A peatland productivity and decomposition parameter database.

A peatland productivity and decomposition parameter database was compiled to estimate parameters for the Canadian Model for Peatlands (CaMP); a module developed by the Canadian Forest Service to address the need for national-scale greenhouse gas emission estimates from peatlands present in the forested area of Canada. Data were compiled for 186 peatland sites from 69 sources. The SITES table contains wetland classification, tree classification, province or state, country, latitude, longitude, and an indication of coordinate accuracy. The NPP ALL table contains annual net primary productivity (NPP; g·m-2 ·yr-1 ) data for cases where one estimate for NPP was reported for all aboveground vegetation. The NPP SHRUB, NPP MOSS, and NPP HERB SEDGE tables each contain a classification of species (if available) or vegetation layer and their NPP (g·m-2 ·yr-1 ). The BIOMASS TREE, BIOMASS SHRUB, BIOMASS HERB SEDGE, and BIOMASS MOSS tables each contain a classification of species (if available) or vegetation layer and their standing aboveground biomass (g/m2 ). Shrubs in the NPP SHRUB and BIOMASS SHRUB tables were further classified into low or tall shrubs, and plants in the NPP HERB and BIOMASS HERB tables into herbs or sedges. The DECAY LITTER table contains decomposition parameters for different litter types and contains a classification of the type of decomposition study, study duration, information on experimental treatments, classification of above- or belowground plant parts, plant species, litter classification, root litter diameter, indication of hardwood or softwood, indication of evergreen or non-evergreen, litter bag depth classification, classes for bag placement relative to the water table and relative to the peat surface, water table depth, decay rate (k exponent), and mass loss values for years 1, 2, 3, 6, 7, 12, and 23 of a decomposition study. The REFERENCES table contains complete citation information and provides links to the source reference pdf file. This data set is vital to the national Canadian peatland modeling effort and should be useful to other peatland scientists and ecosystem modelers. © Her Majesty the Queen in Right of Canada, 2018. Information contained in this publication or product may be reproduced, in part or in whole, and by any means, for personal or public non-commercial purposes, without charge or further permission, unless otherwise specified. You are asked to exercise due diligence in ensuring the accuracy of the materials reproduced; indicate the complete title of the materials reproduced, and the name of the author organization; and indicate that the reproduction is a copy of an official work that is published by Natural Resources Canada (NRCan) and that the reproduction has not been produced in affiliation with, or with the endorsement of, NRCan. Commercial reproduction and distribution are prohibited except with written permission from NRCan. For more information, contact NRCan at copyright.droitdauteur@nrcan-rncan.gc.ca.

A Canadian upland forest soil profile and carbon stocks database.

"A Canadian upland forest soil profile and carbon stocks database" was compiled in phases over a period of 10 years to address various questions related to modeling upland forest soil carbon in a national forest carbon accounting model. For 3,253 pedons, the SITES table contains estimates for soil organic carbon stocks (Mg/ha) in organic horizons and mineral horizons to a 100-cm depth, soil taxonomy, leading tree species, mean annual temperature, annual precipitation, province or territory, terrestrial ecozone, and latitude and longitude, with an assessment of the quality of information about location. The PROFILES table contains profile data (16,167 records by horizon) used to estimate the carbon stocks that appear in the SITES table, plus additional soil chemical and physical data, where provided by the data source. The exceptions to this are estimates for soil carbon stocks based on Canadian National Forest Inventory data (NFI [2006] in REFERENCES table), where data were collected by depth increment rather than horizon and, therefore, total soil carbon stocks were calculated separately before being entered into the SITES table. Data in the PROFILES table include the carbon stock estimate for each horizon (corrected for coarse fragment content), and the data used to calculate the carbon stock estimate, such as horizon thickness, bulk density, and percent organic carbon. The PROFILES table also contains data, when reported by the source, for percent carbonate carbon, pH, percent total nitrogen, particle size distribution (percent sand, silt, clay), texture class, exchangeable cations, cation and total exchange capacity, and percent Fe and Al. An additional table provides references (REFERENCES table) for the source data. Earlier versions of the database were used to develop national soil carbon modeling categories based on differences in carbon stocks linked to soil taxonomy and to examine the potential of using soil taxonomy and leading tree species to improve accuracy in modeled predictions. The current database is being used to develop soil carbon model parameters linked to soil taxonomy and leading tree species and, by various governmental and nongovernmental organizations, to improve digital mapping of ecosite types and soil properties regionally, nationally, and internationally. © Her Majesty the Queen in Right of Canada, 2018. Information contained in this publication or product may be reproduced, in part or in whole, and by any means, for personal or public non-commercial purposes, without charge or further permission, unless otherwise specified. You are asked to: exercise due diligence in ensuring the accuracy of the materials reproduced; indicate the complete title of the materials reproduced, and the name of the author organization; indicate that the reproduction is a copy of an official work that is published by Natural Resources Canada (NRCan) and that the reproduction has not been produced in affiliation with, or with the endorsement of, NRCan. Commercial reproduction and distribution is prohibited except with written permission from NRCan. For more information, contact NRCan at copyright.droitdauteur@nrcan-rncan.gc.ca.

Carbon dynamics on agricultural land reverting to woody land in Ontario, Canada.

The 2015 Paris Agreement reinforces the importance of the land sector and its contribution to greenhouse gas (GHG) reductions. Thus, there is growing interest in improving estimates of the GHG balance in response to land-use changes (LUCs) involving agriculture and forestry, for national-scale reporting, and for carbon (C) offsets. Large agricultural areas in Europe, Russia and North America are reverting to forest, either naturally or through planting, after abandonment of agricultural land, and this trend may have a substantial impact on carbon budgets. We report results of a pilot project in the Mixedwood Plains ecozone of eastern Canada to analyze the change in the C budget on a landscape over 15 years on abandoned cropland where woody vegetation is regenerating. Thirty-six plots (2 km × 2 km) with paired aerial photographs taken circa 1994 and circa 2008 at a scale of 1:10,000 or larger were randomly selected from the 20 km × 20 km National Forest Inventory (NFI) grid. A spatially-explicit version of the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) was used to estimate impacts of LUC on C stocks and fluxes. Polygons identifying areas of LUC within each photo plot were delineated, classified, and evaluated to provide input data for the model. The rate of C accumulation in our study area was found to be relatively constant over the entire simulation period, at 1.07 Mg C/ha/yr. Abandoned agricultural land reverting to woody lands could play an important role in regional and national C sequestration in Canada, but more research is required to quantify the areal extent of this LUC.

Polymorphisms in CYP1B1, GSTM1, GSTT1 and GSTP1, and susceptibility to breast cancer.

Polymorphisms in the cytochrome P450 1B1 (CYP1B1) and glutathione S-transferase (GST) drug metabolic enzymes, which are responsible for metabolic activation/detoxification of estrogen and environmental carcinogens, were analyzed for their association with breast cancer risk in 541 cases and 635 controls from a North Carolina population. Each polymorphism, altering the catalytic function of their respective enzymes, was analyzed in Caucasian and African-American women. As reported in previous studies, individual polymorphisms did not significantly impact breast cancer risk in either Caucasian or African-American women. However, African-American women exhibited a trend towards a protective effect when they had at least one CYP1B1 119S allele (OR=0.53; 95% CI=0.20-1.40) and increased risk for those women harboring at least one CYP1B1 432V allele (OR=5.52; 95% CI=0.50-61.37). Stratified analyses demonstrated significant interactions in younger (age < or =60) Caucasian women with the CYP1B1 119SS genotype (OR=3.09; 95% CI=1.22-7.84) and younger African-American women with the GSTT1 null genotype (OR=4.07; 95% CI=1.12-14.80). A notable trend was also found in Caucasian women with a history of smoking and at least one valine allele at GSTP1 114 (OR=2.12; 95% CI=1.02-4.41). In Caucasian women, the combined GSTP1 105IV/VV and CYP1B1 119AA genotypes resulted in a near 2-fold increase in risk (OR=1.96; 95% CI=1.04-3.72) and the three way combination of GSTP1 105IV/VV, CYP1B1 119AS/SS and GSTT1 null genotypes resulted in an almost 4-fold increase in risk (OR=3.97; 95% CI=1.27-12.40). These results suggest the importance of estrogen/carcinogen metabolic enzymes in the etiology of breast cancer, especially in women before the age of 60, as well as preventative measures such as smoking cessation.

Polymorphisms in drug metabolism genes, smoking, and p53 mutations in breast cancer.

Polymorphisms in phase I and phase II enzymes may enhance the occurrence of mutations at critical tumor suppressor genes, such as p53, and increase breast cancer risk by either increasing the activation or detoxification of carcinogens and/or endogenous estrogens. We analyzed polymorphisms in CYP1B1, GSTM1, GSTT1, and GSTP1 and p53 mutations in 323 breast tumor samples. Approximately 11% of patients exhibited mutations in p53. Women with mutations had a significantly younger age of diagnosis (P = 0.01) and a greater incidence of tumors classified as stage II or higher (P = 0.002). More women with mutations had a history of smoking (55%) compared to women without mutations (39%). Although none of the genotypes alone were associated with p53 mutations, positive smoking history was associated with p53 mutations in women with the GSTM1 null allele [OR = 3.54; 95% CI = 0.97-12.90 P = 0.06] compared to women with the wild-type genotype and smoking history [OR = 0.62, 95% CI = 0.19-2.07], although this association did not reach statistical significance. To test for gene-gene interactions, our exploratory analysis in the Caucasian cases suggested that individuals with the combined GSTP1 105 VV, CYP1B1 432 LV/VV, and GSTM1 positive genotype were more likely to harbor mutations in p53 [OR = 4.94; 95% CI = 1.11-22.06]. Our results suggest that gene-smoking and gene-gene interactions may impact the prevalence of p53 mutations in breast tumors. Elucidating the etiology of breast cancer as a consequence of common genetic polymorphisms and the genotoxic effects of smoking will enable us to improve the design of prevention strategies, such as lifestyle modifications, in genetically susceptible subpopulations.