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UPDATE: The Society of Systematic Biologists (SSB) is now officially sponsoring the Summer School and will provide financial support to help recover the air travel costs of participants from more distant geographic locations!
We invite applications for the Oak Spring eFLOWER Summer School to be held at the Oak Spring Garden Foundation in Upperville, Virginia (USA) from 18 to 27 September 2018. The goal of the Summer School is to deliver high-quality training in the modern comparative methods used to study plant macroevolution. While the methods are general and applicable to any group of organisms, all of our empirical datasets will be drawn from our recent work on flowering plants. A unique feature of this School will be that the students themselves will participate in the creation of the datasets (floral traits and fossil calibrations) in our collaborative database PROTEUS, thereby gaining hands-on experience of the problems and questions associated with compiling data and building real-life datasets for comparative analyses. In doing so, we hope to further promote the rapidly evolving field of macroevolution among graduate students in plant sciences, while also conveying our experience in building high-quality datasets.
The Oak Spring Garden Foundation (OGSF) is a new philanthropic foundation based at the former primary estate of the late Mr. and Mrs. Paul Mellon, who were major philanthropists in the U.S. of the arts, humanities and sciences in the second half of the twentieth century. OSGF is located in the northern Virginia, in the piedmont of the Blue Ridge Mountains (ca. one-hour drive from Washington, D.C.). Led by Sir Peter Crane, the Foundation’s inaugural President, OSGF is an ideal venue for small conferences, workshops, and retreats. It is becoming a new center of stimulation of all things botanical, from fundamental research in plant evolution and conservation, to horticultural and plant conservation practice, to the history and art of plants gardens and landscapes. Part of the role of OSGF will be similar to that played by NESCent (Durham) and NCEAS (Santa Barbara) in encouraging collaborative and synthetic research in evolution and ecology.
The Summer School will be structured around the alternation of ‘Data Days’ and ‘Analytical Days’ (four each). Each Data Day, we will focus on recording floral characters and fossil calibrations for selected groups of flowering plants in PROTEUS, contributing to actual ongoing research led by us (expansion of the eFLOWER dataset). Each Analytical Day will address a specific methodological topic, with a theoretical morning class introducing the fundamentals, and an afternoon hands-on practical to implement methods presented in the morning, using real-life plant datasets provided by us and/or built together over the data days. In addition, a pre-lunch seminar by a Guest Lecturer (see list below), or one of us, on an empirical macroevolutionary study in plants will be scheduled every day. There will also be two pre-dinner ‘Flash Talks’ by participants on their current research.
Topics addressed in the Summer School will include: divergence time estimation using molecular dating methods (relaxed clock methods, fossil calibration), ancestral state reconstruction and correlation of morphological traits (using maximum likelihood and Bayesian methods), and diversification rates and state-dependent diversification. All of these approaches start with phylogenetic trees obtained from molecular or genomic data and thus prior understanding of and experience with phylogenetic reconstruction based on such data is an essential prerequisite for participants, because this topic will not be taught in the Summer School. We also plan to include an outreach component in the form of a short video made by the participants during the course, explaining in simple terms what they have learned and why these methods are important to understand plant evolution.
The 2018 Summer School follows the first eFLOWER Summer School organized in Vienna in July 2013, but has different goals and structure. The Vienna Summer School focused entirely on data entry to build the eFLOWER dataset, whereas the 2018 School will also include methodological training.
The Oak Spring eFLOWER Summer School is free (no registration fee), including food and accommodation on site. All participants will also receive up to US$ 1000 on a reimbursement basis for their economy flights to Dulles Airport (IAD), Washington D.C. In addition, the Society of Systematic Biologists (SSB) will further reimburse up to US$ 500 on air travel costs of up to eight participants from more distant locations. Participants are entirely responsible for arranging their own travel insurance and visas. The School is ideally suited to graduate students (enrolled in a master’s or PhD program) who already have some botanical and phylogenetic background, but who lack training in current macroevolutionary methods. International applicants are welcome (but fluency in English is a requirement of the course) and we will attempt to achieve a good balance in terms of gender and origin in the final Summer School group selected.
To apply, please send a one-page letter of motivation and a CV, as a single PDF file with your name, to firstname.lastname@example.org. Deadline for applications: 1st June, 2018. Decisions will be made by 18th June. Please include the names and contact details of two referees (e.g., current or previous supervisor), but no letter of support (we will contact referees if necessary). Applications that do not meet these requirements may be automatically rejected. The total number of participants will be limited to 15.
- Hervé Sauquet (Royal Botanic Gardens and Domain Trust, Australia)
- Susana Magallón (Universidad Nacional Autónoma de México, Mexico)
- Jürg Schönenberger (University of Vienna, Austria)
- Peter Crane (Oak Spring Garden Foundation, USA)
Confirmed Guest Lecturers:
- Else Marie Friis (Swedish Museum of Natural History, Sweden)
- Laura Lagomarsino (Louisiana State University, USA)
- Stacey Smith (University of Colorado-Boulder, USA)
Links to learn more: