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Long‐term coastal macrobenthic Community Trajectory Analysis reveals habitat‐dependent stability patterns

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Long‐term monitoring programs are fundamental to detect changes in ecosystem health and understand ecological processes. In the current context of increasing anthropogenic threats on marine ecosystems, understanding the dynamics and response of communities becomes essential. We used data collected over 14 years in the REBENT benthic coastal invertebrates monitoring program, at a regional scale in the North‐East Atlantic, covering a total of 26 sites and 979 taxa. Four distinct habitats were studied: two biogenic habitats associated with foundation species in the intertidal and subtidal zones and two bare sedimentary habitats in the same respective tidal zones. We used community trajectory analysis (CTA), a statistical approach that allows for quantitative measures and comparisons of temporal trajectories of ecosystems. We compared observed community trajectories to trajectories simulated under a non‐directional null model in order to better understand the dynamics of the communities, their potential drivers, and the role of the studied habitats in these dynamics. Despite strong differences in the community compositions between sites and habitats, the communities followed non‐directional dynamics during the 14 years monitored, which suggested stability at the regional scale. However, the shape, size, and direction of the trajectories of benthic communities were more similar within than among habitats, also suggesting the influence of the nature of the habitat on community dynamics. Results showed a higher variability in community composition the first years of the monitoring in the intertidal bare habitat and confirmed the role of biogenic habitats in maintaining temporal stability. They also highlighted the need to apprehend the role of transient and rare species and the scale of observation in temporal beta diversity analyses. Finally, our study confirmed the usefulness of CTA to link observed trajectory patterns to fundamental ecological processes.
Title: Long‐term coastal macrobenthic Community Trajectory Analysis reveals habitat‐dependent stability patterns
Description:
Long‐term monitoring programs are fundamental to detect changes in ecosystem health and understand ecological processes.
In the current context of increasing anthropogenic threats on marine ecosystems, understanding the dynamics and response of communities becomes essential.
We used data collected over 14 years in the REBENT benthic coastal invertebrates monitoring program, at a regional scale in the North‐East Atlantic, covering a total of 26 sites and 979 taxa.
Four distinct habitats were studied: two biogenic habitats associated with foundation species in the intertidal and subtidal zones and two bare sedimentary habitats in the same respective tidal zones.
We used community trajectory analysis (CTA), a statistical approach that allows for quantitative measures and comparisons of temporal trajectories of ecosystems.
We compared observed community trajectories to trajectories simulated under a non‐directional null model in order to better understand the dynamics of the communities, their potential drivers, and the role of the studied habitats in these dynamics.
Despite strong differences in the community compositions between sites and habitats, the communities followed non‐directional dynamics during the 14 years monitored, which suggested stability at the regional scale.
However, the shape, size, and direction of the trajectories of benthic communities were more similar within than among habitats, also suggesting the influence of the nature of the habitat on community dynamics.
Results showed a higher variability in community composition the first years of the monitoring in the intertidal bare habitat and confirmed the role of biogenic habitats in maintaining temporal stability.
They also highlighted the need to apprehend the role of transient and rare species and the scale of observation in temporal beta diversity analyses.
Finally, our study confirmed the usefulness of CTA to link observed trajectory patterns to fundamental ecological processes.

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