Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1038/s41598-019-50373-z
Licence creative commons licence
Title (Primary) An approach to study species persistence in unconstrained random networks
Author Fischer, S.M.; Huth, A.
Journal Scientific Reports
Year 2019
Department OESA; iDiv
Volume 9
Page From art. 14110
Language englisch
Supplements https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-019-50373-z/MediaObjects/41598_2019_50373_MOESM1_ESM.pdf
Abstract The connection between structure and stability of ecological networks has been widely studied in the last fifty years. A challenge that scientists continue to face is that in-depth mathematical model analysis is often difficult, unless the considered systems are specifically constrained. This makes it challenging to generalize results. Therefore, methods are needed that relax the required restrictions. Here, we introduce a novel heuristic approach that provides persistence estimates for random systems without limiting the admissible parameter range and system behaviour. We apply our approach to study persistence of species in random generalized Lotka-Volterra systems and present simulation results, which confirm the accuracy of our predictions. Our results suggest that persistence is mainly driven by the linkage density, whereby additional links can both favour and hinder persistence. In particular, we observed ‚Äúpersistence bistability‚ÄĚ, a rarely studied feature of random networks, leading to a dependency of persistence on initial species densities. Networks with this property exhibit tipping points, in which species loss can lead to a cascade of extinctions. The methods developed in this paper may facilitate the study of more general models and thereby provide a step forward towards a unifying framework of network architecture and stability.
Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=22360
Fischer, S.M., Huth, A. (2019):
An approach to study species persistence in unconstrained random networks
Sci. Rep. 9 , art. 14110