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Trichodesmium, a filamentous bloom-forming marine cyanobacterium, plays a key role in the biogeochemistry of oligotrophic ocean regions because of the ability to fix nitrogen. Naturally occurring in the Great Barrier Reef (GBR), the contribution of Trichodesmium to the nutrient budget may be of the same order as that entering the system via catchment runoff. However, the cyclicity of Trichodesmium in the GBR is poorly understood and sparsely documented because of the lack of sufficient observations. This study provides the first systematic analysis of Trichodesmium spatial and temporal occurrences in the GBR over the decade-long MERIS ocean color mission (2002-2012). Trichodesmium surface expressions were detected using the Maximum Chlorophyll Index (MCI) applied to MERIS satellite imagery of the GBR lagoonal waters. The MCI performed well (76%), albeit tested on a limited set of images (N = 25) coincident with field measurements. A north (Cape York) to south (Fitzroy) increase in the extent, frequency and timing of the surface expressions characterized the GBR, with surface expressions extending over several hundreds of kilometers. The two southernmost subregions Mackay and Fitzroy accounted for the most (70%) bloom events. The bloom timing of Trichodesmium varied from May in the north to November in the south, with wet season conditions less favorable to Trichodesmium aggregations. MODIS-Aqua Sea Surface Temperature (SST) datasets, wind speed and field measurements of nutrient concentrations were used in combination with MCI positive instances to assess the blooms' driving factors. Low wind speed (<6 m.s-1) and SST > 24°C were associated with the largest surface aggregations. Generalized additive models (GAM) indicated an increase in bloom occurrences over the 10-year period with seasonal bloom patterns regionally distinct. Interannual variability in SST partially (14%) explained bloom occurrences, and other drivers, such as the subregion and the nutrient budget, likely regulate Trichodesmium surface aggregations in the GBR.
Pubmed ID: 30550568
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View all literature mentionsSoftware application that treats the disease-associated markers and their interactions via a bayesian partitioning model and computes, via Markov chain Monte Carlo, the posterior probability that each marker set is associated with the disease. (entry from Genetic Analysis Software)
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