Effects of postfire climate and seed availability on postfire conifer regeneration.

Large, severe fires are becoming more frequent in many forest types across the western United States and have resulted in tree mortality across tens of thousands of hectares. Conifer regeneration in these areas is limited because seeds must travel long distances to reach the interior of large burned patches and establishment is jeopardized by increasingly hot and dry conditions. To better inform postfire management in low elevation forests of California, USA, we collected 5-yr postfire recovery data from 1,234 study plots in 19 wildfires that burned from 2004-2012 and 18 yrs of seed production data from 216 seed fall traps (1999-2017). We used these data in conjunction with spatially extensive climate, topography, forest composition, and burn severity surfaces to construct taxon-specific, spatially explicit models of conifer regeneration that incorporate climate conditions and seed availability during postfire recovery windows. We found that after accounting for other predictors both postfire and historical precipitation were strong predictors of regeneration, suggesting that both direct effects of postfire moisture conditions and biological inertia from historical climate may play a role in regeneration. Alternatively, postfire regeneration may simply be driven by postfire climate and apparent relationships with historical climate could be spurious. The estimated sensitivity of regeneration to postfire seed availability was strongest in firs and all conifers combined and weaker in pines. Seed production exhibited high temporal variability with seed production varying by over two orders of magnitude among years. Our models indicate that during droughts postfire conifer regeneration declines most substantially in low-to-moderate elevation forests. These findings enhance our mechanistic understanding of forecasted and historically documented shifts in the distribution of trees.

From the stand scale to the landscape scale: predicting the spatial patterns of forest regeneration after disturbance.

Shifting disturbance regimes can have cascading effects on many ecosystems processes. This is particularly true when the scale of the disturbance no longer matches the regeneration strategy of the dominant vegetation. In the yellow pine and mixed conifer forests of California, over a century of fire exclusion and the warming climate are increasing the incidence and extent of stand-replacing wildfire; such changes in severity patterns are altering regeneration dynamics by dramatically increasing the distance from live tree seed sources. This has raised concerns about limitations to natural reforestation and the potential for conversion to non-forested vegetation types, which in turn has implications for shifts in many ecological processes and ecosystem services. We used a California region-wide data set with 1,848 plots across 24 wildfires in yellow pine and mixed conifer forests to build a spatially explicit habitat suitability model for forecasting postfire forest regeneration. To model the effect of seed availability, the critical initial biological filter for regeneration, we used a novel approach to predicting spatial patterns of seed availability by estimating annual seed production from existing basal area and burn severity maps. The probability of observing any conifer seedling in a 60-m2 area (the field plot scale) was highly dependent on 30-yr average annual precipitation, burn severity, and seed availability. We then used this model to predict regeneration probabilities across the entire extent of a "new" fire (the 2014 King Fire), which highlights the spatial variability inherent in postfire regeneration patterns. Such forecasts of postfire regeneration patterns are of importance to land managers and conservationists interested in maintaining forest cover on the landscape. Our tool can also help anticipate shifts in ecosystem properties, supporting researchers interested in investigating questions surrounding alternative stable states, and the interaction of altered disturbance regimes and the changing climate.

Climate influences on whitebark pine mortality from mountain pine beetle in the Greater Yellowstone Ecosystem.

Extensive mortality of whitebark pine, beginning in the early to mid-2000s, occurred in the Greater Yellowstone Ecosystem (GYE) of the western USA, primarily from mountain pine beetle but also from other threats such as white pine blister rust. The climatic drivers of this recent mortality and the potential for future whitebark pine mortality from mountain pine beetle are not well understood, yet are important considerations in whether to list whitebark pine as a threatened or endangered species. We sought to increase the understanding of climate influences on mountain pine beetle outbreaks in whitebark pine forests, which are less well understood than in lodgepole pine, by quantifying climate-beetle relationships, analyzing climate influences during the recent outbreak, and estimating the suitability of future climate for beetle outbreaks. We developed a statistical model of the probability of whitebark pine mortality in the GYE that included temperature effects on beetle development and survival, precipitation effects on host tree condition, beetle population size, and stand characteristics. Estimated probability of whitebark pine mortality increased with higher winter minimum temperature, indicating greater beetle winter survival; higher fall temperature, indicating synchronous beetle emergence; lower two-year summer precipitation, indicating increased potential for host tree stress; increasing beetle populations; stand age; and increasing percent composition of whitebark pine within a stand. The recent outbreak occurred during a period of higher-than-normal regional winter temperatures, suitable fall temperatures, and low summer precipitation. In contrast to lodgepole pine systems, area with mortality was linked to precipitation variability even at high beetle populations. Projections from climate models indicate future climate conditions will likely provide favorable conditions for beetle outbreaks within nearly all current whitebark pine habitat in the GYE by the middle of this century. Therefore, when surviving and regenerating trees reach ages suitable for beetle attack, there is strong potential for continued whitebark pine mortality due to mountain pine beetle.

Climate and weather influences on spatial temporal patterns of mountain pine beetle populations in Washington and Oregon.

Widespread outbreaks of mountain pine beetle in North America have drawn the attention of scientists, forest managers, and the public. There is strong evidence that climate change has contributed to the extent and severity of recent outbreaks. Scientists are interested in quantifying relationships between bark beetle population dynamics and trends in climate. Process models that simulate climate suitability for mountain pine beetle outbreaks have advanced our understanding of beetle population dynamics; however, there are few studies that have assessed their accuracy across multiple outbreaks or at larger spatial scales. This study used the observed number of trees killed by mountain pine beetles per square kilometer in Oregon and Washington, USA, over the past three decades to quantify and assess the influence of climate and weather variables on beetle activity over longer time periods and larger scales than previously studied. Influences of temperature and precipitation in addition to process model output variables were assessed at annual and climatological time scales. The statistical analysis showed that new attacks are more likely to occur at locations with climatological mean August temperatures >15 degrees C. After controlling for beetle pressure, the variables with the largest effect on the odds of an outbreak exceeding a certain size were minimum winter temperature (positive relationship) and drought conditions in current and previous years. Precipitation levels in the year prior to the outbreak had a positive effect, possibly an indication of the influence of this driver on brood size. Two-year cumulative precipitation had a negative effect, a possible indication of the influence of drought on tree stress. Among the process model variables, cold tolerance was the strongest indicator of an outbreak increasing to epidemic size. A weather suitability index developed from the regression analysis indicated a 2.5x increase in the odds of outbreak at locations with highly suitable weather vs. locations with low suitability. The models were useful for estimating expected amounts of damage (total area with outbreaks) and for quantifying the contribution of climate to total damage. Overall, the results confirm the importance of climate and weather on the spatial expansion of bark beetle outbreaks over time.

Network analysis of wildfire transmission and implications for risk governance.

We characterized wildfire transmission and exposure within a matrix of large land tenures (federal, state, and private) surrounding 56 communities within a 3.3 million ha fire prone region of central Oregon US. Wildfire simulation and network analysis were used to quantify the exchange of fire among land tenures and communities and analyze the relative contributions of human versus natural ignitions to wildfire exposure. Among the land tenures examined, the area burned by incoming fires averaged 57% of the total burned area. Community exposure from incoming fires ignited on surrounding land tenures accounted for 67% of the total area burned. The number of land tenures contributing wildfire to individual communities and surrounding wildland urban interface (WUI) varied from 3 to 20. Community firesheds, i.e. the area where ignitions can spawn fires that can burn into the WUI, covered 40% of the landscape, and were 5.5 times larger than the combined area of the community core and WUI. For the major land tenures within the study area, the amount of incoming versus outgoing fire was relatively constant, with some exceptions. The study provides a multi-scale characterization of wildfire networks within a large, mixed tenure and fire prone landscape, and illustrates the connectivity of risk between communities and the surrounding wildlands. We use the findings to discuss how scale mismatches in local wildfire governance result from disconnected planning systems and disparate fire management objectives among the large landowners (federal, state, private) and local communities. Local and regional risk planning processes can adopt our concepts and methods to better define and map the scale of wildfire risk from large fire events and incorporate wildfire network and connectivity concepts into risk assessments.

A statistical model for determining impact of wildland fires on Particulate Matter (PM2.5) in Central California aided by satellite imagery of smoke.

As the climate in California warms and wildfires become larger and more severe, satellite-based observational tools are frequently used for studying impact of those fires on air quality. However little objective work has been done to quantify the skill these satellite observations of smoke plumes have in predicting impacts to PM2.5 concentrations at ground level monitors, especially those monitors used to determine attainment values for air quality under the Clean Air Act. Using PM2.5 monitoring data from a suite of monitors throughout the Central California area, we found a significant, but weak relationship between satellite-observed smoke plumes and PM2.5 concentrations measured at the surface. However, when combined with an autoregressive statistical model that uses weather and seasonal factors to identify thresholds for flagging unusual events at these sites, we found that the presence of smoke plumes could reliably identify periods of wildfire influence with 95% accuracy.

Development of a statistical model for estimating spatial and temporal ambient ozone patterns in the Sierra Nevada, California.

Statistical approaches for modeling spatially and temporally explicit data are discussed for 79 passive sampler sites and 9 active monitors distributed across the Sierra Nevada, California. A generalized additive regression model was used to estimate spatial patterns and relationships between predicted ozone exposure and explanatory variables, and to predict exposure at nonmonitored sites. The fitted model was also used to estimate probability maps for season average ozone levels exceeding critical (or subcritical) levels in the Sierra Nevada region. The explanatory variables--elevation, maximum daily temperature, and precipitation and ozone level at closest active monitor--were significant in the model. There was also a significant mostly east-west spatial trend. The between-site variability had the same magnitude as the error variability. This seems to indicate that there still exist important site features not captured by the variables used in the analysis and that may improve the accuracy of the predictive model in future studies. The fitted model using robust techniques had an overall R2 value of 0.58. The mean standard deviation for a predicted value was 6.68 ppb.

Estimating contribution of wildland fires to ambient ozone levels in National Parks in the Sierra Nevada, California.

Data from four continuous ozone and weather monitoring sites operated by the National Park Service in Sierra Nevada, California, are used to develop an ozone forecasting model and to estimate the contribution of wildland fires on ambient ozone levels. The analyses of weather and ozone data pointed to the transport of ozone precursors from the Central Valley as an important source of pollution in these National Parks. Comparisons of forecasted and observed values demonstrated that accurate forecasts of next-day hourly ozone levels may be achieved by using a time series model with historic averages, expected local weather and modeled PM values as explanatory variables. Results on fire smoke influence indicated occurrence of significant increases in average ozone levels with increasing fire activity. The overall effect on diurnal ozone values, however, was small when compared with the amount of variability attributed to sources other than fire.