897 resultados para Spatio-temporal variability


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Tayrona National Natural Park (TNNP; 11°17' - 11°22' N and 73°53' - 74°12' W) is a hotspot of coral reef biodiversity in the Colombian Caribbean, located between the city of Santa Marta (>455,000 inhabitants) and several smaller river mouths (Rio Piedras, Mendihuaca, Guachaca). The region experiences a strong seasonal variation in physical parameters (temperature, salinity, wind, and water currents) due to alternating dry seasons with coastal upwelling and rainy seasons. Here, a range of water quality parameters relevant for coral reef functioning is provided. Water quality was measured directly above local coral reefs (~10 m water depth) by a monthly monitoring for up to 25 months in the four TNNP bays (Chengue, Gayraca, Neguanje, and Cinto) and at sites with different degree of exposition to winds, waves and water currents (exposed vs. sheltered sites) within each bay. The water quality parameters include: inorganic nutrient (nitrate, nitrite and soluble reactive phosphorus), chlorophyll a, particulate organic carbon and nitrogen concentrations (with a replication of n=3) as well as oxygen availability, biological oxygen demand, seawater pH, and water clarity (with a replication of n=4). This is by far the most comprehensive coral reefs water quality dataset for the region. A detailed description of the methods can be found within the referenced publications.

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This study combined data on fin whale Balaenoptera physalus, humpback whale Megaptera novaeangliae, minke whale B. acutorostrata, and sei whale B. borealis sightings from large-scale visual aerial and ship-based surveys (248 and 157 sightings, respectively) with synoptic acoustic sampling of krill Meganyctiphanes norvegica and Thysanoessa sp. abundance in September 2005 in West Greenland to examine the relationships between whales and their prey. Krill densities were obtained by converting relationships of volume backscattering strengths at multiple frequencies to a numerical density using an estimate of krill target strength. Krill data were vertically integrated in 25 m depth bins between 0 and 300 m to obtain water column biomass (g/m**2) and translated to density surfaces using ordinary kriging. Standard regression models (Generalized Additive Modeling, GAM, and Generalized Linear Modeling, GLM) were developed to identify important explanatory variables relating the presence, absence, and density of large whales to the physical and biological environment and different survey platforms. Large baleen whales were concentrated in 3 focal areas: (1) the northern edge of Lille Hellefiske bank between 65 and 67°N, (2) north of Paamiut at 63°N, and (3) in South Greenland between 60 and 61° N. There was a bimodal pattern of mean krill density between depths, with one peak between 50 and 75 m (mean 0.75 g/m**2, SD 2.74) and another between 225 and 275 m (mean 1.2 to 1.3 g/m**2, SD 23 to 19). Water column krill biomass was 3 times higher in South Greenland than at any other site along the coast. Total depth-integrated krill biomass was 1.3 x 10**9 (CV 0.11). Models indicated the most important parameter in predicting large baleen whale presence was integrated krill abundance, although this relationship was only significant for sightings obtained on the ship survey. This suggests that a high degree of spatio-temporal synchrony in observations is necessary for quantifying predator-prey relationships. Krill biomass was most predictive of whale presence at depths >150 m, suggesting a threshold depth below which it is energetically optimal for baleen whales to forage on krill in West Greenland.

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The MARECHIARA-mesozooplankton dataset contains mesozooplankton data collected in the ongoing time-series at Sation MC (40°48.5' N, 14°15' E) in the Gulf of Naples. This dataset spans over the period 1984-2006 and contains data of mesozooplankton abundance and species composition as well as biomass (as dry weight). Mesozooplankton was regularly sampled in 1984-1990 and 1995-2006, only a few samples were collected in 1991-1992 and no samples in 1993-1994. During the first period of the series sampling frequency was fortnightly, and weekly since 1995.

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Large-scale studies of ocean biogeochemistry and carbon cycling have often partitioned the ocean into regions along lines of latitude and longitude despite the fact that spatially more complex boundaries would be closer to the true biogeography of the ocean. Herein, we define 17 open-ocean biomes classified from four observational data sets: sea surface temperature (SST), spring/summer chlorophyll a concentrations (Chl a), ice fraction, and maximum mixed layer depth (maxMLD) on a 1° × 1° grid. By considering interannual variability for each input, we create dynamic ocean biome boundaries that shift annually between 1998 and 2010. Additionally we create a core biome map, which includes only the grid cells that do not change biome assignment across the 13 years of the time-varying biomes. These biomes can be used in future studies to distinguish large-scale ocean regions based on biogeochemical function.