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      Does host socio-spatial behavior lead to a fine-scale spatial genetic structure in its associated parasites? Translated title: Le comportement socio-spatial de l’hôte conduit-il à une structure génétique à fine échelle de ses parasites ?

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          Gastro-intestinal nematodes, especially Haemonchus contortus, are widespread pathogenic parasites of small ruminants. Studying their spatial genetic structure is as important as studying host genetic structure to fully understand host-parasite interactions and transmission patterns. For parasites having a simple life cycle (e.g., monoxenous parasites), gene flow and spatial genetic structure are expected to strongly rely on the socio-spatial behavior of their hosts. Based on five microsatellite loci, we tested this hypothesis for H. contortus sampled in a wild Mediterranean mouflon population ( Ovis gmelini musimon ×  Ovis sp.) in which species- and environment-related characteristics have been found to generate socio-spatial units. We nevertheless found that their parasites had no spatial genetic structure, suggesting that mouflon behavior was not enough to limit parasite dispersal in this study area and/or that other ecological and biological factors were involved in this process, for example other hosts, the parasite life cycle, or the study area history.

          Translated abstract

          Les nématodes gastro-intestinaux, et plus particulièrement Haemonchus contortus, sont cosmopolites et pathogènes chez les petits ruminants. Étudier leur structure génétique spatiale est aussi important que d’étudier celle des hôtes pour pleinement comprendre les interactions hôtes-parasites et les processus de transmission. Pour les parasites ayant des cycles de vie simples (par exemple, les parasites monoxènes), on s’attend à ce que les flux de gènes et la structure génétique spatiale dépendent fortement du comportement socio-spatial de leurs hôtes. En utilisant cinq loci microsatellites, nous avons testé cette hypothèse pour des H. contortus échantillonnés dans une population sauvage de mouflons méditerranéens ( Ovis gmelini musimon ×  Ovis sp.) dans laquelle les caractéristiques de l’espèce et de l’environnement génèrent des unités socio-spatiales. Nous avons néanmoins mis en évidence que leurs parasites ne présentent pas de structure génétique spatiale, ce qui suggère que le comportement des mouflons ne restreint pas la dispersion des parasites dans cette aire d’étude et/ou que d’autres facteurs biologiques et écologiques tels que d’autres hôtes, le cycle de vie du parasite, ou l’histoire de l’aire d’étude jouent un rôle dans ce processus.

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          Most cited references 84

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          adegenet: a R package for the multivariate analysis of genetic markers.

          The package adegenet for the R software is dedicated to the multivariate analysis of genetic markers. It extends the ade4 package of multivariate methods by implementing formal classes and functions to manipulate and analyse genetic markers. Data can be imported from common population genetics software and exported to other software and R packages. adegenet also implements standard population genetics tools along with more original approaches for spatial genetics and hybridization. Stable version is available from CRAN: http://cran.r-project.org/mirrors.html. Development version is available from adegenet website: http://adegenet.r-forge.r-project.org/. Both versions can be installed directly from R. adegenet is distributed under the GNU General Public Licence (v.2).
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            ggmap: Spatial Visualization with ggplot2

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              Revealing cryptic spatial patterns in genetic variability by a new multivariate method.

              Increasing attention is being devoted to taking landscape information into account in genetic studies. Among landscape variables, space is often considered as one of the most important. To reveal spatial patterns, a statistical method should be spatially explicit, that is, it should directly take spatial information into account as a component of the adjusted model or of the optimized criterion. In this paper we propose a new spatially explicit multivariate method, spatial principal component analysis (sPCA), to investigate the spatial pattern of genetic variability using allelic frequency data of individuals or populations. This analysis does not require data to meet Hardy-Weinberg expectations or linkage equilibrium to exist between loci. The sPCA yields scores summarizing both the genetic variability and the spatial structure among individuals (or populations). Global structures (patches, clines and intermediates) are disentangled from local ones (strong genetic differences between neighbors) and from random noise. Two statistical tests are proposed to detect the existence of both types of patterns. As an illustration, the results of principal component analysis (PCA) and sPCA are compared using simulated datasets and real georeferenced microsatellite data of Scandinavian brown bear individuals (Ursus arctos). sPCA performed better than PCA to reveal spatial genetic patterns. The proposed methodology is implemented in the adegenet package of the free software R.

                Author and article information

                EDP Sciences
                07 November 2019
                : 26
                : ( publisher-idID: parasite/2019/01 )
                [1 ] Université de Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR 5558 69622 Villeurbanne France
                [2 ] Office National de la Chasse et de la Faune Sauvage, Unité Ongulés Sauvages 5 allée de Bethléem, Z.I. Mayencin 38610 Gières France
                [3 ] Department of Comparative Biology and Experimental Medicine, University of Calgary, Faculty of Veterinary Medicine CA-T3B 2C3 Calgary Canada
                [4 ] GIEC du Caroux-Espinouse Fagairolles 34610 Castanet-Le-Haut France
                [5 ] Université de Lyon, VetAgro Sup, Campus Vétérinaire de Lyon 1 Avenue Bourgelat BP 83 69280 Marcy l’Etoile France
                Author notes
                [* ]Corresponding author: elodie.portanier@ 123456gmail.com
                parasite190081 10.1051/parasite/2019062
                © E. Portanier et al., published by EDP Sciences, 2019

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 1, Tables: 1, Equations: 0, References: 83, Pages: 7
                Research Article


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