Fig 1.
Generalized conceptual model for causes and signatures of shifting fire activity.
Scenarios include random variability in “climate” (i.e., a hypothetical metric linked to annual fire activity) which directly determines “fire activity” (e.g., annual area burned or number of large fires). Period 1 is identical in all scenarios, but the y axes are scaled based on values in Period 2. See “Introduction” for a description of each scenario. In all cases of varying fire-climate relationships, a coefficient of efficiency (CE) statistic < 0 indicates a lack of predictive skill (for periods outside of the calibration period). β0 (intercept) and β1 (slope) represent regression parameters; directional changes in parentheses represent hypothetical scenarios not illustrated in the figure.
Fig 2.
The U.S. Northern Rockies study area in Idaho and portions of Montana, west of the Continental Divide.
Area burned, stratified by decade (left), and the three dominant forest types across the study area (right).
Table 1.
Climate metrics used to predict the natural logarithm of annual area burned.
Fig 3.
Fire-danger, water-balance, and climate predictors of annual area burned.
Series are plotted relative to the series mean, with red (blue) representing above (below) average conditions conducive for fire activity. The center row includes annual area burned (gray), expressed as ln(ha), with average values for each of three periods identified via piecewise linear regression (1902–1942, 1943–1984, 1985–2008). Metrics are organized based on their overall score (upper left to lower right; Table 2). Metric type is identified as “FD” (fire danger), “WB” (water balance), or “C” (climate), with units and temporal definitions listed in Table 1.
Table 2.
Results for linear regression models predicting the natural logarithm of annual area burned.
Fig 4.
Calibration accuracy and cross-validation skill of potential predictors of annual area burned.
(A) Accuracy (r2 or R2adj) from the 87 continuous 21-yr regression models. Metrics are ranked (top to bottom) based on the median value. Boxplots display the median, 25th, and 75th quantiles, and whiskers extend to extreme values not considered outliers. (B) Cross-validation skill, CE, for all 87 cross-validation periods. Metrics are ranked from left to right based on the median value; CE ≤ 0 indicates no predictive skill. (C) Calibration accuracy as a function of cross-validation skill, where darker grey indicates a greater proportion of values. Overall metric rank (i.e., CE * r2 or R2adj) is indicated by the within-plot numbers.
Fig 5.
Variability in the strength and nature of fire-climate relationships through time.
(A) Annual area burned in linear and log-transformed space (baseline = series-wide average; thick black line = period averages, as in Fig 3). (B) The changing strength of fire-climate relationships (r2 or R2adj) for each 21-yr model. Metrics with the highest explanatory power are labeled, and the length of each overlapping calibration period is represented in the lower right of the panel (“Calib. window”). (C) Changing skill of fire-climate relationships, CE, as in (B). (D) Changing nature of fire-climate relationships, illustrated by varying model parameters through time. The y-axis is the standardized β1 parameter (for univariate models) or β1, β2 or β3 parameter for the three-variable models (illustrated by dashed lines), with mean 0, and standard deviation 1. Positive values represent a positive influence on area burned. Metrics are ordered from top to bottom based on the overall model score (Table 2).
Fig 6.
Variability in fire-danger, water-balance, and climate metrics across discrete periods of fire activity.
Periods were identified by piecewise linear regression: Period 1 (1902–1942), Period 2 (1943–1984), Period 3 (1985–2008). Metrics are labeled as in Fig 3 and ordered from upper left to lower right based on the overall model score (Table 2). Metrics with significant among-period variability are highlighted with a bold x and y axis, and results of the post-hoc multiple comparison test are illustrated by lower case “a” “b” and “c” below the x axis. See Detecting changing fire-climate relationships… for details.
Fig 7.
Cross-validation skill, model parameters, and strength of fire-climate relationships for top metrics.
Small symbols represent cross-validation skill and the regression parameter for 21-yr calibration windows, stratified by Period 1 (1902–1942, circles), Period 2 (1943–1984, squares) and Period 3 (1984–2008, triangles). The grayscale of each small symbol represents r2 or R2adj for that window (as in Fig 5B), and large symbols represent the centroid of all values within each period, +/- one standard deviation. Regression parameters represent the slope of the model, β1, for single-variable regression models. GDD0 represents β2 from the combined DMC, GDD0 model, while PPTJJA, TJA, and TMAM represent β1, β2, and β3 from the three-variable model, respectively. Parameter values indicated the unit (standard deviation) change in log-transformed area burned for a unit (standard deviation) change in the predictor variable: more extreme values indicated a greater influence on annual area burned. Values below the dashed vertical line on the x axis (CE = 0) lack cross-validation skill. Metrics are ordered from upper left to bottom right based on the overall model score (Table 2).