Direct input of monitoring data into a mechanistic ecological model as a way to identify the phytoplankton growth-rate response to temperature variations
Abstract We present an approach (knowledge-and-data-driven, KDD, modeling) that allows us to get closer to understanding the processes that affect the dynamics of plankton communities. This approach, based on the use of time series obtained as a result of ecosystem monitoring, combines the key featu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-36950-3 |
| _version_ | 1797795704940265472 |
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| author | Alexander B. Medvinsky Nailya I. Nurieva Boris V. Adamovich Nataly P. Radchikova Alexey V. Rusakov |
| author_facet | Alexander B. Medvinsky Nailya I. Nurieva Boris V. Adamovich Nataly P. Radchikova Alexey V. Rusakov |
| author_sort | Alexander B. Medvinsky |
| collection | DOAJ |
| description | Abstract We present an approach (knowledge-and-data-driven, KDD, modeling) that allows us to get closer to understanding the processes that affect the dynamics of plankton communities. This approach, based on the use of time series obtained as a result of ecosystem monitoring, combines the key features of both the knowledge-driven modeling (mechanistic models) and data-driven (DD) modeling. Using a KDD model, we reveal the phytoplankton growth-rate fluctuations in the ecosystem of the Naroch Lakes and determine the degree of phase synchronization between fluctuations in the phytoplankton growth rate and temperature variations. More specifically, we estimate a numerical value of the phase locking index (PLI), which allows us to assess how temperature fluctuations affect the dynamics of phytoplankton growth rates. Since, within the framework of KDD modeling, we directly include the time series obtained as a result of field measurements in the model equations, the dynamics of the phytoplankton growth rate obtained from the KDD model reflect the behavior of the lake ecosystem as a whole, and PLI can be considered as a holistic parameter. |
| first_indexed | 2024-03-13T03:22:03Z |
| format | Article |
| id | doaj.art-46cea2ed237b428b9866393d442491fe |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T03:22:03Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-46cea2ed237b428b9866393d442491fe2023-06-25T11:15:43ZengNature PortfolioScientific Reports2045-23222023-06-0113111010.1038/s41598-023-36950-3Direct input of monitoring data into a mechanistic ecological model as a way to identify the phytoplankton growth-rate response to temperature variationsAlexander B. Medvinsky0Nailya I. Nurieva1Boris V. Adamovich2Nataly P. Radchikova3Alexey V. Rusakov4Institute of Theoretical and Experimental Biophysics, Russian Academy of SciencesInstitute of Theoretical and Experimental Biophysics, Russian Academy of SciencesInstitute of Theoretical and Experimental Biophysics, Russian Academy of SciencesInstitute of Theoretical and Experimental Biophysics, Russian Academy of SciencesInstitute of Theoretical and Experimental Biophysics, Russian Academy of SciencesAbstract We present an approach (knowledge-and-data-driven, KDD, modeling) that allows us to get closer to understanding the processes that affect the dynamics of plankton communities. This approach, based on the use of time series obtained as a result of ecosystem monitoring, combines the key features of both the knowledge-driven modeling (mechanistic models) and data-driven (DD) modeling. Using a KDD model, we reveal the phytoplankton growth-rate fluctuations in the ecosystem of the Naroch Lakes and determine the degree of phase synchronization between fluctuations in the phytoplankton growth rate and temperature variations. More specifically, we estimate a numerical value of the phase locking index (PLI), which allows us to assess how temperature fluctuations affect the dynamics of phytoplankton growth rates. Since, within the framework of KDD modeling, we directly include the time series obtained as a result of field measurements in the model equations, the dynamics of the phytoplankton growth rate obtained from the KDD model reflect the behavior of the lake ecosystem as a whole, and PLI can be considered as a holistic parameter.https://doi.org/10.1038/s41598-023-36950-3 |
| spellingShingle | Alexander B. Medvinsky Nailya I. Nurieva Boris V. Adamovich Nataly P. Radchikova Alexey V. Rusakov Direct input of monitoring data into a mechanistic ecological model as a way to identify the phytoplankton growth-rate response to temperature variations Scientific Reports |
| title | Direct input of monitoring data into a mechanistic ecological model as a way to identify the phytoplankton growth-rate response to temperature variations |
| title_full | Direct input of monitoring data into a mechanistic ecological model as a way to identify the phytoplankton growth-rate response to temperature variations |
| title_fullStr | Direct input of monitoring data into a mechanistic ecological model as a way to identify the phytoplankton growth-rate response to temperature variations |
| title_full_unstemmed | Direct input of monitoring data into a mechanistic ecological model as a way to identify the phytoplankton growth-rate response to temperature variations |
| title_short | Direct input of monitoring data into a mechanistic ecological model as a way to identify the phytoplankton growth-rate response to temperature variations |
| title_sort | direct input of monitoring data into a mechanistic ecological model as a way to identify the phytoplankton growth rate response to temperature variations |
| url | https://doi.org/10.1038/s41598-023-36950-3 |
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