Acknowledgements This model was initially designed during the Purple Patch Pumpkin meeting in the Corbières (France) in October 2018 and we thank all participants for fruitful discussions. Funding AP was funded by a European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 753420 (EcoEvoProspectS project). ; Peer reviewed ; Publisher PDF
International audience Bioturbation is one of the most widespread forms of ecological engineering and has significant implications for the structure and functioning of ecosystems, yet our understanding of the processes involved in biotic mixing remains incomplete. One reason is that, despite their value and utility, most mathematical models currently applied to bioturbation data tend to neglect aspects of the natural complexity of bioturbation in favour of mathematical simplicity. At the same time, the abstract nature of these approaches limits the application of such models to a limited range of users. Here, we contend that a movement towards process-based modelling can improve both the representation of the mechanistic basis of bioturbation and the intuitiveness of modelling approaches. In support of this initiative, we present an open source modelling framework that explicitly simulates particle displacement and a worked example to facilitate application and further development. The framework combines the advantages of rule-based lattice models with the application of parameterisable probability density functions to generate mixing on the lattice. Model parameters can be fitted by experimental data and describe particle displacement at the spatial and temporal scales at which bioturbation data is routinely collected. By using the same model structure across species, but generating species-specific parameters, a generic understanding of species-specific bioturbation behaviour can be achieved. An application to a case study and comparison with a commonly used model attest the predictive power of the approach.
Funding. Marie Sklodowska-Curie grant, grant/Award no.: 661211 and NERC, grant/Award no. NE/J008001/1.7881299 Acknowledgements. The research was authorized by the Kenyan government (NCST 5/002/R/274/4). Ministry for Foreign Affairs of Finland and Academy of Finland for funding BIODEV and TAITAWATER. Research permit from NACOSTI (no. P/18/97336/26355) for SMARTLAND funded by Academy of Finland (no. 318645). ; Peer reviewed ; Postprint
ACKNOWLEDGMENTS AP was funded by a European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 753420 (EcoEvoProspectS project). JMJT and AS were funded by the Biotechnology and Biological Sciences Research Council, project grant BB/S507349/1. ; Peer reviewed ; Publisher PDF
JMJT, RCH and SCFP were funded by NERC (NE/J008001/1). RJM was funded by the Scottish Government's Rural and Environment Science and Analytical Services (RESAS) research program. This project also benefited from funding provided by the Woodland Trust (A13246). KW was supported with funding from the Forestry Commission. ; Peer reviewed ; Publisher PDF
This study was supported by a VLIR-VLADOC scholarship and funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 661211 awarded to JA, research grants G.0055.08 G.0149.09 and G.0308.13 of Research Foundation Flanders (FWO), and the FWO Research Network on Eco-Evolutionary dynamics. SCFP and JMJT were supported by the project TenLamas funded by the French Ministère de l'Energie, de l'Ecologie, du Développement Durable et de la Mer through the EU FP6 BiodivERsA Eranet and by NERC grant NE/J008001/1. GB was supported by the SCALES project (www.scales-project.net). We acknowledge the Taita Research Station of the University of Helsinki for logistic support, and Taita field assistants and students from Ghent University for their help with data collection. ; Peer reviewed ; Publisher PDF
Acknowledgements Project CONTAIN is funded under the Latin American Biodiversity Programme as part of the Newton Fund (NE/S011641/1), with contributions from NERC, the Argentine National Scientific & Technical Research Council (CONICET,-2019- 74-APN-DIR#CONICET), the Brazilian São Paulo Research Foundation (FAPESP 2018/14995-8), the Chilean National Commission for Scientific & Technological Research (CONICYT). AP is supported by CONICYT PIA AFB170008. AF receives grant from CNPq (303988/2018-5), GD receives grant from FAPESP (2018/09054-0). The mink control program "Control Comunitario del Vison" is funded by the regional FNDR Funds, BIP 30484635-0, with the support of the regional government council. Yellowjacket wasp control receive support from municipality of Valdivia. ; Peer reviewed ; Publisher PDF
