This workshop will identify current gaps in our understanding of the role of seed dispersal in plant populations and determine how to address these outstanding gaps in order to move towards a predictive understanding of plant populations under global change. Seed dispersal ecology is largely based on short-term, local-scale empirical studies for a small number of species or on theoretical dispersal models that often make simplified assumptions. These factors limit generality the ability to make quantitative predictions. By integrating data with models, the workshop will lead to computer experiments to:
- gain a mechanistic understanding of the role of dispersal in plant population dynamics;
- test theoretical predictions using empirical data; and
- conduct sensitivity analyses to determine the robustness of conclusions to the type of available data, to missing data, and to different types of models.
Gaps in knowledge and obstacles to progress will be identified, and scientific networking will be enhanced. We have selected a core group of 25 participants representing field ecologists, theoretical ecologists, and mathematical biologists, and are accepting applications to fill the remaining slots for this workshop.
Who can apply? You must be an early career scientist (e.g., grad students, postdocs, pre-tenure faculty) who studies the role of dispersal in populations from an empirical, theoretical, or mathematical approach. You must be able to commit to the entire week, and unfortunately, we cannot support anyone currently employed outside the U.S.
When and where is the workshop? May 9–13, 2016, at SESYNC in Annapolis, Maryland.
What does it cost? All workshop costs (flight from anywhere in the U.S. to Maryland, housing, and food) will be covered for the selected participants.
How do I apply? Send your CV and a cover letter in a single PDF to: email@example.com
In your cover letter, briefly describe the following: 1) your mathematical, theoretical, or empirical approach to studying seed dispersal; 2) why you want to participate in this workshop; and 3) (if applicable) any relevant datasets you are willing to contribute to meta-analyses or review papers.
When are applications due? Applications are due by January 25, 2016.