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A general meta-ecosystem model to predict ecosystem function at landscape extents
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  • Eric Harvey,
  • Justin Marleau,
  • Isabelle Gounand,
  • Shawn Leroux,
  • Carina Rauen Firkowski,
  • Florian Altermatt,
  • F. Guillaume Blanchet,
  • Kevin Cazelles,
  • Cindy Chu,
  • Cassidy D'Aloia,
  • Louis Donelle,
  • Dominique Gravel,
  • Frédéric Guichard,
  • Kevin McCann,
  • Jonathan Ruppert,
  • Colette Ward,
  • Marie-Josee Fortin
Eric Harvey
Université de Montréal
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Justin Marleau
McGill University
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Isabelle Gounand
Institute of Ecology and Environmental Sciences Paris
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Shawn Leroux
Memorial University
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Carina Rauen Firkowski
University of Toronto
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Florian Altermatt
EAWAG
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F. Guillaume Blanchet
Universite de Sherbrooke
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Kevin Cazelles
University of Guelph
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Cindy Chu
Ontario Ministry of Natural Resources and Forestry
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Cassidy D'Aloia
Woods Hole Oceanographic Institution
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Louis Donelle
Concordia University
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Dominique Gravel
Université de Sherbrooke
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Frédéric Guichard
McGill University
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Kevin McCann
University of Guelph
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Jonathan Ruppert
York University
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Colette Ward
University of Guelph
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Marie-Josee Fortin
University of Toronto
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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.