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Predicting Ecosystem Pattern across Landscapes
  • Sean Basquill,
  • Shawn Leroux
Sean Basquill
Memorial University of Newfoundland

Corresponding Author:sean.basquill@novascotia.ca

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Shawn Leroux
Memorial University of Newfoundland
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Abstract

Predictive modelling is fundamental to ecology and essential for objective biodiversity assessment. However, while predictive biodiversity models are generally well-developed, models for predicting patterns within and among ecosystems have not been adequately operationalized. We contend the scarcity of such models marks a concerning gap in the scientific community’s ability to make ecosystem predictions across landscapes, and more broadly for supporting the conservation of biodiversity and ecosystem functions. We propose ecosystem spatial pattern models (ESPM) to fill this gap in modelling capacity. Under our approach to ESPM, spatial patterns of ecosystem properties are the basis for resolving ecosystem organization at local and landscape extents. Our integrative modelling framework differs from others in that it accords biotic and abiotic constituents equally, based on with their joint mechanistic influence on ecosystem dynamics. Development of ESPM is especially timely for ecosystem assessment is undergoing a contemporary groundswell, as scientists and conservation groups propose ambitious targets for ecosystem conservation and restoration.
15 Nov 2022Submitted to Oikos
15 Nov 2022Submission Checks Completed
15 Nov 2022Assigned to Editor
15 Nov 2022Review(s) Completed, Editorial Evaluation Pending
23 Nov 2022Reviewer(s) Assigned
23 Jan 2023Editorial Decision: Revise Major
20 Mar 20231st Revision Received
24 Mar 2023Submission Checks Completed
24 Mar 2023Assigned to Editor
24 Mar 2023Review(s) Completed, Editorial Evaluation Pending
28 Mar 2023Reviewer(s) Assigned
09 Jun 2023Editorial Decision: Revise Minor
16 Jun 20232nd Revision Received
19 Jun 2023Submission Checks Completed
19 Jun 2023Assigned to Editor
19 Jun 2023Review(s) Completed, Editorial Evaluation Pending
22 Jun 2023Editorial Decision: Accept