Tamm Review: Management of mixed-severity fire regime forests in Oregon, Washington, and Northern California

Increasingly, objectives for forests with moderate- or mixed-severity fire regimes are to restore successionally diverse landscapes that are resistant and resilient to current and future stressors. Maintaining native species and characteristic processes requires this successional diversity, but methods to achieve it are poorly explained in the literature. In the Inland Pacific US, large, old, early seral trees were a key historical feature of many young and old forest successional patches, especially where fires frequently occurred. Large, old trees are naturally fire-tolerant, but today are often threatened by dense understory cohorts that create fuel ladders that alter likely post-fire successional pathways. Reducing these understories can contribute to resistance by creating conditions where canopy trees will survive disturbances and climatic stressors; these survivors are important seed sources, soil protectors, and critical habitat elements. Historical timber harvesting has skewed tree size and age class distributions, created hard edges, and altered native patch sizes. Manipulating these altered forests to promote development of larger patches of older, larger, and more widely-spaced trees with diverse understories will increase landscape resistance to severe fires, and enhance wildlife habitat for underrepresented conditions. Closed-canopy, multi-layered patches that develop in hot, dry summer environments are vulnerable to droughts, and they increase landscape vulnerability to insect outbreaks and severe wildfires. These same patches provide habitat for species such as the northern spotted owl, which has benefited from increased habitat area. Regional and local planning will be critical for gauging risks, evaluating trade-offs, and restoring dynamics that can support these and other species. The goal will be to manage for heterogeneous landscapes that include variably-sized patches of (1) young, middle-aged, and old, closed canopy forests growing in upper montane, northerly aspect, and valley bottom settings, (2) a similar diversity of open-canopy, fire-tolerant patches growing on ridgetops, southerly aspects, and lower montane settings, and (3) significant montane chaparral and grassland areas. Tools to achieve this goal include managed wildfire, prescribed burning, and variable density thinning at small to large scales. Specifics on ‘‘how much and where?” will vary according to physiographic, topographic and historical templates, and regulatory requirements, and be determined by means of a socio-ecological process

Tamm Review: Management of mixed-severity fire regime forests in Oregon, Washington, and Northern California

Increasingly, objectives for forests with moderate- or mixed-severity fire regimes are to restore successionally diverse landscapes that are resistant and resilient to current and future stressors. Maintaining native species and characteristic processes requires this successional diversity, but methods to achieve it are poorly explained in the literature. In the Inland Pacific US, large, old, early seral trees were a key historical feature of many young and old forest successional patches, especially where fires frequently occurred. Large, old trees are naturally fire-tolerant, but today are often threatened by dense understory cohorts that create fuel ladders that alter likely post-fire successional pathways. Reducing these understories can contribute to resistance by creating conditions where canopy trees will survive disturbances and climatic stressors; these survivors are important seed sources, soil protectors, and critical habitat elements. Historical timber harvesting has skewed tree size and age class distributions, created hard edges, and altered native patch sizes. Manipulating these altered forests to promote development of larger patches of older, larger, and more widely-spaced trees with diverse understories will increase landscape resistance to severe fires, and enhance wildlife habitat for underrepresented conditions. Closed-canopy, multi-layered patches that develop in hot, dry summer environments are vulnerable to droughts, and they increase landscape vulnerability to insect outbreaks and severe wildfires. These same patches provide habitat for species such as the northern spotted owl, which has benefited from increased habitat area. Regional and local planning will be critical for gauging risks, evaluating trade-offs, and restoring dynamics that can support these and other species. The goal will be to manage for heterogeneous landscapes that include variably-sized patches of (1) young, middle-aged, and old, closed canopy forests growing in upper montane, northerly aspect, and valley bottom settings, (2) a similar diversity of open-canopy, fire-tolerant patches growing on ridgetops, southerly aspects, and lower montane settings, and (3) significant montane chaparral and grassland areas. Tools to achieve this goal include managed wildfire, prescribed burning, and variable density thinning at small to large scales. Specifics on ‘‘how much and where?” will vary according to physiographic, topographic and historical templates, and regulatory requirements, and be determined by means of a socio-ecological process

Reduced fire severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States

Increasing fire severity and warmer, drier postfire conditions are making forests in the western United States (West) vulnerable to ecological transformation. Yet, the relative importance of and interactions between these drivers of forest change remain unresolved, particularly over upcoming decades. Here, we assess how the interactive impacts of changing climate and wildfire activity influenced conifer regeneration after 334 wildfires, using a dataset of postfire conifer regeneration from 10,230 field plots. Our findings highlight declining regeneration capacity across the West over the past four decades for the eight dominant conifer species studied. Postfire regeneration is sensitive to high-severity fire, which limits seed availability, and postfire climate, which influences seedling establishment. In the near-term, projected differences in recruitment probability between low- and high-severity fire scenarios were larger than projected climate change impacts for most species, suggesting that reductions in fire severity, and resultant impacts on seed availability, could partially offset expected climate-driven declines in postfire regeneration. Across 40 to 42% of the study area, we project postfire conifer regeneration to be likely following low-severity but not high-severity fire under future climate scenarios (2031 to 2050). However, increasingly warm, dry climate conditions are projected to eventually outweigh the influence of fire severity and seed availability. The percent of the study area considered unlikely to experience conifer regeneration, regardless of fire severity, increased from 5% in 1981 to 2000 to 26 to 31% by mid-century, highlighting a limited time window over which management actions that reduce fire severity may effectively support postfire conifer regeneration. © 2023 the Author(s)

Reversing the decline of oak forests : are low-intensity treatments effective?

71 leaves : ill. ; 29 cm

Landscape- and regional-level correlative modeling techniques for the prediction of ecological processes

Thesis (Ph.D.)--University of Washington, 2012Quantifying landscape patterns and relating them to key biophysical drivers is often of primary interest in ecological research. Knowledge of such relationships can increase our understanding of pattern-process linkages and aid in making predictions across other spatial or temporal extents. With the recent influx of freely available, high resolution and spatially explicit data sources landscape- and regional-level models have gained in popularity. However, certain challenges arise when modeling ecological process at these scales, which are often overlooked. Processes in ecological systems occur at several spatial scales and interactions among processes are many, complex, and often non-linear and can result in highly heterogeneous conditions across spatial scales. The current research represents three separate applications of landscape- and regional- level correlative modeling. The objectives of each application were to develop correlative models to identify key environmental drivers of ecological process of interest, and to predict the process across a large spatial extent. For each application, I used a multi-model approach where a variety of statistical regression and machine learning methods were tested to objectively identify the model or models that best captured the complexity of an ecological process. The first two studies were located in the southern Appalachian mountain region where stream industrial emissions have acidified stream waters for more than a century. The objectives of these studies were to identify main drivers of stream water acid neutralizing capacity (ANC; a metric related to the ability of a stream to buffer against acidic inputs), and base cation weathering (BCw; the level of base cations supplied to the stream by catchment soils) and to predict ANC and BCw across the study region. The third study was located in the Methow Valley region of eastern Washington, which was aimed at identifying the landscape vegetation components most limiting to cavity-nesting bird populations in the area. Each study had different modeling objectives and different associated nuisances related to the data design, the spatial extent of the study region, and the correlative structure of the modeled process in relation to the environmental drivers

Introduction to R for Terrestrial Ecology

Basics of Numerical Analysis, Mapping, Statistical Tests and Advanced Application of Rstatus: Published onlin

Measuring Initial Attack Suppression Effectiveness through Burn Probability

Most wildfires in North America are quickly extinguished during initial attack (IA), the first phase of suppression. While rates of success are high, it is not clear how much IA suppression reduces annual fire risk across landscapes. This study introduces a method of estimating IA effectiveness by pairing burn probability (BP) analysis with containment probability calculations based on initial fire intensity, spread rate, and crew response time. The method was demonstrated on a study area in Kootenay National Park, Canada by comparing burn probabilities with and without modeled IA suppression. Results produced landscape-level analyses of three variables: burn probability, suppression effectiveness, and conditional escape probability. Overall, IA reduced mean study area BP by 78% as compared to a no-suppression scenario, but the primary finding was marked spatial heterogeneity. IA was most effective in recently burned areas (86% reduction), whereas mature, contiguous fuels moderated its influence (50%). Suppression was least effective in the designated wildfire exclusion zone, suggesting supplementary management approaches may be appropriate. While the framework includes assumptions about IA containment, results offer new insight into emergent risk patterns and how management strategies alter them. Managers can adopt these methods to anticipate, quantify, and compare fine-scale policy outcomes

Quantitative methods for integrating climate adaptation strategies into spatial decision support models

With the onset of rapid climate change and the legacy of past forest management and fire suppression policies, the capacity for forested landscapes to maintain core functionality and processes is being challenged. As such, managers are tasked with increasing the pace and scale of management to mitigate negative impacts of future large disturbances and improve resilience and climate adaptation of large landscapes. Such efforts require consensus building, with partners and stakeholders to determine where to allocate scarce resources. We present a methodology to identify strategic (where to go) and tactical (what to do) priorities across large landscapes to assist in project level planning. The model integrates a spatial assessment of current ecosystem resource conditions and spatial outputs from a landscape succession and disturbance simulation model (LANDIS-II) to assess the potential to achieve desired conditions under climate change with ongoing disturbances. Based on the expected trajectory of landscape conditions over time, the model applies fuzzy logic modeling to provide quantitative support for four management strategies (Monitor, Protect, Adapt, and Transform) across the landscape. We provide an example application of these methods targeting sustainable carbon loads across a 970,000 ha landscape in the central Sierras in California. By including future landscape conditions in the model, decisions made at the stand-level are inherently tied to and influenced by larger landscape-level processes that are likely to have the greatest impact on future landscape dynamics. The methods outlined here are able to incorporate multiple metrics to capture the many resources targeted by management. Model outputs could also be used as inputs into spatial optimization models to assess tradeoffs and synergies among treatment options and to aid in long-term planning

Informing climate adaptation strategies using ecological simulation models and spatial decision support tools

IntroductionForest landscapes offer resources and ecosystem services that are vital to the social, economic, and cultural well-being of human communities, but managing for these provisions can require socially and ecologically relevant trade-offs. We designed a spatial decision support model to reveal trade-offs and synergies between ecosystem services in a large eastern Cascade Mountain landscape in Washington State, USA.MethodsWe used process-based forest landscape (LANDIS-II) and hydrology (DHSVM) models to compare outcomes associated with 100 years of simulated forest and wildfire dynamics for two management scenarios, Wildfire only and Wildfire + Treatments. We then examined the strength and spatial distribution of potential treatment effects and trends in a set of resources and ecosystem services over the simulation period.ResultsWe found that wildfire area burned increased over time, but some impacts could be mitigated by adaptation treatments. Treatment benefits were not limited to treated areas. Interestingly, we observed neighborhood benefits where fire spread and severity were reduced not only in treated patches but in adjacent patches and landscapes as well, creating potential synergies among some resource benefits and services. Ordinations provided further evidence for two main kinds of outcomes. Positive ecological effects of treatments were greatest in upper elevation moist and cold forests, while positive benefits to human communities were aligned with drier, low- and mid-elevation forests closer to the wildland urban interface.DiscussionOur results contribute to improved understanding of synergies and tradeoffs linked to adaptation and restoration efforts in fire-prone forests and can be used to inform management aimed at rebuilding resilient, climate-adapted landscapes

Fuel Treatment Effectiveness In The Context of Landform, Vegetation, and Large, Wind-Driven Wildfires

Large wildfires (\u3e50,000 ha) are becoming increasingly common in semi-arid landscapes of the western United States. Although fuel reduction treatments are used to mitigate potential wildfire effects, they can be overwhelmed in wind-driven wildfire events with extreme fire behavior. We evaluated drivers of fire severity and fuel treatment effectiveness in the 2014 Carlton Complex, a record-setting complex of wildfires in north-central Washington State. Across varied topography, vegetation and distinct fire progressions, we used a combination of simultaneous autoregression (SAR) and random forest (RF) approaches to model drivers of fire severity and evaluated how fuel treatments mitigated fire severity. Predictor variables included fuel treatment type, time since treatment, topographic indices, vegetation and fuels, and weather summarized by progression interval. We found that the two spatial regression methods are generally complementary and are instructive as a combined approach for landscape analyses of fire severity. SAR improves upon traditional linear models by incorporating information about neighboring pixel burn severity, which avoids type I errors in coefficient estimates and incorrect inferences. RF modeling provides a flexible modeling environment capable of capturing complex interactions and non-linearities while still accounting for spatial autocorrelation through the use of spatially explicit predictor variables. All treatment areas burned with higher proportions of moderate and high severity fire during early fire progressions, but thin and underburn, underburn only, and past wildfires were more effective than thin-only and thin and pile burn treatments. Treatment units had much greater percentages of unburned and low severity area in later progressions that burned under milder fire weather conditions, and differences between treatments were less pronounced. Our results provide evidence that strategic placement of fuels reduction treatments can effectively reduce localized fire spread and severity even under severe fire weather. During wind-driven fire spread progressions, fuel treatments that were located on leeward slopes tended to have lower fire severity than treatments located on windward slopes. As fire and fuels managers evaluate options for increasing landscape resilience to future climate change and wildfires, strategic placement of fuel treatments may be guided by retrospective studies of past large wildfire events