CaPTrends: a database of large carnivoran population trends from around the world

Motivation Population trend information is an ‘essential biodiversity variable’ for monitoring change in biodiversity over time. Here, we present a database of 1,122 population trends from around the world, describing changes in abundance over time in large mammal species (n = 50) from four families in the order Carnivora. For this subset of taxa, we provide approximately 21 times more trends than BioTIME and three times more trends than the Living Planet database. Main types of variables included Key data fields for each trend: species, coordinates, trend time-frame, methods of data collection and analysis, and population time series or summarized trend value. Population trend values are reported using quantitative metrics in 75% of records that collectively represent more than 6,500 population estimates. The remaining records qualitatively describe population change (e.g., increase). Spatial location and grain Trends represent 621 unique locations across the globe (latitude: −51.0 to 80.0; longitude: −166.0 to 166.0). Most trends (86%) are found within the Northern Hemisphere. Time period and grain On average (mean), trends are derived from 6.5 abundance observations, and span in time from 1726 to 2017, with 92% of trends starting after 1950. Major taxa and level of measurement We conducted a semi-systematic search for population trend data in 87 species from four families in the order Carnivora: Canidae, Felidae, Hyaenidae and Ursidae. We compiled data for 50 of the 87 species. Software format .csv

Measurement of negative particle multiplicity in S - Pb collisions at 200 GeV/c per nucleon with the NA36 TPC

A high statistics study of the negative multiplicity distribution from S-Pb collisions at 200 GeV/c per nucleon is presented. The NA36 TPC was used to detect charged particles; corrections are based upon the maximum entropy method.A high statistics study of the negative multiplicity distribution from S-Pb collisions at 200 GeV/c per nucleon is presented. The NA36 TPC was used to detect charged particles; corrections are based upon the maximum entropy method.A high statistics study of the negative particle multiplicity distribution from S–Pb collisions at 200 GeV/ c per nucleon is presented. The NA36 TPC was used to detect charged particles; corrections are based upon the maximum entropy method

Tree height integrated into pantropical forest biomass estimates

Copyright © 2012 European Geosciences Union. This is the published version available at http://www.biogeosciences.net/9/3381/2012/bg-9-3381-2012.htmlAboveground tropical tree biomass and carbon storage estimates commonly ignore tree height (H). We estimate the effect of incorporating H on tropics-wide forest biomass estimates in 327 plots across four continents using 42 656 H and diameter measurements and harvested trees from 20 sites to answer the following questions: 1. What is the best H-model form and geographic unit to include in biomass models to minimise site-level uncertainty in estimates of destructive biomass? 2. To what extent does including H estimates derived in (1) reduce uncertainty in biomass estimates across all 327 plots? 3. What effect does accounting for H have on plot- and continental-scale forest biomass estimates? The mean relative error in biomass estimates of destructively harvested trees when including H (mean 0.06), was half that when excluding H (mean 0.13). Power- and Weibull-H models provided the greatest reduction in uncertainty, with regional Weibull-H models preferred because they reduce uncertainty in smaller-diameter classes (≤40 cm D) that store about one-third of biomass per hectare in most forests. Propagating the relationships from destructively harvested tree biomass to each of the 327 plots from across the tropics shows that including H reduces errors from 41.8 Mg ha−1 (range 6.6 to 112.4) to 8.0 Mg ha−1 (−2.5 to 23.0). For all plots, aboveground live biomass was −52.2 Mg ha−1 (−82.0 to −20.3 bootstrapped 95% CI), or 13%, lower when including H estimates, with the greatest relative reductions in estimated biomass in forests of the Brazilian Shield, east Africa, and Australia, and relatively little change in the Guiana Shield, central Africa and southeast Asia. Appreciably different stand structure was observed among regions across the tropical continents, with some storing significantly more biomass in small diameter stems, which affects selection of the best height models to reduce uncertainty and biomass reductions due to H. After accounting for variation in H, total biomass per hectare is greatest in Australia, the Guiana Shield, Asia, central and east Africa, and lowest in east-central Amazonia, W. Africa, W. Amazonia, and the Brazilian Shield (descending order). Thus, if tropical forests span 1668 million km2 and store 285 Pg C (estimate including H), then applying our regional relationships implies that carbon storage is overestimated by 35 Pg C (31–39 bootstrapped 95% CI) if H is ignored, assuming that the sampled plots are an unbiased statistical representation of all tropical forest in terms of biomass and height factors. Our results show that tree H is an important allometric factor that needs to be included in future forest biomass estimates to reduce error in estimates of tropical carbon stocks and emissions due to deforestation

X chromosome inactivation does not necessarily determine the severity of the phenotype in Rett syndrome patients

Rett syndrome (RTT) is a severe neurological disorder usually caused by mutations in the MECP2 gene. Since the MECP2 gene is located on the X chromosome, X chromosome inactivation (XCI) could play a role in the wide range of phenotypic variation of RTT patients; however, classical methylation-based protocols to evaluate XCI could not determine whether the preferentially inactivated X chromosome carried the mutant or the wild-type allele. Therefore, we developed an allele-specific methylation-based assay to evaluate methylation at the loci of several recurrent MECP2 mutations. We analyzed the XCI patterns in the blood of 174 RTT patients, but we did not find a clear correlation between XCI and the clinical presentation. We also compared XCI in blood and brain cortex samples of two patients and found differences between XCI patterns in these tissues. However, RTT mainly being a neurological disease complicates the establishment of a correlation between the XCI in blood and the clinical presentation of the patients. Furthermore, we analyzed MECP2 transcript levels and found differences from the expected levels according to XCI. Many factors other than XCI could affect the RTT phenotype, which in combination could influence the clinical presentation of RTT patients to a greater extent than slight variations in the XCI pattern