Projected change in AAB.

<p>Projections rates are for the period 2010–2039 compared to the period 1961–2004 based on ensemble of A1B emission scenarios. (a) Percent change in AAB resulting from stepwise selection of individual cell-based models, based on AIC model selection criteria; (b) proportion of variance explained (<i>R</i>²). Only significant models (<i>p</i> < 0.05) are plotted.</p

Direct and indirect effects of climate and snow cover on AAB by LDPS regions.

<p>Spring (a) and winter (b) mean (± SE) path coefficients averaged over areas of boreal and western North America with areas of similar snow cover duration (monthly classes of long-term (1972–2006) mean LDPS. Black bars indicate direct effects of temperature on log-AAB; grey bars indirect effects on log-AAB mediated by variation in LDPS. (c) Geographic distribution of monthly mean LDPS.</p

Changes in AAB across United States and Canada by state/province.

<p>(a) Boxplot of percent change in AAB (2010–2039 <i>vs</i>.1961-2004, SRES A1B scenario) binned by US state or Canadian province, based on significant models with <i>p</i> < 0.05. Sharing of any letter (below the graph) indicates lack of significant differences in medians of percent change in AAB based on Bonferroni-corrected <i>a posteriori</i> comparisons of a Kruskal-Wallis median test. Colored boxes indicate groups of states/regions with statistically similar medians ordered from low (green) through high median values (red). (b) States/regions ordered by increasing median change in AAB. Histograms are model projections based on the 1976–2006 baseline period; red dots are extrapolated increases in median AAB and bars are 95% confidence intervals estimated from Theil-Sen trends (1972–2015).</p

Results of PCA analysis of <i>z</i> coefficients of a complete multiple regression model in each grid cell.

<p>Panels show the first four principal components and percent in variation in AAB explained. Seasonal climate variables correlated with PC loadings at <i>r</i> ≥ 0.5 are listed including the sign of the correlation with AAB. (a) PC1, summer temperature (+) and spring precipitation (-); (b) PC2, spring temperature (+); (c) PC3, winter temperature (-); (d) PC4, preceding year summer temperature (+). Red (blue) colors indicate increases (decreases) in log-transformed AAB with increases in variables correlating positively/negatively with PC scores. Note that PC3 is inverted in sign for ease in interpretation. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188486#pone.0188486.s001" target="_blank">S1 Fig</a> for PC5.</p

Temporal trends (1972–2006) in instrumental seasonal climate and snow cover duration.

<p>(a) Winter (JFM) temperature (°C), (b) spring (AMJ) temperature (°C), (c) summer (JAS) temperature (°C), and (d) LDPS (days/decade), based on the Theil-Sen median slope estimator.</p

Average Stand Age from Forest Inventory Plots Does Not Describe Historical Fire Regimes in Ponderosa Pine and Mixed-Conifer Forests of Western North America

<div><p>Quantifying historical fire regimes provides important information for managing contemporary forests. Historical fire frequency and severity can be estimated using several methods; each method has strengths and weaknesses and presents challenges for interpretation and verification. Recent efforts to quantify the timing of historical high-severity fire events in forests of western North America have assumed that the “stand age” variable from the US Forest Service Forest Inventory and Analysis (FIA) program reflects the timing of historical high-severity (i.e. stand-replacing) fire in ponderosa pine and mixed-conifer forests. To test this assumption, we re-analyze the dataset used in a previous analysis, and compare information from fire history records with information from co-located FIA plots. We demonstrate that 1) the FIA stand age variable does not reflect the large range of individual tree ages in the FIA plots: older trees comprised more than 10% of pre-stand age basal area in 58% of plots analyzed and more than 30% of pre-stand age basal area in 32% of plots, and 2) recruitment events are not necessarily related to high-severity fire occurrence. Because the FIA stand age variable is estimated from a sample of tree ages within the tree size class containing a plurality of canopy trees in the plot, it does not necessarily include the oldest trees, especially in uneven-aged stands. Thus, the FIA stand age variable does not indicate whether the trees in the predominant size class established in response to severe fire, or established during the absence of fire. FIA stand age was not designed to measure the time since a stand-replacing disturbance. Quantification of historical “mixed-severity” fire regimes must be explicit about the spatial scale of high-severity fire effects, which is not possible using FIA stand age data.</p></div

Proportion of FIA plots with a percentage of total plot basal area comprised of trees significantly older than the reported FIA stand age.

<p>Proportion of FIA plots with a percentage of total plot basal area comprised of trees significantly older than the reported FIA stand age.</p

FIA plot overlay on fire history data.

<p>Red line indicates FIA stand age, green lines indicate individual tree ages, and black lines indicate fire occurrences based on fire scar data. Blue dot indicates FIA inventory year. McKenna Park, Cerro Balitas and Cerro Rendija sites were in New Mexico; Rollins Pass, Hermosa Creek and Hidden Valley sites were in Colorado; Galahad Point and Peters Flat sites were in Arizona.</p

Distribution of basal area fraction in older trees, relative to stand age in plots sampled by Odion et al [46].

<p>Older trees are defined as either aged trees older than 1.28 times the reported FIA stand age, or unaged trees with diameter greater than the mean diameter of aged trees within 28% of the FIA stand age plus 2 standard deviations. Note that vertical axis scaling varies among panels.</p