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Copyright of Fire Ecology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

One of the crucial components in improving the quality of forests is post-fire vegetation regrowth monitoring. This is done by analyzing the time series of satellite data and studying the severity of forest fires to improve the ability to monitor the dynamic changes of the forest. This study investigates the regeneration process of existing vegetation types in different severities of The Rim fire in Sierra Nevada, California, using the time series of vegetation indices obtained from the MODIS sensor. The Vegetation Return Period (VRP) and the Recovery Rate (RR) after the fire were evaluated to monitor the regrowth of vegetation types. According to the results, the VRP values of the species for low, moderate, and high severity were estimated to be between 22 to 33 months, 33 to 47 months, and about 5 years, in this area. The 8-year changes in the time series of vegetation indices confirm that some vegetation types in this region have not fully recovered. In addition, spatio-temporal variations of the burned regions were examined with Landsat images at 2-year post-fire intervals until 2021. The results showed that in three 2-year periods after the fire, 16,074 hectares, 48,722 hectares, and 27,391 hectares of land were, respectively, converted into unburned areas, and until 2019, about 60% of the burned areas were recovered. Researchers and land managers can use the results of such studies to identify areas that need more attention after a fire. [ABSTRACT FROM AUTHOR]

Fires are a common feature of many landscapes, with numerous and complex ecological consequences. In stream ecosystems, fire can strongly influence fluvial geomorphic characteristics and riparian vegetation, which are structural components of stream–riparian ecosystems that contribute to biodiversity and ecosystem function. However, the effects of fire severity on stream–riparian ecosystems in California's Sierra Nevada region (USA) are not well described, yet critical for effectively informing fire management and policy. At 12 stream reaches paired by fire severity (one high-severity burned, one low-severity burned), no significant differences were found in riparian plant community cover and composition or stream geomorphic characteristics 2–15 years following wildfire. In addition, minimal changes in riparian vegetation and stream geomorphic properties were observed in the first summer following the extensive and severe Rim Fire. However, an upstream-to-downstream influence of multiple fire occurrences was observed over the previous 81 years within each catchment on stream geomorphic metrics, including sediment size, embeddedness and channel geometry, at our study reaches. The inconsistent effects of wildfire on stream–riparian vegetation and geomorphic characteristics over space and time may be related to time since fire and precipitation. Riparian vegetation and geomorphic properties of mountain stream reaches that burned with high- or low-severity fire did not differ from each other in this California, USA study. However, the proportion of each catchment burned multiple times over the past ~80 years was correlated with reach-level geomorphic metrics. [ABSTRACT FROM AUTHOR]

Wildland fires produce smoke plumes that impact air quality and human health. To understand the effects of wildland fire smoke on humans, the amount and composition of the smoke plume must be quantified. Using a fire emissions inventory is one way to determine the emissions rate and composition of smoke plumes from individual fires. There are multiple fire emissions inventories, and each uses a different method to estimate emissions. This paper presents a comparison of four emissions inventories and their products: Fire INventory from NCAR (FINN version 1.5), Global Fire Emissions Database (GFED version 4s), Missoula Fire Labs Emissions Inventory (MFLEI (250 m) and MFLEI (10 km) products), and Wildland Fire Emissions Inventory System (WFEIS (MODIS) and WFEIS (MTBS) products). The outputs from these inventories are compared directly. Because there are no validation datasets for fire emissions, the outlying points from the Bayesian models developed for each inventory were compared with visible images and fire radiative power (FRP) data from satellite remote sensing. This comparison provides a framework to check fire emissions inventory data against additional data by providing a set of days to investigate closely. Results indicate that FINN and GFED likely underestimate emissions, while the MFLEI products likely overestimate emissions. No fire emissions inventory matched the temporal distribution of emissions from an external FRP dataset. A discussion of the differences impacting the emissions estimates from the four fire emissions inventories is provided, including a qualitative comparison of the methods and inputs used by each inventory and the associated strengths and limitations.

Context: As the frequency of large, severe fires increases, detecting the drivers of spatial fire severity patterns is key to predicting controls provided by weather, fuels, topography, and management. Objectives: Identify the biophysical and management drivers of severity patterns and their spatial variability across the 2013 Rim Fire, Sierra Nevada, California, USA. Methods: Random forest models were developed separately for reburned and fire-excluded (> 80 year) areas within Yosemite National Park (NP) and Stanislaus National Forest (NF). Models included biophysical, past disturbance, and spatial autocorrelation (SA) predictors. Variable importance was assessed globally and locally. Variance partitioning was used to assess pure and shared variance among predictors. Results: High spatial variability in the relative dominance of predictors existed across burn days and between land ownerships. Fire weather was a dominant top-down control during plume-dominated fire spread days. However, bottom-up controls from fuels and topography created local, fine-scale heterogeneity throughout. Reburn severity correlated with previous severity suggesting strong landscape memory, particularly in Yosemite NP. SA analysis showed broad-scale spatial dependencies and high shared variance among predictors. Conclusions: Wildfires are inherently a multi-scaled process. Spatial structure in environmental variables create broad-scale patterns and dependencies among drivers leading to regions of similar fire behavior, while local bottom-up drivers generate fine-scaled heterogeneity. Identifying the conditions under which top-down factors overwhelm bottom-up controls can help managers monitor and manage wildfires to achieve both suppression and restoration goals. Restoration targeting both surface and ladder fuels can mediate future fire severity even under extreme weather conditions. [ABSTRACT FROM AUTHOR]

Post-fire tree mortality models are vital tools used by forest land managers to predict fire effects, estimate delayed mortality and develop management prescriptions. We evaluated the performance of mortality models within the First Order Fire Effects Model (FOFEM) software, and compared their performance to locally-parameterised models based on five different forms. We evaluated all models at the individual tree and stand levels with a dataset comprising 34 174 trees from a mixed-conifer forest in the Sierra Nevada, California that burned in the 2013 Rim Fire. We compared stand-level accuracy across a range of spatial scales, and we used point pattern analysis to test the accuracy with which mortality models predict post-fire tree spatial pattern. FOFEM under-predicted mortality for the three conifers, possibly because of the timing of the Rim Fire during a severe multi-year drought. Locally-parameterised models based on crown scorch were most accurate in predicting individual tree mortality, but tree diameter-based models were more accurate at the stand level for Abies concolor and large-diameter Pinus lambertiana , the most abundant trees in this forest. Stand-level accuracy was reduced by spatially correlated error at small spatial scales, but stabilised at scales ≥1 ha. The predictive error of FOFEM generated inaccurate predictions of post-fire spatial pattern at small scales, and this error could be reduced by improving FOFEM model accuracy for small trees. We conducted an evaluation of post-fire tree mortality models with the largest sample to date, and we summarised model accuracy at multiple scales. We found that First Order Fire Effects Models generally had high accuracy for gymnosperms, but accuracy was poor for angiosperms. Fire-related mortality was elevated because of severe multi-year drought, and we provide new mortality models with improved accuracy for fires that occur during drought. [ABSTRACT FROM AUTHOR]

Following changes in vegetation structure and pattern, along with a changing climate, large wildfire incidence has increased in forests throughout the western United States. Given this increase, there is great interest in whether fuels treatments and previous wildfire can alter fire severity patterns in large wildfires. We assessed the relative influence of previous fuels treatments (including wildfire), fire weather, vegetation, and water balance on fire-severity in the Rim Fire of 2013. We did this at three different spatial scales to investigate whether the influences on fire severity changed across scales. Both fuels treatments and previous low to moderate-severity wildfire reduced the prevalence of high-severity fire. In general, areas without recent fuels treatments and areas that previously burned at high severity tended to have a greater proportion of high-severity fire in the Rim Fire. Areas treated with prescribed fire, especially when combined with thinning, had the lowest proportions of high severity. The proportion of the landscape burned at high severity was most strongly influenced by fire weather and proportional area previously treated for fuels or burned by low to moderate severity wildfire. The proportion treated needed to effectively reduce the amount of high severity fire varied by spatial scale of analysis, with smaller spatial scales requiring a greater proportion treated to see an effect on fire severity. When moderate and high-severity fire encountered a previously treated area, fire severity was significantly reduced in the treated area relative to the adjacent untreated area. Our results show that fuels treatments and low to moderate-severity wildfire can reduce fire severity in a subsequent wildfire, even when burning under fire growth conditions. These results serve as further evidence that both fuels treatments and lower severity wildfire can increase forest resilience. [ABSTRACT FROM AUTHOR]

Fire affects environmental attributes associated with the distribution, abundance, and reproduction of butterflies, and studies have demonstrated that species richness and abundance of butterflies respond to fire. However, the effects of fire on resources used by adult butterflies, and on butterfly occupancy, are largely unknown. In 2014 and 2015, we surveyed butterflies and elements ol their habitat within the boundary of the Rim Fire (Stanislaus National Forest, Tuolumne County, California), one of the largest fires known to occur in California during the past century. We examined the extent to which butterfly occupancy and abundance in the first two years following the Rim Fire were associated with environmental attributes that were affected by fire. We also tested whether vegetation and soil burn severity, two indices used by resource management agencies, explained variation in the environmental attributes that we included in models of butterfly occupancy and abundance. We found that the percentage of live ground cover and canopy cover were strongly associated with occupancy of the majority of the species we modeled. In some cases, environmental attributes associated with occupancy also were associated with the abundances of those species. Values of environmental attributes that explained substantial variation in butterfly occupancy and abundance were associated with vegetation and soil burn severity. Understanding how fire affects environmental attributes that are associated with butterfly occupancy and abundance may inform strategies for managing these species with prescribed fire or following wildfire, or when fire treatments are applied for other reasons. [ABSTRACT FROM AUTHOR]

Copyright of Fire Ecology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Disturbance severity and snow duration have species-specific effects on seedlings. • Lower substrate burn severity disadvantaged Abies concolor seedlings. • Earlier snowmelt in the germination year decreased Abies concolor survival. • Post-fire compositional shifts toward drought-tolerant Pinus species are possible. Climate change is altering forest composition through species-specific responses to fire and drought. Future forest composition will depend on how the different regeneration niches of co-occurring species align with current environmental conditions, especially after fire, which can promote germination by exposing mineral soil. Few studies, however, have examined the effects of disturbance severity and microclimate on post-fire regeneration to define and compare the regeneration niches of co-occurring tree species. We used seven years of annual demography and microenvironment data from a 25.6-ha fully censused, stem-mapped forest dynamics plot in California, USA, to examine how disturbance severity, snow duration, and temperature extremes affect the survival of Abies concolor and Pinus lambertiana seedlings that germinated naturally after a low- to moderate-severity fire. We defined disturbance severity at the microsite level, based on characteristics of the substrate, and at the neighborhood level, based on tree mortality. Both disturbance severity and snow duration had species-specific effects on seedling survival, but these differed by life stage. During the germination year, later snow disappearance was associated with a 0.5 increase in survival probability for A. concolor but hardly affected P. lambertiana ; in contrast, higher neighborhood disturbance severity increased survival of both species. After the germination year, higher substrate burn severity was associated with a 0.8 increase in survival probability for A. concolor but hardly affected P. lambertiana ; higher neighborhood disturbance severity and later snow disappearance increased annual survival of both species, but maximum summer temperature had minimal effect. Overall, available seed, higher substrate burn severity, higher neighborhood disturbance severity, and later snow disappearance promoted natural regeneration. However, lower substrate burn severity and earlier snow disappearance in the germination year disadvantaged A. concolor seedlings, increasing the relative abundance of P. lambertiana seedlings compared to the local tree population. Our results indicate that natural post-fire compositional shifts toward drought-tolerant Pinus species–and away from less drought-tolerant Abies species–are possible in the Sierra Nevada, with potential benefits for forest persistence under climate change. Broadly, we show that species differences in regeneration niches shape how disturbance severity and microclimate affect forest species composition. [ABSTRACT FROM AUTHOR]

• Density reduction from fire or mechanical damage increased post-fire growth rates. • Density reduction from bark beetle mortality did not affect post-fire growth rates. • Pinus had a stronger post-fire growth response to competitive release than Abies. • Mortality and diameter distribution, not growth, control composition of large trees. Global increases in large-tree mortality and multiple disturbances such as fire, drought, and pestilence increase the importance of understanding how large-tree growth responds to changes in competitive neighborhoods. We used 34,175 spatially mapped trees within 25.6 ha that burned at low to moderate severity in 2013 to investigate how changes to tree neighborhoods affected growth of 3,652 surviving trees of two species, Abies concolor (white fir) and Pinus lambertiana (sugar pine). Using diameter measurements taken in 2014 and 2019, we estimated post-fire radial growth of each surviving tree. We modeled annual basal area increment as a function of diameter and extracted residuals to yield relative growth rate (RGR). We used a multi-model approach and AIC to compare the species-specific effects on RGR of all post-fire tree mortality within a neighborhood radius versus mortality broken down by cause. To represent density reduction around each surviving tree, we computed crowding metrics for neighborhood radii up to 20 m for all tree mortality combined and for each mortality cause. The best-fit model included terms for density reduction within 10 m of the surviving tree due to 1) fire or mechanical damage and 2) bark beetle mortality, as well as their interactions with species and diameter. Density reduction due to fire or mechanical damage had a positive effect on RGR that was nearly twice as strong for P. lambertiana as A. concolor. Tree diameter interacted with density reduction for just P. lambertiana. Small-diameter (10-cm) P. lambertiana had a fourfold larger increase in basal area increment than large-diameter (60-cm) P. lambertiana as density reduction due to fire or mechanical damage ranged from 0 to 100 %. The strong growth response of medium-diameter P. lambertiana to density reductions could eventually increase the abundance of large-diameter (≥60 cm) P. lambertiana. In conjunction with mortality rates, which were lower for large-diameter P. lambertiana (0.97 %) than A. concolor (3.31 %), this could increase the relative abundance of large-diameter P. lambertiana from 51.2 % in 2020 to 62.6 % by 2050. Changes to competitive neighborhoods can have species-specific effects on large-tree growth, an important consideration for long-term management of fire-prone forests. [ABSTRACT FROM AUTHOR]

Copyright of Fire Ecology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Morel mushrooms are globally distributed, socially and economically important reproductive structures produced by fungi of the genus Morchella . Morels are highly prized edible mushrooms and significant harvests are collected throughout their range, especially in the first year after fire, when some morel species fruit prolifically. Few studies have quantified post-fire morel mushroom abundance, despite their widespread human use. The purpose of this study is to provide the first ever estimate of post-fire morel mushroom abundance in Sierra Nevada mixed-conifer forest. Specifically, we estimate the abundance and spatial variability of morel mushrooms across an intensively mapped and measured forest research site during the first growing season following fire. We conducted this study in the Yosemite Forest Dynamics Plot, a long-term forest research installation located in old-growth mixed-conifer forest of Yosemite National Park, California, USA. We surveyed for morel mushrooms in n = 1119 contiguous circular 3.14 m 2 plots arranged along 2240 m of permanently marked, georeferenced transects. We characterized the spatial correlation of morel plots using k category (multicolor) join count statistics. We analyzed spatial correlations at interplot distances up to 9.0 m. There were 595 morel mushrooms in the 1119 plots we measured. Mushrooms occurred in 17.8% of plots. We estimated a mean standing crop of 1693 morels ha −1 (SE = 155.4 morels ha −1 ). Morel-occupied plots were strongly and significantly spatially autocorrelated. Most of the spatial correlation among morel-occupied plots was apparent at scales up to 7.0 m, and was strongest at scales <3.0 m. This study is one of only four that provide unbiased estimates of post-fire morel abundance. Our morel abundance estimates are generally consistent with prior work in high-latitude North American conifer forests. The strong spatial autocorrelation of morel-occupied microsites at scales <7 m indicates that key factors controlling post-fire morel productivity are heterogeneous at small spatial scales. We propose a simple conceptual model to explain this spatial structure that includes spatial variability of pre-fire Morchella colonies; pre-fire vegetation community and fuelbed; fire behavior, intensity, and effects; and soil moisture and temperature. Relatively liberal harvest limits for recreational and subsistence harvesters appear appropriate and sustainable, at least for coniferous forests in the first year following fire. However, intensive commercial harvest, in jurisdictions where it is allowed, may warrant monitoring to assess potential impacts to long-term morel productivity, conflict with recreational harvesters, and non-target effects. [ABSTRACT FROM AUTHOR]

Abundance, size, and spatial distribution of standing dead trees (snags), are key indicators of forest biodiversity and ecosystem health. These metrics represent critical habitat components for various wildlife species of conservation concern, including the Black-backed Woodpecker ( Picoides arcticus ), which is strongly associated with recently burned conifer forest. We assessed the potential of Airborne Laser Scanning (ALS) to detect and characterize conifer snags and identify Black-backed Woodpecker habitat using previously derived empirical thresholds of conifer snag basal area. Over the footprint of the Rim Fire, a megafire that extended (~ 104,000 ha) through a heterogeneous mosaic of conifer forests, oak woodlands, and meadows in the Sierra Nevada mountains of California, we identified conifer snags and estimated their basal area from single-tree ALS-derived metrics using Gaussian processes in four major steps. First, individual trees were mapped using the Watershed Segmentation algorithm, resulting in 87% detection of trees with stem diameter larger than 30 cm. Second, the snag/live classification model identified snags with an overall accuracy of 91.8%, using the coefficient of variation of height and intensity together with maximum intensity and fractional cover as the most relevant metrics. Third, the conifer/hardwood snag classification model utilizing the maximum height, median height, minimum intensity, and area metrics separated snag forest types with an overall accuracy of 84.8%. Finally, a Gaussian process regression model reliably estimated conifer snag stem diameter ( R 2 = 0.81) using height and crown area, thus significantly outperforming regionally calibrated conifer-specific allometric equations. As a result, ~ 80% of the snag basal area have been mapped. Optimal and potential habitat for Black-backed Woodpecker comprise 53.7 km 2 and 58.4 km 2 , respectively, representing 5.1 and 5.6% of the footprint of the Rim Fire. Our study illustrates the utility of high-density ALS data for characterizing recently burned forests, which, in conjunction with information about the habitat needs of particular snag-dependent wildlife species, can be used to assess habitat characteristics, and thus contribute greatly to forest management and biodiversity conservation. [ABSTRACT FROM AUTHOR]

Recent and projected increases in the frequency and severity of large wildfires in the western U.S. makes understanding the factors that strongly affect landscape fire patterns a management priority for optimizing treatment location. We compared the influence of variations in the local environment on burn severity patterns on the large 2013 Rim fire that burned under extreme drought with those of previous smaller fires for a study area in the Sierra Nevada, California, USA. Although much of the Rim fire burned during plume-dominated conditions resulting in large high-severity patches, our study area burned under milder fire weather resulting in a mix of fire severities. In our study area the Rim fire produced a higher proportion of moderate- and high-severity effects than occurred in previous fires. Random forest modeling explained up to 63% of the Rim fire burn variance using seven predictors: time since previous fire, actual evapotranspiration (AET), climatic water deficit (Deficit), previous maximum burn severity, burning index, slope position, and solar radiation. Models using only a subset of biophysical predictors (AET, Deficit, slope position and steepness, and solar radiation) explained 55% of the Rim fire and 58% of the maximum fire burn severity of previous fires. The relationship of burn severity to patterns of AET, however, reversed for the Rim fire (positive) compared to earlier fires (negative). Measurements of pre-Rim fire forest structure from LiDAR did not improve our ability to explain burn severity patterns. We found that accounting for spatial autocorrelation in burn severity and biophysical environment was important to model quality and stability. Our results suggest water balance and topography can help predict likely burn severity patterns under moderate climate and fire weather conditions, providing managers with general guidance for prioritizing fuel treatments and identifying where fire is less likely to burn with higher severities even for locations with higher forest density and canopy cover. [ABSTRACT FROM AUTHOR]

The number of large, high-severity fires has increased in the western United States over the past 30 years due to climate change and increasing tree density from fire suppression. Fuel quantity, topography, and weather during a burn control fire severity, and the relative contributions of these controls in mixed-severity fires in mountainous terrain are poorly understood. In 2013, the Rim Fire burned a previously studied 2125 ha area of mixed-conifer forest in Yosemite National Park. Data from 84 plots sampled in 2002 revealed increases in tree density, basal area, and fuel buildup since 1899 due to fire exclusion. A dendroecological fire history and reconstruction of forest structure in 1899 showed that this area historically experienced frequent, low-severity fire. In contrast with this region's historical fire regime, burn severity from Landsat imagery showed that this area burned at mixed-severity in the Rim Fire, with 13% of plots classified as unchanged, 31% low severity, 32% moderate severity, and 24% high severity. A random forest model was used to identify the controls of fire severity in this portion of the Rim Fire, using daily area burned, daily fire weather, and fuels and vegetation data for the surface and canopy. Topography, tree species composition, and cover of forbs and shrubs best explained the fire severity. As an example of a re-entry burn, this study demonstrates how fire exclusion alters fire-vegetation interactions, leading to uncharacteristically severe burns and potentially new fire-vegetation dynamics. [ABSTRACT FROM AUTHOR]

The 2013 Rim Fire, originating on Forest Service land, burned into old-growth forests within Yosemite National Park with relatively restored frequent-fire regimes (≥2 predominantly low and moderate severity burns within the last 35 years). Forest structure and fuels data were collected in the field 3-4 years before the fire, providing a rare chance to use pre-existing plot data to analyze fire effects. We used regression tree and random forests analysis to examine the influence of forest structure, fuel, fire history, topographic and weather conditions on observed fire severity in the Rim Fire, as estimated from an initial fire severity assessment based on the relative differenced normalized burn ratio (RdNBR). Plots that burned on days with strong plume activity experienced moderate- to high-severity fire effects regardless of forest conditions, fire history or topography. Fire severity was also highly negatively associated with elevation, with lower severity observed in plots over 1700 m. Burning index (a composite index of fire weather), time since the last fire, and shrub cover had strong positive associations with fire severity. Plots that had experienced fire within the last 14 years burned mainly at low severity in the Rim Fire, while plots that exceeded that time since last fire tended to burn at moderate or high severity. This effect of time since last fire was even more pronounced on days when the burning index was high. Our results suggest that wildfire burning under extreme weather conditions, as is often the case with fires that escape initial attack, can produce large areas of high-severity fire even in fuels-reduced forests with restored fire regimes. [ABSTRACT FROM AUTHOR]

Wildland fires produce smoke plumes that impact air quality and human health. To understand the effects of wildland fire smoke on humans, the amount and composition of the smoke plume must be quantified. Using a fire emissions inventory is one way to determine the emissions rate and composition of smoke plumes from individual fires. There are multiple fire emissions inventories, and each uses a different method to estimate emissions. This paper presents a comparison of four emissions inventories and their products: Fire INventory from NCAR (FINN version 1.5), Global Fire Emissions Database (GFED version 4s), Missoula Fire Labs Emissions Inventory (MFLEI (250 m) and MFLEI (10 km) products), and Wildland Fire Emissions Inventory System (WFEIS (MODIS) and WFEIS (MTBS) products). The outputs from these inventories are compared directly. Because there are no validation datasets for fire emissions, the outlying points from the Bayesian models developed for each inventory were compared with visible images and fire radiative power (FRP) data from satellite remote sensing. This comparison provides a framework to check fire emissions inventory data against additional data by providing a set of days to investigate closely. Results indicate that FINN and GFED likely underestimate emissions, while the MFLEI products likely overestimate emissions. No fire emissions inventory matched the temporal distribution of emissions from an external FRP dataset. A discussion of the differences impacting the emissions estimates from the four fire emissions inventories is provided, including a qualitative comparison of the methods and inputs used by each inventory and the associated strengths and limitations. [ABSTRACT FROM AUTHOR]

• After the 2013 Rim Fire, spotted owls in Yosemite persisted in the fire perimeter. • In the fire perimeter, owl territories moved to areas with <30% high severity burn. • Owl territories found post-fire had less canopy cover than those found pre-fire. • Pre-fire canopy predicted post-fire territories better than post-fire habitat. As fires in the western United States have become larger and more severe over recent decades, understanding how the changing fire regime affects wildlife has become a key issue for conservation. Spotted owls (Strix occidentalis) associate with late-successional forest characteristics and therefore may be particularly sensitive to structural changes in habitat that result from fire. Previous studies have found varying responses of the owls to forest fire. We investigated the effects of the 2013 Rim Fire on territory selection by California spotted owls within Yosemite National Park, which, unlike the surrounding landscape, has been managed with no commercial logging since the early 1900s and minimal fire suppression since the 1970s. We examined specific habitat characteristics associated with spotted owl presence before and after the fire to understand how fire-induced changes in habitat structure may influence spotted owl territory selection. Spotted owls persisted and nested within the fire perimeter throughout the four post-fire years of our study at rates similar to what we observed in areas of Yosemite that were unaffected by the fire. However, within the fire perimeter, spotted owls avoided areas characterized by >30% percent high severity fire. Prior to the fire, spotted owls selected for areas of high canopy cover relative to the rest of the landscape; after the fire, even though territory centers shifted substantially from pre-fire locations, pre-fire canopy cover remained a stronger predictor of spotted owl presence than post-fire canopy cover, or any other pre- or post-fire habitat variables we assessed. The importance of pre-fire forest structure in predicting owl presence after fire suggests that reported variation in spotted owl population response to different fires across the Sierra Nevada may in part reflect variation in pre-fire forest characteristics, and perhaps different forest management regimes that shaped those characteristics. Pre-fire forest characteristics may impart a legacy of post-fire habitat conditions important to owls that commonly used forest and fire metrics do not effectively describe. Further study of owl response to fire in forests with a broader spectrum of pre-fire forest structure and management regimes is needed to better predict and manage effects of the changing fire regime on spotted owls. [ABSTRACT FROM AUTHOR]

Megafires have lasting social, ecological, and economic impacts and are increasing in the western contiguous United States. Because of their infrequent nature, there is a limited sample of megafires to investigate their unique behavior, drivers, and relationship to forest management practices. One approach is to characterize critical information pre-, during, and post-fire using remote sensing. In August 2013, the Rim Fire burned 104,131 ha and in September 2014, the King Fire burned 39,545 ha. Both fires occurred in California's Sierra Nevada. The areas burned by these fires were fortuitously surveyed by airborne campaigns, which provided the most recent remote sensing technologies not currently available from satellite. Technologies include an imaging spectrometer spanning the visible to shortwave infrared (0.38-2.5 μm), a multispectral, high-spatial resolution thermal infrared (3.5-13 μm) spectroradiometer, and Light Detection and Ranging that provide spatial resolutions of pixels from 1 × 1 m to 35 × 35 m. Because of the unique information inherently derived from these technologies before the fires, the areas were subsequently surveyed after the fires. We processed and provide free dissemination of these airborne datasets as products of surface reflectance, spectral metrics and forest structural metrics ( ). These data products provide a unique opportunity to study relationships among and between remote sensing observations and fuel and fire characteristics (e.g., fuel type, condition, structure, and fire severity). The novelty of these data is not only in the unprecedented types of information available from them before, during, and after two megafires, but also in the synergistic use of multiple state of the art technologies for characterizing the environment. The synergy of these data can provide novel information that can improve maps of fuel type, structure, abundance, and condition that may improve predictions of megafire behavior and effects, thus aiding management before, during, and after such events. Key questions that these data could address include: What drives, extinguishes, and results from megafires? How does megafire behavior relate to fire and fuel management? How does the size and severity of a megafire affect the ecological recovery of the system? [ABSTRACT FROM AUTHOR]

The Rim Fire of 2013, the third largest area burned by fire recorded in California history, is simulated by a climate model coupled with a size-resolved aerosol model. Modeled aerosol mass, number, and particle size distribution are within variability of data obtained from multiple-airborne in situ measurements. Simulations suggest that Rim Fire smoke may block 4-6% of sunlight energy reaching the surface, with a dimming efficiency around 120-150 W m -2 per unit aerosol optical depth in the midvisible at 13:00-15:00 local time. Underestimation of simulated smoke single scattering albedo at midvisible by 0.04 suggests that the model overestimates either the particle size or the absorption due to black carbon. This study shows that exceptional events like the 2013 Rim Fire can be simulated by a climate model with 1° resolution with overall good skill, although that resolution is still not sufficient to resolve the smoke peak near the source region.