Feeding habits of Calanus sinicus (Crustacea : Copepoda) during spring and autumn in the Bohai Sea studied with the herbivore index

Pigment ingestion rate (PIR) and egg production rate (EPR) of the dominant copepod Calanus sinicus, as well as chlorophyll-a concentration and phytoplankton assemblages were measured in the Bohai Sea, North China in June 1997, October 1998 and May 1999. A herbivore index (H) was also calculated as the carbon specific ratio of PIR and EPR, in order to investigate its feeding habits in the spring and autumn phytoplankton bloom respectively. On average, chlorophyll-a concentration was relatively similar (1-1.34 mg m(-3)) in the three cruises, but PIR was quite different. It was 3.24 mu g C female(-1) d(-1) in October, equivalent to one half of the PIR for June and one third of the PIR for May. Average EPR was highest in May, and quite similar during the other two months. According to H values, herbivorous feeding contributed 100% of the egg production of C. sinicus in June, 82.5% in May, but only 47.8% in October. It is possible that omnivorous feeding of C. sinicus in October was induced by a prevalence of large-sized diatoms and sufficient non-phytoplankton food resources during the autumn bloom period.

Diurnal, seasonal and annual variation in net ecosystem CO2 exchange of an alpine shrubland on Qinghai-Tibetan plateau

Thus far, grassland ecosystem research has mainly been focused on low-lying grassland areas, whereas research on high-altitude grassland areas, especially on the carbon budget of remote areas like the Qinghai-Tibetan plateau is insufficient. To address this issue, flux of CO2 were measured over an alpine shrubland ecosystem (37 degrees 36'N, 101 degrees 18'E; 325 above sea level [a. s. l.]) on the Qinghai-Tibetan Plateau, China, for 2 years (2003 and 2004) with the eddy covariance method. The vegetation is dominated by formation Potentilla fruticosa L. The soil is Mol-Cryic Cambisols. To interpret the biotic and abiotic factors that modulate CO2 flux over the course of a year we decomposed net ecosystem CO2 exchange (NEE) into its constituent components, and ecosystem respiration (R-eco). Results showed that seasonal trends of annual total biomass and NEE followed closely the change in leaf area index. Integrated NEE were -58.5 and -75.5 g C m(-2), respectively, for the 2003 and 2004 years. Carbon uptake was mainly attributed from June, July, August, and September of the growing season. In July, NEE reached seasonal peaks of similar magnitude (4-5 g C m(-2) day(-1)) each of the 2 years. Also, the integrated night-time NEE reached comparable peak values (1.5-2 g C m(-2) day(-1)) in the 2 years of study. Despite the large difference in time between carbon uptake and release (carbon uptake time < release time), the alpine shrubland was carbon sink. This is probably because the ecosystem respiration at our site was confined significantly by low temperature and small biomass and large day/night temperature difference and usually soil moisture was not limiting factor for carbon uptake. In general, R-eco was an exponential function of soil temperature, but with season-dependent values of Q(10). The temperature-dependent respiration model failed immediately after rain events, when large pulses of R-eco were observed. Thus, for this alpine shrubland in Qinghai-Tibetan plateau, the timing of rain events had more impact than the total amount of precipitation on ecosystem R-eco and NEE.

Assessing the potential of VEGETATION sensor data for mapping snow and ice cover: a Normalized Difference Snow and Ice Index

The VEGETATION (VGT) sensor in SPOT 4 has four spectral bands that are equivalent to Landsat Thematic Mapper (TM) bands (blue, red, near-infrared and mid-infrared spectral bands) and provides daily images of the global land surface at a 1-km spatial resolution. We propose a new index for identifying and mapping of snow ice cover, namely the Normalized Difference Snow/Ice Index (NDSII), which uses reflectance values of red and mid-infrared spectral bands of Landsat TM and VGT. For Landsat TM data, NDSII is calculated as NDSIITM =(TM3 -TM5)/(TM3 +TM5); for VGT data, NDSII is calculated as NDSIIVGT =(B2- MIR)/(B2 + MIR). As a case study we used a Landsat TM image that covers the eastern part of the Qilian mountain range in the Qinghai-Xizang (Tibetan) plateau of China. NDSIITM gave similar estimates of the area and spatial distribution of snow/ice cover to the Normalized Difference Snow Index (NDSI=(TM2-TM5)/(TM2+TM5)) which has been proposed by Hall et al. The results indicated that the VGT sensor might have the potential for operational monitoring and mapping of snow/ice cover from regional to global scales, when using NDSIIVGT.