, Sula nebouxii, and Sula variegata. This article also documents the addition of 39 sequencing primer pairs and 15 allele specific primers or probes for Paralithodes camtschaticus.

genhet: an easy‐to‐use R function to estimate individual heterozygosity

genhet is an R function which calculates the five most used estimates of individual heterozygosity. The advantage of this program is that it can be applied to any diploid genotype dataset, without any limitation in the number of individuals, loci or alleles. Its detailed manual should allow people who have never used R before to make the function work quite easily. The program is freely available at http://www.aureliecoulon.net/research/ac‐computer‐programs.html.

The chemokine receptor CXCR4 strongly promotes neuroblastoma primary tumour and metastatic growth, but not invasion.

Neuroblastoma (NB) is a heterogeneous, and particularly malignant childhood neoplasm in its higher stages, with a propensity to form metastasis in selected organs, in particular liver and bone marrow, and for which there is still no efficient treatment available beyond surgery. Recent evidence indicates that the CXCR4/CXCL12 chemokine/receptor axis may be involved in promoting NB invasion and metastasis. In this study, we explored the potential role of CXCR4 in the malignant behaviour of NB, using a combination of in vitro functional analyses and in vivo growth and metastasis assessment in an orthotopic NB mouse model. We show here that CXCR4 overexpression in non-metastatic CXCR4-negative NB cells IGR-NB8 and in moderately metastatic, CXCR4 expressing NB cells IGR-N91, strongly increased tumour growth of primary tumours and liver metastases, without altering the frequency or the pattern of metastasis. Moreover shRNA-mediated knock-down experiments confirmed our observations by showing that silencing CXCR4 in NB cells impairs in vitro and almost abrogates in vivo growth. High levels of CXCL12 were detected in the mouse adrenal gland (the primary tumour site), and in the liver suggesting a paracrine effect of host-derived CXCL12 on NB growth. In conclusion, this study reveals a yet unreported NB-specific predominant growth and survival-promoting role of CXCR4, which warrants a critical reconsideration of the role of CXCR4 in the malignant behaviour of NB and other cancers.

Testing Global Positioning System Performance for Wildlife Monitoring Using Mobile Collars and Known Reference Points

Abstract To determine the spatial resolution of Global Positioning System (GPS) receiver data, rigorous testing is essential. We tested performance of the Lotek 3300 GPS collar for medium-sized mammals (Lotek Engineering, Inc., Newmarket, ON, Canada). To mimic real wildlife monitoring situations, we performed both static (stationary receiver) and mobile tests, placing the receiver collar on a dog. We compared fix locations of the mobile receiver with the actual trajectory described by a portable Trimble high-precision GPS. We determined performance in relation to habitat type and leaf cover. Location error was habitat-dependent, with the best results in open habitat and much poorer ones in forest, particularly coniferous-dominated forest. For both static and mobile tests, location accuracy was higher when the number of satellites contacted was high and when the residual positional dilution of precision (PDOP) value was low. However, location error was highly variable, even for a given PDOP value and a given number of satellites contacted. Finally, mobile collars performed less well than their static counterparts, presumably because of frequent changes of GPS position and orientation.