Spatially-explicit Agent-based Model Research Articles

Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies

The collective dynamics of self-organised systems emerge from the decision rules agents use to respond to each other and to external forces. This is evident in groups of animals under attack from predators, where understanding collective escape patterns requires evaluating the risks and rewards associated with particular social rules, prey escape behaviour, and predator attack strategies. Here, we find that the emergence of the ‘fountain effect’, a common collective pattern observed when animal groups evade predators, is the outcome of rules designed to maximise individual survival chances given predator hunting decisions. Using drone-based empirical observations of schooling sardine prey (Sardinops sagax caerulea) attacked by striped marlin (Kajikia audax), we first find the majority of attacks produce fountain effects, with the dynamics of these escapes dependent on the predator’s attack direction. Then, using a spatially-explicit agent-based model of predator-prey dynamics, we show that fountain manoeuvres can emerge from combining an optimal individual prey escape angle with social interactions. The escape rule appears to prioritise maximising the distance to the predator and creates conflict in the effectiveness of predators’ attacks and the prey’s avoidance, explaining the empirically observed predators’ attack strategies and the fountain evasions produced by prey. Overall, we identify the proximate and ultimate explanations for fountain effects and more generally highlight that the collective patterns of self-organised predatory-prey systems can be understood by considering both social escape rules and attack strategies.

Visual social information use in collective foraging.

Collective dynamics emerge from individual-level decisions, yet we still poorly understand the link between individual-level decision-making processes and collective outcomes in realistic physical systems. Using collective foraging to study the key trade-off between personal and social information use, we present a mechanistic, spatially-explicit agent-based model that combines individual-level evidence accumulation of personal and (visual) social cues with particle-based movement. Under idealized conditions without physical constraints, our mechanistic framework reproduces findings from established probabilistic models, but explains how individual-level decision processes generate collective outcomes in a bottom-up way. In clustered environments, groups performed best if agents reacted strongly to social information, while in uniform environments, individualistic search was most beneficial. Incorporating different real-world physical and perceptual constraints profoundly shaped collective performance, and could even buffer maladaptive herding by facilitating self-organized exploration. Our study uncovers the mechanisms linking individual cognition to collective outcomes in human and animal foraging and paves the way for decentralized robotic applications.

Assessing the impact of architectural and behavioral interventions for controlling indoor COVID-19 infection risk: An agent-based approach

The COVID-19 pandemic changed our lives, forcing us to reconsider our built environment. In some buildings with high traffic flow, infected individuals release viral particles during movement. The complex interactions between humans, building, and viruses make it difficult to predict indoor infection risk by traditional computational fluid dynamics methods. The paper developed a spatially-explicit agent-based model to simulate indoor respiratory pathogen transmission for buildings with frequent movement of people. The social force model simulating pedestrian movement and a simple forcing method simulating indoor airflow were coupled in an agent-based modeling environment. The impact of architectural and behavioral interventions on the indoor infection risk was then compared by simulating a supermarket case. We found that wearing a mask was the most effective single intervention, with all people wearing masks reducing the percentage of infections to 0.08%. Among the combined interventions, the combination of customer control is the most effective and can reduce the percentage of infections to 0.04%. In addition, the extremely strict combination of all the interventions makes the supermarket free of new infections during its 8-hour operation. The approach can help architects, managers, or the government better understand the effect of nonpharmaceutical interventions to reduce the infection risk and improve the level of indoor safety.

The Formal Model for the solitary bee Osmia bicornis L. agent‑based model

Solitary bees provide an important ecological and agricultural service by pollinating both wild plants and crops, often more effectively than honey bees. In the context of worldwide pollinators' declines, it is important to better understand the functioning of populations under multiple stressors at larger spatial and temporal scales. Here we propose building a detailed, spatially-explicit agent-based model of one of the best-studied species of solitary bees, Osmia bicornis L. In this Formal Model, we review various aspects of O. bicornis biology and ecology in detail and provide descriptions of their planned implementations in the model. We also discuss the model gaps and limitations, as well as inclusions and exclusions, allowing a dialogue with the reviewers about the model's design. The ALMaSS model of O. bicornis aims to provide a realistic and detailed representation of O. bicornis populations in space and time in European agricultural landscapes. The model will be a part of the Animal, Landscape and Man Simulation System (ALMaSS); thus will be able to utilise a highly detailed, dynamic ALMaSS landscape model. It will consider the behaviour of all bee life stages daily and use state transitions to allow each individual to decide their behaviour. The development of egg-to-pupa stages in the nest will be temperature-driven. Adult bees, after they emerge from the nest in spring, will interact with the environment. They will be able to search for suitable nesting locations, provision their brood cells with pollen and reproduce. Modelled females will balance offspring size and number following the optimal allocation theory, but local environmental factors will modify their actual parental investment decisions. The model will include the daily mortality rate for the egg-to-pupa stages, overwintering mortality, and background mortality outside the nest. We will also consider the risk of open-cell parasitism as increasing with the time the brood cell is open. With the level of detail suggested, the model will be able to simulate population-level dynamics in response to multiple factors at the landscape scale over long periods. The European Food Safety Authority (EFSA) has suggested O. bicornis as a model organism for non-Apis solitary bees in the pesticide risk assessment scheme. Therefore, we hope our model will be a first step in building future landscape risk assessments for solitary bees.

Simulating the migration dynamics of juvenile salmonids through rivers and estuaries using a hydrodynamically driven enhanced particle tracking model

Juvenile salmonids migrate hundreds of kilometers from their natal streams to mature in the ocean. Throughout this migration, they respond to environmental cues such as local water velocities and other stimuli to direct and modulate their movements, often through heavily modified riverine and estuarine habitats. Management strategies in an uncertain future of climate change and altered land use regimes depend heavily on being able to reliably predict their ocean entry timings, route use, and survival rates through rivers and estuaries. We developed a spatially-explicit agent-based model of fish movement in response to hydrodynamic flows that uses movement dynamics gleaned from multi-dimensional tracking datasets of acoustically tagged juveniles moving through an urbanized, branched tidal estuary. We demonstrate how such models can be calibrated, and we apply it to the Sacramento-San Joaquin Delta in Central California. The quality of the out-of-sample validation of the model to predict juvenile salmon survival and route selection indicates that the model is versatile and flexible enough to be used in novel hydroclimatological conditions.

Comparing Control Intervention Scenarios for Raccoon Rabies in Southern Ontario between 2015 and 2025.

The largest outbreak of raccoon rabies in Canada was first reported in Hamilton, Ontario, in 2015 following a probable translocation event from the United States. We used a spatially-explicit agent-based model to evaluate the effectiveness of provincial control programs in an urban-centric outbreak if control interventions were used until 2025, 2020, or never used. Calibration tests suggested that a seroprevalence of protective rabies antibodies 2.1 times higher than that inferred from seroprevalence in program assessments was required in simulations to replicate observed raccoon rabies cases. Our simulation results showed that if control interventions with an adjusted seroprevalence were used until 2025 or 2020, the probability of rabies elimination due to control intervention use was 49.2% and 42.1%, respectively. However, if controls were never used, the probability that initial rabies cases failed to establish a sustained outbreak was only 18.2%. In simulations where rabies was not successfully eliminated, using control interventions until 2025 resulted in 67% fewer new infections compared to only applying controls until 2020 and in 90% fewer new infections compared to no control intervention use. However, the model likely underestimated rabies elimination rates since we did not adjust for adaptive control strategies in response to changes in rabies distributions and case numbers, as well as extending control interventions past 2025. Our agent-based model offers a cost-effective strategy to evaluate approaches to rabies control applications.

Modelling lymphatic filariasis elimination in American Samoa: GEOFIL predicts need for new targets and six rounds of mass drug administration

BackgroundAs part of the global effort to eliminate the debilitating mosquito-borne disease lymphatic filariasis (LF), seven rounds of two-drug (diethylcarbamazine and albendazole) mass drug administration (MDA) were conducted in American Samoa over 2000–2006. However subsequent surveys demonstrated ongoing transmission prompting further rounds of three-drug (diethylcarbamazine, albendazole, and ivermectin) MDA starting in 2018. MethodsWe extend GEOFIL, a spatially-explicit agent-based model of LF transmission to predict the probability and timing of the local elimination or resurgence of LF for different MDA scenarios starting in 2018: two-drug vs. three-drug MDA, two to seven annual rounds, and population coverage rates of 55–75%. We developed an interactive visualisation comparing the effect of MDA strategies on different outcomes. ResultsAt least six annual rounds of three-drug MDA treating 75% of the population were required to achieve LF elimination in American Samoa by 2035 in > 50% of simulations. In scenarios where MDA did not achieve elimination, prevalence doubled approximately every three years, even if MDA reduced antigen prevalence to <1% (the target recommended by the World Health Organisation). Prevalence in six- and seven-year-old children was approximately one quarter of the prevalence in the general population. ConclusionThe three rounds of three-drug MDA conducted in 2018, 2019, and 2021 may have come close to WHO targets but are unlikely to interrupt LF transmission in American Samoa without further interventions. The recommended post-MDA surveillance strategy of testing primarily six and seven-year-old children will delay detection of resurgence compared to population representative surveys. The recommended elimination targets (reducing antigen prevalence below 0.5%, 1%, or 2%) may not be sufficient to interrupt transmission in countries with LF epidemiology like American Samoa. Alternative surveillance strategies and interventions designed to identify and eliminate spatially localized residual transmission may need to be considered. Interactive visualisations may assist decision-makers to choose locally appropriate strategies.

Assessing the efficacy of interventions to control indoor SARS-Cov-2 transmission: An agent-based modeling approach

Nonpharmaceutical interventions for minimizing indoor SARS-CoV-2 transmission continue to be critical tools for protecting susceptible individuals from infection, even as effective vaccines are produced and distributed globally. We developed a spatially-explicit agent-based model for simulating indoor respiratory pathogen transmission during discrete events taking place in a single room within a sub-day time frame, and used it to compare effects of four interventions on reducing secondary SARS-CoV-2 attack rates during a superspreading event by simulating a well-known case study. We found that preventing people from moving within the simulated room and efficacious mask usage appear to have the greatest effects on reducing infection risk, but multiple concurrent interventions are required to minimize the proportion of susceptible individuals infected. Social distancing had little effect on reducing transmission if individuals were randomly relocated within the room to simulate activity-related movements during the gathering. Furthermore, our results suggest that there is potential for ventilation airflow to expose susceptible people to aerosolized pathogens even if they are relatively far from infectious individuals. Maximizing the vertical aerosol removal rate is paramount to successful transmission-risk reduction when using ventilation systems as intervention tools.

Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement

African elephants (Loxodonta africana) are well-studied and inhabit diverse landscapes that are being transformed by both humans and natural forces. Most tools currently in use are limited in their ability to predict how elephants will respond to novel changes in the environment. Individual-, or agent-based modeling (ABM), may extend current methods in addressing and predicting spatial responses to environmental conditions over time. We developed a spatially explicit agent-based model to simulate elephant space use and validated the model with movement data from elephants in Kruger National Park (KNP) and Chobe National Park (CNP). We simulated movement at an hourly scale, as this scale can reflect switches in elephant behavior due to changes in internal states and short-term responses to the local availability and distribution of critical resources, including forage, water, and shade. Known internal drivers of elephant movement, including perceived temperature and the time since an individual last visited a water source, were linked to the external environment through behavior-based movement rules. Simulations were run on model landscapes representing the wet season and the hot, dry season for both parks. The model outputs, including home range size, daily displacement distance, net displacement distance, and maximum distance traveled from a permanent water source, were evaluated through qualitative and quantitative comparisons to actual elephant movement data from both KNP and CNP. The ABM was successful in reproducing the differences in daily displacements between seasons in each park, and in distances traveled from a permanent water source between parks and seasons. Other movement characteristics, including differences in home range sizes and net daily displacements, were partially reproduced. Out of the all the statistical comparisons made between the empirical and simulated movement patterns, the majority were classified as discrepancies of medium or small effect size. We have shown that a resource-driven model with relatively simple decision rules generates trajectories with movement characteristics that are mostly comparable to those calculated from empirical data. Simulating hourly movement (as our model does) may be useful in predicting how finer-scale patterns of space use, such as those created by foraging movements, are influenced by finer spatio-temporal changes in the environment.

Understanding transmission and control of the pork tapeworm with CystiAgent: a spatially explicit agent-based model

BackgroundThe pork tapeworm, Taenia solium, is a serious public health problem in rural low-resource areas of Latin America, Africa and Asia, where the associated conditions of nuerocysticercosis (NCC) and porcine cysticercosis cause substantial health and economic harms. An accurate and validated transmission model for T. solium would serve as an important new tool for control and elimination, as it would allow for comparison of available intervention strategies, and prioritization of the most effective strategies for control and elimination efforts.MethodsWe developed a spatially-explicit agent-based model (ABM) for T. solium (“CystiAgent”) that differs from prior T. solium models by including a spatial framework and behavioral parameters such as pig roaming, open human defecation, and human travel. In this article, we introduce the structure and function of the model, describe the data sources used to parameterize the model, and apply sensitivity analyses (Latin hypercube sampling-partial rank correlation coefficient (LHS-PRCC)) to evaluate model parameters.ResultsLHS-PRCC analysis of CystiAgent found that the parameters with the greatest impact on model uncertainty were the roaming range of pigs, the infectious duration of human taeniasis, use of latrines, and the set of “tuning” parameters defining the probabilities of infection in humans and pigs given exposure to T. solium.ConclusionsCystiAgent is a novel ABM that has the ability to model spatial and behavioral features of T. solium transmission not available in other models. There is a small set of impactful model parameters that contribute uncertainty to the model and may impact the accuracy of model projections. Field and laboratory studies to better understand these key components of transmission may help reduce uncertainty, while current applications of CystiAgent may consider calibration of these parameters to improve model performance. These results will ultimately allow for improved interpretation of model validation results, and usage of the model to compare available control and elimination strategies for T. solium.

The impact of proliferation-migration tradeoffs on phenotypic evolution in cancer

Tumors are not static masses of cells but dynamic ecosystems where cancer cells experience constant turnover and evolve fitness-enhancing phenotypes. Selection for different phenotypes may vary with (1) the tumor niche (edge or core), (2) cell turnover rates, (3) the nature of the tradeoff between traits, and (4) whether deaths occur in response to demographic or environmental stochasticity. Using a spatially-explicit agent-based model, we observe how two traits (proliferation rate and migration speed) evolve under different tradeoff conditions with different turnover rates. Migration rate is favored over proliferation at the tumor’s edge and vice-versa for the interior. Increasing cell turnover rates slightly slows tumor growth but accelerates the rate of evolution for both proliferation and migration. The absence of a tradeoff favors ever higher values for proliferation and migration, while a convex tradeoff tends to favor proliferation, often promoting the coexistence of a generalist and specialist phenotype. A concave tradeoff favors migration at low death rates, but switches to proliferation at higher death rates. Mortality via demographic stochasticity favors proliferation, and environmental stochasticity favors migration. While all of these diverse factors contribute to the ecology, heterogeneity, and evolution of a tumor, their effects may be predictable and empirically accessible.

Exploring policy options to spur the expansion of ethanol production and consumption in Brazil: An agent-based modeling approach

The Brazilian government aims to increase the share of biofuels in the energy mix to around 18% by 2030, which implies an increase of ethanol production from currently 27 bln liters to over 50 bln liters per year. Biofuel policies play an important role in ethanol production, consumption, and investment in processing capacity. Nevertheless, a clear understanding of how current policies affect the evolution of the market is lacking. We developed a spatially-explicit agent-based model to analyze the impact of different blend mandates and taxes levied on gasoline, hydrous, and anhydrous ethanol on investment in processing capacity and on production and consumption of ethanol. The model uses land use projections by the PCRaster Land Use Change model and incorporates the institutions governing the actors’ strategic decision making with regard to production and consumption of ethanol, and the institutions governing the interaction among actors. From the investigated mix of policy measures, we find that an increase of the gasoline tax leads to the highest increased investments in sugarcane processing capacity. We also find that a gasoline tax above 1.23 R$/l and a tax exemption for hydrous ethanol may lead to doubling the production of ethanol by 2030 (relative to 2016).

Nearby outdoor recreation modelling: An agent-based approach

Modelling and simulating the movement of humans during outdoor nearby recreational activities can deliver important insight into the landscape services available to people in urbanized areas. Recreational activities such as walking, jogging and cycling are known to have a positive effect on people’s mental and physical health, however in urban areas access to nearby recreation areas is sometimes lacking, under-developed or impeded by man-made infrastructures. In this context, understanding the spatial behaviour of humans during outdoor recreation is a crucial step towards implementing improvement measures. In this paper, we investigated the recreation strategies of multiple individuals and the flow in the movements of these individuals using a spatially-explicit agent-based model. The model combined information on the available infrastructures (roads, housing areas), the recreational potential of the landscape and rule-based movement strategies mimicking recreationists decision-making. At the individual level, we demonstrated that the shortest-path and weighted-path strategies were used the most during nearby recreation activities. At the population level, we successfully predicted recreationist flow patterns, highlighting gaps in path infrastructure and the most preferred path sections in a small town environment. We further illustrated the versatility of the model and its potential to support planning decisions to increase the accessibility of nearby recreation areas.

From oil wealth to green growth - An empirical agent-based model of recession, migration and sustainable urban transition

This paper develops an empirical, multi-layered and spatially-explicit agent-based model that explores sustainable pathways for Aberdeen city and surrounding area to transition from an oil-based economy to green growth. The model takes an integrated, complex systems approach to urban systems and incorporates the interconnectedness between people, households, businesses, industries and neighbourhoods. We find that the oil price collapse could potentially lead to enduring regional decline and recession. With green growth, however, the crisis could be used as an opportunity to restructure the regional economy, reshape its neighbourhoods, and redefine its identity in the global economy. We find that the type of the green growth and the location of the new businesses will have profound ramifications for development outcomes, not only by directly creating businesses and employment opportunities in strategic areas, but also by redirecting households and service businesses to these areas. New residential and business centres emerge as a result of this process. Finally, we argue that industries, businesses and the labour market are essential components of a deeply integrated urban system. To understand urban transition, models should consider both household and industrial aspects.

Predicting monarch butterfly (Danaus plexippus) movement and egg-laying with a spatially-explicit agent-based model: The role of monarch perceptual range and spatial memory

An ∼80% decline in the eastern population of the monarch butterfly (Danaus plexippus) has prompted conservation efforts to increase summer reproductive success in the Midwest United States. Implementation of conservation practices will create a patchwork of milkweed (mainly Asclepias spp.) habitat within agricultural landscapes dominated by corn and soybean production. Since the monarch butterfly is a vagile species, reproductive success is, in part, a function of both the amount and spatial arrangement of habitat patches in a fragmented landscape. To inform conservation planning we developed a spatially-explicit, agent-based model for summer breeding, non-migratory female monarch butterfly movement and egg-laying on an Iowa, USA landscape. Our model employs a unique movement algorithm when monarch agents encounter habitat edges that incorporates monarch perceptual range to their host plant and spatial memory of previously visited habitat. These behavioral factors are rarely incorporated into animal movement algorithms; however, they can influence estimates of resource utilization. Model exploration assessed the distribution and density of eggs laid on a spatially-explicit 148,665 ha landscape comprised of 17 land cover classes with varying milkweed densities. Uncertainty analysis was undertaken by sampling 25 combinations of perceptual range, spatial memory, flight step length and flight directionality parameters from a total of 256 (44) possible combinations. Movement paths simulated with our new movement algorithm show preferential use of high density milkweed areas that would not be simulated using a correlated random walk. Increasing perceptual range caused a decrease in the area used by monarch agents and caused a skewed egg distribution where most eggs were laid in relatively few habitat patches. Increasing spatial memory caused an increase in the area used but decreased the median number of eggs laid in roadside habitat. Current national and regional monarch conservation goals assume a uniform distribution of milkweed in different land cover classes. Translating these goals into spatially-explicit, heterogeneous habitat patches is essential for predicting realized fecundity in the landscape. Our model provides the foundation to link national and regional monarch conservation goals to fine scale spatial configurations of habitat patches in defined landscapes.

Policy instruments to control Amazon fires: A simulation approach

Agricultural fires are a double-edged sword that allow for cost-efficient land management in the tropics but also cause accidental fires and emissions of carbon and pollutants. To control fires in Amazon, it is currently unclear whether policy-makers should prioritize command-and-control or incentive-based instruments such as REDD+. Aiming to generate knowledge about the relative merits of the two policy approaches, this paper presents a spatially-explicit agent-based model that simulates the causal effects of four policy instruments on intended and unintended fires. All instruments proved effective in overturning the predominance of highly profitable but risky fire-use and decreasing accidental fires, but none were free from imperfections. The performance of command-and-control proved highly sensitive to the spatial and social reach of enforcement. Side-effects of incentive-based instruments included a disproportionate increase in controlled fires and a reduced acceptance of conservation subsidies, caused by the prohibition of reckless fires, and also indirect deforestation. The instruments that were most effective in reducing deforestation were not the most effective in reducing fires and vice-versa, which suggests that the two goals cannot be achieved with a single policy intervention.

Linking Bayesian and agent-based models to simulate complex social-ecological systems in semi-arid regions

Interdependencies of ecologic, hydrologic, and social systems challenge traditional approaches to natural resource management in semi-arid regions. As a complex social-ecological system, water demands in the Sonoran Desert from agricultural and urban users often conflicts with water needs for its ecologically-significant riparian corridors. To explore this system, we developed an agent-based model to simulate complex feedbacks between human decisions and environmental conditions in the Rio Sonora Watershed. Cognitive mapping in conjunction with stakeholder participation produced a Bayesian model of conditional probabilities of local human decision-making processes resulting to changes in water demand. Probabilities created in the Bayesian model were incorporated into the agent-based model, so that each agent had a unique probability to make a positive decision based on its perceived environment at each point in time and space. By using a Bayesian approach, uncertainty in the human decision-making process could be incorporated. The spatially-explicit agent-based model simulated changes in depth-to-groundwater by well pumping based on an agent’s water demand. Changes in depth-to-groundwater feedback to influence agent behavior, as well as determine unique vegetation classes within the riparian corridor. Each vegetation class then provides varying stakeholder-defined quality values of ecosystem services. Using this modeling approach allowed us to examine effects on both the ecological and social system of semi-arid riparian corridors under various scenarios. The insight provided by the model contributes to understanding how specific interventions may alter the complex social-ecological system in the future.

A hybrid modeling approach to simulating foot-and-mouth disease outbreaks in Australian livestock

Foot-and-mouth disease (FMD) is a highly contagious and economically important viral disease of cloven-hoofed animals. Australia's freedom from FMD underpins a valuable trade in live animals and animal products. An outbreak of FMD would result in the loss of export markets and cause severe disruption to domestic markets. The prevention of, and contingency planning for, FMD are of key importance to government, industry, producers and the community. The spread and control of FMD is complex and dynamic due to a highly contagious multi-host pathogen operating in a heterogeneous environment across multiple jurisdictions. Epidemiological modelling is increasingly being recognized as a valuable tool for investigating the spread of disease under different conditions and the effectiveness of control strategies. Models of infectious disease can be broadly classified as: population-based models that are formulated from the top-down and employ population-level relationships to describe individual-level behaviour, individual-based models that are formulated from the bottom-up and aggregate individual-level behaviour to reveal population-level relationships, or hybrid models which combine the two approaches into a single model. The Australian Animal Disease Spread (AADIS) hybrid model employs a deterministic equation-based model (EBM) to model within-herd spread of FMD, and a stochastic, spatially-explicit agent-based model (ABM) to model between-herd spread and control. The EBM provides concise and computationally efficient predictions of herd prevalence and clinical signs over time. The ABM captures the complex, stochastic and heterogeneous environment in which an FMD epidemic operates. The AADIS event-driven hybrid EBM/ABM architecture is a flexible, efficient and extensible framework for modelling the spread and control of disease in livestock on a national scale. We present an overview of the AADIS hybrid approach and a description of the model's epidemiological capabilities