A standard protocol for describing individual-based and agent-based models

Wind-driven post-bloom dispersion of Microcystis in a large shallow eutrophic lake: A case study in Lake Taihu

To clarify the wind-driven post-bloom dispersion range of Microcystis, which originally clustered on the water surface, an Individual-Based Model (IBM) of Microcystis movement considering the combined effects of wind and light was developed based on actual hydrodynamic data and Microcystis biomass. After calibrating the effects of hydrodynamics and light, 66 cases of short-term (within a week) post-bloom with satellite images from 2011 to 2017 were simulated. The results showed that there were three short-term post-bloom types: vertical reduction (VR), horizontal reduction (HR) and mixed reduction (MR). For VR type, the cyanobacterial bloom reduction rate was rapid (>160 km2/day), but the dispersion range of Microcystis was limited (<2 km/day), and a larger bloom area was likely to form in the original location when wind speed decreased. For HR type, the cyanobacterial bloom reduction rate was slow (<10 km2/day), but Microcystis exhibited a broad dispersion range (>4 km/day), often leading to smaller, thicker, and longer-lasting cyanobacterial blooms downwind, albeit with a lower probability of occurrence. The characteristics of MR lay between the two aforementioned types.

Misapplied management makes matters worse: Spatially explicit control leverages biotic interactions to slow invasion

A wide range of approaches has been used to manage the spread of invasive species, yet invaders continue to be a challenge to control. In some cases, management actions have no effect or may even inadvertently benefit the targeted invader. Here, we use the mid-20th century management of the Red Imported Fire Ant, Solenopsis invicta, in the US as a motivating case study to explore the conditions under which such wasted management effort may occur. Introduced in approximately 1940, the fire ant spread widely through the southeast US and became a problematic pest. Historically, fire ants were managed with broad-spectrum pesticides; unfortunately, these efforts were largely unsuccessful. One hypothesis suggests that, by also killing native ants, mass pesticide application reduced competitive burdens thereby enabling fire ants to invade more quickly than they would in the absence of management. We use a mechanistic competition model to demonstrate the landscape-level effects of such management. We explicitly model the extent and location of pesticide applications, showing that the same pesticide application can have a positive, neutral, or negative effect on the progress of an invasion, depending on where it is applied on the landscape with respect to the invasion front. When designing management, the target species is often considered alone; however, this work suggests that leveraging existing biotic interactions, specifically competition with native species, can increase the efficacy of management. Our model not only highlights the potential unintended consequences of ignoring biotic interactions, but also provides a framework for developing spatially explicit management strategies that take advantage of these biotic interactions to work smarter, not harder.

Climate change may shift metapopulations towards unstable source-sink dynamics in a fire-killed, serotinous shrub

Climate change, with warming and drying weather conditions, is reducing the growth, seed production, and survival of fire-adapted plants in fire-prone regions such as Mediterranean-type ecosystems. These effects of climate change on local plant demographics have recently been shown to reduce the persistence time of local populations of the fire-killed shrub Banksia hookeriana dramatically. In principle, extinctions of local populations may be partly compensated by recolonization events through long-distance dispersal mechanisms of seeds, such as post-fire wind and bird-mediated dispersal, facilitating persistence in spatially structured metapopulations. However, to what degree and under which assumptions metapopulation dynamics might compensate for the drastically increased local extinction risk remains to be explored. Given the long timespans involved and the complexity of interwoven local and regional processes, mechanistic, process-based models are one of the most suitable approaches to systematically explore the potential role of metapopulation dynamics and its underlying ecological assumptions for fire-prone ecosystems. Here we extend a recent mechanistic, process-based, spatially implicit population model for the well-studied fire-killed and serotinous shrub species B. hookeriana to a spatially explicit metapopulation model. We systematically tested the effects of different ecological processes and assumptions on metapopulation dynamics under past (1988-2002) and current (2003-2017) climatic conditions, including (i) effects of different spatio-temporal fires, (ii) effects of (likely) reduced intraspecific plant competition under current conditions and (iii) effects of variation in plant performance among and within patches. In general, metapopulation dynamics had the potential to increase the overall regional persistence of B. hookeriana. However, increased population persistence only occurred under specific optimistic assumptions. In both climate scenarios, the highest persistence occurred with larger fires and intermediate to long inter-fire intervals. The assumption of lower intraspecific plant competition caused by lower densities under current conditions alone was not sufficient to increase persistence significantly. To achieve long-term persistence (defined as >400 years) it was necessary to additionally consider empirically observed variation in plant performance among and within patches, that is, improved habitat quality in some large habitat patches (≥7) that could function as source patches and a higher survival rate and seed production for a subset of plants, specifically the top 25% of flower producers based on current climate conditions monitoring data. Our model results demonstrate that the impacts of ongoing climate change on plant demographics are so severe that even under optimistic assumptions, the existing metapopulation dynamics shift to an unstable source-sink dynamic state. Based on our findings, we recommend increased research efforts to understand the consequences of intraspecific trait variation on plant demographics, emphasizing the variation of individual traits both among and within populations. From a conservation perspective, we encourage fire and land managers to revise their prescribed fire plans, which are typically short interval, small fires, as they conflict with the ecologically appropriate spatio-temporal fire regime for B. hookeriana, and likely as well for many other fire-killed species.

Brood indicators are an early warning signal of honey bee colony loss-a simulation-based study

Honey bees (Apis mellifera) are exposed to multiple stressors such as pesticides, lack of forage, and diseases. It is therefore a long-standing aim to develop robust and meaningful indicators of bee vitality to assist beekeepers While established indicators often focus on expected colony winter mortality based on adult bee abundance and honey reserves at the beginning of the winter, it would be useful to have indicators that allow detection of stress effects earlier in the year to allow for adaptive management. We used the established honey bee simulation model BEEHAVE to explore the potential of different indicators such as population size, number of capped brood cells, flight activity, abundance of Varroa mites, honey stores and a brood-bee ratio. We implemented two types of stressors in our simulations: 1) parasite pressure, i.e. sub-optimal Varroa treatment by the beekeeper (hereafter referred as Biotic stress) and 2) temporal forage gaps in spring and autumn (hereafter referred as Environmental stress). Neither stressor type could be detected by bee abundance or honey reserves at the end of the first year. However, all response variables used in this study did reveal early warning signals during the course of the year. The most reliable and useful measures seem to be related to brood and the abundance of Varroa mites at the end of the year. However, while in the model we have full access to time series of variables from stressed and unstressed colonies, knowledge of these variables in the field is challenging. We discuss how our findings can nevertheless be used to develop practical early warning indicators. As a next step in the interactive development of such indicators we suggest empirical studies on the importance of the number of capped brood cells at certain times of the year on bee population vitality.

Integrating irrational behavior into flood risk models to test the outcomes of policy interventions

Householders are increasingly responsible for managing residual flood risk at property level. Yet, consumers are observed to adopt irrational behaviors under scenarios of risk, often making suboptimal decisions. Therefore, the question is raised, if householders are required to manage flood risk at household level, how can this be made fair and efficient? Policy instruments often incorporate "fairness" by subsidizing the costs of mitigation options, assuming a linear relationship between available finances and the uptake of risk mitigation measures. To integrate behavior into the assessment of policy instruments, this article develops a method to compare the uptake of flood mitigation between agents following models of Expected Monetary Value (EMV) and Prospect Theory (PT). An agent-based model is created, offering the option to apply either EMV or PT frameworks. EMV represents a rational gamble, determining whether an agent "should" invest in insurance or property-level protection. On the other hand, the PT option incorporates behavioral aspects to examine how agents are likely to respond to investment decisions in flood risk environments. The models are applied to Flood Re, a policy aimed at ensuring affordable insurance access for all individuals in flood risk areas. The results demonstrate that PT models exhibit greater similarity to observed behavior compared to models based on EMV. These findings emphasize the importance of accommodating "irrational" behaviors within the design of policy instruments, promoting fairness and efficiency. Overall, this research provides insights into the integration of behavior in policy assessments and highlights the benefits of considering PT frameworks alongside traditional rational models. By understanding and accounting for human behavior in decision-making processes, policymakers can design more effective and equitable policy instruments for managing flood risk.

Simulating the Simultaneous Impact of Medication for Opioid Use Disorder and Naloxone on Opioid Overdose Death in Eight New York Counties

BACKGROUND

The United States is in the midst of an opioid overdose epidemic; 28.3 per 100,000 people died of opioid overdose in 2020. Simulation models can help understand and address this complex, dynamic, and nonlinear social phenomenon. Using the HEALing Communities Study, aimed at reducing opioid overdoses, and an agent-based model, Simulation of Community-Level Overdose Prevention Strategy, we simulated increases in buprenorphine initiation and retention and naloxone distribution aimed at reducing overdose deaths by 40% in New York Counties.

METHODS

Our simulations covered 2020-2022. The eight counties contrasted urban or rural and high and low baseline rates of opioid use disorder treatment. The model calibrated agent characteristics for opioid use and use disorder, treatments and treatment access, and fatal and nonfatal overdose. Modeled interventions included increased buprenorphine initiation and retention, and naloxone distribution. We predicted a decrease in the rate of fatal opioid overdose 1 year after intervention, given various modeled intervention scenarios.

RESULTS

Counties required unique combinations of modeled interventions to achieve a 40% reduction in overdose deaths. Assuming a 200% increase in naloxone from current levels, high baseline treatment counties achieved a 40% reduction in overdose deaths with a simultaneous 150% increase in buprenorphine initiation. In comparison, low baseline treatment counties required 250-300% increases in buprenorphine initiation coupled with 200-1000% increases in naloxone, depending on the county.

CONCLUSIONS

Results demonstrate the need for tailored county-level interventions to increase service utilization and reduce overdose deaths, as the modeled impact of interventions depended on the county's experience with past and current interventions.

An agent-based model on antimicrobial de-escalation in intensive care units: Implications on clinical trial design

Antimicrobial de-escalation refers to reducing the spectrum of antibiotics used in treating bacterial infections. This strategy is widely recommended in many antimicrobial stewardship programs and is believed to reduce patients' exposure to broad-spectrum antibiotics and prevent resistance. However, the ecological benefits of de-escalation have not been universally observed in clinical studies. This paper conducts computer simulations to assess the ecological effects of de-escalation on the resistance prevalence of Pseudomonas aeruginosa-a frequent pathogen causing nosocomial infections. Synthetic data produced by the models are then used to estimate the sample size and study period needed to observe the predicted effects in clinical trials. Our results show that de-escalation can reduce colonization and infections caused by bacterial strains resistant to the empiric antibiotic, limit the use of broad-spectrum antibiotics, and avoid inappropriate empiric therapies. Further, we show that de-escalation could reduce the overall super-infection incidence, and this benefit becomes more evident under good compliance with hand hygiene protocols among health care workers. Finally, we find that any clinical study aiming to observe the essential effects of de-escalation should involve at least ten arms and last for four years-a size never attained in prior studies. This study explains the controversial findings of de-escalation in previous clinical studies and illustrates how mathematical models can inform outcome expectations and guide the design of clinical studies.

Density-dependent species interactions modulate alpine treeline shifts

Species interactions such as facilitation and competition play a crucial role in driving species range shifts. However, density dependence as a key feature of these processes has received little attention in both empirical and modelling studies. Herein, we used a novel, individual-based treeline model informed by rich in situ observations to quantify the contribution of density-dependent species interactions to alpine treeline dynamics, an iconic biome boundary recognized as an indicator of global warming. We found that competition and facilitation dominate in dense versus sparse vegetation scenarios respectively. The optimal balance between these two effects was identified at an intermediate vegetation thickness where the treeline elevation was the highest. Furthermore, treeline shift rates decreased sharply with vegetation thickness and the associated transition from positive to negative species interactions. We thus postulate that vegetation density must be considered when modelling species range dynamics to avoid inadequate predictions of its responses to climate warming.

It's about time: Feeding competition costs of sociality are affected more by temporal characteristics than spatial distribution

For most herbivorous animals, group-living appears to incur a high cost by intensifying feeding competition. These costs raise the question of how gregariousness (i.e., the tendency to aggregate) could have evolved to such an extent in taxa such as anthropoid primates and ungulates. When attempting to test the potential benefits and costs, previous foraging models demonstrated that group-living might be beneficial by lowering variance in intake, but that it reduces overall foraging success. However, these models did not fully account for the fact that gregariousness has multiple experiences and can vary in relation to ecological variables and foraging competition. Here, we present an agent-based model for testing how ecological variables impact the costs and benefits of gregariousness. In our simulations, primate-like agents forage on a variable resource landscape while maintaining spatial cohesion with conspecifics to varying degrees. The agents' energy intake rate, daily distance traveled, and variance in energy intake were recorded. Using Morris Elementary Effects sensitivity analysis, we tested the sensitivity of 10 model parameters, of which 2 controlled gregarious behavior and 8 controlled food resources, including multiple aspects of temporal and spatial heterogeneity. We found that, while gregariousness generally increased feeding competition, the costs of gregariousness were much lower when resources were less variable over time (i.e., when calorie extraction was slow and resource renewal was frequent). We also found that maintaining proximity to other agents resulted in lower variance in energy intake when resources were more variable over time. Thus, it appears that the costs and benefits of gregariousness are strongly influenced by the temporal characteristics of food resources, giving insight into the pressures that shaped the evolution of sociality and group living, including in our own lineage.

Sex ratio distorting microbes exacerbate arthropod extinction risk in variable environments

Maternally-inherited sex ratio distorting microbes (SRDMs) are common among arthropod species. Typically, these microbes cause female-biased sex ratios in host broods, either by; killing male offspring, feminising male offspring, or inducing parthenogenesis. As a result, infected populations can experience drastic ecological and evolutionary change. The mechanism by which SRDMs operate is likely to alter their impact on host evolutionary ecology; despite this, the current literature is heavily biased towards a single mechanism of sex ratio distortion, male-killing. Furthermore, amidst the growing concerns surrounding the loss of arthropod diversity, research into the impact of SRDMs on the viability of arthropod populations is generally lacking. In this study, using a theoretical approach, we model the epidemiology of an understudied mechanism of microbially-induced sex ratio distortion-feminisation-to ask an understudied question-how do SRDMs impact extinction risk in a changing environment? We constructed an individual-based model and measured host population extinction risk under various environmental and epidemiological scenarios. We also used our model to identify the precise mechanism modulating extinction. We find that the presence of feminisers increases host population extinction risk, an effect that is exacerbated in highly variable environments. We also identified transmission rate as the dominant epidemiological trait responsible for driving extinction. Finally, our model shows that sex ratio skew is the mechanism driving extinction. We highlight feminisers and, more broadly, SRDMs as important determinants of the resilience of arthropod populations to environmental change.

An agent-based model for studying the temperature changes on environments exposed to magnetic fluid hyperthermia

Magnetic fluid hyperthermia (MFH) is a technique whose results show promise in the treatment against cancer, but which still faces obstacles such as controlling the spatial distribution of temperature. The present study developed an agent-based model in order to simulate the temperature changes in an aqueous environment submitted to the magnetic fluid hyperthermia technique. The developed model was built with its parameters based on the clinical treatment protocol for glioblastoma multiforme (GBM). Using thermodynamic properties of magnetic fluid and tissues, we define a specific thermal parameter (α) and evaluate its influence, together with the intensity of the external magnetic field (H), on the dynamics of the temperature of the cancer environment. The temperature evolution generated by the model was in accordance with experimental results known from the subject literature. The parameters evaluation indicates that the temperature stabilization of the tumor environment during MFH treatment is due to the local interactions of energy diffusion, as well as indicating that the α-parameter is a key factor for controlling the temperature and heating speed.

Avoidance, confusion or solitude? Modelling how noise pollution affects whale migration

Many baleen whales are renowned for their acoustic communication. Under pristine conditions, this communication can plausibly occur across hundreds of kilometres. Frequent vocalisations may allow a dispersed migrating group to maintain contact, and therefore benefit from improved navigation via the "wisdom of the crowd". Human activities have considerably inflated ocean noise levels. Here we develop a data-driven mathematical model to investigate how ambient noise levels may inhibit whale migration. Mathematical models allow us to simultaneously simulate collective whale migration behaviour, auditory cue detection, and noise propagation. Rising ambient noise levels are hypothesised to influence navigation through three mechanisms: (i) diminished communication space; (ii) reduced ability to hear external sound cues and; (iii) triggering noise avoidance behaviour. Comparing pristine and current soundscapes, we observe navigation impairment that ranges from mild (increased journey time) to extreme (failed navigation). Notably, the three mechanisms induce qualitatively different impacts on migration behaviour. We demonstrate the model's potential predictive power, exploring the extent to which migration may be altered under future shipping and construction scenarios.

Modelling HIV/AIDS epidemiological complexity: A scoping review of Agent-Based Models and their application

OBJECTIVE

To end the AIDS epidemic by 2030, despite the increasing poverty and inequalities, policies should be designed to deal with population heterogeneity and environmental changes. Bottom-up designs, such as the Agent-Based Model (ABM), can model these features, dealing with such complexity. HIV/AIDS has a complex dynamic of structural factors, risk behaviors, biomedical characteristics and interventions. All embedded in unequal, stigmatized and heterogeneous social structure. To understand how ABMs can model this complexity, we performed a scoping review of HIV applications, highlighting their potentialities.

METHODS

We searched on PubMed, Web of Science, and Scopus repositories following the PRISMA extension for scoping reviews. Our inclusion criteria were HIV/AIDS studies with an ABM application. We identified the main articles using a local co-citation analysis and categorized the overall literature aims, (sub)populations, regions, and if the papers declared the use of ODD protocol and limitations.

RESULTS

We found 154 articles. We identified eleven main papers, and discussed them using the overall category results. Most studies model Transmission Dynamics (37/154), about Men who have sex with Men (MSM) (41/154), or individuals living in the US or South Africa (84/154). Recent studies applied ABM to model PrEP interventions (17/154) and Racial Disparities (12/154). Only six papers declared the use of ODD Protocol (6/154), and 34/154 didn't mention the study limitations.

CONCLUSIONS

While ABM is among the most sophisticated techniques available to model HIV/AIDS complexity. Their applications are still restricted to some realities. However, researchers are challenged to think about social structure due model characteristics, the inclusion of these features is still restricted to case-specific. Data and computational power availability can enhance this feature providing insightful results.

Resource asynchrony and landscape homogenization as drivers of virulence evolution: The case of a directly transmitted disease in a social host

Throughout the last decades, the emergence of zoonotic diseases and the frequency of disease outbreaks have increased substantially, fuelled by habitat encroachment and vectors overlapping with more hosts due to global change. The virulence of pathogens is one key trait for successful invasion. In order to understand how global change drivers such as habitat homogenization and climate change drive pathogen virulence evolution, we adapted an established individual-based model of host-pathogen dynamics. Our model simulates a population of social hosts affected by a directly transmitted evolving pathogen in a dynamic landscape. Pathogen virulence evolution results in multiple strains in the model that differ in their transmission capability and lethality. We represent the effects of global change by simulating environmental changes both in time (resource asynchrony) and space (homogenization). We found an increase in pathogenic virulence and a shift in strain dominance with increasing landscape homogenization. Our model further indicated that lower virulence is dominant in fragmented landscapes, although pulses of highly virulent strains emerged under resource asynchrony. While all landscape scenarios favoured co-occurrence of low- and high-virulent strains, the high-virulence strains capitalized on the possibility for transmission when host density increased and were likely to become dominant. With asynchrony likely to occur more often due to global change, our model showed that a subsequent evolution towards lower virulence could lead to some diseases becoming endemic in their host populations.

Modeling the persistence of Opisthorchis viverrini worm burden after mass-drug administration and education campaigns with systematic adherence

Opisthorchis viverrini is a parasitic liver fluke contracted by consumption of raw fish, which affects over 10 million people in Southeast Asia despite sustained control efforts. Chronic infections are a risk factor for the often fatal bile duct cancer, cholangiocarcinoma. Previous modeling predicted rapid elimination of O. viverrini following yearly mass drug administration (MDA) campaigns. However, field data collected in affected populations shows persistence of infection, including heavy worm burden, after many years of repeated interventions. A plausible explanation for this observation is systematic adherence of individuals in health campaigns, such as MDA and education, with some individuals consistently missing treatment. We developed an agent-based model of O. viverrini which allows us to introduce various heterogeneities including systematic adherence to MDA and education campaigns at the individual level. We validate the agent-based model by comparing it to a previously published population-based model. We estimate the degree of systematic adherence to MDA and education campaigns indirectly, using epidemiological data collected in Lao PDR before and after 5 years of repeated MDA, education and sanitation improvement campaigns. We predict the impact of interventions deployed singly and in combination, with and without the estimated systematic adherence. We show how systematic adherence can substantially increase the time required to achieve reductions in worm burden. However, we predict that yearly MDA campaigns alone can result in a strong reduction of moderate and heavy worm burden, even under systematic adherence. We predict latrines and education campaigns to be particularly important for the reduction in overall prevalence, and therefore, ultimately, elimination. Our findings show how systematic adherence can explain the observed persistence of worm burden; while emphasizing the benefit of interventions for the entire population, even under systematic adherence. At the same time, the results highlight the substantial opportunity to further reduce worm burden if patterns of systematic adherence can be overcome.

The effect of divergent and parallel selection on the genomic landscape of divergence

While the role of selection in divergence along the speciation continuum is theoretically well understood, defining specific signatures of selection in the genomic landscape of divergence is empirically challenging. Modelling approaches can provide insight into the potential role of selection on the emergence of a heterogenous genomic landscape of divergence. Here, we extend and apply an individual-based approach that simulates the phenotypic and genotypic distributions of two populations under a variety of selection regimes, genotype-phenotype maps, modes of migration, and genotype-environment interactions. We show that genomic islands of high differentiation and genomic valleys of similarity may respectively form under divergent and parallel selection between populations. For both types of between-population selection, negative and positive frequency-dependent selection within populations generated genomic islands of higher magnitude and genomic valleys of similarity, respectively. Divergence rates decreased under strong dominance with divergent selection, as well as in models including genotype-environment interactions under parallel selection. For both divergent and parallel selection models, divergence rate was higher under an intermittent migration regime between populations, in contrast to a constant level of migration across generations, despite an equal number of total migrants. We highlight that interpreting a particular evolutionary history from an observed genomic pattern must be done cautiously, as similar patterns may be obtained from different combinations of evolutionary processes. Modelling approaches such as ours provide an opportunity to narrow the potential routes that generate the genomic patterns of specific evolutionary histories.

Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0

Microbial communities are found in all habitable environments and often occur in assemblages with self-organized spatial structures developing over time. This complexity can only be understood, predicted, and managed by combining experiments with mathematical modeling. Individual-based models are particularly suited if individual heterogeneity, local interactions, and adaptive behavior are of interest. Here we present the completely overhauled software platform, the individual-based Dynamics of Microbial Communities Simulator, iDynoMiCS 2.0, which enables researchers to specify a range of different models without having to program. Key new features and improvements are: (1) Substantially enhanced ease of use (graphical user interface, editor for model specification, unit conversions, data analysis and visualization and more). (2) Increased performance and scalability enabling simulations of up to 10 million agents in 3D biofilms. (3) Kinetics can be specified with any arithmetic function. (4) Agent properties can be assembled from orthogonal modules for pick and mix flexibility. (5) Force-based mechanical interaction framework enabling attractive forces and non-spherical agent morphologies as an alternative to the shoving algorithm. The new iDynoMiCS 2.0 has undergone intensive testing, from unit tests to a suite of increasingly complex numerical tests and the standard Benchmark 3 based on nitrifying biofilms. A second test case was based on the "biofilms promote altruism" study previously implemented in BacSim because competition outcomes are highly sensitive to the developing spatial structures due to positive feedback between cooperative individuals. We extended this case study by adding morphology to find that (i) filamentous bacteria outcompete spherical bacteria regardless of growth strategy and (ii) non-cooperating filaments outcompete cooperating filaments because filaments can escape the stronger competition between themselves. In conclusion, the new substantially improved iDynoMiCS 2.0 joins a growing number of platforms for individual-based modeling of microbial communities with specific advantages and disadvantages that we discuss, giving users a wider choice.

The role of plasticity and stochasticity in coexistence

Species coexistence in ecological communities is a central feature of biodiversity. Different concepts, i.e., contemporary niche theory, modern coexistence theory, and the unified neutral theory, have identified many building blocks of such ecological assemblies. However, other factors, such as phenotypic plasticity and stochastic inter-individual variation, have received little attention, in particular in animals. For example, how resource polyphenisms resulting in predator-prey interactions affect coexistence is currently unknown. Here, we present an integrative theoretical-experimental framework using the nematode plasticity model Pristionchus pacificus with its well-studied mouth-form dimorphism resulting in cannibalism. We develop an individual-based model that relies upon synthetic data based on our empirical measurements of fecundity and polyphenism to preserve demographic heterogeneity. We demonstrate how the interplay between plasticity and individual stochasticity result in all-or-nothing outcomes at the local level. Coexistence is made possible when spatial structure is introduced.

Modelling the carbon balance in bryophytes and lichens: Presentation of PoiCarb 1.0, a new model for explaining distribution patterns and predicting climate-change effects

PREMISE

Bryophytes and lichens have important functional roles in many ecosystems. Insight into their CO2 -exchange responses to climatic conditions is essential for understanding current and predicting future productivity and biomass patterns, but responses are hard to quantify at time scales beyond instantaneous measurements. We present PoiCarb 1.0, a model to study how CO2 -exchange rates of these poikilohydric organisms change through time as a function of weather conditions.

METHODS

PoiCarb simulates diel fluctuations of CO2 exchange and estimates long-term carbon balances, identifying optimal and limiting climatic patterns. Modelled processes were net photosynthesis, dark respiration, evaporation and water uptake. Measured CO2 -exchange responses to light, temperature, atmospheric CO2 concentration, and thallus water content (calculated in a separate module) were used to parameterize the model's carbon module. We validated the model by comparing modelled diel courses of net CO2 exchange to such courses from field measurements on the tropical lichen Crocodia aurata. To demonstrate the model's usefulness, we simulated potential climate-change effects.

RESULTS

Diel patterns were reproduced well, and the modelled and observed diel carbon balances were strongly positively correlated. Simulated warming effects via changes in metabolic rates were consistently negative, while effects via faster drying were variable, depending on the timing of hydration.

CONCLUSIONS

Reproducing weather-dependent variation in diel carbon balances is a clear improvement compared to simply extrapolating short-term measurements or potential photosynthetic rates. Apart from predicting climate-change effects, future uses of PoiCarb include testing hypotheses about distribution patterns of poikilohydric organisms and guiding conservation strategies for species.

Mechanistic population models for ecological risk assessment and decision support: The importance of good conceptual model diagrams

The use of mechanistic population models as research and decision-support tools in ecology and ecological risk assessment (ERA) is increasing. This growth has been facilitated by advances in technology, allowing the simulation of more complex systems, as well as by standardized approaches for model development, documentation, and evaluation. Mechanistic population models are particularly useful for simulating complex systems, but the required model complexity can make them challenging to communicate. Conceptual diagrams that summarize key model elements, as well as elements that were considered but not included, can facilitate communication and understanding of models and increase their acceptance as decision-support tools. Currently, however, there are no consistent standards for creating or presenting conceptual model diagrams (CMDs), and both terminology and content vary widely. Here, we argue that greater consistency in CMD development and presentation is an important component of good modeling practice, and we provide recommendations, examples, and a free web app (pop-cmd.com) for achieving this for population models used for decision support in ERAs. Integr Environ Assess Manag 2024;00:1-9. © 2023 SETAC.

Partial privatization and cooperation in biofilms

The evolution of cooperation in microbes is a challenge to explain because microbes producing costly goods for the benefit of any strain types (cooperators) often withstand the threat of elimination by interacting with individuals that exploit these benefits without contributing (defectors). Here we developed an individual-based model to investigate whether partial privatization via the partial secretion of goods can favor cooperation in structured, surface-attaching microbial populations, biofilms. Whether partial secretion can favor cooperation in biofilms is unclear for two reasons. First, while partial privatization has been shown to foster cooperation in unstructured populations, little is known about the role of partial privatization in biofilms. Second, while limited diffusion of goods favors cooperation in biofilms because molecules are more likely to be shared with genetically-related individuals, partial secretion reduces goods that could have been directed towards genetically related individuals. Our results show that although partial secretion weakens the role that limited diffusion has on fostering cooperation, partial secretion favors cooperation in biofilms. Overall, our results provide predictions that future experiments could test to reveal contributions of relatedness and partial secretion to the social evolution of biofilms.

Extinction risks and mitigation for a megaherbivore, the giraffe, in a human-influenced landscape under climate change

Megaherbivores play "outsized" roles in ecosystem functioning but are vulnerable to human impacts such as overhunting, land-use changes, and climate extremes. However, such impacts-and combinations of these impacts-on population dynamics are rarely examined using empirical data. To guide effective conservation actions under increasing global-change pressures, we developed a socially structured individual-based model (IBM) using long-term demographic data from female giraffes (Giraffa camelopardalis) in a human-influenced landscape in northern Tanzania, the Tarangire Ecosystem. This unfenced system includes savanna habitats with a wide gradient of anthropogenic pressures, from national parks, a wildlife ranch and community conservation areas, to unprotected village lands. We then simulated and projected over 50 years how realistic environmental and land-use management changes might affect this metapopulation of female giraffes. Scenarios included: (1) anthropogenic land-use changes including roads and agricultural/urban expansion; (2) reduction or improvement in wildlife law enforcement measures; (3) changes in populations of natural predators and migratory alternative prey; and (4) increases in rainfall as predicted for East Africa. The factor causing the greatest risk of rapid declines in female giraffe abundance in our simulations was a reduction in law enforcement leading to more poaching. Other threats decreased abundances of giraffes, but improving law enforcement in both of the study area's protected areas mitigated these impacts: a 0.01 increase in giraffe survival probability from improved law enforcement mitigated a 25% rise in heavy rainfall events by increasing abundance 19%, and mitigated the expansion of towns and blockage of dispersal movements by increasing abundance 22%. Our IBM enabled us to further quantify fine-scale abundance changes among female giraffe social communities, revealing potential source-sink interactions within the metapopulation. This flexible methodology can be adapted to test additional ecological questions in this landscape, or to model populations of giraffes or other species in different ecosystems.

Spatial-temporal simulation for hospital infection spread and outbreaks of Clostridioides difficile

Validated and curated datasets are essential for studying the spread and control of infectious diseases in hospital settings, requiring clinical information on patients' evolution and their location. The literature shows that approaches based on Artificial Intelligence (AI) in the development of clinical-support systems have benefits that are increasingly recognized. However, there is a lack of available high-volume data, necessary for trusting such AI models. One effective method in this situation involves the simulation of realistic data. Existing simulators primarily focus on implementing compartmental epidemiological models and contact networks to validate epidemiological hypotheses. Nevertheless, other practical aspects such as the hospital building distribution, shifts or safety policies on infections has received minimal attention. In this paper, we propose a novel approach for a simulator of nosocomial infection spread, combining agent-based patient description, spatial-temporal constraints of the hospital settings, and microorganism behavior driven by epidemiological models. The predictive validity of the model was analyzed considering micro and macro-face validation, parameter calibration based on literature review, model alignment, and sensitive analysis with an expert. This simulation model is useful in monitoring infections and in the decision-making process in a hospital, by helping to detect spatial-temporal patterns and predict statistical data about the disease.

Exploring the impact of mobility and selection on stone tool recycling behaviors through agent-based simulation

Recycling behaviors are becoming increasingly recognized as important parts of the production and use of stone tools in the Paleolithic. Yet, there are still no well-defined expectations for how recycling affects the appearance of the archaeological record across landscapes. Using an agent-based model of recycling in surface contexts, this study looks how the archaeological record changes under different conditions of recycling frequency, occupational intensity, mobility, and artifact selection. The simulations also show that while an increased number of recycled artifacts across a landscape does indicate the occurrence of more scavenging and recycling behaviors generally, the location of large numbers of recycled artifacts is not necessarily where the scavenging itself happened. This is particularly true when mobility patterns mean each foraging group spend more time moving around the landscape. The results of the simulations also demonstrate that recycled artifacts are typically those that have been exposed longer in surface contexts, confirming hypothesized relationships between recycling and exposure. In addition to these findings, the recycling simulation shows how archaeological record formation due to recycling behaviors is affected by mobility strategies and selection preferences. While only a simplified model of recycling behaviors, the results of this simulations give us insight into how to better interpret recycling behaviors from the archaeological record, specifically demonstrating the importance of contextualizing the occurrence of recycled artifacts on a wider landscape-level scale.

The relative importance of social information use for population abundance in group-living and non-grouping prey

Predator-prey relationships are fundamental components of ecosystem functioning, within which the spatial consequences of prey social organization can alter predation rates. Group-living (GL) species are known to exploit inadvertent social information (ISI) that facilitates population persistence under predation risk. Still, the extent to which non-grouping (NG) prey can benefit from similar processes is unknown. Here we built an individual-based model to explore and compare the population-level consequences of ISI use in GL and NG prey. We differentiated between GL and NG prey only by the presence or absence of social attraction toward conspecifics that drives individual movement patterns. We found that the extent of the benefits of socially acquired predator information in NG highly depends on the prey's ability to detect nearby predators, prey density and the occurrence of false alarms. Conversely, even moderate probabilities of ISI use and predator detection can lead to maximal population-level benefits in GL prey. This theoretical work provides additional insights into the conditions under which ISI use can facilitate population persistence irrespective of prey social organisation.

Minority-group incubators and majority-group reservoirs support the diffusion of climate change adaptations

Successful climate change adaptation depends on the spread and maintenance of adaptive behaviours. Current theory suggests that the heterogeneity of metapopulation structure can help adaptations diffuse throughout a population. In this paper, we develop an agent-based model of the spread of adaptations in populations with minority-majority metapopulation structure, where subpopulations learn more or less frequently from their own group compared to the other group. In our simulations, minority-majority-structured populations with moderate degrees of in-group preference better spread and maintained an adaptation compared to populations with more equal-sized groups and weak homophily. Minority groups act as incubators for an adaptation, while majority groups act as reservoirs for an adaptation once it has spread widely. This means that adaptations diffuse throughout populations better when minority groups start out knowing an adaptation, as Indigenous populations often do, while cohesion among majority groups further promotes adaptation diffusion. Our work advances the goal of this theme issue by developing new theoretical insights and demonstrating the utility of cultural evolutionary theory and methods as important tools in the nascent science of culture that climate change adaptation needs. This article is part of the theme issue 'Climate change adaptation needs a science of culture'.

The impacts of climate change, energy policy and traditional ecological practices on future firewood availability for Diné (Navajo) People

Local-scale human-environment relationships are fundamental to energy sovereignty, and in many contexts, Indigenous ecological knowledge (IEK) is integral to such relationships. For example, Tribal leaders in southwestern USA identify firewood harvested from local woodlands as vital. For Diné people, firewood is central to cultural and physical survival and offers a reliable fuel for energy embedded in local ecological systems. However, there are two acute problems: first, climate change-induced drought will diminish local sources of firewood; second, policies aimed at reducing reliance on greenhouse-gas-emitting energy sources may limit alternatives like coal for home use, thereby increasing firewood demand to unsustainable levels. We develop an agent-based model trained with ecological and community-generated ethnographic data to assess the future of firewood availability under varying climate, demand and IEK scenarios. We find that the long-term sustainability of Indigenous firewood harvesting is maximized under low-emissions and low-to-moderate demand scenarios when harvesters adhere to IEK guidance. Results show how Indigenous ecological practices and resulting ecological legacies maintain resilient socio-environmental systems. Insights offered focus on creating energy equity for Indigenous people and broad lessons about how Indigenous knowledge is integral for adapting to climate change. This article is part of the theme issue 'Climate change adaptation needs a science of culture'.

Agent-based modeling in medical research, virtual baseline generator and change in patients' profile issue

Simulation studies are promising in medical research in particular to improve drug development. For instance, one can aim to develop In Silico Clinical Trial in order to challenge trial's design parameters in terms of feasibility and probability of success of the trial. Approaches based on agent-based models draw on a particularly useful framework to simulate patients evolution. In this paper, an approach based on agent-based modeling is described and discussed in the context of medical research. An R-vine copula model is used to represent the multivariate distribution of the data. A baseline data cohort can then be simulated and execution models can be developed to simulate the evolution of patients. R-vine copula models are very flexible tools which allow researchers to consider different marginal distributions than the ones observed in the data. It is then possible to perform data augmentation to explore a new population by simulating baseline data which are slightly different than those of the original population. A simulation study illustrates the efficiency of copula modeling to generate data according to specific marginal distributions but also highlights difficulties inherent to data augmentation.

In Situ and Individual-Based Analysis of the Influence of Polystyrene Microplastics on Escherichia coli Conjugative Gene Transfer at the Single-Cell Level

The impact of microplastic particles of micro- and nanometer sizes on microbial horizontal gene transfer (HGT) remains a controversial topic. Existing studies rely on traditional approaches, which analyze population behavior, leading to conflicting conclusions and a limited understanding. The present study addressed these limitations by employing a novel microfluidic chamber system for in situ visualization and precise quantification of the effects of different concentrations of polystyrene (PS) microbeads on microbial HGT at the single-cell level. The statistical analysis indicated no significant difference in the division times of both the donor and recipient bacteria across different PS microbead concentrations. However, as the concentration of PS microbeads increased from 0 to 2000 mg L-1, the average conjugation frequency of Escherichia coli decreased from 0.028 ± 0.015 to 0.004 ± 0.003. Our observations from the microfluidic experiments revealed that 500 nm PS microbeads created a barrier effect on bacterial conjugative transfer. The presence of microbeads resulted in reduced contact and interaction between the donor and recipient strains, thereby causing a decrease in the conjugation transfer frequency. These findings were validated by an individual-based modeling framework parameterized by the data from the individual-level microfluidic experiments. Overall, this study offers a fresh perspective and strategy for investigating the risks associated with the dissemination of antibiotic resistance genes related to microplastics.

Impact of the antidepressant Bupropion on the Dynamic Energy Budget of Daphnia magna

Psychiatric drugs are considered among the emerging contaminants of concern in ecological risk assessment, due to their potential to disrupt homeostasis in aquatic organisms. Bupropion is an antidepressant that acts by selective reuptake inhibition of norepinephrine and dopamine. Little is known about this compound's effects on aquatic organisms, despite being detected in significant concentrations in both water and biota close to waste-water treatment plants and densely populated areas. Dynamic Energy Budget (DEB) models are flexible mechanistic tools that can be applied to understand toxic effects and extrapolate individual responses to higher biological levels and under untested environmental conditions. In this work, we used the stdDEB-TKTD (an application of the DEB theory to ecotoxicology) approach to investigate the possible physiological mode of action of Bupropion on the model organism Daphnia magna. Next, Dynamic Energy Budget Individual-Based Models (DEB-IBM) were used to extrapolate the results to the population level and to predict the combined effects of Bupropion exposure and food availability on the daphnids. Our results revealed an increasing negative effect of this antidepressant on the reproduction and survival of the animals with increasing concentration (0.004, 0.058, 0.58 and 58 μM). At the population level, we found that even the lowest used doses of Bupropion could reduce the population density and its reproductive output. The impacts are predicted to be stronger under limited food conditions.

Negative density-dependence buffers against mismatch-induced population decline in the Sinai baton blue butterfly

Phenological mismatches caused by climate change pose a major threat to global biodiversity, yet relatively few studies have reported population declines resulting from mismatch. It has been hypothesised that density effects may underlie this lack of observed responses by buffering against mismatch-induced population decline. We developed an individual-based model of the critically endangered Sinai baton blue butterfly (Pseudophilotes sinaicus) and its hostplant Sinai thyme (Thymus decussatus), parameterised using real field data, to test this hypothesis. Our model showed that the baton blue experiences demographic consequences under only 5 days of phenological mismatch, but that this threshold was increased to 14 days with the inclusion of density-dependent juvenile mortality. The inclusion of density effects also led to the replication of population cycles observed in nature, supporting the ability of our model to accurately represent the baton blue's ecology. These results add to a growing body of literature suggesting that density effects may underlie the observed lack of demographic responses to mismatch in wild populations. However, these buffers may be short-lived in extreme mismatch scenarios, providing a false sense of security against a looming threat of population collapse.

Circulation of Respiratory Viruses in the City: Towards an Agent-Based Ecosystem model

Mathematical models play an important role in management of outbreaks of acute respiratory infections (ARI). While such models are generally used to study the spread of a solitary virus, in reality multiple viruses co-circulate in the population. These viruses have been studied in detail, including the course of infection and immune defense mechanisms. We developed an agent-based model, called ABM-ARI, assimilating heterogeneous data and theoretical knowledge into a biologically motivated system, that allows to reproduce the seasonal patterns of ARI incidence and simulate interventions. ABM-ARI uses city-specific data to create a synthetic population and to construct realistic contact networks in different activity settings. Characteristics of infection, immune protection and non-specific resistance were varied between individuals to account for the population heterogeneity. For the calibration, we minimised the normalised mean absolute error between simulated and observed epidemic curves. ABM-ARI was built based on the quantitative assessment of features of predominant respiratory viruses and epidemiological characteristics of the population. It provides a good fit to the observed epidemic curves for different age groups and viruses. We also simulated one-week school closures when student absences were at or above 10%, 20% or 30% and found that only 10% and 20% thresholds resulted in a reduction of the incidence. ABM-ARI has a great potential in tackling the challenge of emerging infections by simulating and evaluating the effectiveness of various interventions.

Mathematical modeling at the livestock-wildlife interface: scoping review of drivers of disease transmission between species

Modeling of infectious diseases at the livestock-wildlife interface is a unique subset of mathematical modeling with many innate challenges. To ascertain the characteristics of the models used in these scenarios, a scoping review of the scientific literature was conducted. Fifty-six studies qualified for inclusion. Only 14 diseases at this interface have benefited from the utility of mathematical modeling, despite a far greater number of shared diseases. The most represented species combinations were cattle and badgers (for bovine tuberculosis, 14), and pigs and wild boar [for African (8) and classical (3) swine fever, and foot-and-mouth and disease (1)]. Assessing control strategies was the overwhelming primary research objective (27), with most studies examining control strategies applied to wildlife hosts and the effect on domestic hosts (10) or both wild and domestic hosts (5). In spatially-explicit models, while livestock species can often be represented through explicit and identifiable location data (such as farm, herd, or pasture locations), wildlife locations are often inferred using habitat suitability as a proxy. Though there are innate assumptions that may not be fully accurate when using habitat suitability to represent wildlife presence, especially for wildlife the parsimony principle plays a large role in modeling diseases at this interface, where parameters are difficult to document or require a high level of data for inference. Explaining observed transmission dynamics was another common model objective, though the relative contribution of involved species to epizootic propagation was only ascertained in a few models. More direct evidence of disease spill-over, as can be obtained through genomic approaches based on pathogen sequences, could be a useful complement to further inform such modeling. As computational and programmatic capabilities advance, the resolution of the models and data used in these models will likely be able to increase as well, with a potential goal being the linking of modern complex ecological models with the depth of dynamics responsible for pathogen transmission. Controlling diseases at this interface is a critical step toward improving both livestock and wildlife health, and mechanistic models are becoming increasingly used to explore the strategies needed to confront these diseases.

Shibboleth: An agent-based model of signalling mimicry

Mimicry is an essential strategy for exploiting competitors in competitive co-evolutionary relationships. Protection against mimicry may, furthermore, be a driving force in human linguistic diversity: the potential harm caused by failing to detect mimicked group-identity signals may select for high sensitivity to mimicry of honest group members. Here we describe the results of five agent-based models that simulate multi-generational interactions between two groups of individuals: original members of a group with an honest identity signal, and members of an outsider group who mimic that signal, aiming to pass as members of the in-group. The models correspond to the Biblical story of Shibboleth, where a tribe in conflict with another determines tribe affiliation by asking individuals to pronounce the word, 'Shibboleth.' In the story, failure to reproduce the word phonetically resulted in death. Here, we run five different versions of a 'Shibboleth' model: a first, simple version, which evaluates whether a composite variable of mimicry quality and detection quality is a superior predictor to the model's outcome than is cost of detection. The models thereafter evaluate variations on the simple model, incorporating group-level behaviours such as altruistic punishment. Our results suggest that group members' sensitivity to mimicry of the Shibboleth-signal is a better predictor of whether any signal of group identity goes into fixation in the overall population than is the cost of mimicry detection. Thus, the likelihood of being detected as a mimic may be more important than the costs imposed on mimics who are detected. This suggests that theoretical models in biology should place greater emphasis on the likelihood of detection, which does not explicitly entail costs, rather than on the costs to individuals who are detected. From a language learning perspective, the results suggest that admission to group membership through linguistic signals is powered by the ability to imitate and evade detection as an outsider by existing group members.

An intercomparison of models predicting growth of Antarctic krill (Euphausia superba): The importance of recognizing model specificity

Antarctic krill (Euphausia superba) is a key species of the Southern Ocean, impacted by climate change and human exploitation. Understanding how these changes affect the distribution and abundance of krill is crucial for generating projections of change for Southern Ocean ecosystems. Krill growth is an important indicator of habitat suitability and a series of models have been developed and used to examine krill growth potential at different spatial and temporal scales. The available models have been developed using a range of empirical and mechanistic approaches, providing alternative perspectives and comparative analyses of the key processes influencing krill growth. Here we undertake an intercomparison of a suite of the available models to understand their sensitivities to major driving variables. This illustrates that the results are strongly determined by the model structure and technical characteristics, and the data on which they were developed and validated. Our results emphasize the importance of assessing the constraints and requirements of individual krill growth models to ensure their appropriate application. The study also demonstrates the value of the development of alternative modelling approaches to identify key processes affecting the dynamics of krill. Of critical importance for modelling the growth of krill is appropriately assessing and accounting for differences in estimates of food availability resulting from alternative methods of observation. We suggest that an intercomparison approach is particularly valuable in the development and application of models for the assessment of krill growth potential at circumpolar scales and for future projections. As another result of the intercomparison, the implementations of the models used in this study are now publicly available for future use and analyses.

Data-driven agent-based modelling of incentives for carbon sequestration: The case of sown biodiverse pastures in Portugal

Sown biodiverse permanent pastures rich in legumes (SBP) offset animal farming emissions due to their potential to sequester carbon. From 2009 to 2014 Portugal implemented a programme that provided payments to incentivize the adoption of SBP. However, no proper evaluation of its outcome was conducted. To address this gap, we develop an agent-based model (ABM) at the municipality level to study the adoption of SBP in Portugal and assess the outcome of the programme. We applied the first pure data-driven approach in agricultural land-use ABM, which relies on machine learning algorithms to define the agents' behavioural rules and capture their interaction with biophysical conditions. The ABM confirms that the program effectively expanded the adoption of SBP. However, our estimates indicate that the adoption rate in the absence of payments would have been higher than originally predicted. Furthermore, the existence of the program decreased the adoption rate after its conclusion. These findings underscore the importance of using reliable models and considering residual effects to properly design land use policies. The ABM developed in this study provides a basis for future research aimed at supporting the development of new policies to further promote the adoption of SBP.

Modifying and parameterizing the individual-based model inSTREAM for Atlantic salmon and brown trout in the regulated Gullspång River, Sweden

We modified, parameterized, and applied the individual-based model inSTREAM version 6.1 for lake-migrating populations of landlocked Atlantic salmon (Salmo salar) and brown trout (S. trutta) in a residual flow stretch of the hydropower-regulated Gullspång River, Sweden. This model description is structured according to the TRACE model description framework. Our aim was to model responses in salmonid recruitment to alternative scenarios of flow release and other environmental alterations. The main response variable was the number of large out-migrating juvenile fish per year, with the assumption that individuals are more inclined to out-migrate the larger they get, and that migration is an obligatory strategy. Population and species-specific parameters were set based on local electrofishing surveys, redd surveys, physical habitat surveys, broodstock data as well as scientific literature.•Simulations were set to run over 10 years, with sub-daily time steps, in this spatially and temporally explicit model.•Model calibration and validation of fish growth was done using data on juvenile fish from electrofishing.•The results were found to be sensitive to parameter values for aggregated fish, i.e., "superindividuals" and for the high temperature limit to spawning.

Individual based models for the simulation of fish movement near barriers: Current work and future directions

River fragmentation is an increasing issue for water managers and conservationists. Barriers such as dams interfere with freshwater fish migration, leading to drastic population declines. While there are a range of widely implemented mitigation approaches, e.g. fish passes, such measures are often inefficient due to suboptimal operation and design. There is increasing need to be able to assess mitigation options prior to implementation. Individual based models (IBMs) are a promising option. IBMs can simulate the fine-scale movement of individual fish within a population as they attempt to find a fish pass, incorporating movement processes themselves. Moreover, IBMs have high transferability to other sites or conditions (e.g. changing mitigation, change in flow conditions), making them potentially valuable for freshwater fish conservation yet their application to the fine-scale movement of fish past barriers is still novel. Here, we present an overview of existing IBMs for fine-scale freshwater fish movement, with emphasis on study species and the parameters driving movement in the models. In this review, we focus on IBMs suitable for the simulation of fish tracks as they approach or pass a single barrier. The selected IBMs for modelling fine-scale freshwater fish movement largely focus on salmonids and cyprinid species. IBMs have many applications in the context of fish passage, such as testing different mitigation options or understanding processes behind movement. Existing IBMs include movement processes such as attraction and rejection behaviours, as reported in literature. Yet some factors affecting fish movement e.g. biotic interactions are not covered by existing IBMs. As the technology available for fine scale data collection continues to advance, such as increasing data linking fish behaviour to hydraulics, IBMs could become a more common tool in the design and implementation of fish bypass structures.

A Systematic Review of Systems Science Approaches to Understand and Address Domestic and Gender-Based Violence

Purpose

We aimed to synthesize insights from systems science approaches applied to domestic and gender-based violence.

Methods

We conducted a systematic review of systems science studies (systems thinking, group model-building, agent-based modeling [ABM], system dynamics [SD] modeling, social network analysis [SNA], and network analysis [NA]) applied to domestic or gender-based violence, including victimization, perpetration, prevention, and community responses. We used blinded review to identify papers meeting our inclusion criteria (i.e., peer-reviewed journal article or published book chapter that described a systems science approach to domestic or gender-based violence, broadly defined) and assessed the quality and transparency of each study.

Results

Our search yielded 1,841 studies, and 74 studies met our inclusion criteria (45 SNA, 12 NA, 8 ABM, and 3 SD). Although research aims varied across study types, the included studies highlighted social network influences on risks for domestic violence, clustering of risk factors and violence experiences, and potential targets for intervention. We assessed the quality of the included studies as moderate, though only a minority adhered to best practices in model development and dissemination, including stakeholder engagement and sharing of model code.

Conclusions

Systems science approaches for the study of domestic and gender-based violence have shed light on the complex processes that characterize domestic violence and its broader context. Future research in this area should include greater dialogue between different types of systems science approaches, consideration of peer and family influences in the same models, and expanded use of best practices, including continued engagement of community stakeholders.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10896-023-00578-8.

Effects of extreme events on land-use-related decisions of farmers in Eastern Austria: the role of learning

European farm households will face increasingly challenging conditions in the coming decades due to climate change, as the frequency and severity of extreme weather events rise. This study assesses the complex interrelations between external framework conditions such as climate change or adjustments in the agricultural price and subsidy schemes with farmers' decision-making. As social aspects remain understudied drivers for agricultural decisions, we also consider value-based characteristics of farmers as internal factors relevant for decision-making. We integrate individual learning as response to extreme weather events into an agent-based model that simulates farmers' decision-making. We applied the model to a region in Eastern Austria that already experiences water scarcity and increasing drought risk from climate change and simulated three future scenarios to compare the effects of changes in socio-economic and climatic conditions. In a cross-comparison, we then investigated how farmers can navigate these changes through individual adaptation. The agricultural trajectories project a decline of active farms between -27 and -37% accompanied by a reduction of agricultural area between -20 and -30% until 2053. The results show that regardless of the scenario conditions, adaptation through learning moderates the decline in the number of active farms and farmland compared to scenarios without adaptive learning. However, adaptation increases the workload of farmers. This highlights the need for labor support for farms.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13593-023-00890-z.

Investigating Population-Level Toxicity of the Antidepressant Citalopram in Harpacticoid Copepods Using In Vivo Methods and Bioenergetics-Based Population Modeling

Recent research has revealed various lethal and sublethal effects of the selective serotonin reuptake inhibitor citalopram hydrobromide on the harpacticoid copepod Nitocra spinipes. In the present study, an individual-based model (IBM) grounded in the dynamic energy budget (DEB) theory was developed to extrapolate said effects to the population level. Using a generic DEB-IBM as a template, the model was designed to be as simple as possible, keeping model components that are outside the scope of the core DEB theory to a minimum. To test the model, a 56-day population experiment was performed at 0, 100, and 1000 μg citalopram hydrobromide L^-1 . In the experiment, the populations quickly reached a plateau in the control and at 100 μg L^-1 , which was correctly reproduced by the model and could be explained by food limitations hindering further population growth. At 1000 μg L^-1 , a clear mismatch occurred: Whereas in the experiment the population size increased beyond the supposed (food competition-induced) capacity, the model predicted a suppression of the population size. It is assumed that the IBM still misses important components addressing population density-regulating processes. Particularly crowding effects may have played an important role in the population experiment and should be further investigated to improve the model. Overall, the current DEB IBM for N. spinipes should be seen as a promising starting point for bioenergetics-based copepod population modeling, which-with further improvements-may become a valuable individual-to-population extrapolation tool in the future. Environ Toxicol Chem 2023;42:1094-1108. © 2023 SETAC.

Plasmid Costs Explain Plasmid Maintenance, Irrespective of the Nature of Compensatory Mutations

Conjugative plasmids often carry virulence and antibiotic-resistant genes. Therefore, understanding the behavior of these extra-chromosomal DNA elements gives insights into their spread. Bacteria frequently replicate slower after plasmids' entry, an observation inconsistent with the plasmids' ubiquity in nature. Several hypotheses explain the maintenance of plasmids among bacterial communities. However, the numerous combinations of bacterial species and strains, plasmids, and environments claim a robust elucidatory mechanism of plasmid maintenance. Previous works have shown that donor cells already adapted to the plasmid may use the plasmid as a 'weapon' to compete with non-adapted plasmid-free cells. Computer simulations corroborated this hypothesis with a wide range of parameters. Here we show that donor cells benefit from harboring conjugative plasmids even if compensatory mutations in transconjugant cells occur in the plasmid, not on chromosomes. The advantage's leading causes are as follows: mutations take time to appear, many plasmids remain costly, and re-transfer of mutated plasmids usually occurs in sites distant to the original donors, implying little competition between these cells. Research in previous decades cautioned against uncritical acceptance of the hypothesis that resistance cost helps to preserve antibiotics' effectiveness. This work gives a new twist to this conclusion by showing that costs help antibiotic-resistant bacteria to compete with plasmid-free cells even if compensatory mutations appear in plasmids.

Synergistic Integration Between Machine Learning and Agent-Based Modeling: A Multidisciplinary Review

Agent-based modeling (ABM) involves developing models in which agents make adaptive decisions in a changing environment. Machine-learning (ML) based inference models can improve sequential decision-making by learning agents' behavioral patterns. With the aid of ML, this emerging area can extend traditional agent-based schemes that hardcode agents' behavioral rules into an adaptive model. Even though there are plenty of studies that apply ML in ABMs, the generalized applicable scenarios, frameworks, and procedures for implementations are not well addressed. In this article, we provide a comprehensive review of applying ML in ABM based on four major scenarios, i.e., microagent-level situational awareness learning, microagent-level behavior intervention, macro-ABM-level emulator, and sequential decision-making. For these four scenarios, the related algorithms, frameworks, procedures of implementations, and multidisciplinary applications are thoroughly investigated. We also discuss how ML can improve prediction in ABMs by trading off the variance and bias and how ML can improve the sequential decision-making of microagent and macrolevel policymakers via a mechanism of reinforced behavioral intervention. At the end of this article, future perspectives of applying ML in ABMs are discussed with respect to data acquisition and quality issues, the possible solution of solving the convergence problem of reinforcement learning, interpretable ML applications, and bounded rationality of ABM.

Emergent regulation of ant foraging frequency through a computationally inexpensive forager movement rule

Ant colonies regulate foraging in response to their collective hunger, yet the mechanism behind this distributed regulation remains unclear. Previously, by imaging food flow within ant colonies we showed that the frequency of foraging events declines linearly with colony satiation (Greenwald et al., 2018). Our analysis implied that as a forager distributes food in the nest, two factors affect her decision to exit for another foraging trip: her current food load and its rate of change. Sensing these variables can be attributed to the forager's individual cognitive ability. Here, new analyses of the foragers' trajectories within the nest imply a different way to achieve the observed regulation. Instead of an explicit decision to exit, foragers merely tend toward the depth of the nest when their food load is high and toward the nest exit when it is low. Thus, the colony shapes the forager's trajectory by controlling her unloading rate, while she senses only her current food load. Using an agent-based model and mathematical analysis, we show that this simple mechanism robustly yields emergent regulation of foraging frequency. These findings demonstrate how the embedding of individuals in physical space can reduce their cognitive demands without compromising their computational role in the group.

A survey on agents applications in healthcare: Opportunities, challenges and trends

BACKGROUND AND OBJECTIVE

The agent abstraction is a powerful one, developed decades ago to represent crucial aspects of artificial intelligence research. The meaning has transformed over the years and now there are different nuances across research communities. At its core, an agent is an autonomous computational entity capable of sensing, acting, and capturing interactions with other agents and its environment. This review examines how agent-based techniques have been implemented and evaluated in a specific and very important domain, i.e. healthcare research.

METHODS

We survey key areas of agent-based research in healthcare, e.g. individual and collective behaviours, communicable and non-communicable diseases, and social epidemiology. We propose a systematic search and critical review of relevant recent works, introduced by an exploratory network analysis.

RESULTS

Network analysis enables to devise out 5 main research clusters, the most active authors, and 4 main research topics.

CONCLUSIONS

Our findings support discussion of some future directions for increasing the value of agent-based approaches in healthcare.

Open problems in PDE models for knowledge-based animal movement via nonlocal perception and cognitive mapping

The inclusion of cognitive processes, such as perception, learning and memory, are inevitable in mechanistic animal movement modelling. Cognition is the unique feature that distinguishes animal movement from mere particle movement in chemistry or physics. Hence, it is essential to incorporate such knowledge-based processes into animal movement models. Here, we summarize popular deterministic mathematical models derived from first principles that begin to incorporate such influences on movement behaviour mechanisms. Most generally, these models take the form of nonlocal reaction-diffusion-advection equations, where the nonlocality may appear in the spatial domain, the temporal domain, or both. Mathematical rules of thumb are provided to judge the model rationality, to aid in model development or interpretation, and to streamline an understanding of the range of difficulty in possible model conceptions. To emphasize the importance of biological conclusions drawn from these models, we briefly present available mathematical techniques and introduce some existing "measures of success" to compare and contrast the possible predictions and outcomes. Throughout the review, we propose a large number of open problems relevant to this relatively new area, ranging from precise technical mathematical challenges, to more broad conceptual challenges at the cross-section between mathematics and ecology. This review paper is expected to act as a synthesis of existing efforts while also pushing the boundaries of current modelling perspectives to better understand the influence of cognitive movement mechanisms on movement behaviours and space use outcomes.

Agent-based simulation of COVID-19 containment measures: the case of lockdowns in cities

The effectiveness and political feasibility of COVID-19 containment measures such as lockdowns, are contentious. This stems in part from an absence of tools for their rigorous evaluation. Common epidemiological models such as the SEIR model generally lack the spatial resolution required for micro-level containment actions, the visualization capabilities for communicating measures such as localized lockdowns and the scenario-testing capabilities for assessing different alternatives. We present an individual-level ABM that generates geo-social networks animated by agent-agent and agent-building interactions. The model simulates real-world contexts and is demonstrated for the city of Jerusalem. Simulation outputs yield much useful information for evaluating the effectiveness of lockdowns. These include network-generated socio-spatial contagion chains for individual agents, dynamic building level contagion processes and neighborhood-level patterns of COVID-19 imports and exports useful in identifying super-spreader neighborhoods. The policy implications afforded by these various outputs are discussed.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12076-023-00336-w.

In Search of Proximate Triggers of Anthrax Outbreaks in Wildlife: A Hypothetical Individual-Based Model of Plasmid Transfer within Bacillus Communities

Bacillus anthracis, the causative agent of anthrax in humans, livestock, and wildlife, exists in a community with hundreds of other species of bacteria in the environment. Work on the genetics of these communities has shown that B. anthracis shares a high percentage of chromosomal genes with both B. thuringiensis and B. cereus, and that phenotypic differences among these bacteria can result from extra-chromosomal DNA in the form of plasmids. We developed a simple hypothetical individual-based model to simulate the likelihood of detecting plasmids with genes encoding anthrax toxins within bacterial communities composed of B. anthracis, B. thuringiensis, and B. cereus, and the surrounding matrix of extra-cellular polymeric substances. Simulation results suggest the horizontal transfer of plasmids with genes encoding anthrax toxins among Bacillus species persisting outside the host could function as a proximate factor triggering anthrax outbreaks.