2026-10-11T15:10:16Z https://ibe.pensoft.net/oai.php

10.3897/ibe.1.148200 2025-03-27 ibe

Potsdam University, Potsdam, Germany Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany author Jeltsch, Florian https://orcid.org/https://orcid.org/0000-0002-4670-6469 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany Potsdam University, Potsdam, Germany Leibniz Institute for Zoo and Wildlife Research, Evolutionary Ecology, Berlin, Germany author Roeleke, Manuel https://orcid.org/0000-0001-5298-8071 Leibniz Institute for Agricultural Engineering and Bioeconomy, Microbiome Biotechnology, Potsdam, Germany author Abdelfattah, Ahmed https://orcid.org/0000-0001-6090-7200 Humboldt-Universität zu Berlin, Berlin, Germany Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany author Arlinghaus, Robert https://orcid.org/0000-0003-2861-527X Potsdam University, Potsdam, Germany Graz University of Technology, Graz, Austria Leibniz Institute for Agricultural Engineering and Bioeconomy, Microbiome Biotechnology, Potsdam, Germany author Berg, Gabriele https://orcid.org/0000-0001-9423-3101 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany Potsdam University, Potsdam, Germany author Blaum, Niels https://orcid.org/0000-0001-6807-5162 Leibniz Institute of Freshwater Ecology and Inland Fisheries, Evolutionary and Integrative Ecology, Berlin, Germany KU Leuven, Biology, Freshwater Ecology, Evolution & Conservation, Leuven, Belgium Freie Universität Berlin, Berlin, Germany Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany author De Meester, Luc https://orcid.org/0000-0001-5433-6843 Potsdam University, Potsdam, Germany Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany author Dittmann, Elke https://orcid.org/0000-0002-7549-7918 Potsdam University, Potsdam, Germany Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany author Eccard, Jana https://orcid.org/0000-0002-6151-2128 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany Potsdam University, Potsdam, Germany author Fournier, Bertrand https://orcid.org/0000-0002-7106-6109 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany Potsdam University, Potsdam, Germany author Gaedke, Ursula https://orcid.org/0000-0003-2900-6847 Aarhus University, Roskilde, Denmark author Gallagher, Cara https://orcid.org/0000-0001-7094-1752 Leibniz Institute of Freshwater Ecology and Inland Fisheries, Evolutionary and Integrative Ecology, Berlin, Germany Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany author Govaert, Lynn https://orcid.org/0000-0001-8326-3591 Graduate Center of the City University of New York, Advanced Science Research Center and Programs in Biology and Psychology, New York, United States of America author Hauber, Mark https://orcid.org/https://orcid.org/0000-0003-2014-4928 Freie Universität Berlin, Berlin, Germany Leibniz Institute of Freshwater Ecology and Inland Fisheries, Evolutionary and Integrative Ecology, Berlin, Germany Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany author Jeschke, Jonathan M. https://orcid.org/0000-0003-3328-4217 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany Technische Universität Berlin, Berlin, Germany Leibniz Institute for Zoo and Wildlife Research, Ecological Dynamics, Berlin, Germany author Kramer-Schadt, Stephanie https://orcid.org/0000-0002-9269-4446 Potsdam University, Potsdam, Germany Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany author Linstädter, Anja https://orcid.org/0000-0003-0038-9557 Potsdam University, Potsdam, Germany author Lucke, Ulrike https://orcid.org/0000-0003-4049-8088 University of Tuscia, Viterbo, Italy Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany author Mazza, Valeria https://orcid.org/0000-0002-1634-3417 Potsdam University, Potsdam, Germany Asia Pacific Centre for Theoretical Physics, Pohang, Republic of Korea Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany author Metzler, Ralf https://orcid.org/0000-0002-6013-7020 Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany Potsdam University, Potsdam, Germany author Nendel, Claas https://orcid.org/0000-0001-7608-9097 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany Leibniz Institute for Zoo and Wildlife Research, Ecological Dynamics, Berlin, Germany author Radchuk, Viktoriia https://orcid.org/0000-0003-3072-0095 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany Freie Universität Berlin, Berlin, Germany author Rillig, Matthias https://orcid.org/0000-0003-3541-7853 Brandenburg University of Technology, Cottbus-Senftenberg, Germany Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany author Ryo, Masahiro https://orcid.org/0000-0002-5271-3446 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany Potsdam University, Potsdam, Germany author Scheiter, Katharina https://orcid.org/0000-0002-9397-7544 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany Potsdam University, Potsdam, Germany author Tiedeman, Ralph https://orcid.org/0000-0002-2604-6336 Freie Universität Berlin, Berlin, Germany Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany author Tietjen, Britta https://orcid.org/0000-0003-4767-6406 Potsdam University, Potsdam, Germany Leibniz Institute for Zoo and Wildlife Research, Evolutionary Ecology, Berlin, Germany Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany author Voigt, Christian https://orcid.org/0000-0002-0706-3974 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany Potsdam University, Potsdam, Germany author Weithoff, Guntram https://orcid.org/0000-0003-1420-7152 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany Leibniz Institute of Freshwater Ecology and Inland Fisheries, Evolutionary and Integrative Ecology, Berlin, Germany Freie Universität Berlin, Berlin, Germany author Wolinska, Justyna https://orcid.org/0000-0003-2913-2923 Potsdam University, Potsdam, Germany Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany author Zurell, Damaris https://orcid.org/0000-0002-4628-3558 2025-03-27 2025-03-27 2025 Individual-based Ecology 3033-0947 1 e148200 2025 10.3389/fmicb.2018.00921 10.1126/sciadv.aax8329 10.1016/j.ecolmodel.2021.109685 10.1139/cjfas-2016-0350 10.1111/faf.12176 10.1111/ele.12636 10.1016/j.envsoft.2024.106003 10.3389/fmicb.2020.01598 10.1016/j.tree.2011.01.009 10.1002/eap.2727 10.1038/s41559-023-01985-2 10.1002/ecy.3379 10.1007/s42991-021-00168-5 10.1126/science.1183506 10.1038/s41467-023-43209-y 10.1111/oik.05567 10.1002/ecy.3333 10.1038/s41559-019-0797-2 10.1038/s41598-020-69998-6 10.12703/P6-39 10.1201/9781351073462 10.1007/s11538-021-00927-y 10.1126/science.1127750 10.1016/j.tree.2015.12.009 10.1111/1365-2435.13261 10.1242/jeb.140368 10.1098/rstb.2018.0186 10.1111/ele.13901 10.1098/rspb.2023.0823 The Future of Predictive Ecology. A Elliott-Graves author 2019 text Philosophical Topics 2019 47 1 65 82 10.1086/286212 10.1098/rspb.2020.1799 10.1111/1365-2656.13838 10.1080/01605682.2019.1633232 10.1098/rstb.2019.0449 10.1086/681105 10.1038/s42003-024-07114-4 10.1016/S0304-3800(98)00188-4 10.1515/9781400850624 10.1007/s10021-016-0071-2 10.1016/j.envsoft.2024.106088 10.3897/ibe.@@@.147788 10.1016/j.gecco.2024.e02887 10.1126/science.aat6412 10.1093/biosci/biz095 10.1038/nrmicro.2016.62 10.1111/gean.12267 10.1126/sciadv.abm9982 10.1002/ecs2.2700 10.1007/BF02418571 10.1111/gcb.17397 10.1016/j.ecolind.2021.108466 10.1038/s41598-018-33443-6 10.1111/j.1461-0248.2010.01457.x 10.1002/ece3.7478 10.1146/annurev-ecolsys-102220-011451 10.1146/annurev-ecolsys-102220-024934 10.1111/1365-2656.13930 10.1126/science.abq4062 10.1111/ecog.05689 10.1002/eap.2966 10.1111/gcb.15304 10.1016/j.biocon.2017.01.021 10.1073/pnas.2009451118 10.1111/1365-2664.12482 10.3389/fams.2023.1046185 10.1126/science.abg1780 10.3732/ajb.0900041 10.1038/s41564-022-01173-1 10.1111/1365-2435.13059 10.1016/j.biocon.2013.12.004 10.1016/j.ecolmodel.2015.11.022 10.1126/science.adf0895 10.1002/bes2.1648 10.1002/ecy.3721 10.1073/pnas.2203663119 10.1111/oik.09056 10.1111/ecog.07140 10.1111/1365-2656.13306 10.1002/ecs2.1976 10.1111/ecog.05360 10.1086/718591 10.1038/35098000 10.1126/science.aaf7671 10.1111/oik.07002 10.1098/rspb.2019.2211 10.1111/2041-210X.13235 10.1111/brv.12600 10.1111/1365-2745.14211 10.1002/ecy.2922 10.1186/s40462-022-00333-6 10.1016/j.baae.2022.09.004 10.1111/1365-2656.14134 10.1086/695835 10.1093/biosci/biab099 10.1111/ele.12348 10.1038/s41558-018-0160-7 10.1002/lno.11675 10.1016/j.tree.2011.11.014 10.1111/csp2.12943 10.1111/1462-2920.15418 10.1038/s41559-021-01440-0 10.1038/s41586-025-08604-z 10.1016/j.tree.2012.05.001 10.1002/sae2.12087 10.1111/ecog.05787 10.3897/ibe.1.148200 https://ibe.pensoft.net/article/148200/ https://ibe.pensoft.net/article/148200/download/pdf/ https://ibe.pensoft.net/article/148200/download/xml/ 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. text/html en_US Pensoft Publishers Agent-based biodiversity crisis climate change ecological theory individual trait variation predictions scaling up The need for an individual-based global change ecology Forum Paper

10.3897/ibe.1.147788 2025-03-27 ibe

University of Potsdam, Potsdam, Germany Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany author Grimm, Volker https://orcid.org/https://orcid.org/0000-0002-3221-9512 Graduate Center of the City University of New York, New York, United States of America University of Potsdam, Potsdam, Germany author Hauber, Mark https://orcid.org/https://orcid.org/0000-0003-2014-4928 Technische Universität Dresden, Dresden, Germany author Berger, Uta University of Göttingen, Göttingen, Germany author Meyer, Katrin https://orcid.org/https://orcid.org/0000-0002-9990-4047 Lang Railsback & Associates, Arcata, United States of America author Railsback, Steven https://orcid.org/0000-0002-5923-9847 2025-03-27 2025-03-27 2025 Individual-based Ecology 3033-0947 1 e147788 2025 10.1002/fee.2320 10.15146/5xcp-0d46 10.1111/brv.12729 10.1515/9781400850624 10.1016/j.ecolmodel.2016.01.001 10.1126/science.1116681 10.1016/j.ecolmodel.2014.01.018 10.1007/s10021-016-0071-2 10.1515/9783110848281 10.2307/1310870 10.3897/ibe.1.147788 10.1111/gcb.17397 A Łomnicki author 1988 Population Ecology of Individuals. 1988 240 pp 10.1016/j.baae.2010.08.003 10.3389/fams.2023.1046185 SL Pimm author 1986 1986 10.1111/1365-2656.14134 W Tischler author 1979 Einführung in die Ökologie. 1979 528 pp 10.1098/rstb.1985.0099 10.1098/rspb.2017.2360 10.1023/A:1002670731066 10.1038/sdata.2016.18 10.3897/ibe.1.147788 https://ibe.pensoft.net/article/147788/ https://ibe.pensoft.net/article/147788/download/pdf/ https://ibe.pensoft.net/article/147788/download/xml/ 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. text/html en_US Pensoft Publishers Environmental change individual-based ecology novel conditions paradigm predictions unification A manifesto for Individual-based Ecology Concepts and Synthesis

10.3897/ibe.1.141005 2025-03-27 ibe

Lang Railsback and Associates, Arcata, California, United States of America author Railsback, Steven https://orcid.org/0000-0002-5923-9847 Pacific Southwest Research Station, US Forest Service, Arcata, California, United States of America author Harvey, Bret C. https://orcid.org/0000-0003-0439-9656 2025-03-27 2025-03-27 2025 Individual-based Ecology 3033-0947 1 e141005 2025 10.1016/j.ecolmodel.2024.110987 10.2193/2008-325 10.1002/ece3.4270 10.1016/j.jembe.2019.151270 10.1007/s00265-013-1609-3 10.1007/BF00395696 10.1002/ecy.4429 10.1002/eap.3003 10.1016/j.tree.2019.01.004 10.1890/12-1615.1 10.2307/1939877 10.1515/9781400850624 10.1098/rstb.2011.0180 10.1016/j.ecolmodel.2024.110967 10.1007/b106568 10.1080/00028487.2012.760485 10.1007/s10641-017-0585-2 10.1007/s00442-007-0661-y 10.1111/j.0030-1299.2005.13858.x 10.4039/Ent91293-5 10.1007/s00265-014-1790-z 10.1002/ecs2.3168 10.1007/s10531-015-0993-6 10.1073/pnas.071061998 10.1890/04-0719 10.1016/j.biocon.2019.02.011 10.2307/3071767 10.23943/princeton/9780691195285.001.0001 10.1093/beheco/araa070 10.1002/tafs.10283 10.2737/PSW-GTR-276 10.1007/s00265-006-0172-6 10.3897/ibe.1.141005 https://ibe.pensoft.net/article/141005/ https://ibe.pensoft.net/article/141005/download/pdf/ https://ibe.pensoft.net/article/141005/download/xml/ 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. text/html en_US Pensoft Publishers Modeling Mortality Predation Risk Survival Representing mortality risk in mechanistic models Methods

10.3897/ibe.1.e137398 2025-03-27 ibe

University of Windsor, Windsor, Canada author Church, Kathleen https://orcid.org/https://orcid.org/0000-0002-7096-6869 Humboldt State University, Arcata, United States of America author Railsback, Steven https://orcid.org/0000-0002-5923-9847 University of Windsor, Windsor, Canada author Semeniuk, Christina https://orcid.org/0000-0001-5115-9853 2025-03-27 2025-03-27 2025 Individual-based Ecology 3033-0947 1 e137398 2025 funder Mitacs 10.13039/501100004489 10.1007/s11160-019-09551-5 10.1016/j.ecolmodel.2015.07.026 10.1002/ece3.4270 10.1016/j.fishres.2019.03.021 10.1111/1365-2656.13562 10.18637/jss.v067.i01 10.1016/j.envpol.2017.12.082 10.1674/amid-175-01-55-63.1 10.1002/rra.2590 10.1016/j.tree.2008.04.003 10.1098/rstb.2013.0580 10.1034/j.1600-0706.2001.950204.x 10.1016/j.aquatox.2020.105521 10.1016/j.ecoenv.2022.113301 10.1007/978-3-030-84255-0_6 10.1021/acs.est.9b03083 10.1371/journal.pone.0193308 10.1111/jfb.14303 P Cunningham author 2007 2007 10.1016/j.envpol.2014.10.026 10.1016/j.envpol.2018.03.001 10.1111/j.1365-2656.2007.01284.x 10.1093/oso/9780198546788.001.0001 MEL Felismino author 2023 2023 10.1016/j.aquatox.2015.11.011 10.1002/ecs2.3646 10.1016/j.scitotenv.2018.03.046 10.1897/08-029.1 10.1002/etc.3720 10.1016/j.scitotenv.2018.08.344 10.1073/pnas.1600715113 10.1007/s00128-017-2037-2 10.2307/5123 10.1126/science.1116681 10.1007/s10021-016-0071-2 10.1016/j.aquatox.2018.03.011 10.1007/s10641-013-0186-7 10.1002/fsh.10549 10.1002/etc.5640 10.15447/sfews.2018v16iss2art3 10.1093/icesjms/fst200 10.1111/faf.12701 10.1002/bimj.200810425 10.1007/s11356-019-05414-5 10.1038/s41598-017-01270-w 10.1021/acs.est.7b03567 10.1016/j.chemosphere.2018.09.031 10.1177/1948550617691100 10.1007/s00128-019-02587-0 10.3389/fevo.2020.00086 10.1016/j.scitotenv.2021.151909 10.1139/cjfas-2020-0484 10.1111/gcb.15155 10.1111/j.1469-185X.1983.tb00379.x 10.1371/journal.pone.0239128 10.1111/eva.13263 10.1098/rspb.2014.2454 10.18637/jss.v082.i13 10.1007/s10641-011-9846-7 10.1111/gcb.14957 10.1016/j.watres.2017.12.056 10.1007/s10211-019-00313-x 10.1139/f99-058 10.1016/j.envpol.2018.11.081 10.1111/jfd.12902 10.1080/26395940.2019.1580151 10.1057/jos.2016.7 10.1111/ele.13574 10.1007/978-94-024-1684-8_5 10.1021/es5053655 10.1098/rspb.2020.1947 10.3389/fevo.2019.00022 10.1038/s41598-018-29980-9 10.1016/j.chemosphere.2013.07.036 10.1139/cjfas-2013-0558 10.1139/z95-217 10.1016/j.envpol.2019.113855 10.1016/j.jhazmat.2021.126059 10.1016/j.envpol.2020.115572 10.1002/ecs2.3955 10.1111/faf.12528 10.1016/j.envpol.2016.01.083 10.1007/s10641-014-0222-2 10.1098/rspb.2020.2294 2021 2021 R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ 10.1111/j.1523-1739.2008.00953.x 10.2737/PSW-GTR-218 10.1080/00028487.2015.1079553 10.1093/beheco/araa070 10.1002/rra.3845 10.1002/tafs.10283 10.2737/PSW-GTR-276 10.1111/j.1469-185X.2007.00010.x 10.1111/brv.12480 10.1126/science.aar7734 10.1016/j.cbpc.2020.108862 10.1111/jfd.13585 10.1111/gcb.15570 10.1016/j.aquatox.2004.03.016 10.1016/j.scitotenv.2022.161191 10.3354/esr01064 10.1002/9781119968535 10.1086/422893 10.1016/j.envpol.2016.11.068 10.1016/S1532-0456(03)00139-X 10.1371/journal.pone.0243404 10.1016/j.envpol.2017.03.062 10.1023/A:1025835031366 10.1007/s00114-016-1372-1 10.1038/s41598-021-81499-8 10.1098/rstb.2009.0053 10.1016/j.jhazmat.2021.127237 10.1016/j.anbehav.2016.10.035 10.1139/z86-221 10.1111/j.1558-5646.1989.tb04212.x 10.3390/w13111575 10.1016/j.scitotenv.2020.142099 U Wilensky author 1999 1999 10.1037/0735-7036.107.3.250 10.1093/beheco/aru183 10.1007/s11160-021-09684-6 10.1016/j.envpol.2022.120939 10.1016/j.jhazmat.2018.07.110 10.1577/T05-316.1 10.3897/ibe.1.e137398 https://ibe.pensoft.net/article/137398/ https://ibe.pensoft.net/article/137398/download/pdf/ https://ibe.pensoft.net/article/137398/download/xml/ 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. text/html en_US Pensoft Publishers Agent-based models (ABMs) behaviour microplastics population abundance salmonids Influence of personality traits on the response of a modelled population of stream-dwelling rainbow trout (Oncorhynchus mykiss) to microplastics consumption Research Article

10.3897/ibe.1.e139560 2025-03-27 ibe

Complutense University of Madrid, Madrid, Spain author Ayllón, Daniel https://orcid.org/0000-0001-7539-5287 Cal Poly Humboldt, Arcata, United States of America Lang Railsback & Associates, Arcata, United States of America author Railsback, Steven https://orcid.org/0000-0002-5923-9847 Pacific Southwest Research Station, USDA Forest Service, Arcata, United States of America author Harvey, Bret C. https://orcid.org/0000-0003-0439-9656 Complutense University of Madrid, Madrid, Spain author Nicola, Graciela G. https://orcid.org/0009-0009-0601-0609 Complutense University of Madrid, Madrid, Spain author Elvira, Benigno https://orcid.org/0000-0002-6127-5302 Complutense University of Madrid, Madrid, Spain author Almodóvar, Ana https://orcid.org/0000-0003-1465-3857 2025-03-27 2025-03-27 2025 Individual-based Ecology 3033-0947 1 e139560 2025 10.1111/j.1365-2486.2011.02608.x 10.1111/faf.12720 10.1038/s41558-021-00994-y 10.1890/12-1200.1 10.1371/journal.pone.0081354 10.1016/j.actao.2012.10.007 10.1016/j.scitotenv.2019.133648 10.6084/m9.figshare.28054238 10.1016/j.ecolmodel.2015.07.026 10.1111/gcb.13135 10.1139/f00-253 10.1139/f07-026 D Bronaugh author 2023 2023 10.1111/eva.12135 10.1111/j.1095-8649.2010.02762.x 10.1002/ece3.2177 10.1139/z95-051 10.1098/rspb.1993.0057 10.1111/gcb.16446 10.1242/jeb.204.16.2861 10.18564/jasss.4259 10.1111/cobi.13857 10.1080/00028487.2013.785978 10.1080/00028487.2012.760485 10.1007/s10641-017-0585-2 10.1080/02755947.2013.860062 10.2307/1940796 10.3390/fishes8070337 10.1007/978-94-007-1189-1 10.3354/ab00707 10.47886/9781934874547.ch20 10.1038/s41597-020-0465-z 10.1093/beheco/aru234 10.1016/j.scitotenv.2013.09.031 10.1111/jfb.14620 10.1098/rspb.1998.0464 10.1046/j.1365-2656.1999.00289.x 10.1002/rra.4073 10.1017/9781108758826.010 10.47886/9781934874547.ch21 10.1007/s11160-017-9476-z 10.1111/gcb.16143 10.1139/cjfas-2020-0475 10.1111/j.1365-2656.2006.01088.x 2023 2023 R Core Team (2023) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. Version 4.2.2. https://www.R-project.org/ 10.1002/rra.3845 10.1111/j.1095-8649.2011.03050.x 10.1002/tafs.10411 10.1093/beheco/araa070 10.1002/tafs.10283 10.2737/PSW-GTR-276 10.1007/978-3-031-44389-3_21 10.1093/biosci/biae064 10.1139/cjfas-2012-0274 10.1139/z03-157 10.1175/BAMS-D-11-00094.1 10.1093/biosci/biaa002 10.1093/acprof:osobl/9780199602568.003.0011 10.3897/ibe.1.e139560 https://ibe.pensoft.net/article/139560/ https://ibe.pensoft.net/article/139560/download/pdf/ https://ibe.pensoft.net/article/139560/download/xml/ 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. text/html en_US Pensoft Publishers Activity selection adaptive behaviour climate change diel activity individual-based modelling resource partitioning salmonids Behavioural plasticity in circadian foraging patterns increases resistance of brown trout populations to environmental change Research Article

10.3897/ibe.1.153305 2025-06-13 ibe

Universite de Pau et des Pays de l’Adour, INRAE, ECOBIOP, Saint-Pée-sur-Nivelle, France University of Helsinki, Helsinki, Finland OFB, INRAE, Universite de Pau et des Pays de l’Adour, Institut-Agro, Saint-Pée-sur-Nivelle, France author Lamarins, Amaïa https://orcid.org/0000-0002-6639-9283 AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France author de Coligny, François https://orcid.org/0000-0002-8538-3009 Universite de Pau et des Pays de l’Adour, INRAE, ECOBIOP, Saint-Pée-sur-Nivelle, France OFB, INRAE, Universite de Pau et des Pays de l’Adour, Institut-Agro, Saint-Pée-sur-Nivelle, France author Labonne, Jacques https://orcid.org/0000-0001-5953-3029 OFB, INRAE, Universite de Pau et des Pays de l’Adour, Institut-Agro, Saint-Pée-sur-Nivelle, France Universite de Pau et des Pays de l’Adour, INRAE, ECOBIOP, Saint-Pée-sur-Nivelle, France author Prévost, Etienne CIRAD, UMR CBGP, Montpellier, France author Piou, Cyril https://orcid.org/0000-0002-9378-9404 University of Helsinki, Helsinki, Finland author Primmer, Craig Robert https://orcid.org/0000-0002-3687-8435 OFB, INRAE, Universite de Pau et des Pays de l’Adour, Institut-Agro, Saint-Pée-sur-Nivelle, France Universite de Pau et des Pays de l’Adour, INRAE, ECOBIOP, Saint-Pée-sur-Nivelle, France author Buoro, Mathieu https://orcid.org/0000-0001-7053-3767 2025-06-13 2025-06-13 2025 Individual-based Ecology 3033-0947 1 e153305 2025 funder European Research Council 10.13039/501100000781 10.3897/ibe.1.153305 https://ibe.pensoft.net/article/153305/ https://ibe.pensoft.net/article/153305/download/pdf/ https://ibe.pensoft.net/article/153305/download/xml/ 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. text/html en_US Pensoft Publishers Demo-Genetic Agent-Based Model Anadromous salmonids Life history strategies Evolution Genetic architecture Phenotypic plasticity Dispersal Metapopulation Management IBASAM: a spatially explicit demo-genetic individual-based model for anadromous salmonids (meta)populations Methods

10.3897/ibe.1.146376 2025-07-07 ibe

Tel Aviv University, Tel Aviv, Israel author Crafton, Miranda Kimron Veterinary Institute, Beit Dagan, Israel author Lublin, Avishai Unaffiliated, Kibbuz Maale Hahamisha, Israel author Cahani, Shay Technical University of Munich, Munich, Germany Institute of Environmental Medicine, Helmholtz Munich, Augsburg, Germany Tel Aviv University, Tel Aviv, Israel author Rauer, Luise Tel Aviv University, Tel Aviv, Israel author Spiegel, Orr https://orcid.org/0000-0001-8941-3175 2025-07-07 2025-07-07 2025 Individual-based Ecology 3033-0947 1 e146376 2025 10.1007/s00248-014-0554-7 10.18637/jss.v067.i01 10.3390/ani11020466 JC Carlson author 2011 2011 10.1111/j.1541-0420.2005.00489.x 10.1111/mec.15398 10.1093/jhered/esab078 10.1098/rstb.2014.0107 10.1098/rspb.2017.1807 10.1016/j.onehlt.2017.07.001 10.1111/ele.12917 10.1371/journal.pone.0222398 10.1128/mSystems.00046-16 10.1093/iob/oby009 10.1098/rspb.2015.3078 10.1093/oso/9780192895561.001.0001 10.1101/623660 10.1371/journal.pone.0209200 10.1016/j.cbpb.2019.110332 10.1371/journal.pone.0217804 10.1016/j.jinf.2003.11.001 10.1016/j.tree.2016.09.012 L-AC Hayek author 1997 1997 10.1093/nar/gkac689 10.1128/mSphere.00382-18 10.1111/all.13220 10.1093/af/vfab045 10.1128/msystems.00567-21 J Larsson author 2024 2024 10.1016/j.ecolmodel.2024.110925 10.1007/s004420100716 10.1038/s41598-019-54869-6 10.1071/MA20013 10.1111/1365-2656.13176 10.1371/journal.pone.0061217 10.1016/j.heliyon.2022.e09732 10.1016/j.scitotenv.2020.142372 10.1073/pnas.0800375105 10.1016/j.heliyon.2023.e14997 J Oksanen author 2025 2025 10.1089/fpd.2012.1347 10.1002/ecm.70000 10.1111/mec.16079 10.1016/j.pt.2015.07.002 10.3201/eid1912.130265 10.1371/journal.pone.0189509 10.1139/z01-202 10.3201/eid2313.170418 10.4257/oeco.2020.2404.03 10.1038/s41559-020-1220-8 10.1093/ornithology/ukae050 10.1139/z95-137 10.1111/ele.12708 10.1093/oso/9780192895561.003.0006 10.1371/journal.pone.0287261 10.3389/fpubh.2018.00235 10.1126/science.aam9712 10.1590/1808-1657000412017 10.1016/j.tree.2016.07.005 10.1894/TAL-11.1 10.1098/rstb.2001.0889 10.1136/vr.157.24.778 10.3897/ibe.1.146376 https://ibe.pensoft.net/article/146376/ https://ibe.pensoft.net/article/146376/download/pdf/ https://ibe.pensoft.net/article/146376/download/xml/ 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. text/html en_US Pensoft Publishers Behavioral feedbacks biotelemetry disease ecology individual variation microbiota movement ecology One Health parasite transmission spatial analysis Association between movement patterns, microbiome diversity, and potential pathogens of feral pigeons in dairy farms Research Article

10.3897/ibe.1.151741 2025-08-08 ibe

University of Potsdam, Potsdam, Germany author Szangolies, Leonna https://orcid.org/0000-0003-0266-1163 Berlin-Brandenburg Institute of Advanced Biodiversity Research, Berlin, Germany University of Potsdam, Potsdam, Germany author Jeltsch, Florian https://orcid.org/https://orcid.org/0000-0002-4670-6469 Roehampton University, London, United Kingdom author Halsey, Lewis https://orcid.org/0000-0002-0786-7585 Aarhus Universit, Roskilde, Denmark author Gallagher, Cara https://orcid.org/0000-0001-7094-1752 2025-08-08 2025-08-08 2025 Individual-based Ecology 3033-0947 1 e151741 2025 10.3897/ibe.1.151741 https://ibe.pensoft.net/article/151741/ https://ibe.pensoft.net/article/151741/download/pdf/ https://ibe.pensoft.net/article/151741/download/xml/ 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. text/html en_US Pensoft Publishers Animal movement energetics biodiversity conservation community theory energy dynamics equalizing and stabilizing mechanisms individual-based ecology From metabolism to coexistence: Understanding animal movement and community dynamics through energy Concepts and Synthesis

10.3897/ibe.2.163207 2026-01-05 ibe

Cranfield University, Bedfordshire, United Kingdom author Gold, Harriet https://orcid.org/https://orcid.org/0000-0003-2256-7596 Cranfield University, Bedfordshire, United Kingdom author Johnston, Alice https://orcid.org/0000-0001-8781-4039 Cranfield University, Bedfordshire, United Kingdom author Rust, Will 2026-01-05 2026-01-05 2026 Individual-based Ecology 3033-0947 2 e163207 2026 10.3897/ibe.2.163207 https://ibe.pensoft.net/article/163207/ https://ibe.pensoft.net/article/163207/download/pdf/ https://ibe.pensoft.net/article/163207/download/xml/ 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. text/html en_US Pensoft Publishers Agent-based modelling behaviour decision support landscape meta-modelling physiology population dynamics scale dependency spatial threshold A conceptual framework for scaling up emergent predictions from mechanistic individual-based models Concepts and Synthesis

10.3897/ibe.2.199762 2026-07-16 ibe

Leiden University, Institute of Environmental Sciences, Leiden, Netherlands author Musters, Kees https://orcid.org/0000-0003-1321-6786 Royal Netherlands Academy of Arts and Sciences, Amsterdam, Netherlands Leiden University, Institute of Environmental Sciences, Leiden, Netherlands author de Snoo, Geert R. https://orcid.org/0000-0002-5471-0425 2026-07-16 2026-07-16 2026 Individual-based Ecology 3033-0947 2 e199762 2026 10.3897/ibe.2.199762 https://ibe.pensoft.net/article/199762/ https://ibe.pensoft.net/article/199762/download/pdf/ https://ibe.pensoft.net/article/199762/download/xml/ 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. text/html en_US Pensoft Publishers Deep Learning individual-based information processing organism-based predicting communities sensory instruments training data Challenges and opportunities for Artificial Intelligence for predicting changes in ecological communities Methods

10.3897/ibe.2.185721 2026-07-21 ibe

Institute of Environmental Systems Research, University of Osnabrück, Osnabrück, Germany author Lammers, Dominik https://orcid.org/0009-0007-7997-4279 Université Paris-Saclay, CNRS, IRD, UMR Évolution, Génomes, Comportement et Écologie, Gif-sur-Yvette, France author Requier, Fabrice https://orcid.org/0000-0003-1638-3141 Institute of Environmental Systems Research, University of Osnabrück, Osnabrück, Germany author Focks, Andreas https://orcid.org/0000-0002-9031-0808 Helmholtz Centre for Environmental Research – UFZ, Department of Ecological Modelling, Leipzig, Germany author Groeneveld, Jürgen https://orcid.org/0000-0003-2338-4636 2026-07-21 2026-07-21 2026 Individual-based Ecology 3033-0947 2 e185721 2026 10.3897/ibe.2.185721 https://ibe.pensoft.net/article/185721/ https://ibe.pensoft.net/article/185721/download/pdf/ https://ibe.pensoft.net/article/185721/download/xml/ 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. text/html en_US Pensoft Publishers BEEHAVE brood pheromones egg-laying emergence honey bees multiple stressors pollen storage Food for thought: could the queen's egg-laying rate in the BEEHAVE honey bee model emerge from the effects of brood pheromones, weather conditions, and pollen availability? Research Article

10.3897/ibe.2.174844 2026-07-30 ibe

Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, United States of America author Crouse, Kristin https://orcid.org/https://orcid.org/0000-0002-4878-7477 Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, United States of America Institute on the Environment, University of Minnesota, Saint Paul, United States of America author Lehman, Clarence https://orcid.org/0000-0002-1369-1205 Institute on the Environment, University of Minnesota, Saint Paul, United States of America Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, United States of America author Wilson, Michael https://orcid.org/0000-0003-3073-4518 2026-07-30 2026-07-30 2026 Individual-based Ecology 3033-0947 2 e174844 2026 Abegglen J (1984) On socialization in Hamadryas baboons. Associated University Presses. 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In: Oxford Research Encyclopedia of Environmental Science. 10.1038/scientificamerican1170-116 Grimm V, Railsback SF (2005) Princeton University Press Individual-based Modeling and Ecology. Princeton University Press. 10.1007/s10021-016-0071-2 10.18564/jasss.4259 10.1098/rspb.2021.0839 10.1098/rspb.1999.0646 Hogeweg P (1987) MIRROR Beyond MIRROR: Puddles of LIFE. Alife: 297–316. Holland J (1995) Hidden Order: How Adaptation Builds Complexity. 10.21203/rs.3.rs-6009246/v1 10.2307/1310870 10.1016/j.tree.2021.01.005 10.1007/BF02381450 Krebs J, Davies N (1993) An introduction to behavioural ecology. Blackwell Scientific Publications. 10.1159/isbn.978-3-8055-0286-3 10.1002/ajp.20916 10.1016/j.ecolmodel.2018.10.006 Lotka AJ (1925) Elements of Physical Biology. Williams & Wilkins, 508 pp. 10.1126/science.1238677 10.1086/282454 10.1098/rstb.2019.0104 10.18564/jasss.4174 10.1007/s00265-013-1510-0 10.1007/978-1-4684-7862-4_22 10.1093/oso/9780198815778.001.0001 10.1002/evan.20229 10.22541/au.173679707.75075267/v1 10.1073/pnas.1307903110 10.1080/1747423X.2015.1030463 10.1016/0003-3472(79)90126-X 10.1016/j.anbehav.2012.06.021 10.1007/BF02547828 10.1002/ajp.20951 10.1016/S0003-3472(80)80063-7 10.1016/0167-2789(94)90286-0 10.1002/ajpa.22076 10.1002/ajp.20736 10.1007/s10764-012-9584-5 10.1111/mec.12987 10.2307/2389364 10.1126/science.1259855 10.1093/biosci/biu192 10.4159/9780674054349-010 10.1002/ajpa.21504 10.1002/ecs2.1436 10.1163/156853994X00541 10.1007/s10764-013-9733-5 10.1038/118558a0 10.1016/j.cub.2019.11.081 10.1098/rsos.230967 Wilensky U (1999) NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo/ 10.1006/anbe.2001.1916 10.1146/annurev.anthro.32.061002.120046 10.1038/nature13727 10.1177/053901847901800301 10.1002/(SICI)1096-8644(1999)110:29+%3C1::AID-AJPA2%3E3.0.CO;2-E 10.1038/s41598-025-19816-8 10.1111/j.1600-0706.2009.18284.x 10.3897/ibe.2.174844 https://ibe.pensoft.net/article/174844/ https://ibe.pensoft.net/article/174844/download/pdf/ https://ibe.pensoft.net/article/174844/download/xml/ 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. text/html en_US Pensoft Publishers Agent-based modeling animal behavior behavioral ecology individual-based modeling primate societies spatially explicit stimulus-response rules virtual evolution Simulating the socioecology of virtual primates using B3GET: a generalized behavioral modeling tool Methods

10.3897/ibe.2.203247 2026-08-18 ibe

Helmholtz Centre for Environmental Research – UFZ, Department of Computational Landscape Ecology, Leipzig, Germany author Takola, Elina https://orcid.org/0000-0003-1268-5513 2026-08-18 2026-08-18 2026 Individual-based Ecology 3033-0947 2 e203247 2026 10.1111/j.2007.0906-7590.05236.x 10.1093/icb/icz084 10.1007/s00442-007-0687-1 10.1111/j.1461-0248.2011.01662.x 10.1111/ele.13327 10.1242/jeb.247981 10.1111/ecog.03187 10.32614/CRAN.package.hypervolume 10.1016/j.biocon.2025.111074 10.1890/0012-9658(2002)083%5B2936:MILRS%5D2.0.CO;2 10.1086/343878 10.1073/pnas.0703743104 10.1016/S0169-5347(99)01593-1 10.1111/1365-2656.12359 10.1093/beheco/arac040 10.1016/j.anbehav.2024.10.005 10.1111/1365-2656.13358 10.1111/1365-2656.14037 10.1186/s12862-025-02447-y 10.1016/j.cub.2026.05.041 10.1038/s41467-021-24826-x 10.1086/710388 10.1111/brv.12007 10.1002/ece3.5834 10.1098/rspb.2019.0369 10.1186/s12898-018-0205-9 10.1007/s10530-019-02037-2 10.1111/1365-2656.12144 10.1098/rspb.2004.2680 10.1016/j.tree.2009.07.013 10.5962/bhl.title.7435 10.1111/2041-210x.70263 10.1002/oik.11461 10.32942/X2NS8G 10.1098/rstb.2010.0079 10.1098/rstb.2010.0085 10.1016/S0006-3207(02)00156-8 10.32942/X2ZS88 10.1111/gcb.16486 Grinnell J (1928) Joseph Grinnell’s Philosophy of Nature: Selected Writings of a Western Naturalist, Published in Commemoration of the Seventy-Fifth Anniversary of the Founding of the University of California, Reprint 2020. ed. University of California Press, Berkeley, CA. 10.1111/jzo.13039 10.1093/evlett/qrad004 10.1111/oik.10994 10.1007/BF02071557 Hurlbert S (1981) A gentle depilation of the niche: Dicean resource sets in resource hyperspace. Evolutionary Theory 5: 177–184. 10.1101/SQB.1957.022.01.039 Hutchinson GE (1978) An introduction to population ecology, 4. print. ed. 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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. text/html en_US Pensoft Publishers glmmTMB individual specialization intraspecific resource partitioning Jaccard similarity landscape ecology Movebank remote sensing species–habitat relationships The individualized niche in motion: Quantifying individual specialisation with movement data Methods

10.3897/ibe.2.176474 2026-10-01 ibe

Department of Environmental Science and Policy, George Mason University, Fairfax, United States of America Irish Basking Shark Group, Donegal, Ireland author Gray, Chelsea https://orcid.org/0000-0001-9441-7242 Department of Computational & Data Sciences, George Mason University, Fairfax, United States of America author Rothman, Dale https://orcid.org/0000-0003-1479-2849 College of Education and Human Development, George Mason University, Fairfax, United States of America author Peters-Burton, Erin https://orcid.org/0000-0003-1233-9119 Department of Environmental Science and Policy, George Mason University, Fairfax, United States of America author Smith, Cynthia Centre for Ecology & Conservation, Penryn Campus, University of Exeter, Cornwall, United Kingdom author Parsons, E.C.M https://orcid.org/0000-0002-0464-1046 The Jackson Laboratory for Mammalian Genetics, Bar Harbor, United States of America Graduate School of Biomedical Sciences, Tufts University, Boston, United States of America author Garretson, Alexis https://orcid.org/0000-0002-7260-0131 2026-10-01 2026-10-01 2026 Individual-based Ecology 3033-0947 2 e176474 2026 10.1007/s00227-011-1643-5 10.1016/j.seares.2019.101767 10.31979/etd.zh8m-jsdr Berrow S, Heardman C (1994) The Basking Shark Cetorhinusmaximus (Gunnerus) in Irish Waters: Patterns of Distribution and Abundance. Biology and Environment: Proceedings of the Royal Irish Academy 94B: 101–107. 10.1007/s10641-025-01706-7 10.3389/fmars.2018.00025 10.1016/j.seares.2015.06.009 10.3389/fmars.2024.1430961 10.1038/s41598-019-43078-w 10.1159/000339870 10.1111/j.1365-2419.2005.00331.x 10.1111/jfb.13592 10.1111/jfb.15858 Doherty PD (2017) Basking shark movement ecology in the North-East Atlantic [ProQuest Dissertations Publishing]. http://search.proquest.com/docview/2188989313/?pq-origsite=primo https://doi.org/10.1038/srep42837 10.1016/j.biocon.2017.01.018 10.1201/b22502 10.1038/s41598-019-41212-2 10.3389/fmars.2020.00653 10.1371/journal.pone.0348589 10.3354/meps10724 10.1016/S0169-5347(99)01616-X 10.1007/s10071-014-0815-3 10.5285/A29C5465-B138-234D-E053-6C86ABC040B9 Google (2013) Our mobile planet: Ireland. 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The Shark Trust (2025) Basking Shark Data Directory [Directory]. https://www.sharktrust.org/basking-shark-data-directory 10.1186/s40317-024-00370-5 10.1016/0169-5347(96)20091-6 10.3389/fmars.2016.00157 10.3897/ibe.2.176474 https://ibe.pensoft.net/article/176474/ https://ibe.pensoft.net/article/176474/download/pdf/ https://ibe.pensoft.net/article/176474/download/xml/ 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. text/html en_US Pensoft Publishers Basking sharks Individual-based model marine conservation shark behavior An individual-based model of basking sharks in Ireland Research Article