28 citations as recorded by crossref.

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3. An individual‐based forest model to jointly simulate carbon and tree diversity in Amazonia: description and applications I. Maréchaux & J. Chave https://doi.org/10.1002/ecm.1271
4. Use of inverse modelling and Bayesian optimization for investigating the effect of biochar on soil hydrological properties H. Dokoohaki et al. https://doi.org/10.1016/j.agwat.2018.06.034
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6. Predicting the abundance of forest types across the eastern United States through inverse modelling of tree demography M. Vanderwel et al. https://doi.org/10.1002/eap.1596
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8. Community dynamics under environmental change: How can next generation mechanistic models improve projections of species distributions? A. Singer et al. https://doi.org/10.1016/j.ecolmodel.2015.11.007
9. Quo vadis, agent-based modelling tools? A. Daly et al. https://doi.org/10.1016/j.envsoft.2022.105514
10. Towards Process-based Range Modeling of Many Species M. Evans et al. https://doi.org/10.1016/j.tree.2016.08.005
11. The Latitudinal Diversity Gradient: Novel Understanding through Mechanistic Eco-evolutionary Models M. Pontarp et al. https://doi.org/10.1016/j.tree.2018.11.009
12. Simulating Carbon Stocks and Fluxes of an African Tropical Montane Forest with an Individual-Based Forest Model R. Fischer et al. https://doi.org/10.1371/journal.pone.0123300
13. Predicting frugivore generated seed rain in different environmental contexts: a modelling approach applied to a forest specialist E. Zanette et al. https://doi.org/10.1016/j.ecolmodel.2025.111093
14. From theory to practice in pattern‐oriented modelling: identifying and using empirical patterns in predictive models C. Gallagher et al. https://doi.org/10.1111/brv.12729
15. Assessing the response of forest productivity to climate extremes in Switzerland using model–data fusion V. Trotsiuk et al. https://doi.org/10.1111/gcb.15011
16. Bayesian calibration of a growth‐dependent tree mortality model to simulate the dynamics of European temperate forests M. Cailleret et al. https://doi.org/10.1002/eap.2021
17. Integration of tree hydraulic processes and functional impairment to capture the drought resilience of a semiarid pine forest D. Nadal-Sala et al. https://doi.org/10.5194/bg-21-2973-2024
18. Variational Bayes with synthetic likelihood V. Ong et al. https://doi.org/10.1007/s11222-017-9773-3
19. An extended empirical saddlepoint approximation for intractable likelihoods M. Fasiolo et al. https://doi.org/10.1214/18-EJS1433
20. A simulation method to infer tree allometry and forest structure from airborne laser scanning and forest inventories F. Fischer et al. https://doi.org/10.1016/j.rse.2020.112056
21. Paleorecords Reveal Biological Mechanisms Crucial for Reliable Species Range Shift Projections Amid Rapid Climate Change V. Van der Meersch et al. https://doi.org/10.1111/ele.70080
22. Which demographic processes control competitive equilibria? Bayesian calibration of a size‐structured forest population model L. Heiland et al. https://doi.org/10.1002/ece3.10232
23. Productivity of Fagus sylvatica under climate change – A Bayesian analysis of risk and uncertainty using the model 3-PG A. Augustynczik et al. https://doi.org/10.1016/j.foreco.2017.06.061
24. Tackling unresolved questions in forest ecology: The past and future role of simulation models I. Maréchaux et al. https://doi.org/10.1002/ece3.7391
25. Predicting how many animals will be where: How to build, calibrate and evaluate individual-based models E. van der Vaart et al. https://doi.org/10.1016/j.ecolmodel.2015.08.012
26. Approximate Bayesian computation to recalibrate individual-based models with population data: Illustration with a forest simulation model G. Lagarrigues et al. https://doi.org/10.1016/j.ecolmodel.2014.09.023
27. Global warming likely to enhance black locust (Robinia pseudoacacia L.) growth in a Mediterranean riparian forest D. Nadal-Sala et al. https://doi.org/10.1016/j.foreco.2019.117448
28. Bayesian parameter inference for individual-based models using a Particle Markov Chain Monte Carlo method M. Kattwinkel & P. Reichert https://doi.org/10.1016/j.envsoft.2016.11.001