Filters: Tags: {"type":"Wildlife and Plants"} (X) > partyWithName: U.S. Geological Survey (X)
44 results (15ms)
Filters
Date Range
Extensions Types Contacts
Categories Tag Schemes Tags (with Type=Wildlife and Plants ) |
Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added...
This dataset includes model inputs that describe weather conditions for the 68 lakes included in this study. Weather data comes from gridded estimates (Mitchell et al. 2004). There are two comma-separated files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of lake temperature model inputs and outputs...
This data set includes bi-monthly data on submerged aquatic vegetation species composition, percent cover, above and below ground biomass and environmental data at coastal sites across the fresh to saline gradient in Barataria Bay, LA. This project was co-funded by the South Central Climate Adaptation Science Center and the Gulf Coast Prairie and the Gulf Coastal Plains and Ozarks Landscape Conservation Cooperatives. An alternate reference to this product can be found here.
Categories: Data;
Types: Citation,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Climatology,
Gulf of Mexico,
Louisiana Coastal,
Plants,
Sea-Level Rise and Coasts,
This dataset provides an estimate of 2015 cheatgrass percent cover in the northern Great Basin at 250 meter spatial resolution. The dataset was generated by integrating eMODIS NDVI satellite data with independent variables that influence cheatgrass germination and growth into a regression-tree model. Individual pixel values range from 0 to 100 with an overall mean value of 9.85 and a standard deviation of 12.78. A mask covers areas not classified as shrub/scrub or grass/herbaceous by the 2001 National Land Cover Database. The mask also covers areas higher than 2000 meters in elevation because cheatgrass is unlikely to exist at more than 2% cover above this threshold. Cheatgrass is an invasive grass that has invaded...
Categories: Data;
Types: ArcGIS REST Map Service,
ArcGIS Service Definition,
Downloadable,
Map Service;
Tags: Bromus tectorum,
Cheatgrass,
MODIS,
NDVI,
Northwest CASC,
The files in the sub-folder "1. Juvenile coho salmon abundance and survival" consist of fish survey data and the associated analysis. The file "02_Fish survey data_all events_2019-11-27.csv" contains the actual fish survey data that was collected in Mason Creek, tributary of East Fork Lewis River, SW Washington, during summer of 2017. The protocol for the fish surveys are outline in the file "Fish Rescue Field Protocol_2017_FINAL VERSION_2017-06-01.pdf". The abundance and survival analysis can be found in the file "Juvenile MR abundance_Coho_02Contraints_2019-12-02.R". This file should be loaded through the .Rproj file "Fish Abundance.Rproj". There are many files needed to run the analysis that consist of summaries...
Categories: Data;
Tags: Drought,
Drought, Fire and Extreme Weather,
Fish,
Northwest CASC,
Rivers, Streams and Lakes,
This dataset provides model specifications used to estimate water temperature from a process-based model (Hipsey et al. 2019). The format is a single JSON file indexed for each lake based on the "site_id". This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
Estimates of weather suitability for the occurrence of mortality in whitebark pine from mountain pine beetles as determined from a logistic generalized additive model of the presence of mortality as functions of the number of trees killed last year, the percent whitebark pine in each cell, minimum winter temperature, average fall temperature, avverage April-Aug temperature, and cummulative current and previous year summer precipitation. Analysis done at a 1km grid cell resolution. Weather suitability index calculated by summing the weather terms in the model. Calculated for 2010 through 2099 based on numerous downscaled data under several emissions scenarios. GCMs include: BCC, CanESM, CCSM, CESM, CESM-BGC, CMCC,...
Categories: Data;
Tags: Dendroctonus ponderosae,
Forests,
Landscapes,
Mountain pine beetle,
Northwest CASC,
Estimates of weather suitability for the occurrence of mortality in whitebark pine from mountain pine beetles as determined from a logistic generalized additive model of the presence of mortality as functions of the number of trees killed last year, the percent whitebark pine in each cell, minimum winter temperature, average fall temperature, avverage April-Aug temperature, and cummulative current and previous year summer precipitation. Analysis done at a 1km grid cell resolution. Weather suitability index calculated by summing the weather terms in the model. Calculated for 2010 through 2099 based on downscaled data from various emissions scenarios. GCMs include: BCC, CanESM, CCSM, CESM, CESM-BGC, CMCC, CNRM, Had-CC,...
Categories: Data;
Tags: Dendroctonus ponderosae,
Forests,
Greater Yellowstone Ecosystem,
Idaho,
Landscapes,
This dataset includes model inputs that describe local weather conditions for Sparkling Lake, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded estimates (all other time periods). There are two comma-delimited files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of...
This dataset includes model inputs (specifically, weather and flags for predicted ice-cover) and is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
Climate change has been shown to influence lake temperatures in different ways. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we focused on improving prediction accuracy for daily water temperature profiles in 68 lakes in Minnesota and Wisconsin during 1980-2018. The data are organized into these items: Spatial data - One shapefile of polygons for all 68 lakes in this study (.shp, .shx, .dbf, and .prj files) Model configurations - Model parameters and metadata used to configure models (1 JSON file, with metadata for each of 68 lakes, indexed by "site_id") Model inputs - Data formatted as model inputs for predicting temperature a. Lake...
This dataset includes compiled water temperature data from an instrumented buoy on Lake Mendota, WI and discrete (manually sampled) water temperature records from North Temperate Lakes Long-TERM Ecological Research Program (NTL-LTER; https://lter.limnology.wisc.edu/). The buoy is supported by both the Global Lake Ecological Observatory Network (gleon.org) and the NTL-LTER. This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
Estimates of weather suitability for the occurrence of mortality in whitebark pine from mountain pine beetles as determined from a logistic generalized additive model of the presence of mortality as functions of the number of trees killed last year, the percent whitebark pine in each cell, minimum winter temperature, average fall temperature, avverage April-Aug temperature, and cummulative current and previous year summer precipitation. Analysis done at a 1km grid cell resolution. Weather suitability index calculated by summing the weather terms in the model. Calculated for 2010 through 2099 based on numerous downscaled data under several emissions scenarios. GCMs include: BCC, CanESM, CCSM, CESM, CESM-BGC, CMCC,...
Categories: Data;
Tags: Dendroctonus ponderosae,
Forests,
Greater Yellowstone Ecosystem,
Idaho,
Landscapes,
Data files in this project were used in an integrated population model for three breeding populations of Wilson’s Warbler (Cardellina pusilla). Data span the years 1992-2008. Data include counts from the North American Breeding Bird Survey (BBS; https://www.pwrc.usgs.gov/BBS/RawData/; wiwa_bbs.csv), adult capture histories (wiwa_ch.csv) and age-specific capture data (wiwa_pdat.csv) from the Monitoring Avian Productivity and Survivorship program (MAPS; http://www.birdpop.org/pages/maps.php), and covariate data derived from the ClimateNA (https://sites.ualberta.ca/~ahamann/data/climatena.html; wiwa_cmd_wt.csv and wiwa_tave_sp.csv) and National Centers for Environmental Prediction (NCEP) ⁄ National Center for Atmospheric...
This dataset provides bi-monthly data on seed biomass collected in shallow water habitats across the fresh to saline gradient at coastal sites in Barataria Bay, Louisiana. This project was co-funded by the South Central Climate Adaptation Science Center and the Gulf Coast Prairie and the Gulf Coastal Plains and Ozarks Landscape Conservation Cooperatives. An alternate reference to this product can be found here.
Categories: Data;
Types: Citation;
Tags: Alabama,
Climatology,
Gulf of Mexico,
Louisiana Coastal,
Mississippi Coastal,
The following files are designed to be run using the Path Landscape Model software, version 3.0.4. Later versions of the software cannot run these files. To get a copy of this software, please contact Apex RMS at path@apexrms.com. 1) Path models MUST be run with the provided .MCM and .trd mulitplier files to apply the required transition probability adjustments for procesess such as insect outbreaks, wildfire, and climate change trends. Each Path database is set up with three folders: - The 'Common' folder contains a single Path scenario (also named 'Common'). The Transitions tab within the Common scenario contains the climate-smart STM. - The 'Multipliers' folder contains multipliers specific to each ownership-allocation...
Categories: Data;
Types: Downloadable;
Tags: Birds,
Drought, Fire and Extreme Weather,
Fire,
Forests,
Grasslands and Plains,
The data sets contains hierarchical taxonomic identification information for DNA sequences detected in water samples from 5 locations.
Categories: Data;
Tags: Aquatic Biology,
Decision-Making Support and Tools,
Ecology,
Mammals,
Nevada,
This dataset includes evaluation data ("test" data) and performance metrics for water temperature predictions from multiple modeling frameworks. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added physical constraint for...
This dataset includes model inputs that describe local weather conditions for Lake Mendota, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded estimates (all other time periods). There are two comma-delimited files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of lake...
This dataset includes evaluation data ("test" data) and performance metrics for water temperature predictions from multiple modeling frameworks. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added physical constraint for...
|
|