Abstract
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.
Highlights
Fluctuating abiotic conditions, known as environmental vari ability, and interactions among community members, outlined as system structure, are central to the study of biological communities
Indication of environmental variability based on its direct effects on a community is not robust against con founding spatial and temporal dynamics controlled by community in teractions (James and McCulloch, 1990; Gotelli et al, 2009)
We provide an example application of this framework based on recent functional grouping and qualitative modelling of benthic macroinvertebrate communities in the Rance estuary (Alexandridis et al, 2017a, 2017b), and discuss results in relation to the environ mental history of the system
Summary
Fluctuating abiotic conditions, known as environmental vari ability, and interactions among community members, outlined as system structure, are central to the study of biological communities. Logistical restrictions often limit controlled community experiments that jointly investigate environment and structure to small subsets of natural systems. Such experiments have provided valuable theoretical insight, but their inference potential regarding the behaviour of complex real-world systems remains limited (Wernberg et al, 2012). Observational studies can produce more comprehensive community descriptions, but these are typically based on snapshots of a system that are restricted in space or time. Statistical analysis of such sample data can reveal environmental drivers that shape community composition. This crucial property of biological communities is often dis regarded in favor of acyclic representations (Fan et al, 2016)
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