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Distribution and dynamics of intertidal macrobenthos predicted from remote sensing: response to microphytobenthos and environment
van der Wal, D.; Herman, P.M.J.; Forster, R.M.; Ysebaert, T.J.; Rossi, F.; Knaeps, E.; Plancke, Y.; Ides, S. (2008). Distribution and dynamics of intertidal macrobenthos predicted from remote sensing: response to microphytobenthos and environment. Mar. Ecol. Prog. Ser. 367: 57-72. dx.doi.org/10.3354/meps07535
In: Marine Ecology Progress Series. Inter-Research: Oldendorf/Luhe. ISSN 0171-8630; e-ISSN 1616-1599, meer
Peer reviewed article  
Beschikbaar in  Auteurs | Dataset 
    VLIZ: Open Repository 139645 [ OMA ]
Trefwoorden
    Benthic communities
    Ecosystems
    Environments > Aquatic environment > Marine environment > Intertidal environment
    Mapping
    Monitoring
    Remote sensing
Author keywords
    Hyperspectral remote sensing; Intertidal zone; Ecosystem response modelling; Ecosystem monitoring; Benthic communities; Mapping
Auteurs  Top | Dataset 
  • van der Wal, D.
  • Herman, P.M.J.
  • Forster, R.M.
  • Ysebaert, T.J.
  • Rossi, F.
  • Knaeps, E.
  • Plancke, Y., meer
  • Ides, S., meer
Abstract
    We investigated which variables, including environmental variables and food availability, could predict the spatial distribution and dynamics of benthic macrofauna on an intertidal flat. A time series of macrobenthos and sediment grain size samples was complemented by time series of microphytobenthos and saltmarsh vegetation biomass and sediment grain size from airborne hyperspectral remote sensing, and elevation from laser altimetry. Response models were constructed to predict biomass and species richness of macrobenthos as a function of the environmental variables. Total biomass and species richness was best predicted by a combination of microphytobenthos biomass and sediment characteristics as explanatory variables. Deep deposit feeders and surface deposit feeders also responded best to a combination of variables, with deep deposit feeders responding more strongly to sediment grain size and surface deposit feeders responding more strongly to microphytobenthos biomass. The environmental conditions to reach maximum biomass differed for each macrobenthos species. Application of the response models to the remote sensingderived maps of the environmental variables enabled significant predictions of the spatial distribution of macrobenthos biomass, demonstrating the differences in distribution of the macrobenthos species. The models also revealed the sensitivity of the macrobenthic community to environmental change. In situ and remote sensing data demonstrated a significant fining of the sediment and a (temporal) increase in average microphytobenthos biomass. Field observations also showed a significant increase in species richness and changes in the relative abundance of species, with a decrease in Bathyporeia pilosa, and an increase in Nereis diversicolor, Pygospio elegans and Heteromastus filiformis. Such changes in macrobenthos biomass and species richness were indeed predicted from the response models. The study demonstrates that the synoptic remote sensing techniques combined with field sampling allow efficient ecological mapping and monitoring.
Dataset
  • Hyperspectrale vliegtuigopnamen Schelde-estuarium: Plaat van Walsoorden, Saeftinge en Molenplaat
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