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A general meta-ecosystem model to predict ecosystem function at landscape extents

Abstract : The integration of meta-ecosystem processes over large spatial extent is critical to predicting whether and how global changes might impact biodiversity and ecosystem functions. Yet, there remains an important gap in meta-ecosystem models to predict multiple ecosystem functions (e.g., carbon sequestration, elemental cycling, trophic efficiency) across different ecosystem types (e.g., terrestrial-aquatic, benthic-pelagic). We derive a generic meta-ecosystem model to predict ecosystem function at landscape extents by integrating the spatial dimension of natural systems as spatial networks of different habitat types connected by cross-ecosystem flows of materials and organisms. This model partitions the physical connectedness of ecosystems from the spatial flow rates of materials and organisms, allowing the representation of all types of connectivity across ecosystem boundaries as well as the interaction(s) between them. The model predicts that cross-ecosystem flows maximize the realization of multiple functions at landscape extent. Spatial flows, even the ones that significantly reduce the overall amount of nutrients in the meta-ecosystem, can reallocate nutrients to more efficient ecosystems, leading to greater levels of productivity at both local and regional scales. This ‘cross-ecosystem efficiency hypothesis’ is a general and testable hypothesis emphasizing the complementarity and interconnectedness among ecosystems and the importance of addressing ecosystem diversity for meta-ecosystem function.
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https://hal.archives-ouvertes.fr/hal-03407501
Contributor : Isabelle Gounand Connect in order to contact the contributor
Submitted on : Thursday, October 28, 2021 - 2:49:24 PM
Last modification on : Friday, December 3, 2021 - 11:43:53 AM

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Eric Harvey, Justin Marleau, Isabelle Gounand, Shawn Leroux, Carina Firkowski, et al.. A general meta-ecosystem model to predict ecosystem function at landscape extents. 2021. ⟨hal-03407501⟩

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