294 resultados para PHYTOPLANKTON PATCHINESS


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Phytoplankton abundance in the NW Atlantic was measured by continuous plankton recorder (CPR) sampling along tracks between Iceland and the western Scotian Shelf from 1998 to 2006, when sea-surface chlorophyll (SSChl) measurements were also being made by ocean colour satellite imagery using the SeaWiFS sensor. Seasonal and inter-annual changes in phytoplankton abundance were examined using data collected by both techniques, averaged over each of four shelf regions and four deep ocean regions. CPR sampling had gaps (missing months) in all regions and in the four deep ocean regions satellite observations were too sparse between November and February to be of use. Average seasonal cycles of SSChl were similar to those of total diatom abundance in seven regions, to those of the phytoplankton colour index in six regions, but were not similar to those of total dinoflagellate abundance anywhere. Large inter-annual changes in spring bloom dynamics were captured by both samplers in shelf regions. Changes in annual (or 8 months) averages of SSChl did not generally follow those of the CPR indices within regions and multi-year averages of SSChl, and the three CPR indices were generally higher in shelf than in deep ocean regions. Remote sensing and CPR sampling provide complementary ways of monitoring phytoplankton in the ocean: the former has superior temporal and spatial coverage and temporal resolution, and the latter provides better taxonomic information.

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The circulation of Atlantic water along the European continental slope, in particular the inflow into the North Sea, influences North Sea water characteristics with consequent changes in the environment affecting plankton community dynamics. The long-term effect of fluctuating oceanographic conditions oil the North Sea, pelagic ecosystem is assessed. It is shown that (i) there are similar regime shifts in the inflow through the northern North Sea and in Sea, Surface Temperature, (ii) long-term phytoplankton trends are influenced by the inflow only in some North Sea regions, and (iii) the spatial variability in chemicophysical and biological parameters highlight the influence of smaller scale processes.

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We study the spatial and seasonal variability of phytoplankton biomass (as phytoplankton color) in relation to the environmental conditions in the North Sea using data from the Continuous Plankton Recorder survey. By using only environmental fields and location as predictor variables we developed a nonparametric model (generalized additive model) to empirically explore how key environmental factors modulate the spatio-temporal patterns of the seasonal cycle of algal biomass as well as how these relate to the ,1988 North Sea regime shift. Solar radiation, as manifest through changes of sea surface temperature (SST), was a key factor not only in the seasonal cycle but also as a driver of the shift. The pronounced increase in SST and in wind speed after the 1980s resulted in an extension of the season favorable for phytoplankton growth. Nutrients appeared to be unimportant as explanatory variables for the observed spatio-temporal pattern, implying that they were not generally limiting factors. Under the new climatic regime the carrying capacity of the whole system has been increased and the southern North Sea, where the environmental changes have been more pronounced, reached a new maximum.

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During the 1980s, a rapid increase in the Phytoplankton Colour Index (PCI), a semiquantitative visual estimate of algal biomass, was observed in the North Sea as part of a regionwide regime shift. Two new data sets created from the relationship between the PCI and SeaWiFS chlorophyll a (Chl a) quantify differences in the previous and current regimes for both the anthropogenically affected coastal North Sea and the comparatively unaffected open North Sea. The new regime maintains a 13% higher Chl a concentration in the open North Sea and a 21% higher concentration in coastal North Sea waters. However, the current regime has lower total nitrogen and total phosphorus concentrations than the previous regime, although the molar N: P ratio in coastal waters is now well above the Redfield ratio and continually increasing. Besides becoming warmer, North Sea waters are also becoming clearer (i.e., less turbid), thereby allowing the normally light-limited coastal phytoplankton to more effectively utilize lower concentrations of nutrients. Linear regression analyses indicate that winter Secchi depth and sea surface temperature are the most important predictors of coastal Chl a, while Atlantic inflow is the best predictor of open Chl a; nutrient concentrations are not a significant predictor in either model. Thus, despite decreasing nutrient concentrations, Chl a continues to increase, suggesting that climatic variability and water transparency may be more important than nutrient concentrations to phytoplankton production at the scale of this study.

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Phytoplankton account for approximately 50% of global primary production, form the trophic base of nearly all marine ecosystems, are fundamental in trophic energy transfer and have key roles in climate regulation, carbon sequestration and oxygen production. Boyce et al.1 compiled a chlorophyll index by combining in situ chlorophyll and Secchi disk depth measurements that spanned a more than 100-year time period and showed a decrease in marine phytoplankton biomass of approximately 1% of the global median per year over the past century. Eight decades of data on phytoplankton biomass collected in the North Atlantic by the Continuous Plankton Recorder (CPR) survey2, however, show an increase in an index of chlorophyll (Phytoplankton Colour Index) in both the Northeast and Northwest Atlantic basins3, 4, 5, 6, 7 (Fig. 1), and other long-term time series, including the Hawaii Ocean Time-series (HOT)8, the Bermuda Atlantic Time Series (BATS)8 and the California Cooperative Oceanic Fisheries Investigations (CalCOFI)9 also indicate increased phytoplankton biomass over the last 20–50 years. These findings, which were not discussed by Boyce et al.1, are not in accordance with their conclusions and illustrate the importance of using consistent observations when estimating long-term trends.

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The patterns of copepod species richness (S) and their relationship with phytoplankton productivity, temperature and environmental stability were investigated at climatological, seasonal and year-to-year time scales as well as scales along latitudinal and oceanic–neritic gradients using monthly time series of the Continuous Plankton Recorder (CPR) Survey collected in the North East Atlantic between 1958 and 2006. Time series analyses confirmed previously described geographic patterns. Equatorward and towards neritic environments, the climatological average of S increases and the variance explained by the seasonal cycle decreases. The bi-modal character of seasonality increases equatorward and the timing of the seasonal cycle takes place progressive earlier equatorward and towards neritic environments. In the long-term, the climatological average of S decreased significantly (p < 0.001) between 1958 and 2006 in the Bay of Biscay and North Iberian shelf at a rate of ca. 0.04 year−1, and increased at the same rate between 1991 and 2006 in the northernmost oceanic location. The climatological averages of S correlate positively with those of the index of seasonality of phytoplankton productivity (ratio between the minimum and maximum monthly values of surface chlorophyll) and sea surface temperature, and negatively with those of the proxy for environmental stability (monthly frequency of occurrence of daily averaged wind speed exceeding 10 m s−1). The seasonal cycles of S and phytoplankton productivity (surface chlorophyll as proxy) exhibit similar features in terms of shape, timing and explained variance, but the relationship between the climatological averages of both variables is non-significant. From year-to-year, the annual averages of S correlate negatively with those of phytoplankton productivity and positively with those of sea surface temperature along the latitudinal gradient, and negatively with those of environmental stability along the oceanic–neritic gradient. The annual anomalies of S (i.e. factoring out geographic variation) show a unimodal relationship with those of sea surface temperature and environmental stability, with S peaking at intermediate values of the anomalies of these variables. The results evidence the role of seasonality of phytoplankton productivity on the control of copepod species richness at seasonal and climatological scales, giving support to the species richness–productivity hypothesis. Although sea surface temperature (SST) is indeed a good predictor of richness along the latitudinal gradient, it is unable to predict the increase of richness form oceanic to neritic environments, thus lessening the generality of the species richness–energy hypothesis. Meteo-hydrographic disturbances (i.e. SST and wind speed anomalies as proxies), presumably through its role on mixed layer depth dynamics and turbulence and hence productivity, maximise local diversity when occurring at intermediate frequency and or intensity, thus providing support to the intermediate disturbance hypothesis on the control of copepod diversity.

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Deriving maps of phytoplankton taxa based on remote sensing data using bio-optical properties of phytoplankton alone is challenging. A more holistic approach was developed using artificial neural networks, incorporating ecological and geographical knowledge together with ocean color, bio-optical characteristics, and remotely sensed physical parameters. Results show that the combined remote sensing approach could discriminate four major phytoplankton functional types (diatoms, dinoflagellates, coccolithophores, and silicoflagellates) with an accuracy of more than 70%. Models indicate that the most important information for phytoplankton functional type discrimination is spatio-temporal information and sea surface temperature. This approach can supply data for large-scale maps of predicted phytoplankton functional types, and an example is shown.