The life cycle, the phenology and the interannual variability of jellyfish (i.e. cnidaria medusozoa scyphozoa) are under study across the world as there is debate on their increasing occurrence under human pressure (overfishing, eutrophication, climate change) (Condon et al. 2012). Beside interference in human activities, jellyfish swarms affect the marine food web as these organisms feed on fish eggs and larvae, and compete for food with adult fish (Lynam et al. 2005, Pauly et al. 2009). Whether jellyfish nuisance can be mitigated remains unclear and depends on our understanding of the causes of outbreaks. Most North Sea jellyfish species have a sessile polyp stage as part of their life cycle, and therefore need solid substrate to fix. While A. aurita polyps are visible along the Belgian and Dutch coasts, the location of other species polyps (e.g. Cyanea, Chrysaora) remains largely unknown. Tracing back the origin of an observed jellyfish swarm in the North Sea could help identifying the potential location of polyps and the timing and temperature of strobilation. A Lagrangian particle tracking model parameterized for jellyfish in the English Channel and the southern North Sea is used in backtracking (15 days) and forecast (3 days; forced by UKMO forecast) modes to study the potential origin and fate of jellyfish swarms. A backtracking simulation was applied on a jellyfish swarm observed in 2013 in the Belgian coastal zone . It allowed identifying potential areas of origin for the outbreak, raising new scientific questions. A first sensitivity study illustrates the wind influence on the backtracking simulation.
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Anthropogenic climate change represents a major threat to biodiversity as well as to human well-being. Humanity’s response is to (attempt to) develop and implement mitigation strategies that minimise the speed and eventual level of climate change. Prominent among these is the United Nations scheme known as Reducing Emissions from Deforestation and Degradation, REDD+, which focusses on developing countries (including the DRC). The UN-REDD+ strategy aims at protecting and enhancing biosphere carbon stocks, by conserving tropical rainforest systems, as a means to mitigate global climate change. Biodiversity is generally described as a potential ‘co-benefit’ of forest carbon sequestration, but components of forest biodiversity may overlap to different degrees, trade off with, or be largely independent from those that intervene in carbon storage potential. The spatial congruence of biodiversity and carbon stocks has recently become an upcoming issue in science. In general, biodiversity is positively (but rather weakly) associated with ecosystem carbon, but the association is geographically variable, and even reverses in some regions. This lack of consistent relationships can be attributed to the fact that recent ecosystem mapping analyses are performed at large scales, using only species richness as an indicator for biodiversity. Understanding the relationship between carbon stock and biodiversity is needed to maximize the UN-REDD+ gains, to better address the risks of UN-REDD+ programs, and to avoid substantial biodiversity loss. This study will focus on the local scale relation of carbon stock and biodiversity expressed in multiple diversity parameters over a range of taxa. We will use data from the first multi-taxon inventory in the central Congo basin conducted in the framework of the COBIMFO project (Congo basin integrated monitoring for forest carbon mitigation and biodiversity). The project started in 2010 and measured carbon as well as the diversity of 9 different taxa (eumycetozoa, lichens, trees, fungi, diptera, ants, termites, birds and mammals) in the Yangambi Biosphere Reserve. The sampling and species identification is still ongoing. The first objective of this project is to select a set of parameters that can be calculated for each taxon, and that can be normalised so as to be comparable between taxa. We will assess biotic parameters that describe compositional and functional components of the sampled communities. Secondly, we will investigate the relationship between carbon and biodiversity at both the level of the COBIMFO study plots and across the Yangambi Biosphere Reserve as a whole. The chosen parameters will be calculated for each taxon for each site where the taxon was sampled. For each biodiversity parameter, a generalized linear mixed effects model will be fitted to model biodiversity as a function of carbon. As area-wide data on carbon and biodiversity are not readily available, we will generate area-wide predictions of carbon and biodiversity using BIOMOD, an R-based ensemble modelling framework that simultaneously runs up to 10 different Ecological Niche Modelling techniques, based on the carbon and biodiversity data obtained from the COBIMFO study plots. These extrapolated data will then be used to evaluate the spatial distribution of, and relationships between, carbon and biodiversity on a regional scale. Here we will present the preliminary results of the statistical analysis using taxa and biodiversity parameters for which sufficient data will be available. As a result, we will increase our understanding on the implications of carbon conservation on biodiversity. Furthermore, although we do not aim at identifying the complex mechanisms driving the carbon - biodiversity relation, our fine-scale analysis will promote insight in the underlying ecological drivers.
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