Search publications of the members of the Royal Belgian institute of natural Sciences
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No anonymity for fish: tracing sole juveniles arriving at the Belgian nursery combining genomics, otolith microchemistry and otolith shape analysis
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Past, present and future eutrophication levels in the North East Atlantic
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What is the origin of nitrogen in the North Sea
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Will soles be more numerous and more connected in a warmer world?
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Impact of man-made structures on hard substrate species connectivity patterns in the North Sea
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Calibration et analyse de sensibilité d’un modèle de transport larvaire en Mer du Nord
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Understanding the influence of man-made structures on the ecosystem functions of the North Sea (UNDINE)
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Conclusions and recommendations from the Workshop on Scheldt Eutrophication
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Major hotspots detected along the Scotia Ridge in autumn for southern right whales Eubalaena australis, Antarctic fur seals Arctocephalus gazella and Antarctic prions Pachyptila desolata.
- We investigated the role of hydrological features, such as water masses, fronts, eddies, and sea ice, in affecting the distribution of upper trophic level species in the Scotia Sea region during autumn. On board RV Polarstern, we performed 365 30-min strip transects recording seabirds and marine mammals along the North Scotia Ridge and the South Sandwich Trench in March—April 2013. Among the 7 identified cetacean species recorded, the humpback whale Megaptera novaeangliae was the most abundant baleen whale (40 individuals), and noteworthy were sightings of six southern right whales Eubalaena australis. Pinnipeds (3 species, 1650 individuals) were dominated by Antarctic fur seal Arctocephalus gazella (99%), and seabirds (36 species, 18900 individuals) by Antarctic prion Pachyptila desolata (~50%). The distribution of these top predators was highly patchy with the majority of observations concentrated in a few counts. This heterogeneity is likely a result of prey availability, and we discuss how hydrological features may have caused the patchiness.
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Field data on the little known and endangered Lepilemur mittermeieri.
- Lepilemur mittermeieri is a very little known sportive lemur of the Ampasindava peninsula of Madagascar, presently regarded as endangered. On the basis of genetic material only, obtained from three individuals collected at the same locality. No observation confidently allocated to the species has been reported since. The objectives of our research were to verify that the sportive lemurs found in forests of the Ampasindava peninsula beyond the type-locality of Lepilemur mittermeieri belonged to the same species as the type, to provide morphological and behavioural data for populations confidently attributed to L. mittermeieri and to obtain for these populations preliminary evaluations of density variations within the peninsula. Our surveys were undertaken in March and April 2014 in remnant forest patches of the western part of the Ampasindava peninsula. Linear transects by night and punctual observations by day were conducted. A total of 54 animals were seen along nine transects situated in four forest patches, two at low altitude and two at high altitude. All animals examined and photographed appeared similar, and the impression was gained that a single taxon was involved. Genetic material collected from one dead specimen proved identical to the type of L. mittermeieri which confirmed the identity of the populations we observed. It thus appears that L. mittermeieri is indeed the only sportive lemur present on the peninsula and that it occurs in several forest remnants. We endeavoured to get evaluations of the density and abundance of the species in the four forest patches we studied. We used KAIs (Kilometric Abundance Indices) to evaluate and compare relative densities, and Buckland’s distance sampling method to evaluate absolute densities. The latter suggested a density of 1.9 animals/ ha, a result that must, however, be taken with caution.
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Validation of Historical Operational Data of the Marine Forecasting Centre 2018-2021
- This report presents the validation of the hydrodynamic and wave operational models run by the Marine Forecasting Centre of Belgium, RBINS (MFC) for the period 2018-2021. These models are run twice daily. The hydrodynamic models suite used in this study are set-up with the COHERENS software, a three-dimensional hydrodynamic multi-purpose model for coastal and shelf seas, which resolves mesoscale to seasonal scale processes. The model suite consists of three one-way nested models: the Continental Shelf model (CSM, 2D) covering the European Continental Shelf to its break, the North Sea Shelf (NOS,3D) model, covering the North Sea and part of the English channel and the Belgian Coastal Zone model (BCZ, covering Belgian waters and their immediate surroundings). Two wave models are set-up with the SHYPRO operational system and are called Hypas and Refra, where Refra refines the results of Hypas. There is also a third operational wave model set-up with WAM software (referred to as the WAM-model in this report). The models are validated by measurement data made available on the CMEMS server. Globally, the wave models perform consistently at the different stations, all Taylor diagrams are similar. The correlation is relatively poor, and ranges from 0.3 to 0.8 with an average of ~0.55. The ratio of the standard deviations is usually below 1: the models introduce more variability than is present in the data. The analysis of time series of actual data indicates that the models tend to generate events of high average frequency that do not reflect the reality. On the other hand, the observations show the zero up-crossing frequency varies rapidly in the short term; variability that is not reproduced by the model. In terms of bias, the stations seem to differ, creating a group of off-shore stations (Europlatform, K13) with larger bias or coastal stations (Akkaert, Wandelaar and Westhinder) with more moderate bias. This might reflect the impact of the fetch, given that the wind is usually south-westerly in that part of the North Sea. The RMSE is usually larger than the Bias. The RMSE seems to be sensitive to the overall overestimation of frequencies, and especially to the high frequencies events. There is no clear clustering of stations like was the case for the bias, the tests on the differences in average RMSE are usually not conclusive. Salinity is very sensitive to river plume influences. When studying the background sea surface salinity (filtering out the tidal component), the models show a stable offset in salinity. This offset is best measured by the median of the error distribution. The correlation between data and forecasts is poor because the river discharge uses monthly climatologies. Hence, the models do not have the ability to forecast a drop of salinity due to increased river discharge. On the other hand, a variation in salinity due to hydro-dynamical conditions is reproduced. Statistical tests indicate that the BCZ model forecasts the background salinity with a better accuracy than NOS. The bias analysis of temperature shows that the forecasts are close to the observations and relatively balanced around 0 degrees. The monthly distributions of the error are narrow, with an average span (Q75 – Q25) of 0.268 degrees. Globally, the error seems to remain in the range [-1 ; +1.5] degrees. The sea level forecasts are globally very efficient, with ratio close to 1, and high correlation. The RMSE remains below 30 cm and the bias below 8 cm. The usual statistics are gathered in table 11.4, for all the stations available for each model. According to the metrics, the models perform equally well during low and high tide. The phase shift (tidal timing) however is consistently stronger for the high tides (max 34 minutes) than the low tides (max 17 minutes).
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Environmental benefits of leaving offshore infrastructure in the ocean
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Is the vertical distribution of meiofauna similar in two contrasting microhabitats? A case study of a macrotidal sandy beach
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The effect of bio-irrigation by the polychaete Lanice conchilega on active denitrifiers: Distribution, diversity and composition of nosZ gene
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How is benthic biogeochemical cycling affected by sediment fining arising from human activities?
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Interactions between biological and physical effects modify local biogeochemical cycling processes in offshore wind farms
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Macrobenthos-mediated nutrient cycling in offshore wind farm environments under future ocean climate setting
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Macrobenthos-mediated nutrient cycling in offshore windmill farm environments under future ocean climate settings
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Installation of offshore windfarms can affect local biogeochemical cycling through interactions between biological and physical effects
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Chemical composition: Hearing insect defensive volatiles
