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We analyzed the dynamics of carbon balance components: gross primary production (GPP) and total ecosystem respiration (TER), of a boreal Scots pine forest in Southern Finland. The main focus is on investigations of environmental drivers of GPP and TER and how they affect the inter-annual variation in the carbon balance in autumn (September-December). We used standard climate data and CO2 exchange measurements collected by the eddy covariance (EC) technique over 11 years. EC data revealed that increasing autumn temperature significantly enhances TER: the temperature sensitivity was 9.5 gC m-2 °C-1 for the period September-October (early autumn when high radiation levels still occur) and 3.8 gC m-2 °C-1 for November- December (late autumn with suppressed radiation level). The cumulative GPP was practically independent of the temperature in early autumn. In late autumn, air temperature could explain part of the variation in GPP but the temperature sensitivity was very weak, less than 1 gC m-2 °C-1. Two models, a stand photosynthesis model (COCA) and a global vegetation model (ORCHIDEE), were used for estimating stand GPP and its sensitivity to the temperature. The ORCHIDEE model was tested against the observations of GPP derived from EC data. The stand photosynthesis model COCA predicted that under a predescribed 3-6 °C temperature increase, the temperature sensitivity of 4-5 gC m-2 °C-1 in GPP may appear in early autumn. The analysis by the ORCHIDEE model revealed the model sensitivity to the temporal treatment of meteorological forcing. The model predictions were similar to observed ones when the site level 1/2-hourly time step was applied, but the results calculated by using daily meteorological forcing, interpolated to 1/2-hourly time step, were biased. This is due to the nonlinear relationship between the processes and the environmental factors. [ABSTRACT FROM AUTHOR]

We analyzed the dynamics of carbon balance components: gross primary production (GPP) and total ecosystem respiration (TER), of a boreal Scots pine forest in Southern Finland. Our aim was to study how these dynamics are related to different environmental conditions and how they affect the inter-annual variation in the carbon balance in autumn (September-December). We used standard micrometeorological data and CO2 exchange measurements collected by the eddy covariance (EC) technique over 11 years. The intra-annual relationships between the carbon balance components and the environmental factors were studied by the correlation analysis. Two models, a stand photosynthesis model and a generic dynamic vegetation model (ORCHIDEE), were also applied in the analysis. EC data revealed that increasing autumn temperature significantly enhances TER: the temperature sensitivity was 9.5 gCm-2 °C-1 for the period September-October (early autumn when high radiation levels still occur) and 3.8 gCm-2 °C-1 for November-December (late autumn with suppressed radiation 15 level). The cumulative GPP was practically independent of the temperature in early autumn. In late autumn, air temperature could explain part of the variation in GPP but the temperature sensitivity was very weak, less than 1 gCm-2 °C-1. The stand photosynthesis model predicted that under a predescribed 3-6°C temperature increase, the temperature sensitivity of 4-5 gCm-2 °C-1 in GPP may appear in early autumn. The 20 TER and GPP sensitivities, produced by the ORCHIDEE model, were similar to observed ones when the site level 1/2 h time step was applied, but the results calculated by using daily meteorological forcing, interpolated to 1/2 h time step, were biased stemming from the nonlinear relationship between the processes and the environmental factors. [ABSTRACT FROM AUTHOR]

We describe a new method for finding haplotype blocks based on the use of the minimum description length principle. We give a rigorous definition of the quality of a segmentation of a genomic region into blocks, and describe a dynamic programming algorithm for finding the optimal segmentation with respect to this measure. We also describe a method for finding the probability of a block boundary for each pair of adjacent markers: this gives a tool for evaluating the significance of each block boundary. We have applied the method to the published data of Daly et al. The results are in relatively good agreement with the published results, but also show clear differences in the predicted block boundaries and their strengths. We also give results on the block structure in population isolates.