This map represents estimated stream flowlines from a hydrologically corrected digital elevation model. The lines were derived in GRASS GIS using a single flow-direction (D8) algorithm that does not allow channel braiding. Each stream segment is identified by a unique integer. Stream lines were deli

This map represents estimated stream flowlines from a hydrologically corrected digital elevation model. The lines were derived in GRASS GIS using a multi-direction algorithm that allows channel braiding. Each stream segment is identified by a unique integer. Stream lines were delineated for drainage

This map represents estimated stream flowlines from a hydrologically corrected digital elevation model. The lines were derived in GRASS GIS using a single-flow direction algorithm that does not allow channel braiding. Each stream segment is identified by a unique integer.Stream lines were delineated

This map represents estimated stream flowlines from a hydrologically corrected digital elevation model. The lines were derived in the GRASS GIS module r.watershed using a multi-flow direction algorithm that allows channel braiding. Each stream segment is identified by a unique integer.

This map represents estimated flow accumulation from a hydrologically corrected digital elevation model. The map was derived in GRASS GIS using a single flow-direction (D8) algorithm that does not allow channel braiding. Values represent the area (in square meters) of land that contributes flow to a

This dataset represents estimated flow accumulation from a hydrologically corrected digital elevation model. The map was derived in GRASS GIS using a multi-flow direction algorithm that allows channel braiding. Values represent the area (in square meters) of land that contributes flow to a given cel

Many biotic interactions influence community structure, yet most distribution models for plants have focused on plant competition or used only abiotic variables to predict plant abundance. Furthermore, biotic interactions are commonly context-dependent across abiotic gradients. For example, plant-pl

Elevation difference (snow depth estimate for exposed ground surfaces) between co-registered WorldView-3 optical stereo DSM products from 2016-09-25 (snow-off) and 2017-02-01 (snow-on). These are preliminary products from the Stereo2SWE workflow, used to derive snow depth estimates from time series

Elevation difference (snow depth estimate for exposed ground surfaces) between co-registered WorldView-3 optical stereo DSM products from 2016-09-25 (snow-off) and 2017-02-01 (snow-on). These are preliminary products from the Stereo2SWE workflow, used to derive snow depth estimates from time series

Elevation difference (snow depth estimate for exposed ground surfaces) between co-registered WorldView-3 optical stereo DSM products from 2016-09-25 (snow-off) and 2017-02-01 (snow-on). These are preliminary products from the Stereo2SWE workflow, used to derive snow depth estimates from time series

Elevation difference (snow depth estimate for exposed ground surfaces) between co-registered WorldView-3 optical stereo DSM products from 2016-09-25 (snow-off) and 2017-02-01 (snow-on). These are preliminary products from the Stereo2SWE workflow, used to derive snow depth estimates from time series

Elevation difference (snow depth estimate for exposed ground surfaces) between co-registered WorldView-3 optical stereo DSM products from 2016-09-25 (snow-off) and 2017-02-01 (snow-on). These are preliminary products from the Stereo2SWE workflow, used to derive snow depth estimates from time series

Elevation difference (snow depth estimate for exposed ground surfaces) between co-registered WorldView-3 optical stereo DSM products from 2016-09-25 (snow-off) and 2017-02-01 (snow-on). These are preliminary products from the Stereo2SWE workflow, used to derive snow depth estimates from time series

This folder contains scripts and datasets used in the manuscript titled " HydroMix v1.0: a new Bayesian mixing framework for attributing uncertain hydrological sources " in the journal Geoscientific Model Development . The article is available for reading at https://www.geosci-model-dev-discuss.net/

Complete census data in all plots for years 2014-2017, transformed into format suitable for demographic regression (CSV format with text metadata).

Data from: Thoma, D.P., S.M. Munson D.L. Witwicki 2018. Landscape pivot points and responses to water balance in national parks of the southwest U.S. Contact: David Thoma Dave_thoma@nps.gov 406-994-7725 These data are the polygon attributes and linear regression coefficients of iNDVI and water balan

Whether species interactions are static or change over time has wide-reaching ecological and evolutionary consequences. However, species interaction networks are typically constructed from temporally aggregated interaction data, thereby implicitly assuming that interactions are fixed. This approach

Aim: Ecologically driven diversification can create spectacular diversity in both species numbers and form. However, the prediction that the match between intrinsic (e.g. functional trait) and extrinsic (e.g. climatic niche) variables may lead to evolutionary radiation has not been critically tested