Theoretical Ecology Lab Tea

In the face of an epidemic, policies may be put in place to alter the behavior of the population (e.g. quarantining) for the good of the public health. While classical compartmental models and measures such as R_0 have provided insights into epidemics, which can be adopted into relevant policies, adaptive human behavior is usually not explicitly considered, even though humans often change economic activities and social plans during, and in anticipation of an outbreak. Considering contact rate to be the control variable, we pose a mathematical model of this problem as a deterministic mean-field game with discrete states and continuous time. In addition to the SIR dynamics, each compartment solves the optimal control problem, considering the tradeoff between present and future utility, as well as the strategies of the other compartments. We can compute the mean-field equilibrium, which is the fixed point of the coupled system of Bellman equations and the SIR differential equations. In addition, we compare the mean-field equilibrium with the socially optimal solution and prove that the contact rate of the infected should always be lowered to achieve social optimum. We also compute the price of anarchy of this system along the different parameters to find the characteristics of a disease, which in the case of an outbreak, we may benefit more by moving towards the social optimum via public policy.

Climate affects the seasonality of several directly-transmitted diseases, with implications for climate change. Here, I will compare and contrast results from two recent projects looking at respiratory syncytial virus and influenza, discussing similarities in the climate drivers of transmission, resulting epidemic dynamics and future projections. I will also consider how combined climatic and demographic changes may alter the future landscape of infectious disease, with particular implications for the evolution of influenza.

Safely achieving the goals of the Paris Climate Agreement requires a worldwide transformation to carbon-neutral societies within the next 30 y. Accelerated technological progress and policy implementations are required to deliver emissions reductions at rates sufficiently fast to avoid crossing dangerous tipping points in the Earth’s climate system. Here, we discuss and evaluate the potential of social tipping interventions (STIs) that can activate contagious processes of rapidly spreading technologies, behaviors, social norms, and structural reorganization within their functional domains that we refer to as social tipping elements (STEs). STEs are subdomains of the planetary socioeconomic system where the required disruptive change may take place and lead to a sufficiently fast reduction in anthropogenic greenhouse gas emissions. The results are based on online expert elicitation, a subsequent expert workshop, and a literature review. The STIs that could trigger the tipping of STE subsystems include 1) removing fossil-fuel subsidies and incentivizing decentralized energy generation (STE1, energy production and storage systems), 2) building carbon-neutral cities (STE2, human settlements), 3) divesting from assets linked to fossil fuels (STE3, financial markets), 4) revealing the moral implications of fossil fuels (STE4, norms and value systems), 5) strengthening climate education and engagement (STE5, education system), and 6) disclosing information on greenhouse gas emissions (STE6, information feedbacks). Our research reveals important areas of focus for larger-scale empirical and modeling efforts to better understand the potentials of harnessing social tipping dynamics for climate change mitigation.

The regulation of the biological pump plays a central role in the global carbon cycle. Recent laboratory studies and field observations demonstrate considerable variation in C:N:P of both plankton growing under different environmental conditions and in particulate organic matter across regions. Multiple distinct environmental drivers, including both temperature and inorganic nutrient concentrations, have been hypothesized as mechanisms responsible for these variations, each implying different feedbacks in the Carbon cycle. To reconcile the biological and geochemical measurements of C:N:P and to determine the extent to which temperature and nutrients control C:N:P, we here developed a trait-based model (ATOM, Adaptive Trait Optimization Model) designed to capture known metabolic regulation of different cellular biochemical components and estimated variation in C:N:P along environmental gradients. Bayesian optimization of this model against a newly compiled dataset of both C:N! :P measurements and high-resolution measurements of inorganic N and P revealed that nutrient supply rates followed by nutrient levels were the strongest regulators of C:N:P. We then explored using metagenomic data on the abundance of N and P transporters and found that we could improve predictions compared to models based on surface nutrients. Integrating ATOM with a global-scale model of Nitrogen and Phosphate cycling led to improved an improved fit of carbon export to nutrient traps compared to both a static Redfield model and a model based on nutrient concentrations only. Thus, the biological regulation of C:N:P captured a global rearrangement in carbon export with important implications for the carbon cycle.

Understanding biome distributions is a critical issue in modern ecology, especially in the context of predictive models of past and future climate change. While we can explain the current distribution of many biomes accurately, our predictions are less successful in dynamic systems where vegetation-environment feedbacks are significant. Spatial interactions make this even more difficult, and alter the predictions of mean-field models substantially. Savannas and grasslands cover ~40% of the Earth's land surface and forests cover another ~30%. Understanding the dynamics among these biomes will help explain biosphere dynamics, past, present and into the future. I will discuss some of our recent and ongoing work modeling within-patch and spatial dynamics of savanna-forest vegetation dynamics to offer a perspective on the stability of savanna distributions, despite variability in vegetation structure within the biome.

In this talk, we will discuss the question of evolution of cooperation in group-structured populations in the presence of competition occurring at two levels: individuals competing with peer group members based on individual payoff and groups competing against other groups based on the collective payoffs of group members. Two additional mechanisms often proposed for promotion of cooperation via individual selection are like-with-like assortment and interactions taking place on networks. Here, we will explore multilevel selection in which game-theoretic interactions between peer group members are assortative or take place on networks, resulting in greater clustering of cooperators with cooperators and defectors with defectors. We will show that within-group population structure has a synergistic effect working in concert with multilevel selection, decreasing the threshold for between-group competition strength needed to sustain cooperation and increasing the fraction of cooperation at steady state.

The shift from solitary individuals to eusocial colonies represents one of the major transitions in evolution. As such, there is great interest in understanding the ecological environment that enables such a transition. In this talk, I will introduce a theoretical model to test the effects of two specific environmental components on the emergence of social behavior: egg-to-adult development time and season length. This stochastic model incorporates development time, season length, and helping behaviors to model the competition between solitary and social populations. Two different equilibria emerge in the model: one where social populations dominate, and one where solitary populations dominate. Which equilibria emerges is associated with egg-to-adult development time and season length. These findings are similar to those we find in bee distribution data from the Swiss Alps and the United States.

The generation and control of pattern and form is still a challenging problem in the biomedical sciences. I shall briefly describe the mechanical theory of morphogenesis and the discovery of morphogenetic laws in limb development. I shall show how to move evolution backwards, describe its experimental verification and why there are no 3-headed monsters.

A central goal of quantitative community ecology is to develop a predictive and empirically parametrizable theory of coexistence. In the standard Lotka-Volterra modeling approach, coexistence in a diverse ecosystem is determined by all pairwise interactions between the constituent species. However, when there are more than two species, higher-order interactions can emerge. For example, suppose one microbial species harms a second species by secreting an antibiotic, while a third species secretes a molecule that degrades this antibiotic. Then, the interaction between the first two species is attenuated by the concentration of the antibiotic-degrading molecule, and it therefore depends on the abundance of the third species. Models based on pairwise interactions fail to measure this effect, and therefore may give incorrect predictions for the community as a whole. Here, I formulate a simple extension of the generalized Lotka-Volterra model that includes higher-order interactions between three species. Using the cavity method from statistical physics, I predict the fraction of species that coexist in large, randomly parametrized ecosystems with higher-order interactions. If I re-distribute some of the interactions in a community of pure pairwise interactions into higher-order interactions, more species coexist. These results provide a null expectation for how higher-order interactions can impact coexistence in diverse ecosystems.

Humans have impacted ecosystems worldwide for the past ~200,000 years, yet many studies of ecosystems do not include humans, preferring to place humans apart from the natural environment. This approach denies the fact that every ecosystem worldwide is impacted by people; to have a more sustainable future we need to use computational social science approaches to understand the human place in ecosystems. Here I highlight my work as an “archaeo-ecologist” of embedding past human societies within ecosystems. The ability to model societies where we can examine beginning and end points can enable scientists to see the past as “experiments” in sustainability, learning from the archaeological record to improve our understanding of how to enable more resilient interactions with our own environments today. I show how the past can be calibration data for the present by using food web models to examine the unique ways humans provide key functions to the biotic environments they inhabit. Ultimately, this work suggests the complicated ways humans can be both beneficial and detrimental to ecosystems.

Unraveling the drivers controlling community assembly is a central issue in ecology. Although selection, dispersal, diversification and drift are conceptually accepted as major community assembly processes, defining their relative importance in governing biodiversity is very challenging. Here, we present a novel framework to quantitatively infer community assembly mechanisms by phylogenetic bin-based null model analysis (iCAMP). Analyses with simulated microbial communities showed that iCAMP has very high accuracy, precision, sensitivity, and specificity. Application of iCAMP to grassland microbial communities revealed that homogeneous selection and “drift” played dominant roles in controlling grassland soil microbial community assembly in response to experimental warming. Interestingly, warming decreased “drift” over time, but enhanced homogeneous selection. In addition, iCAMP was also used to examine the changes of the assembly mechanisms of forest or groundwater microbial communities along geographical or environmental stress gradients. The general framework developed here provides an effective and robust tool to quantify community assembly processes in microbial ecology, and it should also be useful for plant and animal ecology.