Novel model coupling approach for resilience analysis of coastal plant communities
- Resilience is a major research focus covering a wide range of topics from biodiversity conservation to ecosystem (service) management. Model simulations can assess the resilience of, for example, plant species, measured as the return time to conditions prior to a disturbance. This requires process-based models (PBM) that implement relevant processes such as regeneration and reproduction and thus successfully reproduce transient dynamics after disturbances. Such models are often complex and thus limited to either short-term or small-scale applications, whereas many research questions require species predictions across larger spatial and temporal scales. We suggest a framework to couple a PBM and a statistical species distribution model (SDM), which transfers the results of a resilience analysis by the PBM to SDM predictions. The resulting hybrid model combines the advantages of both approaches: the convenient applicability of SDMs and the relevant process detail of PBMs in abrupt environmental change situations. First, we simulateResilience is a major research focus covering a wide range of topics from biodiversity conservation to ecosystem (service) management. Model simulations can assess the resilience of, for example, plant species, measured as the return time to conditions prior to a disturbance. This requires process-based models (PBM) that implement relevant processes such as regeneration and reproduction and thus successfully reproduce transient dynamics after disturbances. Such models are often complex and thus limited to either short-term or small-scale applications, whereas many research questions require species predictions across larger spatial and temporal scales. We suggest a framework to couple a PBM and a statistical species distribution model (SDM), which transfers the results of a resilience analysis by the PBM to SDM predictions. The resulting hybrid model combines the advantages of both approaches: the convenient applicability of SDMs and the relevant process detail of PBMs in abrupt environmental change situations. First, we simulate dynamic responses of species communities to a disturbance event with a PBM. We aggregate the response behavior in two resilience metrics: return time and amplitude of the response peak. These metrics are then used to complement long-term SDM projections with dynamic short-term responses to disturbance. To illustrate our framework, we investigate the effect of abrupt short-term groundwater level and salinity changes on coastal vegetation at the German Baltic Sea. We found two example species to be largely resilient, and, consequently, modifications of SDM predictions consisted mostly of smoothing out peaks in the occurrence probability that were not confirmed by the PBM. Discrepancies between SDM- and PBM-predicted species responses were caused by community dynamics simulated in the PBM and absent from the SDM. Although demonstrated with boosted regression trees (SDM) and an existing individual-based model, IBC-grass (PBM), our flexible framework can easily be applied to other PBM and SDM types, as well as other definitions of short-term disturbances or long-term trends of environmental change. Thus, our framework allows accounting for biological feedbacks in the response to short- and long-term environmental changes as a major advancement in predictive vegetation modeling.…
Author details: | Anett SchibalskiORCiDGND, Katrin Körner, Martin MaierORCiD, Florian JeltschORCiDGND, Boris SchröderORCiDGND |
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DOI: | https://doi.org/10.1002/eap.1758 |
ISSN: | 1051-0761 |
ISSN: | 1939-5582 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/29862603 |
Title of parent work (English): | Ecological applications : a publication of the Ecological Society of America |
Publisher: | Wiley |
Place of publishing: | Hoboken |
Publication type: | Article |
Language: | English |
Date of first publication: | 2018/06/04 |
Publication year: | 2018 |
Release date: | 2021/10/08 |
Tag: | Baltic Sea; Lolium perenne; Scirpus maritimus; hybrid model; model coupling; transient dynamics |
Volume: | 28 |
Issue: | 6 |
Number of pages: | 15 |
First page: | 1640 |
Last Page: | 1654 |
Funding institution: | German Federal Ministry of Education and ResearchFederal Ministry of Education & Research (BMBF) [01LL0911] |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie |
DDC classification: | 5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie |
Peer review: | Referiert |