Revealing perturbation responses with limited observations of biological communities
Restrictions in empirical research of biological communities have limited our understanding of the combined influence of environmental variability and system structure on community composition. Spatial patterns of community composition in less accessible systems, such as marine benthos, can often no...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/6e1068e966134289971720a668124848 |
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| Sumario: | Restrictions in empirical research of biological communities have limited our understanding of the combined influence of environmental variability and system structure on community composition. Spatial patterns of community composition in less accessible systems, such as marine benthos, can often not be explained by many factors beyond the direct impact of the environment on community members. We present a method that combines commonly collected data of community composition with analyses of qualitative mathematical models, to assess not only direct impacts of environmental variability, but also the propagation of impacts through complex interaction networks. Transformed spatial data of community composition describe the community members’ observed similarity of response to an external input. The output of qualitative mathematical models describes the community members’ predicted similarity of response to input entering the system through any of its variables. A statistically significant agreement between the observed and any of the predicted response similarities indicates the respective system variable as a likely gateway for environmental variability into the system. The method is applied to benthic macroinvertebrate communities in the Rance estuary (Brittany, France). Organisms identified as likely gateways have traits that agree with their predicted response to documented spatially and temporally structured environmental variability. We suggest use of this novel framework for more comprehensive identification of environmental drivers of community change, including gateway community members and cascades of environmentally driven change through community structure. |
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