Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session G08: Ecological Dynamics II |
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Sponsoring Units: DBIO Chair: Pankaj Mehta, Boston University Room: Room 131 |
Tuesday, March 7, 2023 11:30AM - 11:42AM |
G08.00001: Coexistence in diverse communities with higher-order interactions Theo L Gibbs, Simon A Levin, Jonathan M Levine A central assumption in most ecological models is that the interactions in a community operate only between pairs of species. However, two species may interactively affect the growth of a focal species. Although interactions among three or more species, called higher-order interactions, have the potential to modify our theoretical understanding of coexistence, ecologists lack clear expectations for how these interactions shape community structure. Here we analytically predict and numerically confirm how the variability and strength of higher-order interactions affect species coexistence. We found that as higher-order interaction strengths became more variable across species, fewer species could coexist, echoing the behavior of pairwise models. If interspecific higher-order interactions became too harmful relative to self-regulation, coexistence in diverse communities was destabilized, but coexistence was also lost when these interactions were too weak and mutualistic higher-order effects became prevalent. This behavior depended on the functional form of the interactions as the destabilizing effects of the mutualistic higher-order interactions were ameliorated when their strength saturated with species' densities. Last, we showed that more species-rich communities structured by higher-order interactions lose species more readily than their species-poor counterparts, generalizing classic results for community stability. Our work provides needed theoretical expectations for how higher-order interactions impact species coexistence in diverse communities. |
Tuesday, March 7, 2023 11:42AM - 11:54AM |
G08.00002: Emergent competition shapes the ecological properties of multi-trophic ecosystems Zhijie Feng, Pankaj Mehta, Jason W Rocks, Robert A Marsland Ecosystems are commonly organized into trophic levels --organisms that occupy the same level in a food chain (e.g., primary producers, herbivores, carnivores). A fundamental question in theoretical ecology is to understand how the interplay between trophic structure, diversity, and competition shapes the ecological properties of ecosystems. To address this problem, we analyze a generalized Consumer Resource Model with three trophic layers using the cavity method and numerical simulations. Unlike many previous works, our model explicitly includes many different types of species at each trophic level. We find that intra-trophic diversity gives rise to ``emergent competition" between species within a trophic level due to feedbacks mediated by other trophic levels. We show that this emergent competition mediates a crossover from a regime of top-down control (populations are limited by predators) to a regime of bottom-up control (populations are limited by primary producers) and that the crossover between these regimes can be characterized by a simple order parameter related to species packing and the number of surviving species. Collectively, these results highlight the importance of intra-trophic diversity for understanding multitrophic ecosystems and suggest a simple criterion for determining whether an ecosystem exhibits top-down or bottom-up control. |
Tuesday, March 7, 2023 11:54AM - 12:06PM |
G08.00003: Predicting microbial community compositions using compressive sensing Shreya Arya, Ashish B George, James P O'Dwyer A primary goal of microbiome engineering is to use synthetic and natural microbial communities for improving outcomes in human health, agriculture, and climate. For these methods to succeed, it is crucial to reliably predict the final community composition from the initial composition, in a well-defined environment. However, our ability to learn community compositions is hindered by the vast number of experiments required to assemble all communities possible from a given pool of species. Thus far, predictive methods have focussed on fitting parametric, mechanistic models, like the generalized Lotka-Volterra model, to limited empirical data. |
Tuesday, March 7, 2023 12:06PM - 12:18PM |
G08.00004: A new geometric framework for niche theory and consumer resource models Emmy Blumenthal, Pankaj Mehta A fundamental problem in ecology is to understand when species can coexist in an ecosystem. One major paradigm for addressing this is Niche theory and Consumer Resource Models (CRMs). Niche theory and CRMs have played a foundational role in our ecological understanding by highlighting the central role of ecological competition in shaping ecosystem function and structure. Many of the central intuitions of niche theory (Tilman's R*, species coexistence cones) were developed using geometric arguments for analyzing CRMs. Here, we present a new, simple yet powerful, geometric framework for understanding species coexistence in CRMs based on convex polytopes in the space of consumer preferences. We show that this new geometric picture can be used to predict which species can co-exist, enumerate all possible ecologically stable steady-states, and all allowed transitions between these steady-states. Our geometric picture also naturally allows us to understand how changing species' attributes affects species co-existence and niche differentiation. Collectively, these results constitute a qualitatively new way of understanding the role of species' consumption preferences in ecosystems and niche theory. |
Tuesday, March 7, 2023 12:18PM - 12:30PM |
G08.00005: Predicting the First Steps of Evolution in Randomly Assembled Microbial Communities John D McEnany, Benjamin H Good Microbial communities can self-assemble into highly diverse states with predictable community-level phenotypes. However, the residents of these communities can rapidly evolve over time by acquiring additional mutations in their genomes. When a mutant invades a population, it competes for ecological niches both with its parent strain and with the other strains in the surrounding community. This complex interplay between ecology and evolution is difficult to capture with existing community assembly theory. Here, we introduce a replica-theoretic approach for predicting the first steps of evolution in randomly assembled communities that compete for substitutable resources. We show how the invasion fitness of a mutant and the probability that it coexists with its parent depends on the size of the community, the saturation of its niches, and the metabolic overlap between its members. We find that successful mutations are often able to coexist with their parent strains, even in saturated communities with low niche availability. This coexistence probability increases further when the mutations impose a cost on the maximum growth rate. These results suggest that even small amounts of evolution can produce microbial communities with multiple coexisting strains of the same species. |
Tuesday, March 7, 2023 12:30PM - 12:42PM |
G08.00006: Effects of a periodically changing environment on a predator-prey ecology. Mohamed Swailem, Uwe C Tauber We study the Lotka-Volterra model for predator-prey competition subject to a periodically varying carrying capacity. The Lotka-Volterra model with on-site lattice occupation restrictions (i.e., finite local carrying capacity) that represent finite food resources for the prey population, exhibits a continuous active-to-absorbing phase transition. Monte Carlo simulations on a two-dimensional lattice are utilized to investigate the effect of seasonal variations of the environment on species coexistence. The results of our simulations are also compared to the mean-field analysis in order to specifically delineate the impact of stochastic fluctuations and spatial correlations. Different effective static environments are explored in the extreme limits of fast and slow periodic switching. Analysis of the mean-field equations in the fast-switching regime enables a semi-quantitative description of the (quasi-)stationary state. |
Tuesday, March 7, 2023 12:42PM - 12:54PM |
G08.00007: Theory: Predicting and controlling evolving ecological communities Dervis C Vural, Vu A Nguyen In a complex community, species continuously adapt to each other. On rare occasions, the adaptation of one species can lead to the extinction of others, and even its own. "Adaptive Dynamics" is the standard framework to describe evolutionary changes to community interactions, and in particular, to predict adaptation driven extinction. Unfortunately, most of the literature in this field is dominated by computer simulations which must make a large number of arbitrary assumptions about a large number of parameters governing interspecies interactions (e.g. random matrices). |
Tuesday, March 7, 2023 12:54PM - 1:06PM |
G08.00008: Dynamical phase transitions in the eco-evolutionary dynamics of complex ecosystems Jim Wu, Trevor K GrandPre, Anne-Florence Bitbol, David J Schwab In complex ecosystems such as microbial communities, there is constant ecological and evolutionary feedback between the residing species and the environment occurring on concurrent timescales. Species respond and adapt to their surroundings by modifying their phenotypic traits, which in turn alters their environment and the resources available. To study this interplay between ecological and evolutionary mechanisms, we construct a consumer-resource model coupled with phenotypic mutations. Drawing from non-equilibrium statistical physics, we define time-integrated observables to characterize the effects of frequency-dependent selection and adaptation rate on the community structure in phenotype space and the temporal dynamics. Using these dynamical observables, we identify various phases that the ecosystem can exhibit and discuss the conditions for dynamical phase transitions and their ecological implications. |
Tuesday, March 7, 2023 1:06PM - 1:18PM |
G08.00009: An age-structured Lotka-Volterra model and the emergence of overcompensation Mingtao Xia, Xiangting Li, Tom Chou There has been renewed interest in understanding the mathematical structure of ecological population models that lead to overcompensation, the process by which a population recovers to a higher level after suffering an increase in predation or harvesting. We construct an age-structured single-species population model that incorporates a Lotka-Volterra-type cannibalism interaction. Depending on the structure of the interaction, our model can exhibit overcompensation as well as oscillations of the total population; both phenomena have been observed in ecological systems. Analytic and numerical analysis of our model reveals sufficient conditions for overcompensation and oscillations. We also show how our structured population PDE model can be reduced to coupled ODE models representing piecewise constant parameter domains, providing additional mathematical insight into the emergence of overcompensation. |
Tuesday, March 7, 2023 1:18PM - 1:30PM |
G08.00010: Impact of oceanic frontal divergence on phytoplankton community composition Abigail Plummer, Mara Freilich, Roberto Benzi, Chang Jae Choi, Lisa Sudek, Alexandra Z Worden, Federico Toschi, Amala Mahadevan As phytoplankton reproduce and compete, their motion is often prescribed by ocean currents. These currents therefore influence the distribution and diversity of phytoplankton, although the details of this biophysical interaction are not well characterized due to observational and computational limitations at small spatial and temporal scales. Here, we combine observations, simulations, and theory to consider the impact of realistic flow fields with resolved submesoscale dynamics on plankton community composition. We find that the regions of divergence in our flow field can substantially modify competition events in simulations, and propose that submesoscale frontal divergence is a plausible explanation for observed taxonomic variability in oceanic fronts. The change in community composition of neutral competitors is linearly related to the divergence, with regions of positive divergence supporting local populations and regions of negative divergence suppressing them. |
Tuesday, March 7, 2023 1:30PM - 1:42PM |
G08.00011: Dynamic coexistence due to growth succession in cyclic microbial ecosystems Avaneesh V Narla, Terence T Hwa, Arvind Murugan Microbial ecosystems are commonly modeled by fixed interactions between microbes in steady physiological states, typically the exponential growth state. However, ecological dynamics often feature large self-generated environmental changes which drive microbes through distinct physiological states manifested by very different growth rates. Examples of such dynamics include succession dynamics in nature and simple growth-dilution cycles in the laboratory. Here, we introduce a phenomenological model to gain insight into the dynamic coexistence of microbes due to changes in physiological states in cyclic environments. Our model allows us to bypass specific interactions leading to different physiological states (e.g., nutrient starvation, stress, aggregation, contact-dependent killing, etc.), by considering the growth of each species according to a global ecological coordinate, taken here to be the total community biomass. Analysis of this model provides rigorous, quantitative criteria for the dynamic coexistence of many species in terms of differential species’ dominance (“growth niches”) along the ecological coordinates. Our model shifts the focus of ecosystem dynamics from bottom-up studies based on inter-species interaction to top-down studies based on accessible macroscopic observables such as growth rates and total biomass, thereby allowing quantitative examination of community-wide characteristics. |
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