Marine Biology

, 163:114 | Cite as

Thriving of Zostera noltei under intertidal conditions: implications for the modelling of seagrass populations

  • Ana Azevedo
  • João Miguel Dias
  • Ana Isabel Lillebø


The distribution of Zostera noltii seagrass in Ria de Aveiro lagoon (NW Portugal) is presently characterised by intertidal monospecific meadows. Numerical modelling approaches are useful supportive tools for environmental management, although the available numerical models for seagrasses generally overlook the intertidal processes, such as the water loss during air exposure periods. To achieve a better comprehension of seagrass dynamics and contribute to the development of more realistic models, this work aims to quantify the relative water content (RWC) of intertidal seagrass Z. noltii (RWCseagrass) in the aforementioned mesotidal lagoon. Experimental design included the sampling of Z. noltii shoots on ebbing and flooding tides, along two transects with different sediment types (medium sand vs. fine sand) and distinct intertidal heights (high, medium and lower intertidal), to assess the RWC during a tidal cycle under extreme spring tide conditions. Sediment descriptors such as the relative water content (RWCsediment), organic matter and grain size were also determined. The results showed no regular pattern between RWCseagrass variation and the different intertidal heights. Nevertheless, the seagrasses colonising the medium-sized sand sediments showed a higher RWCseagrass lost (about 20–31 %), when comparing with the samples from the finer sediments (8–16 %). Thus, these results suggest that the relative importance of sediment grain size is more effective in conditioning the RWCseagrass over a tidal cycle than the intertidal height itself (i.e. than different exposure times). This work highlights the importance of considering sediment type in the development of tools for seagrass recovery.


Relative Water Content Tidal Cycle Seagrass Meadow High Relative Water Content Mira Channel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The Portuguese Foundation for Science and Technology (FCT) supported this study through the research project LTER-RAVE (LTER/BIA-BEC/0063/2009), co-funded by MCES (PIDDAC), and LTER/BIA-BEC/0063/2009, funded through national FCT/MCES (PIDDAC), co-funded by COMPETE/FEDER/UE, as well as through the PhD grant SFRH/BD/84613/2012 (A. Azevedo). Thanks are also due, for the financial support to CESAM (UID/AMB/50017/2013), to FCT/MEC through national funds, and the co-funding by the FEDER, within the PT2020 Partnership Agreement and Compete 2020. The European Commission, under the 7th Framework Programme, also supported this study through the collaborative research project LAGOONS (contract no 283157). The authors also thank two reviewers for their comments and suggestions which greatly improved the manuscript.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Physics, CESAMUniversity of AveiroAveiroPortugal
  2. 2.Department of Biology, CESAMUniversity of AveiroAveiroPortugal

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