Modeling Response of N Addition on C and N Allocation in Scandinavian Norway Spruce Stands

Because nitrogen is considered to be the major growth-limiting element in boreal forests, the increasing nitrogen availability from deposition should lead to increasing growth. We have tested this assumption by simulating, with a simple model, carbon and nitrogen development in seven long-term fertilization experiments in three Nordic countries. The only differences between sites in the model are climate, the ambient nitrogen deposition, nitrogen fertilization regimes, and the initial conditions at the start of the experiment. The model simulates the observed stand development well as long as nitrogen remains the limiting factor. The simulated retention of deposited nitrogen is in general low (less than 50%), whereas retention of fertilizer nitrogen is higher. This seems to imply that the higher production in the fertilized stands will not be maintained once the fertilization is stopped. The model results also indicate that the major effect of climate on site productivity is through soil processes, not tree physiology.     

Sampling strategies for biotope definition: minimal sampling area for selected groups of macroinvertebrates in the rocky subtidal of São Miguel, Azores

Azorean rocky shores are mainly characterized by patchy algae-based communities with variable associated macrofauna. Characterization studies should therefore include quantitative information for both algae and macroinvertebrates. Unlike for the algae, minimal sampling areas are undefined for macroinvertebrates in the Azores. The present study defines the minimal area to be used for the assessment of the abundance of conspicuous benthic macroinvertebrate abundance. This study proposes methodologies to be used for a selected group of invertebrates when simultaneously undertaking quantifications of macroalgae.     

Hematopoietic chimerism monitoring based on STRs: quantitative platform performance on sequential samples.

Hematopoietic stem cell transplantation (HSCT) creates a donor-recipient cellular chimerism in the patient, which is quantitatively assayed from peripheral blood based on STR-DNA. Since chimerism values often vary across a patient's samples, it is important to determine to what extent this variability reflects technical aspects of platform performance. This issue is systematically assessed in the current study for the first time. Using the SGM Plus multiplex PCR kit and ABI platform, the longitudinal performance of STR markers was quantitatively evaluated in two chimeric models with true values, and in patient samples (n >500 marker loci). Computation of percent chimerism for each marker, and mean (sample) percent chimerism, standard deviation, and coefficient of variance was performed by our ChimerTrack utility. In chimeric models with known values, individual markers exhibited an accuracy (observed/true) of 88-98%; replication precision was 92-100% true, with a mean error of 2%. Fragment size calling was greater than 99% accurate and precise. Patient results were comparable for markers, relaive to sample means. One source of technical variability in chimerism estimation was allelic differential amplification efficiency. The latter was influenced by signal amplitude, dye label, marker size, and allelic size interval. It can be concluded that long-term chimeric tracking is routinely feasible using this platform in conjunction with ChimerTrack software. Importantly, mean percent chimerism, for any sample, should closely approximate the true chimeric status, with a technical accuracy of 98%. Guidelines are presented for selecting an optimized marker profile.     

Spatial heterogeneity and population risk analysis of cotton bollworm, Helicoverpa armigera, in China

In this study, semivariance was used to quantitatively measure the spatial heterogeneity for the egg population of cotton bollworm during a growing season. The typical characteristic parameters of theoretical semivariance models against lag distances were applied to measure components of spatial heterogeneity： trend, range, spatial dependence, and the strength of spatial dependence. Then, kriging interpolation was used to evaluate the population risk of cotton bollworm exceeding economic thresholds. From early June through early September, the population densities were sampled 10 times in the study field. Results showed that the spatial patterns were related to population density. For its low-density population, the spatially heterogeneous trends were usually of spherical shapes; but for highdensity ones, the trends shifted to Gaussian shapes. The spatial dependence appeared at varied distances ranging from 52 meters to 936 meters, and the spatial dependence was in the range of 0.39-288.60, which changed with population densities. While having high heterogeneity, the strength of spatial dependence became much stronger. Results of population risk analysis showed that there was a high risk during its early stages, especially in mid-June. In August, population risk was so low that it did not need to be controlled.