Dr. Tom Van Engeland
Post-doctoral researcher
Department: Ecosystem Studies
T. +31 (0) 113 577 473
F. +31 (0) 113 573 616
Tom.Van.Engeland(at)nioz.nl

Visiting address:

Korringaweg 7

4401 NT Yerseke

The Netherlands

Postal address:

Postbus 140

4400 AC Yerseke

The Netherlands

• Sediment biogeochemistry
• Benthic-pelagic coupling
• Seagrass ecology
• Dissolved Organic Matter dynamics
• Eutrophication

• Aquatic Eddy Correlation
• Time series analysis and signal processing
• Ecological modelling and model uncertainty analysis
• Stable isotopes tracers
• Compound-specific analysis (fatty acids and amino acids)

Oxygen flux determination by aquatic eddy correlation?

Aquatic eddy correlation (EC) to measure benthic oxygen fluxes was first introduced by Peter Berg and co-workers (2003). The eddy correlation technique is based on high-frequency measurements of vertical current velocity and oxygen concentration at the same point. In contrast to the traditionally used sediment core and benthic chamber incubations, EC oxygen flux measurements do not affect advective transport of oxygen, which is particularly important for permeable sediments or habitats characterised by strong hydrodynamic influences or interactions, such as macrophyte-dominated systems. In addition, the measurement setup can be deployed in locations that are not accessible with the standard techniques, such as rocky substrates, and reefs.

We are currently moving the eddy correlation research within NIOZ from a test phase to a level of routine deployment. This involves optimisation of practical issues and signal processing software. Development of a package for the R Statistical Software is planned for the near future in collaboration with Tom Cox (Antwerp University). The EC techniques is gradually integrated in larger surveys, while emphasis is shifted from pure methodology to more scientifically relevant research questions.

Modelling effects of ocean acidification on pelagic ecosystems

Ocean acidification, often referred to as the other CO₂ problem, affects marine biota in various ways (e.g. physiology of larval stages, calcifying organisms, ....). However, in contrast to the physico-chemical mechanisms underlying the cause, the effects are often unpredictable and not well understood. Simple ecosystem models can be used to help interpret experimental data. Within the European Project of Ocean Acidification (EPOCA) our task was to quantify the uncertainty in predictions and output of such simple dynamic models.

Part of this uncertainty originates from uncertainty in experimental data.  This data uncertainty which can be unexplained variability or simply a lack of data is via calibration (inverse modelling) transferred to parameter uncertainty, which in turn translates to uncertainty in model output (forward modelling) and predictions. Investigation of this model uncertainty in the light of data uncertainty can help in documenting and understanding the experiment from which the data was derived.

Dissolved organic nitrogen dynamics in coastal ecosystems

In recent decades, it has become clear that many organisms are able to use dissolved organic nitrogen (DON) as a source of fixed nitrogen. Particularly in oligotrophic ecosystem or ecosystem undergoing oligotrophication DON is often the largest fraction of total dissolved nitrogen, and therefore a potentially important source to some primary producers that can make use of it. This implies that under regimes of increasing oligotrophication, important shifts in community composition and ecosystem functioning can occur.

We investigated the long-term, seasonal and spatial variability in DON on the Dutch continental shelf using frequency-domain and wavelet-based time series analyses (Van Engeland et al. 2010) . This study showed a clear contrast in temporal DON variability between coastal and offshore monitoring stations, that can be understood by considering that dissolved nitrogen contains different classes of molecules with contrasting bioavailability, that have specific turn-over times.

As a follow up on these results, a number of experiments were run in the inner bay of Cadiz (Spain). During this campaign we investigate the ability of different actors (bacteria, phytoplankton, seagrasses, macroalgae) in a macrophyte-dominated ecosystem to take up nitrogen from inorganic and organic sources with contrasting chemical stability and metabolic function. Van Engeland et al. (2011) showed that the investigated seagrasses and macroalga were able to take up nitrogen from organic compounds, even with a strongly reduced bacterial community present. A field study, focussing on uptake from the water column, confirmed these lab results in the field, but at the same time showed that most of the nitrogen in these macrophyte systems was taken up by a pelagic microbial community (Van Engeland et al. 2013). The observed preference for inorganic nitrogen and the contrasting uptake capacities for different ecosystem actors and different substrates supported the hypothesis that was put forward to explain the contrasting modes of variability in coastal and offshore DON observed in the monitoring time series (Van Engeland et al. 2010).

 Please find all my publications at ResearchGate. 

Kromkamp, J. C., & Van Engeland, T. (2010). Changes in phytoplankton biomass in the Western Scheldt estuary during the period 1978–2006. Estuaries and Coasts, 33(2): 270-285.

Van Engeland, T., Soetaert, K., Knuijt, A., Laane, R. W. P. M., & Middelburg, J. J. (2010). Dissolved organic nitrogen dynamics in the North Sea: A time series analysis (1995–2005). Estuarine, Coastal and Shelf Science, 89(1), 31-42. doi:10.1016/j.ecss.2010.05.009

Van Engeland, T., Bouma, T. J., Morris, E. P., Brun, F. G., Peralta, G., Lara, M., ... & Middelburg, J. J. (2011). Potential uptake of dissolved organic matter by seagrasses and macroalgae. Marine Ecology-Progress Series, 471: 71-81.

Van Engeland, T., Bouma, T. J., Morris, E. P., Brun, F. G., Peralta, G., Lara, M., ... & Middelburg, J. J. (2013). Dissolved organic matter uptake in a temperate seagrass ecosystem. Marine Ecology-Progress Series, 478: 87-100.