Habitat fragmentation is a major threat to biodiversity. Nonetheless, habitat fragmentation may have an unequal influence over genetic flow depending on specie life history traits and dispersion capacities (Baguette et al., 2013). Generally, the effects of fragmentation on genetic flow is more pronounced for sessile organisms, compared with more mobile or vagile ones (Callens et al., 2011), and for specialists compared with generalists (Bonte et al., 2003; Entling et al., 2011). The plant species Geum urbanum (wood avens: on the left picture below) and Primula elatior (true oxlip: on the right picture below) are common within highly fragmented European temperate forests. They are nonetheless two contrasting models in terms of dispersion abilities and ecological specialisation: P. elatior being a notorious forest specialist with limited dispersal abilities.
The proposed internship aims at evaluating functional connectivity in a fragmented system (temperate deciduous forests) by means of indirect estimators for this two contrasting model species. Contemporary and past genetic diversity will be measured using cytoplasmic genes (cpDNA – historic genetic variation) and microsatellites loci (contemporary gene flow) (Arens et al., 2004; Van Geert et al., 2006; Seino et al., 2014). Two landscape windows were selected for both the Hauts-de-France region (Thiérache) and the Brittany region (Zone Atelier Armorique): one window is characterized by forest patches interconnected through a dense hedgerow network (locally called ‘bocage’) while the other window is characterized by a highly fragmented system with few to no hedgerows. For the Hauts-de-France region, a third landscape window was selected as a control or baseline unfragmented system based on ‘virtual forest patches’ spread out across a forest matrix. Individuals were sampled in forest patches (or virtual forest patches) and in hedgerows during two field seasons (i.e. in 2017 and 2018). Functional connectivity and population genetic structure will be investigated using classic methods (DAPC) (Jombart et al., 2008) and Bayesian assigning tests (Pritchard et al., 2000; Guillot et al., 2005) for characterizing genetic structure. The role of landscape features on genetic structure will be assessed by means of correlative approaches and graph-theory methods. The successful applicant will be highly encouraged to propose other analytical methods if he/she deems fit.
We seek a highly motivated and curious candidate. The successful candidate will be closely supervised by Professor Annie Guiller, Senior Researcher Jonathan Lenoir and PhD student Pedro Poli during his/her internship. He/She should be able to work independently. Strong interest in population genetics, landscape ecology, evolution and biostatistics are recommended, meaning that a good background in those domains is desired, but not mandatory. Those skills will be developed during the internship.
Funding: We acknowledge a grant from the “Fondation pour la Recherche sur la Biodiversité” (FRB, AO 2018).
Internship stipend: About 500 EUR per month during 6 months (January-June 2019).
Starting date and duration: The succesful applicant is expected to start his/her internship on the 7th of January 2019 for a 6 month duration.
Host Institute: Edysan is a mixed research unit involving the CNRS and Université de Picardie Jules Verne institutes. The research work developed in Edysan aims at understanding natural and semi-natural ecosystem functioning and impacts environment changes over those systems. We are well placed in Amiens, a nice and active city in the Picardy region. The laboratory has an ongoing partnership with Regional Molecular Biology Centre (CRRBM) that dispose of state of the art equipment and infrastructure.
Supervisors: Annie Guiller (Professeur), Jonathan Lenoir (CR CNRS) and Pedro Poli (PhD student).
Application deadline: 30th November 2018. Potential candidates should send a CV and a cover letter (in English or French) to: annie.guiller@u-picardie.fr, jonathan.lenoir@u-picardie.fr and pedro.poli@u-picardie.fr
Jonathan Lenoir 