TY - JOUR A1 - Romero-Mujalli, Daniel A1 - Jeltsch, Florian A1 - Tiedemann, Ralph T1 - Individual-based modeling of eco-evolutionary dynamics BT - state of the art and future directions JF - Regional environmental change N2 - A challenge for eco-evolutionary research is to better understand the effect of climate and landscape changes on species and their distribution. Populations of species can respond to changes in their environment through local genetic adaptation or plasticity, dispersal, or local extinction. The individual-based modeling (IBM) approach has been repeatedly applied to assess organismic responses to environmental changes. IBMs simulate emerging adaptive behaviors from the basic entities upon which both ecological and evolutionary mechanisms act. The objective of this review is to summarize the state of the art of eco-evolutionary IBMs and to explore to what degree they already address the key responses of organisms to environmental change. In this, we identify promising approaches and potential knowledge gaps in the implementation of eco-evolutionary mechanisms to motivate future research. Using mainly the ISI Web of Science, we reveal that most of the progress in eco-evolutionary IBMs in the last decades was achieved for genetic adaptation to novel local environmental conditions. There is, however, not a single eco-evolutionary IBM addressing the three potential adaptive responses simultaneously. Additionally, IBMs implementing adaptive phenotypic plasticity are rare. Most commonly, plasticity was implemented as random noise or reaction norms. Our review further identifies a current lack of models where plasticity is an evolving trait. Future eco-evolutionary models should consider dispersal and plasticity as evolving traits with their associated costs and benefits. Such an integrated approach could help to identify conditions promoting population persistence depending on the life history strategy of organisms and the environment they experience. KW - Modeling KW - Individual-based models KW - Ecology KW - Evolution KW - Eco-evolutionary dynamics Y1 - 2018 U6 - https://doi.org/10.1007/s10113-018-1406-7 SN - 1436-3798 SN - 1436-378X VL - 19 IS - 1 SP - 1 EP - 12 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Schneider, Anne-Kathrin A1 - Schröder-Esselbach, Boris T1 - Perspectives in modelling earthworm dynamics and their feedbacks with abiotic soil properties JF - Applied soil ecology : a section of agriculture, ecosystems & environment N2 - Effects of earthworms on soil abiotic properties are well documented from several decades of laboratory and mesocosm experiments, and they are supposed to affect large-scale soil ecosystem functioning. The prediction of the spatiotemporal occurrence of earthworms and the related functional effects in the field or at larger scales, however, is constrained by adequate modelling approaches. Correlative, phenomenological methods, such as species distribution models, facilitate the identification of factors that drive species' distributions. However, these methods ignore the ability of earthworms to select and modify their own habitat and therefore may lead to unreliable predictions. Understanding these feedbacks between earthworms and abiotic soil properties is a key requisite to better understand their spatiotemporal distribution as well as to quantify the various functional effects of earthworms in soil ecosystems. Process-based models that investigate either effects or responses of earthworms on soil environmental conditions are mostly applied in ecotoxicological and bioturbation studies. Process-based models that describe feedbacks between earthworms and soil abiotic properties explicitly are rare. In this review, we analysed 18 process-based earthworm dynamic modelling studies pointing out the current gaps and future challenges in feedback modelling. We identify three main challenges: (i) adequate and reliable process identification in model development at and across relevant spatiotemporal scales (individual behaviour and population dynamics of earthworms), (ii) use of information from different data sources in one model (laboratory or field experiments, earthworm species or functional type) and (iii) quantification of uncertainties in data (e.g. spatiotemporal variability of earthworm abundances and soil hydraulic properties) and derived parameters (e.g. population growth rate and hydraulic conductivity) that are used in the model. KW - Oligochaeta KW - Feedback biotic-abiotic KW - Functional effect KW - Population dynamics KW - Modeling KW - Ecosystem engineer Y1 - 2012 U6 - https://doi.org/10.1016/j.apsoil.2012.02.020 SN - 0929-1393 VL - 58 IS - 1 SP - 29 EP - 36 PB - Elsevier CY - Amsterdam ER -