Geology topics

J. Morgan Varner

Publications and source records attributed to J. Morgan Varner.

11 recordsLinked to original sources

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↗

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

Reimagine fire science for the anthropocene

Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

PNAS Nexus↗

USGS2021

Robust projections of future fire probability for the conterminous United States

Globally increasing wildfires have been attributed to anthropogenic climate change. However, providing decision makers with a clear understanding of how future planetary warming could affect fire regimes is complicated by confounding land use factors that influence wildfire and by uncertainty associated with model simulations of climate change. We use an ensemble of statistically downscaled Global Climate Models in combination with the Physical Chemistry Fire Frequency Model (PC2FM) to project changing potential fire probabilities in the conterminous United States for two scenarios representing lower (RCP 4.5) and higher (RCP 8.5) greenhouse gas emission futures. PC2FM is a physically-based and scale-independent model that predicts mean fire return intervals from both fire reactant and reaction variables, which are largely dependent on a locale's climate. Our results overwhelmingly depict increasing potential fire probabilities across the conterminous US for both climate scenarios. The primary mechanism for the projected increases is rising temperatures, reflecting changes in the chemical reaction environment commensurate with enhanced photosynthetic rates and available thermal molecular energy. Existing high risk areas, such as the Cascade Range and the Coastal California Mountains, are projected to experience greater annual fire occurrence probabilities, with relative increases of 122% and 67%, respectively, under RCP 8.5 compared to increases of 63% and 38% under RCP 4.5. Regions not currently associated with frequently occurring wildfires, such as New England and the Great Lakes, are projected to experience a doubling of occurrence probabilities by 2100 under RCP 8.5. This high resolution, continental-scale modeling study of climate change impacts on potential fire probability accounts for shifting background environmental conditions across regions that will interact with topographic drivers to significantly alter future fire probabilities. The ensemble modeling approach presents a useful planning tool for mitigation and adaptation strategies in regions of increasing wildfire risk.

Science of the Total Environment↗

USGS2020

A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species

Background Predictive models of post-fire tree and stem mortality are vital for management planning and understanding fire effects. Post-fire tree and stem mortality have been traditionally modeled as a simple empirical function of tree defenses ( e.g., bark thickness) and fire injury ( e.g., crown scorch). We used the Fire and Tree Mortality database (FTM)—which includes observations of tree mortality in obligate seeders and stem mortality in basal resprouting species from across the USA—to evaluate the accuracy of post-fire mortality models used in the First Order Fire Effects Model (FOFEM) software system. The basic model in FOFEM, the Ryan and Amman (R-A) model, uses bark thickness and percentage of crown volume scorched to predict post-fire mortality and can be applied to any species for which bark thickness can be calculated (184 species-level coefficients are included in the program). FOFEM (v6.7) also includes 38 species-specific tree mortality models (26 for gymnosperms, 12 for angiosperms), with unique predictors and coefficients. We assessed accuracy of the R-A model for 44 tree species and accuracy of 24 species-specific models for 13 species, using data from 93 438 tree-level observations and 351 fires that occurred from 1981 to 2016. Results For each model, we calculated performance statistics and provided an assessment of the representativeness of the evaluation data. We identified probability thresholds for which the model performed best, and the best thresholds with either ≥80% sensitivity or specificity. Of the 68 models evaluated, 43 had Area Under the Receiver Operating Characteristic Curve (AUC) values ≥0.80, indicating excellent performance, and 14 had AUCs <0.7, indicating poor performance. The R-A model often over-predicted mortality for angiosperms; 5 of 11 angiosperms had AUCs <0.7. For conifers, R-A over-predicted mortality for thin-barked species and for small diameter trees. The species-specific models had significantly higher AUCs than the R-A models for 10 of the 22 models, and five additional species-specific models had more balanced errors than R-A models, even though their AUCs were not significantly different or were significantly lower. Conclusions Approximately 75% of models tested had acceptable, excellent, or outstanding predictive ability. The models that performed poorly were primarily models predicting stem mortality of angiosperms or tree mortality of thin-barked conifers. This suggests that different approaches—such as different model forms, better estimates of bark thickness, and additional predictors—may be warranted for these taxa. Future data collection and research should target the geographical and taxonomic data gaps and poorly performing models identified in this study. Our evaluation of post-fire tree mortality models is the most comprehensive effort to date and allows users to have a clear understanding of the expected accuracy in predicting tree death from fire for 44 species.

Fire Ecology↗

USGS2020

The Fire and Tree Mortality Database, for empirical modeling of individual tree mortality after fire

Wildland fires have a multitude of ecological effects in forests, woodlands, and savannas across the globe. A major focus of past research has been on tree mortality from fire, as trees provide a vast range of biological services. We assembled a database of individual-tree records from prescribed fires and wildfires in the United States. The Fire and Tree Mortality (FTM) database includes records from 164,293 individual trees with records of fire injury (crown scorch, bole char, etc.), tree diameter, and either mortality or top-kill up to ten years post-fire. Data span 142 species and 62 genera, from 409 fires occurring from 1981-2016. Additional variables such as insect attack are included when available. The FTM database can be used to evaluate individual fire-caused mortality models for pre-fire planning and post-fire decision support, to develop improved models, and to explore general patterns of individual fire-induced tree death. The database can also be used to identify knowledge gaps that could be addressed in future research.

Scientific Data↗

USGS2020

Biogeography of fire regimes in western US conifer forests: A trait-based approach

Aim Functional traits are a critical link between species distributions and the ecosystem processes that structure those species’ niches. Concurrent increases in the availability of functional trait data and our ability to model species distributions present an opportunity to develop functional trait biogeography, i.e. the mapping of functional traits across space. Functional trait biogeography can improve process-based predictions about the resistance of certain species assemblages to changing environmental conditions across landscape scales. We illustrate this concept by developing the first trait-based, quantitative ranking of fire resistance (adult tree survival) in North American conifer species, and mapping that fire resistance across space. Location and Time period Western Continental United States, present-day. Major taxa studied 29 common conifer tree species. Methods We compiled six traits for each species: three relating to tree morphology and three relating to litter flammability. We combined these traits into a single fire resistance score, and used community-weighted averaging to estimate the fire resistance scores of different forest communities, using interpolated species distribution and relative abundance data. Results Species associated with historically frequent fire have high fire resistance scores (e.g., Pinus ponderosa), reflected by thick bark, tall crowns, and flammable litter. Species associated with subalpine or arid conditions have low fire resistance scores (e.g., Picea engelmannii and Pinus edulis), reflected by thin bark, short stature, poor self-pruning and low litter flammability. A map of forest community fire resistance across the western US reveals agreement with independent assessments of historical fire regimes, while also identifying areas where community-wide species traits may be mismatched with historical fire regimes. Main conclusions Quantifying the functional traits that confer resistance to tree-killing fire provides a direct link between ecosystem disturbance and community resistance. Understanding this link is critical to evaluating long-term resilience of different forest types under dynamic fire regimes. Our work represents the first known spatial representation of fire-resistance traits at a regional scale, and as such provides a link between functional traits and biogeography relevant to a critical ecosystem process.

Global Ecology and Biogeography↗

USGS2020

Evaluating and optimizing the use of logistic regression for tree mortality models in the First Order Fire Effects Model (FOFEM)

Wildland fires burn millions of forested hectares annually around the world, affecting biodiversity, carbon storage, hydrologic processes, and ecosystem services largely through fire-induced tree mortality (Bond-Lamberty et al. 2007; Dantas et al. 2016). In spite of this widespread importance, the underlying mechanisms of fire-caused tree mortality remain poorly understood, (Hood et al. 2018). Post-fire tree mortality has been traditionally modeled as an empirical function of tree defenses (bark thickness) and fire injury (crown scorch, stem char) (Ryan and Amman 1996; Woolley et al. 2012). Empirical models are commonly used in fire management to predict fire effects (Reinhardt et al. 1997), from the finescale software tools for fire management planning, to process-based succession models (Keane et al. 2011), and global models of the terrestrial carbon cycle (Hantson et al. 2016). Nevertheless, many fire-caused tree mortality models have undergone little evaluation.

Conference Paper↗

USGS2018

Fire and tree death: Understanding and improving modeling of fire-induced tree mortality

Each year wildland fires kill and injure trees on millions of forested hectares globally, affecting plant and animal biodiversity, carbon storage, hydrologic processes, and ecosystem services. The underlying mechanisms of fire-caused tree mortality remain poorly understood, however, limiting the ability to accurately predict mortality and develop robust modeling applications, especially under novel future climates. Virtually all post-fire tree mortality prediction systems are based on the same underlying empirical model described in Ryan and Reinhardt (1988 Can. J. For. Res. 18 1291–7), which was developed from a limited number of species, stretching model assumptions beyond intended limits. We review the current understanding of the mechanisms of fire-induced tree mortality, provide recommended standardized terminology, describe model applications and limitations, and conclude with key knowledge gaps and future directions for research. We suggest a two-pronged approach to future research: (1) continued improvements and evaluations of empirical models to quantify uncertainty and incorporate new regions and species and (2) acceleration of basic, physiological research on the proximate and ultimate causes of fire-induced tree mortality to incorporate processes of tree death into models. Advances in both empirical and process fire-induced tree modeling will allow creation of hybrid models that could advance understanding of how fire injures and kills trees, while improving prediction accuracy of fire-driven feedbacks on ecosystems and landscapes, particularly under novel future conditions.

Environmental Research Letters↗

USGS2017

Characterizing interactions between fire and other disturbances and their impacts on tree mortality in western U.S. Forests

Increasing evidence that pervasive warming trends are altering disturbance regimes and their interactions with fire has generated substantial interest and debate over the implications of these changes. Previous work has primarily focused on conditions that promote non-additive interactions of linked and compounded disturbances, but the spectrum of potential interaction patterns has not been fully considered. Here we develop and define terminology, expand on the existing conceptual framework and review the patterns and mechanisms of disturbance interactions with a focus on interactions between fire and other forest disturbances and a specific emphasis on resulting tree mortality. The types of interactions reflect the positive, negative, or neutral responses to the incidence, intensity, and effects of the interaction. These types of interactions are not always mutually exclusive, but can be distinct. The collective effect of the interactions will determine the longer-term ecosystem response that can result in a resistant, resilient, or compounded interaction. Our review indicates that the interactions of drought, bark beetles , or pathogens with fire often result in neutral or maintained interactions that do not negatively or positively influence the incidence or intensity following fire. The effect of these disturbance interactions on tree mortality ranged from antagonistic (reduced mortality compared to individual disturbances) to synergistic (greater mortality compared to individual disturbances) within and among disturbance interaction types but often resulted in additive effects (mortality is consistent with the summation of the two disturbances). Synergistic effects on tree mortality have been observed when the severity of the initial disturbance is moderate to high and time between disturbances is relatively short. When the sequence of disturbance interaction is reversed (e.g., fire precedes other disturbances) the conditions can generally promote impeded interactions (lower incidence of interaction), reduced interactions (lower intensity of interaction), and antagonistic interactions (lower tree mortality). While recent research on fire-disturbance interactions has increased over the last decade and provided important insights, more research that identifies the specific thresholds of incidence, intensity, and effects of interaction by region and forest type are needed to better assist management solutions that promote desired outcomes in rapidly changing ecosystems.

Forest Ecology and Management↗

USGS2017

Biogeography: An interweave of climate, fire, and humans

Longleaf pine (Pinus palustris) is an icon of the southeastern United States and has been considered a foundation species in forests, woodlands, and savannas of the region (Schwarz 1907; Platt 1999). Longleaf pine is an avatar for the extensive pine-dominated, fire-dependent ecosystems (Figure 2.1) that provide habitats for thousands of species and have largely vanished from the landscape. Longleaf pine is one of the world's most resilient and fire-adapted trees (Keeley and Zedler 1998), widely perceived as the sole dominant in forests across a large area of the Southeast (Sargent 1884; Mohr 1896; Wahlenberg 1946). Longleaf pine was once a primary natural resource, providing high-quality timber, resins, and naval stores that fueled social changes and economic growth through the 19th and early 20th centuries.

Book chapter↗