Example intertidal food webs from alaska, sampled at increasingly large spatial areas from 0.25 m2 (top) to 24 km2 (bottom). spheres represent taxa and lines between them represent directional feeding links. Primary producers, invertebrates, and vertebrates are shown in green, yellow, and red, respectively. the vertical axis indicates short-weighted trophic level.
A few decades ago, ecologists used cartoonish data and models to describe species interactions in ecosystems. Now, with advances in empiricism, computing power, and modeling, the complex structures and nonlinear dynamics of highly interconnected ecological networks can be simulated and assessed, allowing researchers to characterize how energy flows through ecosystems in far more sophisticated ways than was previously possible.
While today’s technologies are advanced enough to quantify and predict complex food web structures and dynamics, researchers have traditionally had only an idiosyncratic datasets with which to do so. Novel, emerging databases comprised of tens to hundreds of replicate networks along spatial, temporal, and environmental gradients are providing researchers new ways to study such influences as spatial scaling, habitat change, re-colonization after disturbance, and natural and anthropogenic climate variability on food web structure, dynamics, and function.
SFI Professor and VP for Science Jennifer Dunne organized a working group at SFI March 2-4 to take this type of research a step further. “Gradient-based research presents unique opportunities and challenges, both in terms of ecological dynamics and fundamental network theory,” she says.
The working group, a follow-on to a 2013 meeting, brought together ecologists with expertise in ecological network data, experiments, and modeling with researchers who have expertise in network theory to further solidify this as coherent research agenda, to advance particular research projects, and to take on the hard question of how to compare the structure of dozens of networks that include enriched node, link, and system data, she says.
Several earlier-career colleagues “who are doing very cutting-edge research in this area” brought new dimensions to the working group, Dunne says. They include SFI Omidyar Fellows Josh Grochow and Justin Yeakel, postdoctoral fellows Fernanda Valdovinos (University of Arizona) and Luis Gilarranz (Estación Biológica de Doñana EBD-CSIC), and Ph.D. student Ashkaan Fahimipour (UC Riverside).