Abstract: While many studies have addressed the effect of individual stresses on plant–plant associations, few
have addressed the effects of co-occurring stresses. We therefore investigated how associations between Wyoming big
sagebrush (Artemisia tridentata ssp. wyomingensis) and 2 native grasses (Poa secunda and Elymus elymoides) responded
to different combinations of grazing and moisture stresses in the Great Basin, USA. Positive (i.e., facilitative) interactions
between nurse plants and their beneficiaries are predicted to increase with increasing moisture limitation and
grazing stress, but these interactions may break down at extreme levels of stress. We hypothesized that (1) competitive
interactions and negative shrub-grass spatial associations would occur under the least stressful conditions (low grazing
intensity /high precipitation); (2) positive shrub-grass spatial associations would dominate at intermediate levels of stress
(high grazing intensity /high precipitation and low grazing intensity /low precipitation); and (3) negative grass-shrub
relationships would dominate at extreme levels of stress (high grazing /low precipitation). We sampled 5 site pairs (high
vs. low grazing intensity) that occurred over a precipitation gradient. We assessed how abundance of the 2 grasses
P. secunda and E. elymoides responded to sagebrush microsite (canopy vs. interspace), grazing intensity, and precipitation.
We found that both grass species were positively associated with A. tridentata canopy microsites at low annual
precipitation levels. However, grazing stress appeared to weaken this effect for P. secunda, indicating, as we predicted, a
potential breakdown of facilitative interactions in highly stressful conditions. Although we predicted that facilitation
would dominate in moderately stressful conditions, we only found this to be true (for both grasses) in one of the 2 moderately
stressful scenarios (low grazing /low precipitation). Our results provide insights into how Great Basin plant
communities may respond to the separate and combined effects of grazing and drought stresses, both of which may
intensify in the future.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).