How Model Organisms and Model Uncertainty Impact Our Understanding of the Risk of Sublethal Impacts of Toxicants to Survival and Growth of Ecologically Relevant Species

Understanding how sublethal impacts of toxicants affect population-relevant outcomes for organisms is challenging. We tested the hypotheses that the well-known sublethal impacts of methylmercury (MeHg) and a polychlorinated biphenyl (PCB126) would have meaningful impacts on cohort growth and survival in yellow perch (Perca flavescens) and Atlantic killifish (Fundulus heteroclitus) populations, that inclusion of model uncertainty is important for understanding the sublethal impacts of toxicants, and that a model organism (zebrafish Danio rerio) is an appropriate substitute for ecologically relevant species (yellow perch, killifish). Our simulations showed that MeHg did not have meaningful impacts on growth or survival in a simulated environment except to increase survival and growth in low mercury exposures in yellow perch and killifish. For PCB126, the high level of exposure resulted in lower survival for killifish only. Uncertainty analyses increased the variability and lowered average survival estimates across all species and toxicants, providing a more conservative estimate of risk. We demonstrate that using a model organism instead of the species of interest does not necessarily give the same results, suggesting that using zebrafish as a surrogate for yellow perch and killifish may not be appropriate for predicting contaminant impacts on larval cohort growth and survival in ecologically relevant species. Our analysis also reinforces the notion that uncertainty analyses are necessary in any modeling assessment of the impacts of toxicants on a population because it provides a more conservative, and arguably realistic, estimate of impact. Environ Toxicol Chem 2024;43:2122-2133. © 2024 SETAC.

Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.

Predator-prey interactions across hunting mode, spatial domain size, and habitat complexities

Predator-prey interactions are a fundamental part of community ecology, yet the relative importance of consumptive and nonconsumptive effects (NCEs) (defined as a risk-induced response that alters prey fitness) has not been resolved. Theory suggests that the emergence and subsequent predominance of consumptive or NCEs depend on the given habitat's complexity as well as predator hunting mode and spatial domain sizes of both predator and prey, but their relative influence on the outcome of predator-prey interactions is unknown. We built agent-based models in NetLogo to simulate predator-prey interactions for three hunting modes-sit-and-wait, sit-and-pursue, and active-while concurrently simulating large versus small spatial domain sizes for both predators and prey. We studied (1) how hunting mode and spatial domain size interact to influence the emergence of consumptive or NCEs and (2) how, when NCEs do dominate, hunting mode and spatial domain separately or additively determine prey shifts in time, space, and habitat use. Our results indicate consumptive effects only dominate for active predators when prey habitat domains overlap completely with the predator's spatial domain and when sit-and-wait and sit-and-pursue predators and their prey both have large spatial domains. Prey are most likely to survive when they shift their time but most frequently shift their habitat. Our paper helps to better understand the underlying mechanisms that drive consumptive or NCEs to be most dominant.

Agent-based simulations improve abundance estimation

Abundance is a fundamental characteristic of every biological population and is the focus of many research programs in ecology and conservation. In this paper I give an overview of the challenges of estimating abundance. I argue that truly understanding, validating, and refining the field techniques and quantitative methods used to estimate abundance can largely benefit from agent-based simulations. I illustrate this through the example of bird point counts and introduce the software bSims to test statistical and biological assumptions for estimating abundance and to aid survey design.

Big boned: How fat storage and other adaptations influenced large theropod foraging ecology

Dinosaur foraging ecology has been the subject of scientific interest for decades, yet much of what we understand about it remains hypothetical. We wrote an agent-based model (ABM) to simulate meat energy sources present in dinosaur environments, including carcasses of giant sauropods, along with living, huntable prey. Theropod dinosaurs modeled in this environment (specifically allosauroids, and more particularly, Allosaurus Marsh, 1877) were instantiated with heritable traits favorable to either hunting success or scavenging success. If hunter phenotypes were more reproductively successful, their traits were propagated into the population through their offspring, resulting in predator specialists. If selective pressure favored scavenger phenotypes, the population would evolve to acquire most of their calories from carrion. Data generated from this model strongly suggest that theropods in sauropod-dominated systems evolved to detect carcasses, consume and store large quantities of fat, and dominate carcass sites. Broadly speaking, selective forces did not favor predatory adaptations, because sauropod carrion resource pools, as we modeled them, were too profitable for prey-based resource pools to be significant. This is the first research to test selective pressure patterns in dinosaurs, and the first to estimate theropod mass based on metabolic constraints.

The Joint Evolution of Animal Movement and Competition Strategies

AbstractCompetition typically takes place in a spatial context, but eco-evolutionary models rarely address the joint evolution of movement and competition strategies. Here we investigate a spatially explicit forager-kleptoparasite model where consumers can either forage on a heterogeneous resource landscape or steal resource items from conspecifics (kleptoparasitism). We consider three scenarios: (1) foragers without kleptoparasites, (2) consumers specializing as foragers or as kleptoparasites, and (3) consumers that can switch between foraging and kleptoparasitism depending on local conditions. We model movement strategies as individual-specific combinations of preferences for environmental cues, similar to step-selection coefficients. Using mechanistic, individual-based simulations, we study the joint evolution of movement and competition strategies, and we investigate the implications for the distribution of consumers over this landscape. Movement and competition strategies evolve rapidly and consistently across scenarios, with marked differences among scenarios, leading to differences in resource exploitation patterns. In scenario 1, foragers evolve considerable individual variation in movement strategies, while in scenario 2, movement strategies show a swift divergence between foragers and kleptoparasites. In scenario 3, where individuals' competition strategies are conditional on local cues, movement strategies facilitate kleptoparasitism, and individual consistency in competition strategy also emerges. Even in the absence of kleptoparasitism (scenario 1), the distribution of consumers deviates considerably from predictions of ideal free distribution models because of the intrinsic difficulty of moving effectively on a depleted resource landscape with few reliable cues. Our study emphasizes the advantages of a mechanistic approach when studying competition in a spatial context and suggests how evolutionary modeling can be integrated with current work in animal movement ecology.

Novel pathogen introduction triggers rapid evolution in animal social movement strategies

Animal sociality emerges from individual decisions on how to balance the costs and benefits of being sociable. Novel pathogens introduced into wildlife populations should increase the costs of sociality, selecting against gregariousness. Using an individual-based model that captures essential features of pathogen transmission among social hosts, we show how novel pathogen introduction provokes the rapid evolutionary emergence and coexistence of distinct social movement strategies. These strategies differ in how they trade the benefits of social information against the risk of infection. Overall, pathogen-risk-adapted populations move more and have fewer associations with other individuals than their pathogen-risk-naive ancestors, reducing disease spread. Host evolution to be less social can be sufficient to cause a pathogen to be eliminated from a population, which is followed by a rapid recovery in social tendency. Our conceptual model is broadly applicable to a wide range of potential host-pathogen introductions and offers initial predictions for the eco-evolutionary consequences of wildlife pathogen spillover scenarios and a template for the development of theory in the ecology and evolution of animals' movement decisions.

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.

A hospital demand and capacity intervention approach for COVID-19

The mathematical interpretation of interventions for the mitigation of epidemics in the literature often involves finding the optimal time to initiate an intervention and/or the use of the number of infections to manage impact. Whilst these methods may work in theory, in order to implement effectively they may require information which is not likely to be available in the midst of an epidemic, or they may require impeccable data about infection levels in the community. In reality, testing and cases data can only be as good as the policy of implementation and the compliance of the individuals, which implies that accurately estimating the levels of infections becomes difficult or complicated from the data that is provided. In this paper, we demonstrate a different approach to the mathematical modelling of interventions, not based on optimality or cases, but based on demand and capacity of hospitals who have to deal with the epidemic on a day to day basis. In particular, we use data-driven modelling to calibrate a susceptible-exposed-infectious-recovered-died type model to infer parameters that depict the dynamics of the epidemic in several regions of the UK. We use the calibrated parameters for forecasting scenarios and understand, given a maximum capacity of hospital healthcare services, how the timing of interventions, severity of interventions, and conditions for the releasing of interventions affect the overall epidemic-picture. We provide an optimisation method to capture when, in terms of healthcare demand, an intervention should be put into place given a maximum capacity on the service. By using an equivalent agent-based approach, we demonstrate uncertainty quantification on the likelihood that capacity is not breached, by how much if it does, and the limit on demand that almost guarantees capacity is not breached.

Emerging computational paradigms to address the complex role of gut microbial metabolism in cardiovascular diseases

The human gut microbiota and its associated perturbations are implicated in a variety of cardiovascular diseases (CVDs). There is evidence that the structure and metabolic composition of the gut microbiome and some of its metabolites have mechanistic associations with several CVDs. Nevertheless, there is a need to unravel metabolic behavior and underlying mechanisms of microbiome-host interactions. This need is even more highlighted when considering that microbiome-secreted metabolites contributing to CVDs are the subject of intensive research to develop new prevention and therapeutic techniques. In addition to the application of high-throughput data used in microbiome-related studies, advanced computational tools enable us to integrate omics into different mathematical models, including constraint-based models, dynamic models, agent-based models, and machine learning tools, to build a holistic picture of metabolic pathological mechanisms. In this article, we aim to review and introduce state-of-the-art mathematical models and computational approaches addressing the link between the microbiome and CVDs.

Conceptualisation of multiple impacts interacting in the marine environment using marine infrastructure as an example

The human population is increasingly reliant on the marine environment for food, trade, tourism, transport, communication and other vital ecosystem services. These services require extensive marine infrastructure, all of which have direct or indirect ecological impacts on marine environments. The rise in global marine infrastructure has led to light, noise and chemical pollution, as well as facilitation of biological invasions. As a result, marine systems and associated species are under increased pressure from habitat loss and degradation, formation of ecological traps and increased mortality, all of which can lead to reduced resilience and consequently increased invasive species establishment. Whereas the cumulative bearings of collective human impacts on marine populations have previously been demonstrated, the multiple impacts associated with marine infrastructure have not been well explored. Here, building on ecological literature, we explore the impacts that are associated with marine infrastructure, conceptualising the notion of correlative, interactive and cumulative effects of anthropogenic activities on the marine environment. By reviewing the range of mitigation approaches that are currently available, we consider the role that eco-engineering, marine spatial planning and agent-based modelling plays in complementing the design and placement of marine structures to incorporate the existing connectivity pathways, ecological principles and complexity of the environment. Because the effect of human-induced, rapid environmental change is predicted to increase in response to the growth of the human population, this study demonstrates that the development and implementation of legislative framework, innovative technologies and nature-informed solutions are vital, preventative measures to mitigate the multiple impacts associated with marine infrastructure.

Comparative Agent-Based Simulations on Levels of Multiplicity Using a Network Regression: A Mobile Dating Use-Case

We demonstrate the use of agent-based models to simulate the interactions of two mobile dating applications that possess divergent interaction features. We reproduce several expected outcomes when compared to extant literature. We also demonstrate the use of a standard social network analysis technique—the network regression, Multiple Regression Quadratic Assignment Procedure—in conducting a principled and interpretable comparison between the two models with strong results. This combined approach is novel and allows complex system modelers who utilize agent-based models to reduce their reliance on idealized network structures (small world, scale-free, erdos-renyi) when applying underlying network interactions to agent-based models that can often skew results and mislead from a full picture of system-level properties. This work serves as a proof-of-concept in the integration of classical social network analysis methods and contemporary agent-based modeling to compare software designs and to enhance the policy-generation process of online social networks.

Agent-Based Modeling of Autosomal Recessive Deafness 1A (DFNB1A) Prevalence with Regard to Intensity of Selection Pressure in Isolated Human Population

An increase in the prevalence of autosomal recessive deafness 1A (DFNB1A) in populations of European descent was shown to be promoted by assortative marriages among deaf people. Assortative marriages became possible with the widespread introduction of sign language, resulting in increased genetic fitness of deaf individuals and, thereby, relaxing selection against deafness. However, the effect of this phenomenon was not previously studied in populations with different genetic structures. We developed an agent-based computer model for the analysis of the spread of DFNB1A. Using this model, we tested the impact of different intensities of selection pressure against deafness in an isolated human population over 400 years. Modeling of the "purifying" selection pressure on deafness ("No deaf mating" scenario) resulted in a decrease in the proportion of deaf individuals and the pathogenic allele frequency. Modeling of the "relaxed" selection ("Assortative mating" scenario) resulted in an increase in the proportion of deaf individuals in the first four generations, which then quickly plateaued with a subsequent decline and a decrease in the pathogenic allele frequency. The results of neutral selection pressure modeling ("Random mating" scenario) showed no significant changes in the proportion of deaf individuals or the pathogenic allele frequency after 400 years.

A novel agent-based, evolutionary model for expressing the dynamics of creative open-problem solving in small groups

Understanding the process of producing creative responses to open-ended problems solved in small groups is important for many modern domains, like health care, manufacturing, education, banking, and investment. Some of the main theoretical challenges include characterizing and measuring the dynamics of responses, relating social and individual aspects in group problem solving, incorporating soft skills (e.g., experience, social aspects, and emotions) to the theory of decision making in groups, and understanding the evolution of processes guided by soft utilities (hard-to-quantify utilities), e.g., social interactions and emotional rewards. This paper presents a novel theoretical model (TM) that describes the process of solving open-ended problems in small groups. It mathematically presents the connection between group member characteristics, interactions in a group, group knowledge evolution, and overall novelty of the responses created by a group as a whole. Each member is modeled as an agent with local knowledge, a way of interpreting the knowledge, resources, social skills, and emotional levels associated to problem goals and concepts. Five solving strategies can be employed by an agent to generate new knowledge. Group responses form a solution space, in which responses are grouped into categories based on their similarity and organized in abstraction levels. The solution space includes concrete features and samples, as well as the causal sequences that logically connect concepts with each other. The model was used to explain how member characteristics, e.g., the degree to which their knowledge is similar, relate to the solution novelty of the group. Model validation compared model simulations against results obtained through behavioral experiments with teams of human subjects, and suggests that TMs are a useful tool in improving the effectiveness of small teams.

From Flatland to Jupiter: Searching for Rules of Interaction Across Biological Scales

In this future-spanning perspective, we examine how an agent based model could be used to define general rules for interactions across biological systems and evolutionary time. To date there have been a number of attempts to simulate the emergence of ecological communities using agent-based models of individuals that have evolving traits. Here we speculate whether it is possible to use this computational modeling to simulate self-organizing systems and, importantly, to decipher universal principles that govern biological interactions. This perspective is a thought exercise, meant to extrapolate from current knowledge to how we may make Jupiter-shot leaps to further advance the biosciences in the 21st century.

Magnesium ions regulate mesenchymal stem cells population and osteogenic differentiation: A fuzzy agent-based modeling approach

Mesenchymal stem cells (MSCs) are proliferative and multipotent cells that play a key role in the bone regeneration process. Empirical data have repeatedly shown the bioregulatory importance of magnesium (Mg) ions in MSC growth and osteogenesis. In this study, we propose an agent-based model to predict the spatiotemporal dynamics of the MSC population and osteogenic differentiation in response to Mg2+ ions. A fuzzy-logic controller was designed to govern the decision-making process of cells by predicting four cellular processes of proliferation, differentiation, migration, and mortality in response to several important bioregulatory factors such as Mg2+ ions, pH, BMP2, and TGF-β1. The model was calibrated using the empirical data obtained from three sets of cell culture experiments. The model successfully reproduced the empirical observations regarding live cell count, viability, DNA content, and the differentiation-related markers of alkaline phosphate (ALP) and osteocalcin (OC). The simulation results, in agreement with the empirical data, showed that Mg2+ ions within 3-6 mM concentration have the highest stimulation effect on cell population growth. The model also correctly reproduced the stimulatory effect of Mg2+ ions on ALP and its inhibitory effect on OC as the early and late differentiation markers, respectively. Besides, the numerical simulation shed light on the innate cellular differences of the cells cultured in different experiments in terms of the proliferative capacity as well as sensitivity to Mg2+ ions. The proposed model can be adopted in the study of the osteogenesis around Mg-based implants where ions released due to degradation interact with local cells and regulate bone regeneration.

Anticipation of common buzzard population patterns in the changing UK landscape

Harmonious coexistence between humans, other animals and ecosystem services they support is a complex issue, typically impacted by landscape change, which affects animal distribution and abundance. In the last 30 years, afforestation on grasslands across Great Britain has been increasing, motivated by socio-economic reasons and climate change mitigation. Beyond expected benefits, an obvious question is what are the consequences for wider biodiversity of this scale of landscape change. Here, we explore the impact of such change on the expanding population of common buzzards Buteo buteo, a raptor with a history of human-induced setbacks. Using Resource-Area-Dependence Analysis (RADA), with which we estimated individuals' resource needs using 10-day radio-tracking sessions and the 1990s Land Cover Map of GB, and agent-based modelling, we predict that buzzards in our study area in lowland UK had fully recovered (to 2.2 ind km^-2) by 1995. We also anticipate that the conversion of 30%, 60% and 90% of economically viable meadow into woodland would reduce buzzard abundance nonlinearly by 15%, 38% and 74%, respectively. The same approach used here could allow for cost-effective anticipation of other animals' population patterns in changing landscapes, thus helping to harmonize economy, landscape change and biodiversity.

Social Barriers and the Hiatus from Successful Green Stormwater Infrastructure Implementation across the US

Green stormwater infrastructure (GSI), a nature-inspired, engineered stormwater management approach, has been increasingly implemented and studied especially over the last two decades. Though recent studies have elucidated the social benefits of GSI implementation in addition to its environmental and economic benefits, the social factors that influence its implementation remain under-explored thus, there remains a need to understand social barriers on decisions for GSI. This review draws interdisciplinary research attention to the connections between such social barriers and the potentially underlying cognitive biases that can influence rational decision making. Subsequently, this study reviewed the agent-based modeling (ABM) approach in decision support for promoting innovative strategies in water management for long-term resilience at an individual level. It is suggested that a collaborative and simultaneous effort in governance transitioning, public engagement, and adequate considerations of demographic constraints are crucial to successful GSI acceptance and implementation in the US.

Computational animal welfare: towards cognitive architecture models of animal sentience, emotion and wellbeing

To understand animal wellbeing, we need to consider subjective phenomena and sentience. This is challenging, since these properties are private and cannot be observed directly. Certain motivations, emotions and related internal states can be inferred in animals through experiments that involve choice, learning, generalization and decision-making. Yet, even though there is significant progress in elucidating the neurobiology of human consciousness, animal consciousness is still a mystery. We propose that computational animal welfare science emerges at the intersection of animal behaviour, welfare and computational cognition. By using ideas from cognitive science, we develop a functional and generic definition of subjective phenomena as any process or state of the organism that exists from the first-person perspective and cannot be isolated from the animal subject. We then outline a general cognitive architecture to model simple forms of subjective processes and sentience. This includes evolutionary adaptation which contains top-down attention modulation, predictive processing and subjective simulation by re-entrant (recursive) computations. Thereafter, we show how this approach uses major characteristics of the subjective experience: elementary self-awareness, global workspace and qualia with unity and continuity. This provides a formal framework for process-based modelling of animal needs, subjective states, sentience and wellbeing.

Benthic habitat is an integral part of freshwater Mysis ecology

Diel vertical migration (DVM) is common in aquatic organisms. The trade-off between reduced predation risk in deeper, darker waters during the day and increased foraging opportunities closer to the surface at night is a leading hypothesis for DVM behaviour.Diel vertical migration behaviour has dominated research and assessment frameworks for Mysis, an omnivorous mid-trophic level macroinvertebrate that exhibits strong DVM between benthic and pelagic habitats and plays key roles in many deep lake ecosystems. However, some historical literature and more recent evidence indicate that mysids also remain on the bottom at night, counter to expectations of DVM.We surveyed the freshwater Mysis literature using Web of Science (WoS; 1945-2019) to quantify the frequency of studies on demographics, diets, and feeding experiments that considered, assessed, or included Mysis that did not migrate vertically but remained in benthic habitats. We supplemented our WoS survey with literature searches for relevant papers published prior to 1945, journal articles and theses not listed in WoS, and additional references known to the authors but missing from WoS (e.g. only 47% of the papers used to evaluate in situ diets were identified by WoS).Results from the survey suggest that relatively little attention has been paid to the benthic components of Mysis ecology. Moreover, the literature suggests that reliance on Mysis sampling protocols using pelagic gear at night provides an incomplete picture of Mysis populations and their role in ecosystem structure and function.We summarise current knowledge of Mysis DVM and provide an expanded framework that more fully considers the role of benthic habitat. Acknowledging benthic habitat as an integral part of Mysis ecology will enable research to better understand the role of Mysis in food web processes.

Agent-Based Modeling and Simulation in Mathematics and Biology Education

With advances in computing, agent-based models (ABMs) have become a feasible and appealing tool to study biological systems. ABMs are seeing increased incorporation into both the biology and mathematics classrooms as powerful modeling tools to study processes involving substantial amounts of stochasticity, nonlinear interactions, and/or heterogeneous spatial structures. Here we present a brief synopsis of the agent-based modeling approach with an emphasis on its use to simulate biological systems, and provide a discussion of its role and limitations in both the biology and mathematics classrooms.

Improving Representation of Decision Rules in LUCC-ABM: An Example with an Elicitation of Farmers’ Decision Making for Landscape Restoration in Central Malawi

Restoring interlocking forest-agricultural landscapes—forest-agricscapes—to sustainably supply ecosystem services for socio-ecological well-being is one of Malawi’s priorities. Engaging local farmers is crucial in implementing restoration schemes. While farmers’ land-use decisions shape land-use/cover and changes (LUCC) and ecological conditions, why and how they decide to embrace restoration activities is poorly understood and neglected in forest-agricscape restoration. We analyze the nature of farmers’ restoration decisions, both individually and collectively, in Central Malawi using a mixed-method analysis. We characterize, qualitatively and quantitatively, the underlying contextual rationales, motives, benefits, and incentives. Identified decision-making rules reflect diverse and nuanced goal frames of relative importance that are featured in various combinations. We categorize the decision-making rules as: problem-solving oriented, resource/material-constrained, benefits-oriented, incentive-based, peers/leaders-influenced, knowledge/skill-dependent, altruistic-oriented, rules/norms-constrained, economic capacity-dependent, awareness-dependent, and risk averse-oriented. We link them with the corresponding vegetation- and non-vegetation-based restoration practices to depict the overall decision-making processes. Findings advance the representation of farmers’ decision rules and behavioral responses in computational agent-based modeling (ABM), through the decomposition of empirical data. The approach used can inform other modeling works attempting to better capture social actors’ decision rules. Such LUCC-ABMs are valuable for exploring spatially explicit outcomes of restoration investments by modeling such decision-making processes and policy scenarios.

The boon and bane of boldness: movement syndrome as saviour and sink for population genetic diversity

BACKGROUND

Many felid species are of high conservation concern, and with increasing human disturbance the situation is worsening. Small isolated populations are at risk of genetic impoverishment decreasing within-species biodiversity. Movement is known to be a key behavioural trait that shapes both demographic and genetic dynamics and affects population survival. However, we have limited knowledge on how different manifestations of movement behaviour translate to population processes. In this study, we aimed to 1) understand the potential effects of movement behaviour on the genetic diversity of small felid populations in heterogeneous landscapes, while 2) presenting a simulation tool that can help inform conservation practitioners following, or considering, population management actions targeting the risk of genetic impoverishment.

METHODS

We developed a spatially explicit individual-based population model including neutral genetic markers for felids and applied this to the example of Eurasian lynx. Using a neutral landscape approach, we simulated reintroductions into a three-patch system, comprising two breeding patches separated by a larger patch of differing landscape heterogeneity, and tested for the effects of various behavioural movement syndromes and founder population sizes. We explored a range of movement syndromes by simulating populations with various movement model parametrisations that range from 'shy' to 'bold' movement behaviour.

RESULTS

We find that movement syndromes can lead to a higher loss of genetic diversity and an increase in between population genetic structure for both "bold" and "shy" movement behaviours, depending on landscape conditions, with larger decreases in genetic diversity and larger increases in genetic differentiation associated with bold movement syndromes, where the first colonisers quickly reproduce and subsequently dominate the gene pool. In addition, we underline the fact that a larger founder population can offset the genetic losses associated with subpopulation isolation and gene pool dominance.

CONCLUSIONS

We identified a movement syndrome trade-off for population genetic variation, whereby bold-explorers could be saviours - by connecting populations and promoting panmixia, or sinks - by increasing genetic losses via a 'founder takes all' effect, whereas shy-stayers maintain a more gradual genetic drift due to their more cautious behaviour. Simulations should incorporate movement behaviour to provide better projections of long-term population viability and within-species biodiversity, which includes genetic diversity. Simulations incorporating demographics and genetics have great potential for informing conservation management actions, such as population reintroductions or reinforcements. Here, we present such a simulation tool for solitary felids.

Learned emergence in selfish collective motion

To understand the collective motion of many individuals, we often rely on agent-based models with rules that may be computationally complex and involved. For biologically inspired systems in particular, this raises questions about whether the imposed rules are necessarily an accurate reflection of what is being followed. The basic premise of updating one's state according to some underlying motivation is well suited to the realm of reservoir computing; however, entire swarms of individuals are yet to be tasked with learning movement in this framework. This work focuses on the specific case of many selfish individuals simultaneously optimizing their domains in a manner conducive to reducing their personal risk of predation. Using an echo state network and data generated from the agent-based model, we show that, with an appropriate representation of input and output states, this selfish movement can be learned. This suggests that a more sophisticated neural network, such as a brain, could also learn this behavior and provides an avenue to further the search for realistic movement rules in systems of autonomous individuals.