Accuracy statistics for the oil and gas prediction model.

<p>Accuracy statistics for the oil and gas prediction model.</p

Oil and gas simulation results for the two scenarios.

<p>This map illustrates the location and extent of expected development in the two scenarios. Areas in orange depict growth for the anticipated scenario. Areas in red depict growth for the unrestrained scenario. Bar graphs show the quantity of development projected for each scenario. Core areas for sage-grouse are shown to highlight expected areas of future conflict (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007400#pone.0007400-Doherty1" target="_blank">[46]</a>).</p

Oil and gas development potential in the US Intermountain West.

<p>(A) This map shows the potential for oil and gas development from low to high. Areas in red have the highest potential and tan have the lowest. Black dots show producing (active or inactive) well locations (IHS, Inc.). (B) Percent of federal minerals leased by oil and gas potential category (C) Validation of oil and gas potential model comparing predictions based on pre-1986 data to post-1986 wells drilled by quintile-derived oil and gas potential categories.</p

Area of expected development for each scenario.

<p>Area (sq km) is divided into development rights sold (black) and development rights available (light gray).</p

Comparison of design based BPOP estimates compared to population estimates generated by summing Random Forest predictions 2000–2002.

<p>We computed yearly 95% CI’s from transect and species-specific SE’s. We only compared BPOP versus Random Forest spatial methods for strata that had almost complete overlap (strata 26–28, 30, 32–35, 38–41, 45–47). For the years we modeled, summation of random forest spatial models across all overlapping strata predicted higher population estimates than the designed based BPOP estimates (Mean = 10.6% increase (range -1.6% [2002] to 15.3% [2007]), however estimates were within the 95% confidence intervals and population trends tracked each other.</p

Abundance and distribution of 5 species of dabbling ducks across the traditional BPOP survey areas in the Prairie Pothole Region during 2002–2010.

<p>These species included; blue-winged teal (<i>Anas discors</i>), gadwall (<i>A. strepera</i>), mallard (<i>A. platyrhynchos</i>), northern pintail (<i>A. acuta</i>), and northern shoveler (<i>A. clypeata</i>). Population estimates derived from our spatially explicit models were summed across the entire landscape and grouped into 10 percent bins, such that a value of 10 represents the smallest area in which 10% of the population is contained relative to each year. Our spatially explicit population estimates show large variation in both population estimates and settling patterns across the years we modeled. Models explained between 64% and 79% of the variation in population counts.</p

Linear regression of mean year and stratum level BPOP estimates as predicted by compared random forest stratum level population estimates from 2002–2010.

<p>Random forest estimates predicted BPOP estimates well with an r² = 0.977 and a regression coefficient of 1.005. Plots of BPOP estimates versus random forest predictions highlight a good model fit, but also show variation for certain transect and year combinations.</p

Description of the explanatory variables used to predict the abundance of count of 5 species of dabbling ducks within a ∼ 11 km2 scale within the Prairie Pothole Region of the U.S. and Canada during 2002–2010.

<p>These species included; blue-winged teal (Anas discors), gadwall (A. strepera), mallard (A. platyrhynchos), northern pintail (A. acuta), and northern shoveler (A. clypeata).</p><p>Footnote: data source abbreviations in order of appearance: National Wetlands Inventory (NWI); (CanVec); Ducks Unlimited Canada (DUC); United States Fish and Wildlife Service and Canadian Wildlife Service Breeding Population Survey (BPOP); National Aeronautics and Space Administration (NASA), Earth Observation Data Portal (EODP), Moderate Resolution Imaging Spectrometer (MODIS); Shuttle Radar Topography Mission (SRTM); National Landcover Dataset (NLCD), Agriculture Agri-Food Canada (AAFC). All data layers are available from <a href="https://www.sciencebase.gov/catalog/item/535fa1aae4b078dca33ae3ad?community=LC+MAP+-+Landscape+Conservation+Management+and+Analysis+Portal" target="_blank">https://www.sciencebase.gov/catalog/item/535fa1aae4b078dca33ae3ad?community=LC+MAP+-+Landscape+Conservation+Management+and+Analysis+Portal</a>.</p><p>Description of the explanatory variables used to predict the abundance of count of 5 species of dabbling ducks within a ∼ 11 km2 scale within the Prairie Pothole Region of the U.S. and Canada during 2002–2010.</p

Abundance and distribution of 5 species of dabbling ducks across the U.S. and Canadian Prairie Pothole Region.

<p>These species included; blue-winged teal (<i>Anas discors</i>), gadwall (<i>A. strepera</i>), mallard (<i>A. platyrhynchos</i>), northern pintail (<i>A. acuta</i>), and northern shoveler (<i>A. clypeata</i>). Maps depict the mean and standard deviation of our yearly predictions from 2002–2010. For the mean population estimate (left inset) estimates were summed across the entire landscape and grouped into 10 percent bins, such that a value of 10 represents the smallest area in which 10% of the population is contained relative to each year.</p

The functional response of waterfowl abundance to wetlands density varied with changing population sizes within the Prairie Pothole Region of the U.S. and Canada during 2002–2010.

<p>Waterfowl abundance was positively associated with wetlands density regardless of time lags tested, wetland density, or overall population size within the PPR. For each panel in the figure, the x-axis is the count of wetlands (0 to 100) and the y-axis is the count of 5 species of dabbling ducks (0 to 300) within a ∼ 11 km² scale. Functional responses were generated using Loess smoothing functions in R.</p