Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session J23: Evolutionary and Ecological Dynamics II: Communities and NetworksFocus
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Sponsoring Units: DBIO GSNP Chair: Mikhail Tikhonov, Washington University, St. Louis Room: 304 |
Tuesday, March 3, 2020 2:30PM - 2:42PM |
J23.00001: Resource-rich environments can reduce stability and diversity in microbial communities by strengthening interspecies interactions Christoph Ratzke, Juliene Barrere, Jeffrey Gore Organisms – especially microbes – tend to live in complex communities. While some of these ecosystems are very bio-diverse, others aren’t, and while some are very stable over time others undergo strong temporal fluctuations. Despite a long history of research and a plethora of data it is not fully understood what sets biodiversity and stability of ecosystems. Theory as well as experiments suggest a connection between species interaction, biodiversity, and stability of ecosystems, where an increase of ecosystem stability with biodiversity is often observed. However, what causes these connections remains unclear. Here we show in microbial ecosystems in the lab that the concentrations of available nutrients can set the strength of interactions between bacteria. At high nutrient concentrations, extensive microbial growth leads to strong chemical modifications of the environment, causing more negative interactions between species. These stronger interactions exclude more species from the community – resulting in a loss of biodiversity. At the same time, these stronger interactions also decrease the stability of the microbial communities, providing a mechanistic link between species interaction, biodiversity and stability. |
Tuesday, March 3, 2020 2:42PM - 2:54PM |
J23.00002: Niche-Neutral Transition in a Lotka-Volterra Model of Diverse Ecosystems Jim Wu, David J. Schwab, Pankaj Mehta Niche and neutral theory are two prevailing, yet much debated, ideas in ecology proposed to explain the patterns of biodiversity. Whereas niche theory emphasizes selective differences between species and interspecific interactions in shaping the community, neutral theory supposes functional equivalence between species and points to stochasticity as the primary driver of ecological dynamics. In this work, we draw a bridge between these two opposing theories. Starting from a Lotka-Volterra (LV) model with demographic noise and random symmetric interactions, we analytically derive the stationary species abundance distribution and extract the population statistics. Using these results, we demonstrate the existence of a phase transition between niche and neutral regimes, thus reconciling how neutral-like statistics may arise from a diverse community of species with different traits. |
Tuesday, March 3, 2020 2:54PM - 3:06PM |
J23.00003: Competition-driven strategies for controlling multistable microbial communities Veronika Dubinkina, Akshit Goyal, Yulia Fridman, Parth Pratim Pandey, Sergei Maslov Microbial communities often need particular species compositions, or community states, to perform a certain function. Because these communities play crucial roles in human health and industry, we need to maintain them in particular desirable states, and prevent them from reaching undesirable ones. What makes this task especially challenging is that microbial communities are often multistable; both desirable and undesirable states may be possible in the same environmental conditions. Due to these complications, we still lack pragmatic and reliable strategies to fulfill this task. Here, we propose two strategies to control multistable microbial communities: that of controlling the colonization order in which species are introduced into a community, and that of controlling the supply of nutrients to a community. Both strategies are driven by competition for nutrients between microbial species. As a proof of concept, we illustrate their implementation in a resource-explicit model. Our proposed strategies have the potential to greatly improve existing methods to engineer and manipulate real microbial communities, such as in industrial bioreactors and the human gut. |
Tuesday, March 3, 2020 3:06PM - 3:18PM |
J23.00004: Long-range patterns of activity shaped by signaling interactions within bacterial communities James Boedicker, Kalinga Pavan T Silva, Tahir Yusufaly Bacteria communicate to coordinate macro-scale patterns of gene expression and associated group behaviors such as biofilm formation. Cells secrete, sense, and respond to small chemical signals in a process known as quorum sensing. Within diverse bacterial communities, signal exchange is modulated by signaling interactions between different cell types, including interference through signal crosstalk and signal destruction. Our work explored how signaling interactions between multiple cell types shape global patterns of gene expression. We examined how community composition modulated signal-dependent gene expression using both experiments and reaction-diffusion models. In some communities, the pattern of gene expression followed a 2D percolation transition, controlled by the ratio of signal producing and signal destroying strains in the community. At a critical amount of interference, signal exchange was disrupted and long-range communication was suppressed. We explored the limitations of diffusive communication and strategies cells could use to coordinate behavior at length scales exceeding distances over which diffusion is effective. |
Tuesday, March 3, 2020 3:18PM - 3:30PM |
J23.00005: Ecological interactions constrain the coexistence of generalists and specialists during coevolution in microbial communities Akshit Goyal Natural microbial communities are complex ecosystems in which species with different metabolic strategies—both generalists and specialists—stably coexist. We do not understand how coevolution within a community can both lead to, and stabilize, such coexistence, and how generic we expect it to be. Here, we propose and study a minimal model of a co-evolving microbial community shaped by ecological interactions. Our model combines competition and cooperation for nutrients with evolutionary game theory. In doing so, it makes three broad and surprising predictions about co-evolved microbial communities. First, we find that generalists and specialists coexist only in a narrow range of ecological interactions; in all other regimes, specialists dominate. Second, we find that co-evolving with others makes it strikingly difficult for generalists to evolve a correlation between their nutrient preferences and growth rates; such a correlation is often observed in experiments. Finally, we find that communities with a greater fraction of generalists are also likely to have more stable states, which can be tested. This model provides a simple framework through which to quantify and make testable predictions about coevolution in microbial communities. |
Tuesday, March 3, 2020 3:30PM - 3:42PM |
J23.00006: Criticality on topologically disordered systems and the Harris criterion Hatem Barghathi, Thomas Vojta To test the stability of clean critical points against quenched spatial disorder, Harris introduced the criterion dν>2. Its predictions are in agreement with the vast majority of analytical and numerical results on phase transition in disordered systems. However, in systems where disorder arises from random connectivity, a number of violations of the Harris criterion have been reported. We recently introduced [1] a modified stability criterion, (d+1)ν>2, for systems in which the presence of topological constraints suppresses disorder fluctuations, resulting in a violation of the usual Harris criterion. However, some recent results on topologically disordered systems appear to violate even the modified criterion. To uncover the source of such apparent violations we perform a detailed statistical analysis of such systems together with large-scale Monte Carlo simulations. |
Tuesday, March 3, 2020 3:42PM - 3:54PM |
J23.00007: The Emergence of Spatial Patterns in Tree Yield: A New Model for the Masting Phenomenon Shadisadat Esmaeili, Alan Hastings, Karen Abbott, Jonathan Machta, Vahini Reddy Nareddy The emergence of patterns of synchrony is ubiquitous across many fields, including ecological systems in which synchrony can be both favorable and detrimental. The prevalence of synchrony creates the expectation of the existence of detail-independent principles that can explain and predict this phenomenon and its emergence. A notable example of synchrony is the “masting” phenomenon observed in many plant species in which individual plants show variable annual production (bearing), which is spatially correlated. External forces and local dynamics can lead to the emergence of spatial patterns or full synchrony in such systems. Currently, existing models for masting phenomenon, while proposing a mechanism for alternate bearing, do not address the spatial patterns observed in real data. In this talk, we introduce a new model to emulate the observed spatial patterns and study the effects of local coupling and external forces on the dynamics of the system. |
Tuesday, March 3, 2020 3:54PM - 4:06PM |
J23.00008: Binary Decisions of Large Cliques of Evidence Accumulators Bhargav Karamched, Zachary Kilpatrick, Kresimir Josic, Megan Stickler, Will Ott, Benjamin Lindner We consider cliques of N evidence accumulators making a binary decision based on noisy observations. Each agent's evidence is a drift-diffusion process on a symmetric, bounded domain with absorbing boundaries. An agent makes an immutable decision when their evidence reaches one of the boundaries (thresholds). Prior to a decision, each agent's evidence is an independent stochastic process. Following a decision, each agent's evidence receives a bump equal to the value of the threshold corresponding to the decision. In large cliques, such a decision can induce a large fraction of the agents to agree with the initial decider. If the first decider is correct, this bodes well for the overall performance of the clique. However, if the initial decider is incorrect, the overall performance of the clique can be disastrous. We derive asymptotic results conveying what fraction of agents agree with the initial decider upon observing their decision and how the remaining agents decide therafter to agree with the initial decider or collectively disagree with them. We also show how the framework of the evidence accumulation in cliques can be modified so that it is self-correcting and, even if the first decision is wrong, have the majority of the clique choose the correct decision. |
Tuesday, March 3, 2020 4:06PM - 4:18PM |
J23.00009: Identifying Suspicious Users and Products to Predict New Opinions Sukhwan Chung An opinion network describes how a person values an object, which can be another person or product. This type of network is ubiquitous because humans constantly evaluate their surroundings. Analyzing an opinion network can bring deeper understanding of what and how people are thinking, but at the same time, a malicious attempt to influence such network can be harmful to the public. One problem naturally arising while studying opinion networks is identifying users leaving intentionally fake opinions to influence the opinions of others. Another question that can be asked is how to predict a person’s opinion towards another. In this work, a solution to those issues is suggested using the Rev2 algorithm (developed by S. Kumar, et al) and the principle of maximum entropy. The Rev2 algorithm is used to assign a suspiciousness score to users based on how much the user’s action deviates from the rest. Also, a quality index will be assigned to products based on reliable opinions. Using this information, the principle of maximum entropy is invoked to produce the most unbiased prediction of new opinions. The proposed solution is tested against public network data including a user-to-user trust network of Bitcoin platform users and a user-to-product rating network of Netflix users. |
Tuesday, March 3, 2020 4:18PM - 4:30PM |
J23.00010: Optimal evidence accumulation on social networks Bhargav Karamched, Simon Stolarczyk, Kresimir Josic, Zachary Kilpatrick To make decisions we are guided by the evidence we collect, as well as the opinions of friends and neighbors. How do we integrate our private beliefs with information we obtain from our social network? To understand the strategies humans use to do so, it is useful to compare them to observers that optimally integrate all evidence. Here we derive network models of rational agents who accumulate private measurements and observe decisions of their neighbors to choose between two options. The resulting information exchange dynamics has interesting properties: When one option is preferred, the absence of a decision can be increasingly informative over time. In recurrent networks an absence of a decision can lead to a sequence of belief updates akin to those in the literature on common knowledge. In large networks, a single decision can trigger a cascade of agreements and disagreements that depend on the private information agents have gathered. Our approach provides a bridge between social decision making models in the economics literature, which largely ignore the temporal dynamics of decisions, and the single-observer evidence accumulator models used widely in neuroscience and psychology. |
Tuesday, March 3, 2020 4:30PM - 4:42PM |
J23.00011: Sensitivity of collective outcomes identifies pivotal components Edward Lee, Daniel M Katz, Michael J Bommarito, Paul Ginsparg The relation between collective outcomes and individual behavior is a central question in social science, biology, and statistical physics. Using the information geometry of minimal models from statistical physics, we develop a general approach for identifying key "pivotal" components on which aggregate statistics depend most sensitively. For political voting, pivotal blocs are like swing voters on whom the distribution of majority-minority divisions depends most sensitively. Analogously, collective market movement may be characterized by a few important stock indices, or the identity of a community on Twitter may hinge on a few individuals. In neural networks, pivotal components may be important for determining collective states. Our approach may help evaluate how political bodies change with membership or analyze the robustness of social institutions and biological networks to targeted perturbation. |
Tuesday, March 3, 2020 4:42PM - 4:54PM |
J23.00012: Community optimization and ruggedness of ecological landscapes Ashish B. George, Kirill S Korolev Many applications require the assembly of an optimal microbial community that maximizes biofuel production, crop yield, or remediation potential. The number of combinations in which these communities can be assembled grows exponentially with the number of species. Therefore, optimization strategies have to rely on heuristic algorithms that iteratively select the best community from a small set of trial communities. The success of such strategies depends on the ruggedness of the landscape of community function. We show that consumer-resource models with and without cross-feeding have unique steady states that depend only on the presence or absence of species in the community. Thus, community selection is a search on an ecological landscape in close analogy with evolution on fitness landscapes in population genetics. We report typical ruggednesses of such landscapes, and discuss how they depend on inter-specific interactions and environmental conditions. We also determine the conditions under which landscape ruggedness can be estimated from incomplete data. |
Tuesday, March 3, 2020 4:54PM - 5:30PM |
J23.00013: History-dependent tradeoffs in changing environments Invited Speaker: Mikhail Tikhonov Performance tradeoffs, which simple models typically postulate, are known to themselves evolve. This leads to a curious feedback loop: evolutionary history shapes tradeoff strength, which, in turn, shapes evolutionary future. Using a simple model, I will show that this feedback can lead to counterintuitive consequences in the context of multiple or changing environments: specifically, a direct exposure to some environment of interest will, in general, no longer be the most effective way of achieving highest fitness in it. I will demonstrate three different mechanisms for how alternate exposure strategies can prove more effective: inducing a more evolvable architecture, counteracting a form of “use it or lose it”, or relying on horizontal gene transfer; and will discuss the prospects of linking these theoretical expectations to experiments. |
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