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A general meta‐ecosystem model to predict ecosystem functions at landscape extents.

Authors :
Harvey, Eric
Marleau, Justin N.
Gounand, Isabelle
Leroux, Shawn J.
Firkowski, Carina R.
Altermatt, Florian
Guillaume Blanchet, F.
Cazelles, Kevin
Chu, Cindy
D'Aloia, Cassidy C.
Donelle, Louis
Gravel, Dominique
Guichard, Frédéric
McCann, Kevin
Ruppert, Jonathan L. W.
Ward, Colette
Fortin, Marie‐Josée
Source :
Ecography; Nov2023, Vol. 2023 Issue 11, p1-16, 16p
Publication Year :
2023

Abstract

The integration of ecosystem processes over large spatial extents is critical to predicting whether and how local and global changes may impact biodiversity and ecosystem functions. Yet, there remains an important gap in meta‐ecosystem models to predict multiple functions (e.g. carbon sequestration, elemental cycling, trophic efficiency) across ecosystem types (e.g. terrestrial‐aquatic, benthic‐pelagic). We derive a flexible meta‐ecosystem model to predict ecosystem functions 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. We partition 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. Through simulating a forest‐lake‐stream meta‐ecosystem, our model illustrates that even if spatial flows induced significant local losses of nutrients, differences in local ecosystem efficiencies could lead to increased secondary production at regional scale. This emergent result, which we dub the 'cross‐ecosystem efficiency hypothesis', emphasizes the importance of integrating ecosystem diversity and complementarity in meta‐ecosystem models to generate empirically testable hypotheses for ecosystem functions. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09067590
Volume :
2023
Issue :
11
Database :
Complementary Index
Journal :
Ecography
Publication Type :
Academic Journal
Accession number :
173397456
Full Text :
https://doi.org/10.1111/ecog.06790