Geology topics

Micah C. Wright

Publications and source records attributed to Micah C. Wright.

At least 19 recordsLinked to original sources

USGS2026

Estimating basal area change by tree size with Sentinel-2 imagery following four fires in California, USA

Background Failure to account for tree size when estimating burn severity may not accurately capture post-fire tree mortality and post-fire forest structure. Aims We explored whether basal area mortality by tree size class could be determined from remotely-sensed burn severity indices based solely on Sentinel-2 satellite imagery. Methods We used data collected in four large California wildfires to model the relationship between proportional basal area mortality and burn severity indices derived from Sentinel-2 imagery for three tree diameter class thresholds: small (15 to 30 cm), medium (30 to 50 cm) and large (>50 cm). Key results Our models showed that for a given burn severity index value, the proportion of mortality was greater overall in smaller trees, and that the proportion of mortality in large trees changed more slowly than that of smaller trees with changing burn severity index values. Conclusions We found that models that accounted for tree size can more precisely estimate changes in forest size structure than a similar model that did not account for tree size. Implications Explicitly accounting for tree size can improve estimates of post-fire forest structure, including for large trees which make up the bulk of stand biomass and post-fire seed sources.

California↗

USGS2026

Recovery trajectories of surface fuels and forest trees following prescribed fire in low elevation conifer forests of California and southern Oregon

Background Prescribed fire is commonly used to manage surface fuels and stand structure in dry coniferous forests in the western United States. While the effectiveness of prescribed fire to manage fuel loads and live forest structure has been well documented, less is known about the shape of recovery trajectories more than a decade following treatment. We used up to 30 years of longitudinal observations from > 180 monitoring plots treated with a single prescribed fire-only treatment across six national parks in California and Oregon to compare competing recovery trajectory models: generalized linear models (GLM) that presumed exponential trajectories over time, generalized additive models (GAM) that had maximum flexibility to fit responses, and generalized Michaelis–Menten models that describe an asymptotic recovery trajectory. Results The Michaelis–Menten model had the lowest median RMSE of the fuel recovery models, with predicted recovery times and maximal fuel loads tending to increase with increasing fuel particle size. However, predictive performance was similar between model forms. GAM best predicted forest structure, showing that stem density of live overstory trees (> 15 cm stem diameter) declined immediately following prescribed fire without appreciable postfire accumulation. Results for live stem biomass were similar, but with less pronounced reductions. Conclusions We found that reductions in surface fuel biomass from a single prescribed fire-only treatment can last for more than a decade for some fuel types. Our analysis also demonstrates that asymptotic models reasonably describe surface fuel recovery trajectories. However, we lack the evidence to definitively conclude that asymptotic models best describe postfire recovery, even with repeated observations of up to three decades postfire. We also found that stem density and biomass of live trees with stem diameter > 15 cm both declined in the first few years immediately following the fire, after which they remained relatively unchanged for the remainder of the observation period.

California, Oregon↗

USGS2026

Effects of repeat prescribed burning in dry coniferous forests in national parks of California

Background Prescribed fire is a common approach to reduce fuels and mitigate fire hazards. The accumulation of live and dead fuels following initial treatment means that repeated application of prescribed fire could be used to maintain this benefit. However, the effect of repeated prescribed fires is not well documented in many dry coniferous forests in the western United States. Here, we present observations of changes in live trees and surface fuels following two prescribed fires in dry coniferous forests in national parks of California. Results Changes in forest structure and accumulation of surface fuels were similar over time following initial-entry and second-entry fires. An exception was that repeated fires were associated with substantial reductions in stem density. There were smaller changes in live tree basal area and stem biomass. Conclusions Our results indicate that following initial-entry fires, subsequent burning maintained reductions in surface fuel loads without major inadvertent losses of live tree basal area and stem biomass, implying the survival of large trees.

California↗

USGS2026

Quantifying post-fire live tree presence and spatial variation using Sentinel-2 time series

Accurate mapping of post-fire surviving trees is important for tracking forest recovery and prioritizing land management decisions. Satellite-based remote sensing is an effective method to assess post-fire forest conditions. Traditionally, differenced satellite-derived burn severity indices are computed by differencing one year pre- and post-fire spectral reflectance values. Differenced burn severity indices are useful for quantifying and mapping the magnitude of ecological change, but their application to detecting and mapping post-fire live trees may not be as appropriate, particularly for delayed tree mortality. Delayed tree mortality (“delayed mortality”) is a phenomenon where trees that initially survive fire then die over an extended period (between one and five years), and it can be challenging to measure and predict. In this study, we demonstrate the potential of mapping delayed mortality using readily available remotely sensed imagery alone. We used random forest models to detect post-fire live trees using 10-m resolution Sentinel-2 data at one-, three-, and five-years post-fire for four fires in the southern Sierra Nevada, California, USA. Using imagery from the National Agriculture Imagery Program (NAIP; 60-cm resolution), we manually classified live tree presence in 6000 Sentinel-2 pixels (500 pixels for each fire-year combination) to calibrate and validate models. Sentinel-2 based model accuracies ranged from 65 % to 86 % with F-scores ranging from 0.52 to 0.86, and their predictions of live pixel area were on average 44 % lower than inferred from more traditional indices such as relative differenced normalized burn ratio (RdNBR). This work represents a promising first step in using freely available post-fire spectral reflectance imagery to detect live trees over an extended period to support post-fire management.

California↗

USGS2026

Basal area loss from fire using field-calibrated remote sensing refines western US fire severity measurements

The spatial patterns of fire effects and tree mortality have profound consequences for forest resilience. Cost-effective, medium-resolution, and spatiotemporally extensive fire severity measurements are essential for informing post-fire restoration and improving our understanding of wildfires—from forest stands to continents and from days to decades. Remote sensing advancements have improved burn severity mapping, but methods vary in interpretability, scalability, generalizability, and alignment with field measurements. One meaningful metric of fire effects on forests is proportion basal area loss, but existing methods are limited by a lack of region-specific field reference data and a scalable mapping framework. To address these issues, we compiled 3280 field reference plots from 123 fires in forests across the Western US to calculate the proportion of fire-induced basal area loss. We then used spatially cross-validated machine learning models with concurrent hyperparameter tuning to select a skillful, parsimonious model from a large candidate set of remotely-sensed, climatic, and topographic predictors. Spectral-only measures of severity over- or underestimated basal area loss in dry versus wet years and across aspects, demonstrating the value of incorporating climatic and topographic context. We also tested model performance on a separate holdout dataset in the Southwest US as a demonstration of reproducibility and transparency. We provide a Google Earth Engine tool for estimating proportional basal area loss for any fire perimeter in the Western US, enabling rapid map creation for land management and ecological modeling. All code, model parameters, and training data are released to support reproducibility, community adoption, regional refinement, and adaptation to new regions.

western United States↗

USGS2025

Recent large-scale prescribed fire treatments reduced Carr Fire severity at Whiskeytown National Recreation Area

Background Severe fire weather is becoming more common throughout the western United States. Changing conditions demand a better understanding of how prescribed fire treatments perform under extreme burning conditions, including the interactive influence of the age of treatments, vegetation, and fire weather. The Carr Fire of July 2018 burned nearly the entire land area of Whiskeytown National Recreation Area (NRA) under extreme fuel moisture and temperature conditions. Prior to the Carr Fire and since 1997, staff at Whiskeytown NRA treated 23% of the 15,756-ha NRA using large-scale prescribed fire (underburn) treatments ranging in size from 40 to 400 hectares. Methods We used simultaneous autoregressive (SAR) models to describe the effects of landscape-scale fuel treatments on wildfire severity under extreme burning conditions and across diverse biophysical settings at Whiskeytown NRA. Because vegetation type and structure are known drivers of fire severity in diverse ecosystems such as at Whiskeytown NRA, we also considered three different sources of vegetation structure data, including a 2006 physiognomic-floristic classification, a 2011 lidar-based forest structure classification, and a 2016 Landfire map of existing vegetation physiognomy-subclass. Results The greatest effect on 2018 Carr Fire severity was time since treatment of underburn treatments, but treatment effectiveness on fire severity dissipated rapidly—showing notable effectiveness within 5 years of underburning but virtually no effectiveness beyond 10 years post-treatment. Additional factors related to severity included vegetation structure type, topographic position index, aspect, slope, temperature, and wind gust speed. Model variance explained and model parameters, including the effect of underburn treatments, were similar regardless of the source of vegetation structure data. Conclusions Our results show that large-scale underburning treatments can reduce wildfire severity even under extreme fire weather conditions but suggest that frequent maintenance intervals are required to maintain treatment effectiveness ahead of severe wildfire events.

California↗

USGS2025

Effects of restoration thinning on live tree carbon in northern secondary coastal redwood forests

At Redwood National and State parks restoration efforts are underway to promote the recovery of forests following a history of intense logging. Unmanaged secondary forests at the parks have high stem density with slow stand development. Restoration thinning treatments of these stands are designed to promote the representation of redwood and improve the growth of residual trees. Restoration treatments, by definition, remove live tree carbon, so there is a potential conflict with restoration and carbon sequestration goals. We used data from 151 monitoring plots to determine early treatment effects (mostly ≤ 10 years following treatments) on live tree forest carbon in thinned and unthinned stands. Aboveground live tree carbon in untreated secondary forests was primarily related to stand age. Both treated and untreated stands accumulated carbon over time, with evidence that treated stands had a slightly faster rate of carbon accumulation. For a subset of plots where we had sufficient replicates of thinning prescriptions, we found thinning resulted in reductions in live tree aboveground carbon, particularly for heavy thinning, but with plots accumulating live tree carbon stocks similar to pre-treatment levels within 6–10 years. Stand projections suggested that both thinned and unthinned stands have the capacity to greatly increase aboveground live tree carbon in coming decades, with thinned stands accumulating aboveground live tree carbon at a slightly faster pace.

California↗

USGS2025

Sentinel imagery detects the presence of live trees following large wildfires in California

Identifying live tree presence following wildfire is important for burn damage assessments and decision making, as these trees serve as seed sources for recovery. Satellite-based remote sensing offers an efficient means to assess burn severity with products representing vegetation greenness and char/ash presence and their change from pre- to post-fire imagery. While effective at assessing burn severity (e.g. ecosystem change), there remain limitations in identifying fire refugia (surviving trees), due to the difficulty of teasing apart different green vegetation types (e.g. trees, shrubs, grasses). In this paper, we use 10 m Sentinel-2 satellite data to predict live tree presence across three sites impacted by the 2021 California fire season. We used vegetation indices (VIs) from post-fire imagery (normalized difference vegetation index [NDVI], normalized burn ratio [NBR], normalized difference water index [NDWI], visible atmospherically resistant index [VARI], and burn area index [BAI]), differential VIs from pre- and post-fire imagery (dNDVI, dNBR, RdNBR, dNDWI, dVARI), and direct reflectance bands (all bands model; visible, near-infrared, and shortwave infrared; B1–B12) to predict live tree presence via random forest modeling. To calibrate and validate the random forest models, we photointerpreted ∼2300 pixels per fire region using 2022 National Agriculture Imagery Program imagery. We performed additional field-based validation using tree presence/absence data two years post-fire ( n = 296 observations across two sites). At the site level, the all bands model outperformed the vegetation index-based models (80%–85% vs 65%–79% accuracy). Errors were mainly false positives attributed to pixels with green understory vegetation but no live trees. In cross-site inference, which involved pooling two sites for model calibration to test on the third site, the all bands model retained good performance (76%–81% accuracy). Evaluation against field survey data demonstrated a larger range of performance (50%–87% accuracy) that highlights limitations based on tree isolation and crown percent greenness. Relative to differential-based VIs, our results highlight potential advantages of using post-fire Sentinel-2 imagery and random forest modeling for identifying live tree presence and scaling to full fire extents.

California↗

USGS2025

Vegetation community monitoring: Forest structure in Klamath Network parks

The Klamath Network, comprising six national park units in northern California and southern Oregon, initiated a vegetation monitoring protocol in 2011 to discern ecologically significant vegetation trends in these parks. The premise of the protocol is that multivariate analyses of species composition data is the most robust means for early detection of vegetation change over time. Here, we present these community metrics based on our sampling efforts from 2011 to 2019. Observations from the first sampling event (2011–2013) were used to establish baseline conditions for the vegetation communities. Observations from subsequent sampling in 2014–2019 were used to identify potential temporal variation in forest structure across habitat types and parks. Park landscapes were categorized into three strata: matrix (low- to mid-elevation upland habitats), riparian (within 10 m of a perennial stream), and high-elevation (above a predefined elevation, park-specific). At the onset of the network’s vegetation monitoring protocol, 241 permanent plots were established at random locations across the three strata. We present summary statistics from three repeated samplings (2011–2019) of each plot, describing variation in forest structure across broad habitat types and parks. Observable differences in forest structure aligned with expected productivity gradients across the parks. Measures of forest structure (vegetation cover, stem density, basal area, tree heights, height to live crown, shrub cover, and surface fuels) were generally higher in mesic sites, compared to sites located in more arid, continental climates. Differences across sampling frames also followed this general pattern of productivity. Matrix and riparian sampling frames had similar ranges of values in most cases, while high elevation sites had relatively lower stem density, basal area, shrub cover, fuels, and recruitment. Notably, we observed a relative lack of change in forest structure over time. This is not surprising given the relatively short (six-year) timespan of observations in each park. The fourth set of Klamath Network surveys (2021–2023) is likely to show substantial changes in vegetation cover and forest structure, particularly for parks that have recently experienced major fires. Continued long-term vegetation monitoring is crucial for understanding ecosystem responses to a rapidly changing world. This report on vegetation composition is the second in a series; upcoming reports will analyze structure and function, aiming to detect spatiotemporal trends.

California, Oregon↗

USGS2024

Drought before fire increases tree mortality after fire

Fire and drought are expected to increase in frequency and severity in temperate forests due to climate change. To evaluate whether drought increases the likelihood of post-fire tree mortality, we used a large database of tree survival and mortality from 32 years of wildland fires covering four dominant western North American conifers. We used Bayesian hierarchical modeling to predict the probability of individual tree mortality after fire based on species— Pinus contorta (lodgepole pine), Abies concolor (white fir), Pseudotsuga menziesii (Douglas-fir), and Pinus ponderosa (ponderosa pine)—bark thickness, bark char, percentage live tree crown scorched or consumed crown volume scorch (CVS), and mean annual climatic water deficit (CWD) anomalies the year pre-fire and fire year relative to the 1985–2015 reference period. Although crown injury was the primary determinant of tree mortality after fire, drought increased likelihood of death, with a 2-SD increase in CWD (+115.7) resulting in a 78% increase in the probability of mortality. We assessed the crown scorch level expected to result in >50% probability of mortality under different CWD scenarios: observed CWD, CWD of +2, and +4°C warming scenarios. Increased climatic moisture stress amplified tree death, reducing the threshold that causes tree mortality across all conifers under +4°C warming, with more subtle and species-specific reductions for the +2°C scenario. Models predicting post-fire tree mortality are components of global and regional carbon estimates, habitat suitability assessments, and forest management planning and decision support systems. The amplifying effects of drought on post-fire tree mortality and predicted future climates are likely to lead to higher tree mortality following fires in forested landscapes of western North America and may have cascading effects on ecosystem services and future forest resilience.

western United States↗

USGS2024

Learning from a high-severity fire event—Conditions following the 2018 Carr Fire at Whiskeytown National Recreation Area

The 2018 Carr Fire burned more than 90 percent of Whiskeytown National Recreation Area, with much of the park burning at high severity. California yellow pine and mixed conifer forests are not well adapted to large, high-severity fires, and forest recovery after these events may be problematic. Large, high-severity fire patches pose difficulties for recruitment with interiors that are long distances from potential seed trees and may develop fuel structures that can promote further high-severity fire. This report details patterns of forest structure derived from field plots measured 2–3 years after the Carr Fire, providing a characterization of immediate fire effects. We coupled these observations with remotely sensed information, including data collected from unoccupied aircraft system surveys. The remotely sensed data were used to depict erosion after the Carr Fire as well as to create a high-resolution land cover classification map, a debris flow risk map and hazard assessment, and a post-fire canopy vegetation loss map. Results indicated high levels of tree mortality after the Carr Fire, including high-value old growth forest stands, supporting remotely sensed assessments of fire severity. The high-resolution tree mortality model also aligned well with other remotely sensed estimates of immediate burn severity. Results of the land cover classification illustrated the high percentage of dead vegetation remaining in the understory and canopy 8 months post-fire. Changes in vegetation height identified areas with canopy vegetation loss from 1- to 8-months post-fire. Pairing the post-fire debris accumulation with debris flow probabilities may identify high-risk debris flow areas. The results of this study will help inform future decisions concerning wildland fire and vegetation management strategies at Whiskeytown National Recreation Area and are broadly relevant for management in the aftermath of large, high-severity fires in mixed, dry coniferous forests in the western United States.

California↗

USGS2023

Spatially explicit models of seed availability improve predictions of conifer regeneration following the 2018 Carr Fire in northern California

For many conifer species in dry conifer forests of North America, seeds must be present for postfire regeneration to occur, suggesting that seed dispersal from surviving trees plays a critical role in postfire forest recovery. However, the application of tree fecundity and spatial arrangement to postfire conifer recovery predictions have only recently become more common, and is often included at relatively coarse scales (i.e., 30 meters). In this study, we mapped surviving trees using lidar and created a spatially explicit estimate of seed density (seed shadows) with 10 m, 50 m, and 100 m median dispersal distances. We estimated the number of seeds produced by each tree using allometric relationships between tree size and fecundity. Along with the seed shadows, we used a suite of topographic variables as inputs to negative binomial hurdle models to predict conifer seedling abundance in 131 plots following the 2018 Carr Fire in northern California, USA. We compared models using each of the seed shadows to each other as well as to a model using the distance to the nearest surviving tree, which served as a baseline. All model formulations indicated that estimated seed availability was positively associated with conifer regeneration. Despite the importance of seed availability plays in regeneration and the substantial differences in seed availability represented by the different seed shadows in our analysis, we found surprisingly little difference in model performance regardless of which seed shadow was used. However, the models employing seed shadows outperformed the models with distance to the nearest live tree. Although we have demonstrated a modest improvement in predicting postfire conifer regeneration, the uncertainty in our results highlights the importance of tree detection and classification in future studies of this kind. Future studies may find it useful to consider other factors such as predation, site suitability, and seed mortality as potential drivers of discrepancies between total and realized dispersal kernels.

California↗

USGS2023

Best practices for distributing and deploying U.S. Geological Survey Shiny applications

A goal of U.S. Geological Survey (USGS) research is to create actionable science for U.S. Department of the Interior managers and partners. Properly managed and released software tools can quickly, accurately, and easily translate scientific results to aid managers’ decision-making. The USGS policies on software development and distribution affect how authors of USGS software products must navigate the review and approval process. Understanding these rules and procedures will save time and effort in making science useful and visible through software development. This document outlines USGS policies on software development as they pertain to Shiny applications, which use R code to create interactive web-based applications. The requirements and procedures described here will also be relevant for other USGS software applications. This document is not intended to be a step-by-step guide on building a Shiny application or to go into detail about any of the referenced software programs. An understanding of the referenced software programs, including R, Git, and hypertext markup language/cascading style sheets (HTML/CSS) is needed to use this document efficiently, though links for more thorough guidance are included throughout.

Techniques and Methods↗

USGS2023

Forest structure and residual tree growth at the Northwest Gateway project area, Lassen Volcanic National Park, California

Mechanical thinning and prescribed fire are common mitigation treatments to reduce fire hazards. However, these treatments are infrequently applied together within national parks. The Northwest Gateway project at Lassen Volcanic National Park is an exception to this pattern. Various thinning prescriptions were applied to the project area in 2014, with a subset of the area prescribed burned in 2018 and 2019. To determine responses to these treatments, we analyzed forest structure and fuels data across a network of long-term monitoring plots measured before treatments and in multiple years following treatments. Additionally, we assessed patterns in individual tree growth from cores taken from 101 individual yellow pines (ponderosa pine, Pinus ponderosa Douglas ex Lawson & C. Lawson, and Jeffery pine, P. jeffreyi Grev. & Balf.) within the project unit. Basal area and stem density were reduced following thinning treatments for pole-sized (≤15-centimeter diameter at base height) and overstory trees (>15-centimeter diameter at base height), with sharper reductions in pole-sized trees. Proportional change in live basal area after thinning was highest for pole-size Abies , with more than 80-percent basal area and stem density removed on average. There were large reductions in pole-sized Pinus and Populus . However, Populus trees were not targeted for removal, suggesting that these trees died via other mechanisms. Thinning treatments also resulted in reductions in stand density index values and in surface fuel loading when followed by prescribed fire, particularly for small fuels size classes (such as litter/duff, 1-hour, and 10-hour fuels). Growth of individual residual yellow pine, measured in terms of annual basal area increment, indicated a strong growth release in the years following thinning treatments. Taken together, these results indicate that forest restoration treatments at the Northwest Gateway project area were effective at reducing stand density and encouraging growth of residual Pinus . Interestingly, our results also indicated that although thinning followed by prescribed fire was most effective at reducing surface fuel loads, harvest techniques such as whole tree yarding may effectively reduce the accumulation of post-treatment residual fuels, especially when combined with hand piling and other targeted treatments.

California↗

USGS2022

Predictive accuracy of post-fire conifer death declines over time in models based on crown and bole injury

A key uncertainty of empirical models of post-fire tree mortality is understanding the drivers of elevated post-fire mortality several years following fire, known as delayed mortality. Delayed mortality can represent a substantial fraction of mortality, particularly for large trees that are a conservation focus in western US coniferous forests. Current post-fire tree mortality models have undergone limited evaluation of how injury level and time since fire interact to influence model accuracy and predictor variable importance. Less severe injuries potentially serve as an indicator for vulnerability to additional stressors such as bark beetle attack or moisture stress. We used a collection of 164,293 individual tree records to examine post-fire tree mortality in eight western USA conifers: Abies concolor , A. grandis , Calocedrus decurrens , Larix occidentalis , Pinus contorta , P. lambertiana , P. ponderosa , and Pseudotsuga menziesii . We evaluated the importance of fire injury predictors on discriminating between surviving trees versus immediate and delayed post-fire mortality. We fit balanced random forest models for each species using cumulative tree mortality from 1–5-years post-fire. We compared these results to multi-class random forest models using first-year mortality, 2–5-year mortality, and survival 5-years post-fire as a response variable. Crown volume scorched, diameter at breast height, and relative bark char height, were used as predictor variables. The cumulative mortality models all predicted trees that died within 1-year of fire with high accuracy but failed to predict 2–5-year mortality. The multi-class models were an improvement but had lower accuracy for predicting 2–5-year mortality. Multi-class model accuracies ranged from 85–95% across all species for predicting 1-year post-fire mortality, 42–71% for predicting 2–5-year mortality, and 64–85% for predicting trees that lived past 5-years. Our study highlights the differences in tree species tolerance to fire injury and suggests that including second-order predictors such as beetle attack or climatic water stress before and after fire will be critical to improve accuracy and better understand the mechanisms and patterns of fire-caused tree death. Random forest models have potential for management applications such as post-fire harvesting and simulating future stand dynamics.

western United States↗

USGS2022

Long-term effects of prescribed fire on large tree growth in mixed conifer forests at Lassen Volcanic National Park, California

Prescribed fire in dry coniferous forests of the western U.S. is used to reduce fire hazards. How large, old trees respond to these treatments is an important management consideration. Growth is a key indicator of residual tree condition, which can be predictive of mortality and response to future disturbance. Using a combination of long-term plot records and dendrochronological samples, we analyzed the effects of prescribed fire treatments from the early 1990 s on forest structure and individual tree growth in mixed-conifer forests of Lassen Volcanic National Park in northern California. Prescribed fire reduced stand live tree basal area and stem density at our sites up to 10 years following fire. Within two prescribed fire burn units and two adjacent unburned stands, we analyzed tree cores from 136 large (mean stem diameter > 70 cm) yellow pine ( Pinus jeffreyi and P. ponderosa ) and 136 large (mean stem diameter > 50 cm) white fir ( Abies concolor ). After accounting for annual precipitation, basal area increment for individual trees initially declined up to < 3 years post-fire for white fir and > 10 years post-fire for yellow pine, presumably in response to tree injuries. Growth improved for both species at a site that was burned twice, particularly for white fir. Recent average basal area increment was positively related to crown ratio and negatively associated with an index of local competition. Our findings suggest that forest management, such as prescribed fire and mechanical thinning, may be beneficial in terms of maintaining or improving tree growth among large residual trees. However, managers may want to balance the benefits of these treatments against inadvertent injury and mortality of large trees.

California↗

USGS2021

Patterns of conifer invasion following prescribed fire in grasslands and oak woodlands of Redwood National Park, California

The invasion, or “encroachment”, of native conifers commonly occurs in the absence of frequent fire in deciduous woodlands and grasslands of the Pacific Northwest, USA. To effectively target restoration activities, managers require a better understanding of the outcomes of prescribed fire and the spatial patterns of conifer invasions. We examined the duration of prescribed fire effectiveness for controlling conifer invasions, as well as multiple site characteristics (including distance to potential seed trees, prescribed fire history, and topographic variables) that influenced conifer invasions following fire in grassland and oak woodland communities in the Bald Hills of Redwood National Park, California. Prescribed fire substantially reduced counts of small conifers (< 0.91 m in height), but reinvasion was rapid for sites ≤75 m from the forest edge, returning to pre‐fire levels by 2 years post‐fire. Following prescribed fires the presence of conifers was largely determined by proximity of overstory trees, with more than 95% of conifer seedlings (stems <1.37 m in height) found within 44 m of an overstory conifer. Number of fires and years since the most recent fire were not strongly related to counts of conifer seedlings and density of conifer saplings (stems from 0.1 to 10 cm diameter at breast height, 1.37 m). Our results suggest that in the Bald Hills vulnerability to conifer invasion is principally a function of proximity to seed sources, and the frequent application of prescribed fire or surrogate treatments are needed to prevent conifer seedlings from attaining fire‐resistant sizes.

California↗

USGS2021

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‐year postfire recovery data from 1,234 study plots in 19 wildfires that burned from 2004–2012 and 18 years of seed production data from 216 seed fall traps (1999–2017). We used this data in conjunction with spatially extensive estimates of climate, topography, forest composition, and burn severity to construct taxon‐specific, spatially explicit models of conifer regeneration that incorporate estimated 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.

California↗