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Figure 5: Estimates (With 95% Confidence Intervals) of the Effect of an Increase in One SD (0.8%) in the Areal Disturbance Due to Well Pads (A) Within 3.2 Km and (B) the Pacific Decadal Oscillation Index (0.82).
doi 10.7717/peerj.5417/fig-5
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Figure 12: Estimates (With 95% Confidence Intervals) of the Effect of an Increase in One SD (0.31%) in the Areal Disturbance Due to Well Pads Within 3.2 Km of All Leks and the Pacific Decadal Oscillation Index (0.86) by the Statistic Used to Combine Repeated Lek Counts (Mean or Max) and the Period (A–C).
Table 4: The Relative Importance (Wi) of the Lag in the Pacific Decadal Oscillation Index as a Predictor of the Change in the Population Density Across All Models With a Lek Distance of 3.2 Km and the Areal Disturbance Due to Well Pads Lagged 1–4 Years.
Figure 11: Bayesian Estimates (With 95% Credible Intervals) of the Effect of the Percent Areal Disturbance Due to Oil and Gas Well Pads on the Expected Carrying Capacity at a Typical Working Group.
Table 3: The Relative Importance (Wi) of the Lag in the Areal Disturbance Due to Well Pads as a Predictor of the Change in the Population Density Across All Models With a Lek Distance of 3.2 Km and the Pacific Decadal Oscillation Index Independently Lagged 1–4 Years.
Table 2: The Relative Importance (Wi) of the Lag in the Pacific Decadal Oscillation Index Density as a Predictor of the Count of Male Sage-Grouse at Individual Leks Across All Models With a Lek Distance of 0.8, 1.6, 3.2, 6.4 and 12.8 Km and the Areal Disturbance Due to Well Pads Independently Lagged 1–4 Years.
The Pacific Decadal Oscillation, Revisited
Journal of Climate
Atmospheric Science
An Evaluation of Reproducibility of the Pacific Decadal Oscillation in the CMIP3 Simulations
Journal of the Meteorological Society of Japan
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Decadal Variability in the Terrestrial Carbon Budget Caused by the Pacific Decadal Oscillation and Atlantic Multidecadal Oscillation
Journal of the Meteorological Society of Japan
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Table S2: Parameter Estimates and 95% Confidence Intervals Derived by Model Averaging Across the Confidence Set of Models
