DOI: 10.3897/ibe.2.176474

Authors: Chelsea Gray, Dale S. Rothman, Erin Peters-Burton, Cynthia Smith, E.C.M Parsons, Alexis Garretson

Abstract: Basking sharks (Cetorhinus maximus) are largely solitary, following highly individualized migration routes, yet they exhibit poorly understood aggregation behaviors, ranging from pairs to groups of up to 1,000 individuals. These surface aggregations put sharks at risk of boat strikes and are thought to be driven in part by localized zooplankton availability, though observational evidence suggests that food alone may not fully explain this behavior. Previous modeling approaches have successfully identified long-range basking shark migration trends but have not accurately reproduced behavior at finer spatial scales, and commonly used chlorophyll a proxies for zooplankton have produced inconsistent results when compared with sightings data. This study presents the first individual-based model (IBM) of basking shark aggregation behavior, simulating shark movement around the Inishowen Peninsula, Ireland, from 1982 to 2018. Rather than relying on a phytoplankton proxy, zooplankton was simulated directly to capture fine-scale prey patchiness. Four behavioral submodels were tested and compared with three decades of sightings data from the Irish Basking Shark Group and Irish Whale and Dolphin Group: sharks seeking food only, sharks seeking other sharks only, sharks seeking both food and other sharks, and a random-movement control. The combined food-and-social submodel produced the most realistic results, with lower error and greater qualitative correspondence to observed sightings data than the food-only, social-only, or random submodels, which rarely generated aggregations larger than two individuals. These findings indicate that basking shark aggregations emerge from a combination of environmental and social drivers rather than food availability or chance alone. This model demonstrates the viability of IBMs for simulating basking shark behavior, offers a flexible framework that can incorporate improved environmental and behavioral data as they become available, and provides a foundation for future research into the drivers of basking shark aggregation.

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DOI: 10.3897/ibe.2.203247

Authors: Elina Takola

Abstract: Individuals of the same species often differ consistently in their use of resources, their responses to environmental gradients, and their movement decisions. Between-individual variation across niche axes has been shown to have important ecological consequences. However, practical frameworks that translate modern tracking data and space-use patterns into operational, comparable measures of individual specialization in habitat use remain limited. The individualized niche concept has inherited the plasticity of the ecological niche concept, and its quantification is considered a laborious task. Here, a workflow that estimates realized and potential individualized niches from movement data using mixed-effects resource selection functions and niche multidimensional hypervolumes is presented. The workflow combines random-intercept and random-slope resource selection functions with relative selection probabilities and ecological niche indices. The outputs of resource selection functions are used to create a kernel density-estimated hypervolume, and individualized niches are quantified using niche breadth and pairwise niche overlap indices. In addition, repeatability estimates are used to capture consistent among-individual differences and allow direct comparisons between realized and potential niche components. The workflow is implemented exclusively in the programming language R. A case study using publicly available northern lapwing (Vanellus vanellus) tracking data illustrates how the workflow can integrate multiple environmental variables, including earthworm abundance, human presence, pesticides, management, soil variables, and vegetation, to characterize individualized niches. The proposed workflow is transferable across taxa of mobile organisms and can facilitate the study of niche specialization and population-level heterogeneity across scales.

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DOI: 10.3897/ibe.2.174844

Authors: Kristin N. Crouse, Clarence L. Lehman, Michael L. Wilson

Abstract: Understanding how individual behaviors scale up to population and ecosystem patterns remains a central challenge in ecology. Here, we introduce B3GET (Behavior, Ecology, Genetics, Evolution, and Tradeoffs), a generalized agent-based modeling framework that links individual stimulus–response rules to emergent ecological, evolutionary, and social dynamics. Stimuli include the observable traits of individuals, such as their sex, age, and fertility status, among other phenotypes and signals; responses include movement, body growth, and inclination to mate, eat, fight, and perform other behaviors. These rules are heritable and can mutate and recombine during sexual reproduction, resulting in the emergence of adaptive strategies and counterstrategies. We conducted 288 simulations totaling 370,199 individuals, divided equally among four emulated primate species: chimpanzees, geladas, hamadryas baboons, and olive baboons. We demonstrate that (1) B3GET can generate patchy and homogeneous virtual plant food environments; (2) virtual organisms can live and thrive in these habitats and persist for generations; (3) populations can evolve over generations; and (4) these virtual primate counterparts resembled the target species in terms of their dispersal patterns, mating strategies, and ranging behaviors. B3GET users can edit the starting files to create species-specific virtual organisms and collect simulation data for testing hypotheses. Because B3GET can emulate a broad range of animal species, it provides a promising approach for investigating broader questions in animal behavior and primate socioecology, both for research and education.

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DOI: 10.3897/ibe.2.185721

Authors: Dominik Lammers, Fabrice Requier, Andreas Focks, Jürgen Groeneveld

Abstract: The BEEHAVE honey bee model is widely recognised for its ability to analyse the impact of local land use, including pesticide use, weather conditions, mite infestations, and beekeeping practices, on the risk of colony loss. However, it is based on an imposed egg-laying rate that represents only average conditions in Central Europe while also being the main driving factor in colony dynamics. A modified version of the model, called BEEHAVE-PPE (Pollen Pheromone Egg-laying), was developed, in which positive feedback between pollen foraging and the egg-laying rate was implemented. It was assumed that daily pollen foraging depends not only on weather conditions but also on brood size, brood pheromones, and temperature. The six parameters of the new BEEHAVE-PPE module were calibrated using demographic and weather data from a bee colony monitoring area in France. This produced realistic colony dynamics under average weather conditions but not under prolonged periods of adverse weather that prevented foraging. Although the model is based on unverified heuristic assumptions regarding brood pheromone production and dynamics, the results suggest that it is plausible that the egg-laying rate in honey bee colonies is influenced by local weather conditions and feedback from the brood. The new version, BEEHAVE-PPE, is not intended for direct use but rather to stimulate empirical research on queen egg-laying, brood pheromone signalling, and theoretical work on colony feedback dynamics.

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DOI: 10.3897/ibe.2.199762

Authors: C. J. M. Musters, Geert R. de Snoo

Abstract: Ecology should be capable of convincingly demonstrating the consequences of changes in advance to become the strong predictive science that it needs to be. Predictive ecology could greatly assist conservation policy-makers by enabling them to understand and assess alternative management scenarios. Predicting changes in ecological communities require complex models and large empirical datasets. Deep Learning (DL), in combination with Organism-based Ecology (OE), could be used to construct models capable of predicting how organisms interact with each other and with their environment and how these interactions drive community change. At present, traditional ecological data are insufficient to train such models. A completely new type of data is needed, data that captures the information organisms use to guide their actions and responses to changes within their communities. To address this, we propose developing sensory instruments that simulate organisms’ sensory organs to collect these data. We also call for the explicit study and publication of the limits of applicability of DL models and we propose establishing a set of ethically responsible rules for applying DL models in ecology.

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DOI: 10.3897/ibe.2.163207

Authors: Harriet M. Gold, Alice S. A. Johnston, William D. Rust

Abstract: Environmental decision-makers need robust, landscape-level predictions of population responses to inform management decisions before implementation. Mechanistic individual-based models (IBMs) can capture how individual behaviour and interactions in heterogeneous environments generate emergent population dynamics, but high computational costs typically restrict applications to small spatial extents. To address this limitation, we synthesise spatial modelling strategies across subfields of ecology and introduce the Spatial Threshold of Emergent Behaviour Stabilisation (STEBS) framework. STEBS capitalises on the biological realism of mechanistic IBMs through an in silico modelling experiment to quantify the Critical Emergence Threshold (CET) — the smallest spatial extent at which emergent system behaviour stabilises. The CET provides a biologically meaningful and computationally efficient scale for developing meta-models that relate environmental variables to emergent population patterns, which can then be extrapolated across unsimulated regions to predict landscape-level dynamics. This approach enables tractable scaling up of mechanistic IBMs, while retaining their biological realism. STEBS therefore offers a systematic pathway for applying IBMs to real-world environmental challenges, enhancing the evidence base for policy and management under accelerating global change. Future development of STEBS into an operational and transferable tool will require empirical validation across diverse species, landscapes and IBM structures, alongside evaluation of whether the upfront investment required to estimate CETs improves predictive efficiency compared to brute-force scaling approaches.

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DOI: 10.3897/ibe.1.151741

Authors: Leonna Szangolies, Florian Jeltsch, Lewis G. Halsey, Cara A. Gallagher

Abstract: To counteract ongoing biodiversity loss due to global change, we need a deeper understanding of when and how species coexist. Recent work has begun to uncover mechanistic links between species coexistence and the movement of individual animals, revealing how individual behaviour can shape community dynamics. This movement behaviour is both motivated by and constrains an animal’s energy state, which in turn drives organism fitness and species interactions, for example by minimizing energy costs or maximizing energy intake. Advancing our understanding of individual energetic mechanisms can therefore reveal key drivers of coexistence. Here, we propose a conceptual framework linking animal energetics, movement behaviour and coexistence to explore how energy fluxes drive movement, mediate species interactions and shape community dynamics, extending former theories that address subsets of these relationships. Energetics is an important process influencing whether, how, where and when animals move, and underpins both equalizing (e.g., similar energy balances among species) and stabilizing (e.g., energy costs that limit large populations) mechanisms of coexistence. By synthesizing insights from community ecology, movement ecology and ecophysiology, we highlight how the integration of these fields reveals a fundamental set of interconnected mechanisms shaping species coexistence. We advocate for this mechanistic framework to improve our understanding of diversity dynamics and predictions of the impacts of environmental change on coexistence and biodiversity. We call for the development of interdisciplinary methods to test predictions evolving in this area and provide examples of how this framework can be applied to advance understanding across varied ecological systems.

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DOI: 10.3897/ibe.1.146376

Authors: Miranda M. Crafton, Avishai Lublin, Shay Cahani, Luise Rauer, Orr Spiegel

Abstract: The feedback between host behavior and disease transmission is well acknowledged, but empirical studies demonstrating the strength and direction of the associations between individuals’ pathogens or microbiota composition and their movement are rare. We investigated these associations in feral pigeons (Columba livia domestica), a synanthrope species known to host a plethora of zoonotic pathogens, predicting a positive association between individual’s movement behavior and microbiota alpha diversity. We captured pigeons in three dairy farms along an urbanization gradient in central Israel and combined GPS-tracking with total RNA-Sequencing to characterize pigeons’ movement and microbiota, respectively. We found that pigeons roosted primarily in human settlements, with frequent visits to dairy farms and other agricultural sites. Microbiota diversity and composition varied between sites and the individuals within them, and several pathogens relevant to poultry, cattle, and human-health were frequently detected (e.g. Escherichia and Clostridium). Pigeons in the urban site covered shorter distances and carried a greater diversity of bacteria compared to those in rural sites. Beyond these among-site differences, exploratory individuals (measured by the number of unique locations they visited) had more diverse microbiota, implying a role for individual behavioral traits in disease exposure and spread. We conclude that pigeons can potentially serve as transmission vectors among wildlife, livestock, and humans. Further, the associations between host behavior and microbiota diversity emphasize the relevance of individually-targeted wildlife movement analyses for disease ecology and One Health.

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DOI: 10.3897/ibe.1.153305

Authors: Amaïa Lamarins, François de Coligny, Jacques Labonne, Etienne Prévost, Cyril Piou, Craig R. Primmer, Mathieu Buoro

Abstract: To improve our understanding and management of species’ response to environmental and anthropogenic changes, there is a critical need for integrative modeling approaches that encompass demographic, ecological and evolutionary processes in a unified framework. This is especially crucial for anadromous salmonid fishes that hold significant socio-economic value. Mechanistic models, particularly Demo-Genetic Agent-Based Models (DG-ABMs), are promising tools to address this challenge. Building on previous work, we present IBASAM, an Individual Based Anadromous SAlmonids Model. It incorporates significant advancements, including new processes such as dispersal and in-river movement, enhanced flexibility for simulating diverse spatial configurations- from river dendritic networks to metapopulations and multiple marine areas- and the integration of variation in life history phenology and the genetic architecture of traits. Additionally, a user-friendly interface has been developed to facilitate broader application. This improved versatility enables IBASAM application to a number of populations and species of anadromous salmonids, making it well-suited for a wide range of ecological and evolutionary studies. The model is particularly suited for investigating (meta)population eco-evolutionary dynamics, persistence, and adaptation to environmental changes, as well as exploring management options including fisheries regulation and river connectivity restoration. We summarize the model’s structure and illustrate some of its novelties, parameterization, and emerging patterns with the case study of a naturally reproducing Atlantic salmon population simulated over 100 years, with constant fishing rates and no climatic trend. Our simulations successfully reproduced 30 variables observed over 30 years in the Scorff population. IBASAM opens up new opportunities for research on the response of salmonid meta-populations to environmental changes, the exploration of management practices, with further developments possible.

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DOI: 10.3897/ibe.1.e139560

Authors: Daniel Ayllón, Steven F. Railsback, Bret C. Harvey, Graciela G. Nicola, Benigno Elvira, Ana Almodóvar

Abstract: Stream-dwelling salmonids in the low-latitude and -altitude margins of their range are particularly threatened by climate change. However, they possess a variety of evolutionary, plastic, and behavioural mechanisms that provide resistance against rapid changes in their environment. Behavioural plasticity can be important under rapid environmental change because it is relatively fast and flexible. In particular, salmonids can exhibit flexible diel activity patterns in response to new environmental conditions, but the consequences of this capability for long-term population persistence in the face of climate change remain unclear. We used an individual-based model to simulate the trajectory of a brown trout population at the warmest edge of its range under three environmental-change scenarios of increasing warming and streamflow reduction. We assessed (1) how simulated trout responded behaviourally to climate change by modifying their circadian foraging patterns, and (2) how much this behavioural plasticity buffered the population-level consequences of environmental change. Our simulations showed that under current conditions trout of different age classes segregated foraging both temporally and spatially. The most consistent response to environmental change was more diurnal feeding in all age classes and under all scenarios, with the strength of this response increasing with the severity of change. In addition, total daily foraging activity increased in all age classes. A second experiment indicated that virtual populations of individuals capable of flexible circadian feeding were more resistant to environmental change than populations restricted to fixed feeding patterns. Thus, our computational experiment suggests that the ability of fish to adaptively select when as well as where to feed, well-documented at the individual level in the empirical literature, could potentially buffer the demographic impacts of long-term environmental change.

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DOI: 10.3897/ibe.1.e137398

Authors: Kathleen D. W. Church, Steven F. Railsback, Christina A. D. Semeniuk

Abstract: Microplastics in freshwater habitats are consumed by fish, including stream-dwelling salmonids, which can alter food consumption or negatively affect swimming and foraging behaviour. As population-level effects are largely unknown, a population of stream-dwelling rainbow trout (Oncorhynchus mykiss) was simulated using the agent-based model ‘inSTREAM 7’ to model population-level effects (biomass) of behavioural changes caused by microplastics consumption. Individual fish were assigned all possible combinations of two personality traits (dominance, boldness/shyness) and consumed microplastics while foraging; and their microplastics consumption, body length and abundance were tracked for three different life stages (fry, juvenile, adult) for a period of 10 years. Three scenarios were explored: a low-impact scenario with microplastics causing decreased food consumption, a medium-impact scenario which added lower swimming speed and a high-impact scenario with additional reductions of foraging efficiency. Each scenario was tested for microplastics concentrations of 0%, 1% (i.e. current levels) and 3% (i.e. future levels) of drift food. Overall, microplastics consumption did not strongly affect trout abundance. Dominant adult trout consumed disproportionally more microplastics than all other fish, especially at higher microplastic concentrations. Personality traits influenced the response of the trout to microplastics ingestion: dominant and bold adults were smaller when food consumption was reduced; shy and subordinate adults were smaller when swimming speed was lowered; and all dominant adults, regardless of boldness, were smaller when foraging efficiency was impeded, with dominant and bold fry also less abundant in this scenario. However, effects on fish body length were only found at microplastic concentrations of 3%, indicating these outcomes can be prevented, as current levels of microplastics pollution are below this concentration. Nevertheless, microplastics ingestion may become an additional stressor that interacts with the myriad of mostly anthropogenic stressors that already affect wild salmonid populations.

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DOI: 10.3897/ibe.1.141005

Authors: Steven F. Railsback, Bret C. Harvey

Abstract: Mortality risk is a critical and complex component of individual fitness and individual-based ecology, especially when risk-avoidance behaviors are considered. Organisms are subject to multiple kinds of risk that can vary with habitat, time, individual state, individual activity and behavior, and population status. Yet risk is often represented very simply in models and there is little literature on practical ways to model its variation. In our experience, desirable characteristics of risk models include: (a) survival probability can vary with multiple variables of individuals, habitat, and other entities; (b) relations between survival and specific variables can be added or removed or modified without re-fitting the entire model; (c) relations between variables and survival can take different forms, including continuous and nonlinear functions and discrete values; (d) relations between variables and survival are easy to understand and fit to many kinds of data or assumptions; and (e) they can be calibrated by adjusting only one parameter. We review the terminology and conventions that ecologists often use to model risk, and provide a mathematical framework for modeling risk. For complex risks, we describe and illustrate a method with “survival increase functions” that each relate survival probability to one variable. These functions can have a different form for each variable and can each be based on different information. The multiple functions are combined into a single survival probability value that is easily calibrated. We discuss methods for evaluating survival increase functions, ranging from general field observations to controlled field experiments, knowledge and data on mechanisms driving survival, and even conceptual models of those mechanisms. This approach has proven practical for representing complex effects of multiple variables on survival probability in models that represent how individual behavior and fitness depend on risk.

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DOI: 10.3897/ibe.1.147788

Authors: Volker Grimm, Mark E. Hauber, Uta Berger, Katrin M. Meyer, Steven F. Railsback

Abstract: Ecology traditionally has been fragmented into separate branches that emphasise different localities, ecosystems, habitats, levels of organisation, applications, etc., and that use distinct terminologies and methods. Individual-based ecology (IBE) can unify these branches. By taking into account the variation, behaviours, and interactions of individual organisms, IBE links the responses of organisms to the responses of ecological systems: if we understand enough about individuals, we can predict complex system dynamics, even under novel conditions. But we must also consider the system level to know what characteristics of individuals are important. In IBE, both individual and system levels are considered simultaneously, either by collecting data at both levels or at least by linking data observed at one level to existing patterns or theories at the other level. Through this comprehensive perspective, IBE unifies the many separate branches of ecology. Methodological and conceptual advances already developed in the 21st century allow us both to make observations at both individual and system levels and to link them using individual-based models. These advances are transforming IBE from a vision into a feasible and necessary approach that makes ecology fit for addressing the shifts and challenges of the future.

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DOI: 10.3897/ibe.1.148200

Authors: Florian Jeltsch, Manuel Roeleke, Ahmed Abdelfattah, Robert Arlinghaus, Gabriele Berg, Niels Blaum, Luc De Meester, Elke Dittmann, Jana Anja Eccard, Bertrand Fournier, Ursula Gaedke, Cara Gallagher, Lynn Govaert, Mark Hauber, Jonathan M. Jeschke, Stephanie Kramer-Schadt, Anja Linstädter, Ulrike Lucke, Valeria Mazza, Ralf Metzler, Claas Nendel, Viktoriia Radchuk, Matthias C. Rillig, Masahiro Ryo, Katharina Scheiter, Ralph Tiedemann, Britta Tietjen, Christian C. Voigt, Guntram Weithoff, Justyna Wolinska, Damaris Zurell

Abstract: Biodiversity loss and widespread ecosystem degradation are among the most pressing challenges of our time, requiring urgent action. Yet our understanding of their causes remains limited because prevailing ecological concepts and approaches often overlook the underlying complex interactions of individuals of the same or different species, interacting with each other and with their environment. We propose a paradigm shift in ecological science, moving from simplifying frameworks that use species, population or community averages to an integrative approach that recognizes individual organisms as fundamental agents of ecological change. The urgency of the biodiversity crisis requires such a paradigm shift to advance ecology towards a predictive science by elucidating the causal mechanisms linking individual variation and adaptive behaviour to emergent properties of populations, communities, ecosystems, and ecological interactions with human interventions. Recent advances in computational technologies, sensors, and analytical tools now offer unprecedented opportunities to overcome past challenges and lay the foundation for a truly integrated Individual-Based Global Change Ecology (IBGCE). Unravelling the potential role of individual variability in global change impact analyses will require a systematic combination of empirical, experimental and modelling studies across systems, while taking into account multiple drivers of global change and their interactions. Key priorities include refining theoretical frameworks, developing benchmark models and standardized toolsets, and systematically incorporating individual variation and adaptive behaviour into empirical field work, experiments and predictive models. The emerging synergies between individual-based modelling, big data approaches, and machine learning hold great promise for addressing the inherent complexity of ecosystems. Each step in the development of IBGCE must systematically balance the complexity of the individual perspective with parsimony, computational efficiency, and experimental feasibility. IBGCE aims to unravel and predict the dynamics of biodiversity in the Anthropocene through a comprehensive study of individual organisms, their variability and their interactions. It will provide a critical foundation for considering individual variation and behaviour for future conservation and sustainability management, taking into account individual-to-ecosystem pathways and feedbacks.

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