Projecting the distribution of forests in New England in response to climate change

Aim To project the distribution of three major forest types in the northeastern USA in response to expected climate change. Location The New England region of the United States. Methods We modelled the potential distribution of boreal conifer, northern deciduous hardwood and mixed oak-hickory forests using the process-based BIOME4 vegetation model parameterized for regional forests under historic and projected future climate conditions. Projections of future climate were derived from three general circulation models forced by three global warming scenarios that span the range of likely anthropogenic greenhouse gas emissions. Results Annual temperature in New England is projected to increase by 2.2-3.3 °C by 2041-70 and by 3.0-5.2 °C by 2071-99 with corresponding increases in precipitation of 4.7-9.5% and 6.4-11.4%, respectively. We project that regional warming will result in the loss of 71-100% of boreal conifer forest in New England by the late 21st century. The range of mixed oak-hickory forests will shift northward by 1.0-2.1 latitudinal degrees (c. 100-200 km) and will increase in area by 149-431% by the end of the 21st century. Northern deciduous hardwoods are expected to decrease in area by 26% and move upslope by 76 m on average. The upslope movement of the northern deciduous hardwoods and the increase in oak-hickory forests coincide with an approximate 556 m upslope retreat of the boreal conifer forest by 2071-99. In our simulations, rising atmospheric CO2 concentrations reduce the losses of boreal conifer forest in New England from expected losses based on climatic change alone. Main conclusion Projected climate warming in the 21st century is likely to cause the extensive loss of boreal conifer forests, reduce the extent of northern hardwood deciduous forests, and result in large increases of mixed oak-hickory forest in New England. © 2010 Blackwell Publishing Ltd

Estimating potential forest NPP, biomass and their climatic sensitivity in New England using a dynamic ecosystem model

Accurate estimation of forest net primary productivity (NPP), biomass, and their sensitivity to changes in temperature and precipitation is important for understanding the fluxes and pools of terrestrial carbon resulting from anthropogenically driven climate change. The objectives of this study were to (1) estimate potential forest NPP and biomass for New England using a regional ecosystem model, (2) compare modeled forest NPP and biomass with other reported data for New England, and (3) examine the sensitivity of modeled forest NPP to historical climatic variation. We addressed these objectives using the regional ecosystem model LPJ-GUESS implemented with eight plant functional types representing New England forests. We ran the model using 30-arc second spatial resolution climate data in monthly timesteps for the period 1901-2006. The modeled forest NPP and biomass were compared to empirically-based MODIS and FIA estimates of NPP and U.S. forest biomass. Our results indicate that forest NPP in New England averages 428 g C m-2yr-1 and ranges from 333 to 541 g C m-2yr-1 for the baseline period (1971- 2000), while forest biomass averages 135 Mg/ha and ranges from 77 to 242 Mg/ha. Modeled forest biomass decreased at a rate of 0.11 Mg/ha (R2=0.74) per year in the period 1901-1949 but increased at a rate of 0.25 Mg/ha (R2=0.95) per year in the period 1950-2006. Estimates of NPP and biomass depend on forest type: spruce-fir had the lowest mean of 395 g C m-2yr-1 and oak forest had the highest mean of 468 g C m-2yr-1. Similarly, forest biomass was highest in oak (153 Mg/ha) and lowest in red-jack pine (118 Mg/ ha) forests. The modeled NPP for New England agrees well with FIA-based estimates from similar forests in the mid-Atlantic region but was smaller than MODIS NPP estimates for New England. Nevertheless, the modeled inter-annual variability of NPP was strongly correlated with the MODIS NPP data. The modeled biomass agrees well with U.S. forest biomass data for New England but was less than FIA-based estimates in the mid-Atlantic region. For the region as a whole, the modeled NPP and biomass are within the ranges of MODIS- and FIA-based estimates. Forest NPP was sensitive to changes in temperature and precipitation: NPP was positively related to temperatures in April, May and October but negatively related to summer temperature. Increases in precipitation in the growing season enhanced forest NPP. © 2010 Tang et al

A Kind of New Surface Modeling Method Based on DEM Data

Surface elevation changes greatly in the river erosion area. Due to the limitation of the acquisition equipment and cost, the traditional seismic acquisition data has sparse physical points both horizontally and longitudinally, the density of surface measurement data is not enough to survey the surface structure in detail. With the development of science and technology, and the application of satellite technology, the DEM elevation data obtained from the geographic information system (GIS) are becoming more and more accurate. In this paper, a precise modeling is performed on the surface based on the geographic information from the river erosion area and combined with the results of the surface survey control points, a good effect is achieved.Key words: River erosion area; Geographic information; Similarity coefficient; Kriging interpolation; Surface modeling; High and low frequency static

KCAT: A Knowledge-Constraint Typing Annotation Tool

Fine-grained Entity Typing is a tough task which suffers from noise samples extracted from distant supervision. Thousands of manually annotated samples can achieve greater performance than millions of samples generated by the previous distant supervision method. Whereas, it's hard for human beings to differentiate and memorize thousands of types, thus making large-scale human labeling hardly possible. In this paper, we introduce a Knowledge-Constraint Typing Annotation Tool (KCAT), which is efficient for fine-grained entity typing annotation. KCAT reduces the size of candidate types to an acceptable range for human beings through entity linking and provides a Multi-step Typing scheme to revise the entity linking result. Moreover, KCAT provides an efficient Annotator Client to accelerate the annotation process and a comprehensive Manager Module to analyse crowdsourcing annotations. Experiment shows that KCAT can significantly improve annotation efficiency, the time consumption increases slowly as the size of type set expands.Comment: 6 pages, acl2019 demo pape

Gene expression profile analysis of human hepatocellular carcinoma using SAGE and LongSAGE

<p>Abstract</p> <p>Background</p> <p>Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and the second cancer killer in China. The initiation and malignant transformation of cancer result from accumulation of genetic changes in the sequences or expression level of cancer-related genes. It is of particular importance to determine gene expression profiles of cancers on a global scale. SAGE and LongSAGE have been developed for this purpose.</p> <p>Methods</p> <p>We performed SAGE in normal liver and HCC samples as well as the liver cancer cell line HepG2. Meanwhile, the same HCC sample was simultaneously analyzed using LongSAGE. Computational analysis was carried out to identify differentially expressed genes between normal liver and HCC which were further validated by real-time quantitative RT-PCR.</p> <p>Results</p> <p>Approximately 50,000 tags were sequenced for each of the four libraries. Analysis of the technical replicates of HCC indicated that excluding the low abundance tags, the reproducibility of SAGE data is high (R = 0.97). Compared with the gene expression profile of normal liver, 224 genes related to biosynthesis, cell proliferation, signal transduction, cellular metabolism and transport were identified to be differentially expressed in HCC. Overexpression of some transcripts selected from SAGE data was validated by real-time quantitative RT-PCR. Interestingly, sarcoglycan-ε (SGCE) and paternally expressed gene (PEG10) which is a pair of close neighboring genes on chromosome 7q21, showed similar enhanced expression patterns in HCC, implicating that a common mechanism of deregulation may be shared by these two genes.</p> <p>Conclusion</p> <p>Our study depicted the expression profile of HCC on a genome-wide scale without the restriction of annotation databases, and provided novel candidate genes that might be related to HCC.</p

A Comparison of Co-methylation Relationships Between Rheumatoid Arthritis and Parkinson's Disease

Rheumatoid arthritis (RA) is a complex autoimmune disease. Recent studies have identified the DNA methylation loci associated with RA and found that DNA methylation was a potential mediator of genetic risk. Parkinson's disease (PD) is a common neurodegenerative disease. Several studies have indicated that DNA methylation levels are linked to PD, and genes related to the immune system are significantly enriched in PD-related methylation modules. Although recent studies have provided profound insights into the DNA methylation of both RA and PD, no shared co-methylation relationships have been identified to date. Therefore, we sought to identify shared co-methylation relationships linked to RA and PD. Here, we calculated the Pearson's correlation coefficient (PCC) of 225,239,700 gene pairs and determined the differences and similarities between the two diseases. The global co-methylation change between in PD cases and controls was larger than that between RA cases and controls. We found 337 gene pairs with large changes that were shared between RA and PD. This co-methylation relationship study represents a new area of study for both RA and PD and provides new ideas for further study of the shared biological mechanisms of RA and PD