arXiv subjects

Sarah Iams

Publications and source records attributed to Sarah Iams.

9 recordsLinked to original sources

arXiv2024

Planned behavior, perceptual biases, and the dynamics of collective action

Many classical models of collective behavior assume that emergent dynamics result from external and observable interactions among individuals. However, how collective dynamics in human populations depend on the internal psychological processes of individuals remains underexplored. Here, we develop a mathematical model to investigate the effects of internal psychology on the dynamics of collective action. Our model is grounded in the theory of planned behavior -- a well-established conceptual framework in social psychology that links intrinsic beliefs to behavior. By incorporating temporal biases in social perception and individual differences in decision-making processes into our model, we find that the interplay between internal and external drivers of behavior can produce diverse outcomes, ranging from partial participation in collective action to rapid or delayed cascades of action-taking. These distinct outcomes are preceded by transient dynamics that are qualitatively similar to one another, which, just as in real-world scenarios, makes it difficult to predict long-term collective dynamics from early observations. Our model thus provides a useful test bed for methods that aim to predict the emergence of collective action, and it lays the groundwork for studying the nuanced dynamics of collective human behavior arising from the interaction between psychological processes and observable actions.

physics.soc-ph↗

arXiv2021

Impulsive fire disturbance in a savanna model: Tree-grass coexistence states, multiple stable system states, and resilience

Savanna ecosystems are shaped by the frequency and intensity of regular fires. We model savannas via an ordinary differential equation (ODE) encoding a one-sided inhibitory Lotka-Volterra interaction between trees and grass. By applying fire as a discrete disturbance, we create an impulsive dynamical system that allows us to identify the impact of variation in fire frequency and intensity. The model exhibits three different bistability regimes: between savanna and grassland; two savanna states; and savanna and woodland. The impulsive model reveals rich bifurcation structures in response to changes in fire intensity and frequency -- structures that are largely invisible to analogous ODE models with continuous fire. In addition, by using the amount of grass as an example of a socially-valued function of the system state, we examine the resilience of the social value to different disturbance regimes. We find that large transitions ("tipping") in the valued quantity can be triggered by small changes in disturbance regime.

q-bio.PE↗

arXiv2021

Model and Data Reduction for Data Assimilation: Particle Filters Employing Projected Forecasts and Data with Application to a Shallow Water Model

The understanding of nonlinear, high dimensional flows, e.g, atmospheric and ocean flows, is critical to address the impacts of global climate change. Data Assimilation techniques combine physical models and observational data, often in a Bayesian framework, to predict the future state of the model and the uncertainty in this prediction. Inherent in these systems are noise (Gaussian and non-Gaussian), nonlinearity, and high dimensionality that pose challenges to making accurate predictions. To address these issues we investigate the use of both model and data dimension reduction based on techniques including Assimilation in Unstable Subspaces, Proper Orthogonal Decomposition, and Dynamic Mode Decomposition. Algorithms that take advantage of projected physical and data models may be combined with Data Analysis techniques such as Ensemble Kalman Filter and Particle Filter variants. The projected Data Assimilation techniques are developed for the optimal proposal particle filter and applied to the Lorenz'96 and Shallow Water Equations to test the efficacy of our techniques in high dimensional, nonlinear systems.

math.DS↗

arXiv2020

A Fast-Slow Model of Banded Vegetation Pattern Formation in Drylands

From infiltration of water into the soil during rainstorms to seasonal plant growth and death, the ecohydrological processes that are thought to be relevant to the formation of banded vegetation patterns in drylands occur across multiple timescales. We propose a new fast-slow switching model in order to capture these processes on appropriate timescales within a conceptual modeling framework based on reaction-advection-diffusion equations. The fast system captures hydrological processes that occur on minute to hour timescales during and shortly after major rainstorms, assuming a fixed vegetation distribution. These include key feedbacks between vegetation biomass and downhill surface water transport, as well as between biomass and infiltration rate. The slow system acts between rain events, on a timescale of days to months, and evolves vegetation and soil moisture. Modeling processes at the appropriate timescales allows parameter values to be set by the actual processes they capture. This reduces the number of parameters that are chosen expressly to fit pattern characteristics, or to artificially slow down fast processes by the orders of magnitude required to align their timescales with the biomass dynamics. We explore the fast-slow switching model through numerical simulation on a one-dimensional hillslope, and find agreement with certain observations about the pattern formation phenomenon, including band spacing and upslope colonization rates. We also find that the predicted soil moisture dynamics are consistent with time series data that has been collected at a banded vegetation site. This fast-slow model framework introduces a tool for investigating the possible impact of changes to frequency and intensity of rain events in dryland ecosystems.

nlin.PS↗

arXiv2019

Vegetation Pattern Formation in Drylands

This is a book chapter, written as a contribution to a new edition of "Dryland Ecohydrology", edited by Paolo D'Odorico, Amilcare Porporato, and Christiane Runyan, (to appear, Springer 2019). It aims to (1) describe some of the background to conceptual mathematical models of spontaneous pattern formation, in the context of dryland vegetation patterns, and (2) review some of the observational studies of the phenomenon. The chapter also highlights challenges and opportunities associated with the development of the models in light of increasing availability of remote sensing data. This includes both satellite imagery of the patterns and elevation data of the topography. The vast scales, in time and space, associated with the key processes further suggest avenues for improved mathematical modeling paradigms.

nlin.PS↗

arXiv2018

A topographic mechanism for arcing of dryland vegetation bands

Banded patterns consisting of alternating bare soil and dense vegetation have been observed in water-limited ecosystems across the globe, often appearing along gently sloped terrain with the stripes aligned transverse to the elevation gradient. In many cases these vegetation bands are arced, with field observations suggesting a link between the orientation of arcing relative to the grade and the curvature of the underlying terrain. We modify the water transport in the Klausmeier model of water-biomass interactions, originally posed on a uniform hillslope, to qualitatively capture the influence of terrain curvature on the vegetation patterns. Numerical simulations of this modified model indicate that the vegetation bands change arcing-direction from convex-downslope when growing on top of a ridge to convex-upslope when growing in a valley. This behavior is consistent with observations from remote sensing data that we present here. Model simulations show further that whether bands grow on ridges, valleys, or both depends on the precipitation level. A survey of three banded vegetation sites, each with a different aridity level, indicates qualitatively similar behavior.

nlin.PS↗

arXiv2018

Quantifying resilience to recurrent ecosystem disturbances using flow-kick dynamics

Shifting ecosystem disturbance patterns due to climate change (e.g. storms, droughts, wildfires) or direct human interference (e.g. harvests, nutrient loading) highlight the importance of quantifying and strengthening the resilience of desired ecological regimes. Although existing metrics capture resilience to isolated shocks, gradual parameter changes, and continuous noise, quantifying resilience to repeated, discrete disturbances requires novel analytical tools. Here we introduce a flow-kick framework that quantifies resilience to disturbances explicitly in terms of their magnitude and frequency. We present a resilience boundary between disturbances that cause either escape from a basin of attraction or stabilization within it, and use the resilience boundary to build resilience metrics tailored to repeated, discrete disturbances. The flow-kick model suggests that the distance-to-threshold resilience metric overestimates resilience in the context of repeated disturbances. It also reveals counterintuitive triggers for regime shifts, such as increasing recovery times between disturbances, or increasing disturbance magnitude and recovery times proportionately.

q-bio.PE↗

arXiv2018

Signatures of human impact on self-organized vegetation in the Horn of Africa

In many dryland environments, vegetation self-organizes into bands that can be clearly identified in remotely-sensed imagery. The status of individual bands can be tracked over time, allowing for a detailed remote analysis of how human populations affect the vital balance of dryland ecosystems. In this study, we characterize vegetation change in areas of the Horn of Africa where imagery taken in the early 1950s is available. We find that substantial change is associated with steep increases in human activity, which we infer primarily through the extent of road and dirt track development. A seemingly paradoxical signature of human impact appears as an increase in the widths of the vegetation bands, which effectively increases the extent of vegetation cover in many areas. We show that this widening occurs due to altered rates of vegetation colonization and mortality at the edges of the bands, and conjecture that such changes are driven by human-induced shifts in plant species composition. Our findings suggest signatures of human impact that may aid in identifying and monitoring vulnerable drylands in the Horn of Africa.

nlin.PS↗

arXiv2016

Assessing the robustness of spatial pattern sequences in a dryland vegetation model

A particular sequence of patterns, "$\text{gaps} \to \text{labyrinth} \to \text{spots}$," occurs with decreasing precipitation in previously reported numerical simulations of PDE dryland vegetation models. These observations have led to the suggestion that this sequence of patterns can serve as an early indicator of desertification in some ecosystems. Since parameter values can take on a range of plausible values in the vegetation models, it is important to investigate whether the pattern sequence prediction is robust to variation. For a particular model, we find that a quantity calculated via bifurcation-theoretic analysis appears to serve as a proxy for the pattern sequences that occur in numerical simulations across a range of parameter values. We find in further analysis that the quantity takes on values consistent with the standard sequence in an ecologically relevant limit of the model parameter values. This suggests that the standard sequence is a robust prediction of the model, and we conclude by proposing a methodology for assessing the robustness of the standard sequence in other models and formulations.

nlin.PS↗