About

Alan Hastings is a faculty member associated with the Hastings Lab at UC Davis. His research focuses on ecological and evolutionary dynamics, as indicated by the lab's emphasis on current research and the lab members' scholarly activities. The webpage references his CV and highlights his role within the lab, which includes mentoring graduate students and postdoctoral researchers. His contributions are recognized within the academic community, and he maintains an active presence in research, as evidenced by recent publications by lab members. Further details about his background, specific research interests, and key contributions are available through the lab's website and his CV.

Research topics

• Computer Science
• Sociology
• Ecology
• Biology
• Artificial Intelligence
• Psychology
• Medical education
• Physics
• Demography
• Environmental science
• Geography
• Medicine
• Risk analysis (engineering)
• Engineering ethics
• Engineering
• Environmental resource management
• Social psychology
• Pedagogy
• Business

Selected publications

• Tradeoffs in planning marine protected areas for kelp forest resilience: protecting climate refugia is not always the best solution

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-04

  articleOpen access

  Abstract Marine protected areas (MPAs) are increasingly promoted as climate mitigation tools, yet guidance on their placement to maximize resilience against climate stressors like marine heatwaves remains limited. Here, we develop MPA placement guidelines that explicitly consider a mechanistic pathway through which MPAs could enhance kelp forest resilience to heatwaves: protecting fishery-targeted urchin predators to prevent kelp overgrazing. Using a spatially explicit, tri-trophic model of California kelp forests, we evaluate alternative MPA configurations across a hypothetical coastline where half the habitat experiences an increased probability of experiencing heatwaves. We found that effective MPA placement depends on whether MPAs are being newly established or reconfigured within an existing network, and that among-patch connectivity and spillover played vital roles in the relative effectiveness of different MPA configurations. Changes in resilience occurred primarily at the patch scale, with trade-offs between increased within-MPA resilience and decreased resilience in some fished areas, resulting in minimal coastwide population effects. For example, for new MPAs, large single MPAs within heatwave-prone areas maximized within-MPA resilience gains, while multiple small MPAs in heatwave refugia best supported whole-coast resilience. When reconfiguring established networks, expanding existing MPAs in refugia areas was most effective. We also demonstrate the importance of considering MPA recovery timescales: for example, relocating old MPAs to heatwave refugia yielded minimal short-term benefits due to the loss of rebuilt, previously fished, predator biomass. Our findings demonstrate that climate-adaptive marine planning should explicitly consider the spatiotemporal implications of trophic cascades, connectivity, and transient population dynamics to support ecosystem resilience.

  PublisherOA PDFDOI

• Reordered hierarchical complexity in ecosystems with delayed interactions

  PNAS Nexus · 2025-06-30

  articleOpen access

  It was once believed that large ecosystems with random interactions are unstable, limiting their complexity. Thus, large community size or numerous interactions are rare in nature. Later, a strict hierarchical complexity was revealed: competitive and mutualistic communities have the least complexity, followed by random ones, and then predator-prey communities. Recently, a hierarchy of recovery times for ecosystems with identical complexity was found, influenced by discrete time delays. A key question is whether this hierarchical complexity holds under noninstantaneous interactions. We surprisingly show that it does not. Specifically, the complexity of predator-prey communities is significantly affected by time delays, reordering the hierarchy at a critical threshold. These changes exhibit nonmonotonic behavior with continuous time delays, another realistic interaction type. We validated our findings in various realistic ecosystems. Our results indicate that incorporating factors like time delays and their appropriate forms can lead to correct and even deeper understanding about complexity of large ecosystems and other biophysical systems.

  PublisherOA PDFDOI

• Long-range dispersal promotes spatial synchrony but reduces the length and time scales of synchronous fluctuations

  ArXiv.org · 2025-06-10

  preprintOpen accessSenior author

  Synchronous oscillations of spatially disjunct populations are widely observed in ecology. Even in the absence of spatially synchronized exogenous forces, metapopulations may synchronize via dispersal. For many species, most dispersal is local, but rare long-distance dispersal events also occur. While even small amounts of long-range dispersal are known to be important for processes like invasion and spatial spread rates, their potential influence on population synchrony is often overlooked, since local dispersal on its own can be strongly synchronizing. In this work, we investigate the effect of random, rare, long-range dispersal on the spatial synchrony of a metapopulation and find profound effects not only on synchrony but also on properties of the resulting spatial patterns. While controlling for the overall amount of emigration from each local subpopulation, we vary the fraction of dispersal that occurs locally (to nearest neighbors) versus globally (to random locations, irrespective of distance). Using a metric that measures the instantaneous level of global synchrony, we show that this form of long-range dispersal significantly favors the spatially synchronous state and homogenizes the population by decreasing the size of clusters of subpopulations that are out of phase with the rest of the metapopulation. Moreover, the addition of non-local dispersal significantly decreases the equilibration time of the metapopulation.

  PublisherOA PDFDOI

• Effect of Spatial Heterogeneity in Spatial Metapopulation Models

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Competition–colonization trade-off can explain any observed abundances and assumed competitive hierarchies

  Proceedings of the National Academy of Sciences · 2025-12-05

  articleOpen access1st authorCorresponding

  The competition-colonization trade-off is a possible explanation for coexistence of species in a metacommunity context that has been intensively studied for decades. Nonetheless, questions about the ubiquity and generality of the mechanism remain. The outcome of the model, equilibrium species abundances, are relatively easy to measure. However, the input into the basic model, the competitive hierarchy and the colonization rates, are not easy to measure in the field. We propose an approach that starts with an observed equilibrium configuration. We show that for any assumed competitive hierarchy we can find a corresponding set of colonization rates that would produce the observed equilibrium. We also find a simple formula for the colonization rates in terms of the observed abundances. This approach both shows that any observed set of abundances can result from a competition-colonization trade-off, and provides a method for future analyses. Additionally, generalizations of our approach can apply to generalizations of the basic competition-colonization model that avoid any biologically questionable assumptions.

  PublisherDOI

• Fluctuating selection breaks Hamilton’s rule for the evolution of altruism

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-04-22

  preprintOpen access

  Abstract Altruistic behaviours, those that benefit recipients at a cost to donors, have long posed a puzzle in evolutionary biology and sociobiology. Established theories, such as kin selection, group selection, and reciprocal altruism, explain altruism via assortment mechanisms that depend on preferential interactions among altruists to ensure a higher average payoff than selfish individuals. Hamilton’s rule defines the minimum level of such assortative interactions required for altruism to evolve. However, in populations with limited growth, increased competition can erode the selective advantage of altruism and thus require more stringent Hamilton’s rule. Here, we propose a fundamentally different mechanism: fluctuating selection driven by increased competition due to added social benefits of altruism in populations with limited carrying capacity. Under fluctuating selection, altruists can invade a selfish population without the need of assortment mechanisms and outcompete selfish individuals through a transient phase, even when the cost of altruism exceeds its direct benefit. Classical invasion analysis, which compares the long-term growth rates of rare altruistic mutants and resident selfish individuals, fails in our model because both strategies exhibit zero long-term growth. Instead, we show that altruism can invade when its short-term, time-averaging growth rate exceeds that of selfish individuals, which necessitates a prolonged transient phase. This invasion is enabled by payoff-regulated density dependence, which captures the dynamic interplay between payoff and density on fitness under fluctuating selection. Our findings challenge the necessity of Hamilton’s rule and show that genuine altruism, with less average payoff than selfish individuals, even extremely self-sacrificial, can emerge under natural selection. This suggests that altruism may arise not from preferential interaction, but as an adaptive response to population fluctuations in constrained environments, providing an alternative paradigm for understanding the evolution of altruistic behaviour.

  PublisherOA PDFDOI

• Behavior of Ising spins and ecological oscillators on dynamically rewired small-world networks

  Physical review. E · 2025-06-18 · 1 citations

  articleSenior author

  Many ecological populations are known to display a cyclic behavior with period 2. Previous work has shown that when a metapopulation (a group of coupled populations) with such dynamics is allowed to interact via nearest-neighbor dispersal in two dimensions, it undergoes a phase transition from disordered (spatially asynchronous) to ordered (spatially synchronous) that falls under the two-dimensional Ising universality class. While nearest-neighbor dispersal may satisfactorily describe how most individuals migrate between habitats, we should expect a small fraction of individuals to venture on a journey to further locations. We model this behavior by considering ecological oscillators on dynamically rewired small-world networks, in which at each time step a fraction p of the nearest-neighbor interactions is replaced by a new interaction with a random node on the network. We measure how this connectivity change affects the critical point for synchronizing ecological oscillators. Our results indicate that increasing the amount of long-range interaction (increasing p) favors the ordered regime, but the presence of memory in ecological oscillators leads to quantitative differences in how much long-range dispersal is needed to order the network, relative to an analogous network of Ising spins. We also show that, even for very small values of p, the phase transition falls into the mean-field universality class, and we argue that ecosystems where dispersal can occasionally happen across the system's length scale will display a phase transition in the mean-field universality class.

  PublisherDOI

• Quantifying local fishing mortality rates to inform monitoring design for marine reserves

  Theoretical Ecology · 2025-05-22

  articleOpen access
  PublisherOA PDFDOI

• Learning to learn ecosystems from limited data

  Proceedings of the National Academy of Sciences · 2025-12-17 · 2 citations

  articleOpen access

  A fundamental challenge in developing data-driven approaches to ecological systems for tasks such as state estimation and prediction is the paucity of the observational or measurement data. For example, modern machine-learning techniques such as deep learning or reservoir computing typically require a large quantity of data. Leveraging synthetic data from paradigmatic nonlinear but non-ecological dynamical systems, we develop a meta-learning framework with time-delayed feedforward neural networks to predict the long-term behaviors of ecological systems as characterized by their attractors. We show that the framework is capable of accurately reconstructing the "dynamical climate" of the ecological system with limited data. Three benchmark population models in ecology, namely the Hastings-Powell model, its variant, and the Lotka-Volterra system, are used to demonstrate the performance of the meta-learning based prediction framework. In all cases, enhanced accuracy and robustness have been achieved using five to seven times less training data as compared with the corresponding machine-learning method trained solely from the ecosystem data. In addition, two real-world ecological benchmark datasets: the microbial time-series dataset and global population dynamics database, are tested to demonstrate the applicability of the meta-learning framework to the real world. A number of issues affecting the prediction performance are addressed.

  PublisherDOI

• Short‐Term Management of Kelp Forests for Marine Heatwaves Requires Planning

  Conservation Letters · 2025-07-01

  articleOpen access

  ABSTRACT Heatwaves are now pervasive stressors to marine ecosystems, and it is urgent to consider mitigation tools that support ecosystem resilience and persistence in the immediate future. We modeled a system of kelp, herbivorous urchin, and predatory fish to compare how potential management actions (kelp seeding, urchin removal, and fishery closures) could reduce the likelihood of a heatwave shifting a kelp forest into a degraded urchin barren state. We found that those interventions were most effective when begun alongside or before the start of a heatwave. Closing the predatory fish fishery was more effective when done earlier and for longer, while urchin removal and kelp seeding were more effective when begun alongside and continued throughout the heatwave. Kelp seeding was notably less effective than other interventions. Our results suggest the need for improved heatwave forecasting and nimble management protocols to enact mitigation actions quickly if a heatwave is forecasted or occurs.

  PublisherOA PDFDOI

Recent grants

• QEIB: Stochastic Spatial Spread: Models and Experiments

  NSF · $292k · 2005–2009

• Collaborative:MSPA-CSE: Analysis and Detection of Transient Dynamics in Ecological Systems

  NSF · $186k · 2004–2008

• Metacommunity Dynamics: Integrating Local Dynamics, Stochasticity, and Connectivity

  NSF · $291k · 2018–2023

• Collaborative Research: MTM 2:Searching for General Rules Governing Microbiome Dynamics using Anaerobic Digesters as Model Systems

  NSF · $696k · 2020–2026

• Collaborative Research: Range Limits and Their Response to Environmental Change: Experiments and Stochastic Models

  NSF · $305k · 2009–2014

Frequent coauthors

• Louis W. Botsford

  University of California, Davis

  50 shared

• Jonathan Machta

  Santa Fe Institute

  40 shared

• Karen C. Abbott

  Case Western Reserve University

  36 shared

• Kim Cuddington

  University of Waterloo

  32 shared

• Andrew Morozov

  Severtsov Institute of Ecology and Evolution

  29 shared

• J. Wilson White

  Oregon State University

  28 shared

• Sergei Petrovskii

  Peoples' Friendship University of Russia

  28 shared

• Easton R. White

  University of New Hampshire

  24 shared

Labs

• Hastings LabPI