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This dataset represents the connections between each nearby pair of 2020 cores for Blackburnian warbler. It is intended to highlight areas important for connecting cores and to visually represent the connections among cores.
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This dataset represents the connections between each nearby pair of 2080 cores for American woodcock. It is intended to highlight areas important for connecting cores and to visually represent the connections among refugia cores.
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Within large-river ecosystems, floodplains serve a variety of important ecological functions. A recent survey of 80 managers of floodplain conservation lands along the Upper and Middle Mississippi and Lower Missouri Rivers in the central United States found that the most critical information needed to improve floodplain management centered on metrics for characterizing depth, extent, frequency, duration, and timing of inundation. These metrics can be delivered to managers efficiently through cloud-based interactive maps. To calculate these metrics, we interpolated an existing one-dimensional HEC-RAS hydraulic model for the Lower Missouri River, which simulated water surface elevations at cross sections spaced (<1...
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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...
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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...
The timing of life-history events in many plants and animals depend on specific environmental conditions that fluctuate with seasonal conditions. Climate change is altering environmental regimes and disrupting natural cycles and patterns across communities. Anadromous fishes that migrate between marine and freshwater habitats to spawn are particularly sensitive to shifting environmental conditions, and thus are vulnerable to the effects of climate change. However, for many anadromous fish species the specific environmental mechanisms driving migration and spawning patterns are not well understood. The data in this release are a supplement to the publication Legett et al. (2021). Daily patterns of river herring (Alosa...
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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).
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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...
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This dataset represents the connections between each nearby pair of 2080 cores for Cerulean warbler. It is intended to highlight areas important for connecting cores and to visually represent the connections among refugia cores.
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For 7 species: American woodcock (amwo), Bicknell's thrush (bith), Blackburnian warbler (blbw), Box turtle (teca), Cerulean warbler (cerw), Moose (moose), and Saltmarsh sparrow (sals) we provide five distinct datasets. Species codes (in parenthesis) are used in file names and represented by "[species]" below. 1. A set of conservation cores that represent areas of high local, relative value to the species either in the present, the future, or both: [species]_allcores.shp 2. Conductance among the present condition cores: [species]_conduct.tif 3. Conductance among the future cores: [species]_conduct_futr.tif 4. The landscape capability of the species in 2020 (an input dataset to this analysis): [species]_LC_2020_v5.1.tif...
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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).
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This dataset represents the connections between each nearby pair of 2020 cores for Moose. It is intended to highlight areas important for connecting cores and to visually represent the connections among cores.
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This dataset represents the connections between each nearby pair of 2020 cores for Saltmarsh sparrow. It is intended to highlight areas important for connecting cores and to visually represent the connections among cores.
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This dataset represents the connections between each nearby pair of 2080 cores for Box turtle. It is intended to highlight areas important for connecting cores and to visually represent the connections among refugia cores.
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This dataset represents the connections between each nearby pair of 2020 cores for Bicknell's thrush. It is intended to highlight areas important for connecting cores and to visually represent the connections among cores.
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This dataset represents the connections between each nearby pair of 2080 cores for Blackburnian warbler. It is intended to highlight areas important for connecting cores and to visually represent the connections among refugia cores.
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This dataset represents the connections between each nearby pair of 2020 cores for Box turtle. It is intended to highlight areas important for connecting cores and to visually represent the connections among cores.
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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...
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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).
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The U.S. Geological Survey has been forecasting sea-level rise impacts on the landscape to evaluate where coastal land will be available for future use. The purpose of this project is to develop a spatially explicit, probabilistic model of coastal response for the Northeastern U.S. to a variety of sea-level scenarios that take into account the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Model results provide predictions of adjusted land elevation ranges (AE) with respect to forecast sea-levels, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static or dynamic. The predictions span the...


map background search result map search result map Probability of Predicted Elevation with respect to projected sea levels for the Northeastern U.S. from Maine to Virginia for the 2020s, 2030s, 2050s and 2080s (Albers, NAD 83) Climate Change Scenario Inundation Metrics along the Upper and Middle Mississippi and Lower Missouri Rivers Current condition and refugia conservation cores for seven species and conductance among them Process-guided deep learning predictions of lake water temperature Process-guided deep learning water temperature predictions: 3 Model inputs (meteorological inputs and ice flags) Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Process-guided deep learning water temperature predictions: 4a Lake Mendota detailed training data Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Process-guided deep learning water temperature predictions: 3c All lakes historical inputs Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Pairwise comparisons of river herring run dynamics and environmental regimes among Massachusetts streams American woodcock 2080 conductance Bicknell's thrush 2020 conductance Blackburnian warbler 2020 conductance Blackburnian warbler 2080 conductance Cerulean warbler Climate Refugia 2080 Moose 2020 conductance Box turtle 2020 conductance Box turtle Landscape Capability Saltmarsh sparrow 2020 conductance Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Process-guided deep learning water temperature predictions: 4a Lake Mendota detailed training data Pairwise comparisons of river herring run dynamics and environmental regimes among Massachusetts streams Climate Change Scenario Inundation Metrics along the Upper and Middle Mississippi and Lower Missouri Rivers Process-guided deep learning predictions of lake water temperature Process-guided deep learning water temperature predictions: 3 Model inputs (meteorological inputs and ice flags) Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Process-guided deep learning water temperature predictions: 3c All lakes historical inputs Probability of Predicted Elevation with respect to projected sea levels for the Northeastern U.S. from Maine to Virginia for the 2020s, 2030s, 2050s and 2080s (Albers, NAD 83) Current condition and refugia conservation cores for seven species and conductance among them Moose 2020 conductance Box turtle 2020 conductance Saltmarsh sparrow 2020 conductance Blackburnian warbler 2020 conductance Blackburnian warbler 2080 conductance Cerulean warbler Climate Refugia 2080 Box turtle Landscape Capability American woodcock 2080 conductance Bicknell's thrush 2020 conductance