Protein predictions using Augustus web for the fungi Neonectria coccinea and N. faginata, as well as protein prediction of closely related species N. ditissima, and Corinectria fuckeliana.

EM-DAT contains essential core data on the occurrence and effects of over 22,000 mass disasters in the world from 1900 to the present day., including floods, droughts, storm events, famine and disease epidemics.The database is compiled from various sources, including UN agencies, non-governmental organisations, insurance companies, research institutes and press agencies.

The Water Data Centre provides the European entry point for water related data as part of the Water Information System for Europe (WISE). Data covers the following broad (non-exhaustive) themes: Water quality and contamination including surface and groundwater resource contamination with Nitrate, Phosphorous etc. Waterbase Water Quality database. Water treatment and investment water biodiversity marine environmental condition Runoff trends (including minimum flow return period analysis) Drought frequency and extent flood risk and extent including climate change impact Freshwater abstraction by source Water use by sector Economic value added and water extraction Water scarcity exposure water intensity water accounting by river basin Water and food-borne diseases water and crop production and irrigation, including land area water abstraction in candidate countries Water Statistics (Eurostat) Europe water asset accounts

For large areas, like Washington State, download as a file geodatabase.  Large data sets like this one, for the State of Washington, may exceed the limits for downloading as shape files, excel files, or KML files.  For areas less than a county, you may use the map to zoom to your area and download as shape file, excel or KML, if that format is desired.Every summer for approximately the past seventy years, an insect and disease aerial detection survey has been flown of all the forested acres of Washington state (except where noted in the digital data by large 'NF' (not flown) areas). This survey is a cooperative effort between the U.S. Forest Service and the WADNR with two different flight observers each sketching a two mile swath out their side of the plane. The primary mission of the survey is to record recently killed and defoliated groups of trees throughout the state, and to continually build a historical record of these trends. The vast majority of damage found is caused by insect and disease damage agents; however, trees killed by early spring feeding of black bears or by events such as winter storms, fires, floods and landslides are recorded as well. Current defoliation can be detected as soon as the affected foliage changes color that year. However, whole tree mortality is not current since only flagged trees (i.e., trees which have a bright red, orange, or yellow foliage color) are recorded. This means that trees killed the year of the survey will not have changed color yet and so a one year lag time results. Since only this distinctive color or "signature" of the tree can be seen. It is an educated guess as to the causal agent. We therefore use ground surveys to reinforce our estimates as much as possible. Example: When bear damage is spotted while surveying, a polygon is drawn on the map of the size and location of the damage. The polygon is then labeled with the appropriate damage agent (i.e. Bear) and the number of trees affected rounded to the nearest five. No vertical data is recorded.

[NOTE - 11/24/2021: this dataset is superseded by an updated version https://doi.org/10.15482/USDA.ADC/1524292 ] Data Sources: Time series data on cattle fever tick incidence, 1959-2017, and climate variables January 1950 through December 2017, form the core information in this analysis. All variables are monthly averages or sums over the fiscal year (FY), October 01 (of the prior calendar year, y-1) through September 30 of the current calendar year (y). Annual records on detections of Rhipicephalus (Boophilus) microplus and R. (B). annulatus (cattle fever tick, CFT) on premises within the Permanent Quarantine Zone (PQZ) were obtained from CFTEP (USDA-APHIS and the USDA- ARS). Details of tick survey procedures, CFTEP program goals and history, and the geographic extent of the Permanent Quarantine Zone are in SI (Introduction, details). Solar radio flux data as well as Pacific Ocean El Niño Oscillation index data, 1950-2017, are accessed at from NOAA ESRL (2018b). Predicted values for on-going Solar Cycle 24 are from NOAA SWPC (2018). Accumulated Cyclone Energy Index (ACE) data are from the NOAA ESRL (2018a) database. Hurricane incidence data over the PQZ are accessed at the NOAA (2018) tropical storm database. Local meteorology data are from the NOAA NCDC (2018) climate portal for three weather stations (Del Rio International Airport TX, Laredo Municipal Airport TX, and Brownsville South Padre Island International Airport TX). Details on these stations and data are in the SI (Methods and Data, additional details). Data Pre-treatment: Global climate indicators, local meteorology, and CFT variables are assembled into a single MS Excel matrix. To address the low signal-to-noise ratio and non-independence of time series common in weather data (SI Methods and Data, additional details, tests). We transform all predictor and response variables using a series of five consecutive steps: 1) first differences (year n minus year n-1) were calculated; 2) and these converted to z scores (z = (x- μ) / σ); 3) linear regression was used to remove directional trends; 4) moving averages were calculated for each data vector, and; 5) a lag was optionally applied. The transformed data variables were then tested for predictive ability using simple correlation, probability of, error and level of significance. Bivariate and Multivariate Regression Analysis: Four bivariate Best Model regressions of climate predictors on CFT are developed using XLSTAT software (Addinsoft Inc. 2018); three multivariate models include regression with no interactions, with level 2 interactions, and with variables restricted to two and to four variables minimum. To validate each model, we withhold the first and last 29 observations points. Nine model evaluation and three summary statistics are identified in SI (Methods and Data, additional details, definitions). Reconstruction of Complete CFT Cycle, and Projection: It is generally recognized that the onset year of the first outbreak is not 1959 but some earlier point in the decade. Likewise, 2017 is unlikely the final end-year of the current outbreak. Given the lack of complete data on any one CFT outbreak cycle, we average CFT levels over Outbreak 1 and Outbreak 2, using 1987 as the mid-point between outbreaks (see Fig. 2). Using a Hurricane-Hale Cycle construct (SI Fig. 1), we hypothesize Outbreak 1 starts in 1943 and ends in 1987; Outbreak 2 starts in 1987 and ends in 2030-2031. We then extend the full CFT pattern one cycle into the future to forecast likely incidence beyond 2030.

[NOTE - 11/24/2021: this dataset supersedes an earlier version https://doi.org/10.15482/USDA.ADC/1518654 ] Data sources. Time series data on cattle fever tick incidence, 1959-2020, and climate variables January 1950 through December 2020, form the core information in this analysis. All variables are monthly averages or sums over the fiscal year, October 01 (of the prior calendar year, y-1) through September 30 of the current calendar year (y). Annual records on monthly new detections of Rhipicephalus microplus and R. annulatus (cattle fever tick, CFT) on premises within the Permanent Quarantine Zone (PQZ) were obtained from the Cattle Fever Tick Eradication Program (CFTEP) maintained jointly by the United States Department of Agriculture (USDA), Animal Plant Health Inspection Service and the USDA Animal Research Service in Laredo, Texas. Details of tick survey procedures, CFTEP program goals and history, and the geographic extent of the PQZ are in the main text, and in the Supporting Information (SI) of the associated paper. Data sources on oceanic indicators, on local meteorology, and their pretreatment are detailed in SI. Data pretreatment. To address the low signal-to-noise ratio and non-independence of observations common in time series, we transformed all explanatory and response variables by using a series of six consecutive steps: (i) First differences (year y minus year y-1) were calculated, (ii) these were then converted to z scores (z = (x- μ) / σ, where x is the raw value, μ is the population mean, σ is the standard deviation of the population), (iii) linear regression was applied to remove any directional trends, (iv) moving averages (typically 11-year point-centered moving averages) were calculated for each variable, (v) a lag was applied if/when deemed necessary, and (vi) statistics calculated (r, n, df, P<, p<). Principal component analysis (PCA). A matrix of z-score first differences of the 13 climate variables, and CFT (1960-2020), was entered into XLSTAT principal components analysis routine; we used Pearson correlation of the 14 x 60 matrix, and Varimax rotation of the first two components. Autoregressive Integrated Moving Average (ARIMA). An ARIMA (2,0,0) model was selected among 7 test models in which the p, d, and q terms were varied, and selection made on the basis of lowest RMSE and AIC statistics, and reduction of partial autocorrelation outcomes. A best model linear regression of CFT values on ARIMA-predicted CFT was developed using XLSTAT linear regression software with the objective of examining statistical properties (r, n, df, P<, p<), including the Durbin-Watson index of order-1 autocorrelation, and Cook’s Di distance index. Cross-validation of the model was made by withholding the last 30, and then the first 30 observations in a pair of regressions. Forecast of the next major CFT outbreak. It is generally recognized that the onset year of the first major CFT outbreak was not 1959, but may have occurred earlier in the decade. We postulated the actual underlying pattern is fully 44 years from the start to the end of a CFT cycle linked to external climatic drivers. (SI Appendix, Hypothesis on CFT cycles). The hypothetical reconstruction was projected one full CFT cycle into the future. To substantiate the projected trend, we generated a power spectrum analysis based on 1-year values of the 1959-2020 CFT dataset using SYSTAT AutoSignal software. The outcome included a forecast to 2100; this was compared to the hypothetical reconstruction and projection. Any differences were noted, and the start and end dates of the next major CFT outbreak identified.

Recent USDA/ARS patented technologies on animal production and protection that are available for licensing are described, including summary, contact, benefits, and applications. Updated June 2018.