Site-level and spatially-explicit modelling provides some insights on key factors driving seasonal dynamics of an intertidal seagrass DOI Creative Commons
Héloise Müller,

Etienne Auclair,

Aubin Woehrel

et al.

Ecological Modelling, Journal Year: 2024, Volume and Issue: 495, P. 110802 - 110802

Published: July 19, 2024

In a context of worldwide decline and given the critical ecological role marine seagrasses to coastal ecosystem structure functioning, regional conservation initiatives have emerged over past thirty years protect these important habitat-forming species.Yet, effective interventions need account for site-specific processes stressors.Thus, our ability accurately predict seagrass dynamics is pivotal support management interventions.To date, determinist process-based modelling has provided insights on drivers dynamics.Here, we developed an original model framework that combines hydrodynamics ocean with local data-driven models rely Boosted Regression Trees seasonal patch-level plant-level features as function environmental conditions.Based only 12-month monitoring across nine sites, traits successfully reproduce overall based mostly inferred relationships monthly light temperature, lesser extent, exposure physical stressors (i.e., currents waves).While fail finely capture spatial discrepancies all sites (especially where demonstrates higher growth potential), spatially-explicit simulations highlight how seagrass-hydrodynamics feedback whole bay can dampen potential due shear stress.However, this offers simulate long-term changes in extent status meadows Arcachon Bay, explicit resolving hydro-sediment effects appears priority better range between conditions.

Language: Английский

Site-level and spatially-explicit modelling provides some insights on key factors driving seasonal dynamics of an intertidal seagrass DOI Creative Commons
Héloise Müller,

Etienne Auclair,

Aubin Woehrel

et al.

Ecological Modelling, Journal Year: 2024, Volume and Issue: 495, P. 110802 - 110802

Published: July 19, 2024

In a context of worldwide decline and given the critical ecological role marine seagrasses to coastal ecosystem structure functioning, regional conservation initiatives have emerged over past thirty years protect these important habitat-forming species.Yet, effective interventions need account for site-specific processes stressors.Thus, our ability accurately predict seagrass dynamics is pivotal support management interventions.To date, determinist process-based modelling has provided insights on drivers dynamics.Here, we developed an original model framework that combines hydrodynamics ocean with local data-driven models rely Boosted Regression Trees seasonal patch-level plant-level features as function environmental conditions.Based only 12-month monitoring across nine sites, traits successfully reproduce overall based mostly inferred relationships monthly light temperature, lesser extent, exposure physical stressors (i.e., currents waves).While fail finely capture spatial discrepancies all sites (especially where demonstrates higher growth potential), spatially-explicit simulations highlight how seagrass-hydrodynamics feedback whole bay can dampen potential due shear stress.However, this offers simulate long-term changes in extent status meadows Arcachon Bay, explicit resolving hydro-sediment effects appears priority better range between conditions.

Language: Английский

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