Effects of increased pCO(2) and temperature on the North Atlantic spring bloom. I. The phytoplankton community and biogeochemical response

The North Atlantic spring bloom is one of the largest annual biological events in the ocean, and is characterized by dominance transitions from siliceous (diatoms) to calcareous (coccolithophores) algal groups. To study the effects of future global change on these phytoplankton and the biogeochemical cycles they mediate, a shipboard continuous culture experiment (Ecostat) was conducted in June 2005 during this transition period. Four treatments were examined: (1) 12 degrees C and 390 ppm CO2 (ambient control), (2) 12 degrees C and 690 ppm CO2 (high pCO(2)) (3) 16 degrees C and 390 ppm CO2 (high temperature), and (4) 16 degrees C and 690 ppm CO2 ('greenhouse'). Nutrient availability in all treatments was designed to reproduce the low silicate conditions typical of this late stage of the bloom. Both elevated pCO(2) and temperature resulted in changes in phytoplankton community structure. Increased temperature promoted whole community photosynthesis and particulate organic carbon (POC) production rates per unit chlorophyll a. Despite much higher coccolithophore abundance in the greenhouse treatment, particulate inorganic carbon production (calcification) was significantly decreased by the combination of increased pCO(2) and temperature. Our experiments suggest that future trends during the bloom could include greatly reduced export of calcium carbonate relative to POC, thus providing a potential negative feedback to atmospheric CO2 concentration. Other trends with potential climate feedback effects include decreased community biogenic silica to POC ratios at higher temperature. These shipboard experiments suggest the need to examine whether future pCO2 and temperature increases on longer decadal timescales will similarly alter the biological and biogeochemical dynamics of the North Atlantic spring bloom.

Community structure of macrobenthos in coastal water off Rushan, southern Shandong Peninsula, and the relationships with environmental factors

To understand the present actuality of the marine ecosystem in the southern coastal water region of the Shandong Peninsula and the impact of the global change and the human activities to the marine ecosystem of the region, the macrobenthic community structure was researched based on data from 26 sampling stations carried out on four seasonal cruises from December 2006 to November 2007. The data was analyzed using PRIMER 6.0 and SPSS 15.0 software packages. The results showed that 236 macrobenthic species in total were collected from the research region by the field works. Most of the species belong to Polychaeta (76 species), Mollusca (75) and Crustacea (60). Of which, 33 species were common species by the four cruises. The dominant species were different among the four seasons, however, the polychaete species Nephtys oligobranchia and Sternaspis scutata were always dominant in the four seasons. The abundances and biomasses of the macrobenthos from the research region were variable in tire four seasons. The results of CLUSTER and MDS analysis showed that the similarities of macrobenthic structures among the stations were low, most of the similarities were at about 40% of similarity values, only that of two stations were up to 60%. In accordance with the similarity values of the macrobenthic structures, the 26 stations were clustered as six groups at arbitrary similarity level of 30%. The ABC curve indicated that the marcofauna communities in the research region had riot been disturbed distinctly. The results of BIOENV and BVSTEP (Spearman) analysis implied that the concentrations of organic matter in bottom water and heavy metal copper in sediment, water depth and temperature of bottom were the most significant environmental factors to affect the macrobentic community.

Cellular automata: Simulating alpine tundra vegetation dynamics in response to global warming

This study attempts to model alpine tundra vegetation dynamics in a tundra region in the Qinghai Province of China in response to global warming. We used Raster-based cellular automata and a Geographic Information System to study the spatial and temporal vegetation dynamics. The cellular automata model is implemented with IDRISI's Multi-Criteria Evaluation functionality to simulate the spatial patterns of vegetation change assuming certain scenarios of global mean temperature increase over time. The Vegetation Dynamic Simulation Model calculates a probability surface for each vegetation type, and then combines all vegetation types into a composite map, determined by the maximum likelihood that each vegetation type should distribute to each raster unit. With scenarios of global temperature increase of I to 3 degrees C, the vegetation types such as Dry Kobresia Meadow and Dry Potentilla Shrub that are adapted to warm and dry conditions tend to become more dominant in the study area.