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Towards a system-level causative knowledge of pollinator communities

View ORCID ProfileSerguei Saavedra, View ORCID ProfileIgnasi Bartomeus, View ORCID ProfileOscar Godoy, View ORCID ProfileRudolf P. Rohr, View ORCID ProfilePenguan Zu
doi: https://doi.org/10.1101/2021.09.23.461517
Serguei Saavedra
1Department of Civil and Environmental Engineering, MIT, 77 Massachusetts Av., 02139 Cambridge, MA, USA
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  • For correspondence: sersaa@mit.edu
Ignasi Bartomeus
2Estación Biológica de Doñana (EBD-CSIC), Seville, Spain
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Oscar Godoy
3Departamento de Biología, Instituto Universitario de Ciencias del Mar (INMAR), Universidad de Cádiz, E-11510, Royal Port, Spain
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Rudolf P. Rohr
4Department of Biology - Ecology and Evolution, University of Fribourg, Chemin du Musée 10, CH-1700 Fribourg, Switzerland
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Penguan Zu
5Department of Environmental Systems Science, ETH Zurich, Schmelzbergstrasse 9, CH-8092, Zurich, Switzerland
6Department Fish Ecology & Evolution, Swiss Federal Institute of Aquatic Science and Technology (Eawag), Seestrasse 79, CH-6047, Kastanienbaum, Switzerland
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Abstract

Pollination plays a central role both in the maintenance of biodiversity and in crop production. However, habitat loss, pesticides, invasive species, and larger environmental fluctuations are contributing to a dramatic decline of numerous pollinators world-wide. This has increased the need for interventions to protect the composition, functioning, and dynamics of pollinator communities. Yet, how to make these interventions successful at the system level remains extremely challenging due to the complex nature of species interactions and the various unknown or unmeasured confounding ecological factors. Here, we propose that this knowledge can be derived by following a probabilistic causal analysis of pollinator communities. This analysis implies the inference of interventional expectations from the integration of observational and synthetic data. We propose that such synthetic data can be generated using theoretical models that can enable the tractability and scalability of unseen confounding ecological factors affecting the behavior of pollinator communities. We discuss a road map for how this probabilistic causal analysis can be accomplished to increase our system-level causative knowledge of natural communities.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/MITEcology/Saavedra_etal_causal_example

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 24, 2021.
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Towards a system-level causative knowledge of pollinator communities
Serguei Saavedra, Ignasi Bartomeus, Oscar Godoy, Rudolf P. Rohr, Penguan Zu
bioRxiv 2021.09.23.461517; doi: https://doi.org/10.1101/2021.09.23.461517
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Towards a system-level causative knowledge of pollinator communities
Serguei Saavedra, Ignasi Bartomeus, Oscar Godoy, Rudolf P. Rohr, Penguan Zu
bioRxiv 2021.09.23.461517; doi: https://doi.org/10.1101/2021.09.23.461517

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