Abstract: A mathematical model incorporating the effects of possibly asymmetric frequency dependent interactions is proposed. Model predictions for an idealized two-species annual plant community with asymmetric linear frequency dependence are explored using (i) analytic mean field equilibrium predictions, (ii) deterministic, discrete-time, finite-population, mean field predictions, and (iii) stochastic, discrete-time, cellular automata predictions for a variety of sizes of the spatial interaction and dispersal neighborhoods. We define species interaction factors, ranging from 0 to 1, which incorporate both frequency independent and frequency dependent terms. The maximum competitive ability of a species is reduced unless species frequency is optimal based on species-specific frequency dependence coefficients, ranging from −1 to 1. Assuming that maximum competitive ability is identical for two species, they can coexist indefinitely when they have equal absolute magnitude or both have sufficiently negative frequency dependence. Although smaller scales of spatial interactions reduce the region of the parameter space in which stable coexistence is pre- dicted, the time to extinction of one species can be significantly increased or decreased by the locality of interactions, depending on whether the losing species has positive or negative frequency dependence, respectively. The sensitivity to initial conditions in the community at large is dramatically reduced as the spatial scale of interactions is decreased. As a consequence, smaller spatial interaction neighborhoods increase the ability of introduced species to invade established communities in regions of the parameter space not predicted by mean field approximations. In the 'loser positive, winner positive' regions, smaller scales of interaction dramatically increased invasiveness. In the 'loser positive, winner negative' regions of the parameter space, invasion success decreases, but time to extinction of the resident species during successful invasions increases, with an increase in the spatial scale of interactions. The 'loser negative, winner positive' regions were relatively insensitive to initial conditions, so invasion success was relatively high at a variety of spatial scales. Surprisingly, invasions in parts of this region are most often successful with intermediate neighborhood sizes, although the maximum time that the losing species could persist before being driven to extinction increases with an increase in the spatial scale of interactions. These results are explained by understanding cluster formation and density and the relative local interspecific dynamics in cluster interiors, exteriors, and boundaries. In summary, frequency dependent interactions, and the spatial scale on which these interactions occur, can have a big impact on spatio-temporal community dynamics, with implications regarding species coexistence and invasiveness. The model proposed herein provides a theoretical frame- work for studying frequency dependent interactions that may shed light on spatio-temporal dynamics in real ecological communities.
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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).