Recipient Organization
CAL POLYTECHNIC STATE UNIV
(N/A)
SAN LUIS OBISPO,CA 93407
Performing Department
Natural Resources Management and Environmental Sciences
Non Technical Summary
The costs of Costal California wildfires over the last decade, particularly in 2020, are clear to most residents and can be measured in terms of trauma and health impacts from wildfire and smoke. These impacts have been substantial in both cities and rural areas demonstrating a need for the broadest of cross sections of the public to come together to address these growing threats. This project aims to identify management at stand and landscape scales to simultaneous mitigate the impacts of fire, and tree-mortality driven fuels accumulation. The project focuses on addressing fire suppression and sudden oak death, collectively the most important drivers of fuel accumulation in the coast range. Our project combines an unprecedented database of wildfire impacts and wildfire interactions with fuels conditions to understand the scope of the problem as well as its costs to critical ecological resources. This database of wildfire impacts will be paired with a new set of forest-level experiments with the goal of mitigating these fuels at the stand level. These results provide the basis for improving management actions for a range of vegetation conditions in these disease and fire impacted forests. While fire is a fact of life in California coastal ecosystems, it has not always been such as severe threat to the wellbeing of the people who live within this landscape. Landscape-level management of wildfire has been accomplished elsewhere in Mediterranean environments of the world and it was dominant in California within the last 200 years. Of course, the changing economy, substantially larger population, and range of infrastructure also influences how wildfire mitigation can be achieved, but it must be accomplished none the less. This study is a step on the path to sustainable and safe wildlands in coastal California.
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
This two-part project aims to link forest assessment and whole-stand experiments to identify optimal management treatments in coastal forests. The project is divided into two principlThis two-part project aims to link forest assessment and whole-stand experiments to identify optimal management treatments in coastal forests. The project is divided into two principle objectives (with matching funds objectives noted within each):1) Determination of timing, magnitude, and severity of biological driven fuels accumulation on fire impacts including forest carbon in living, dead, and soil organic matter1a) Annual monitoring of P. ramorum populations and associated tree mortality in post-fire vegetation recovery (the goal is shared with the ARI matching funds)1b) Laboratory analysis of post fire soil carbon2) Assessment of a range of management interventions in a cost-benefit framework aiming to match field technique with objectives, ecological factors, and trade-offs among fuels, pathogen, and carbon sequestration goals (also supported by ARI matching funds) 1a) Assess a range of management treatments at the Soquel Demonstration State Forest (Figure 2), a long-term management and research site which has been highly impacted by sudden oak death, and establish pre-treatment conditions at Swanton Pacific Ranch sites identified for post-fire restoration treatmentse objectives (with matching funds objectives noted within each):1) Determination of timing, magnitude, and severity of biological driven fuels accumulation on fire impacts including forest carbon in living, dead, and soil organic matter1a) Annual monitoring of P. ramorum populations and associated tree mortality in post-fire vegetation recovery (the goal is shared with the ARI matching funds)1b) Laboratory analysis of post fire soil carbon2) Assessment of a range of management interventions in a cost-benefit framework aiming to match field technique with objectives, ecological factors, and trade-offs among fuels, pathogen, and carbon sequestration goals (also supported by ARI matching funds) 1a) Assess a range of management treatments at the Soquel Demonstration State Forest (Figure 2), a long-term management and research site which has been highly impacted by sudden oak death, and establish pre-treatment conditions at Swanton Pacific Ranch sites identified for post-fire restoration treatments
Project Methods
Goal 1: The Big Sur plot network consists of 280 long-term monitoring plots established in 2006 and three landscape-level fires with varying degrees of overlap and variable past fire frequency. The region is primarily a resource for understanding wildfire dynamics and fuel-fire interactions in the absence of management. The plot network has been sampled every three years with some variability in effort focused on a core set of 120 plots which have been burned in the three fires that followed plot establishment: the 2008 Basin Fire, 2016 Soberanes Fire, and 2020 Dolan Fire. Regularly surveyed plots can be categorized as burning once during each fire, during the Soberanes and Basin Fire, and the Basin and Dolan Fires. For each of these five categories, the two dominant forest types (mixed evergreen and redwood) are represented by at least 10 plots. Numerous and comparable reference plots are represented in the dataset. This creates a constellation of burn vs unburned and time since fire comparisons. For each case, live and dead fuels have been monitored pre and post fire and this project would assess post-fire fuels levels and tree mortality associated with the 2020 Dolan Fire. The Sonoma plot network consists of numerous state parks, land trust, and private ownerships. The network is also a resource for understanding wildland fire, disease, and their interactions with the exception that fuels and disease mitigation has increased over the last decade in many of the private landholdings. The plot network consists of 120 plots established in 2004 which were monitored annually for sudden oak death dynamics until 2014 and biannually thereafter. Good relationships with the private landowner participants have provided additional information on thinning treatments including prescriptions, timing, and costs when these have occurred (about 30% of all private study plots). Portions of the Sonoma Plot network were burned in the 2017 and 2020 wildfires with overlap (twice burned) in 12 plots. This plot network also includes pre-fire live and dead fuels with a full representation of tree mortality for interior mixed evergreen forests.Goal 2: Both Swanton Pacific Ranch (SPR) and Soquel Demonstration State Forest (SDSF) represent single ownerships with rich past histories of fire, forest management, and extensive sudden oak death impacts. Each site is representative of conditions in the 2020 San Mateo - Santa Cruz Unit Fire (CZU) with extensive fire impacts occurring at Swanton and unburned conditions represented by SDSU. Each of the sites has monitoring infrastructure and this project will establish a common methodology for forest and fuels assessment at each site.Plot measurement approaches are identical for both goals 1 and 2: In each plot, we will conduct a full census of standing biomass (living and dead) for all stems > 1 cm diameter at breast height (dbh) including mapping and tagging within plots. These measurements are paired with Browns transect fuels quantification (Brown et al. 1981), a common method of fuels measurement, a full census of coarse woody debris (Cobb et al., 2012), and shallow soil carbon quantification (Cobb et al. 2016). These methods have been previously shown to adequately estimate dead fuel (and carbon) generated by the disease (Cobb et al. 2012a, Metz et al. 2013). Data will be analyzed in R using techniques appropriate for time series data with observational and experimental approaches. Models for designed experiments can be appropriate for these datasets given the describe timing of fire and the adequate replication (N is at least 10) for the major forest types, fire incidence, and disease combinations (Metz et al. 2011, Cobb et al. 2016). Structural equation modeling and information-driven model selection have also been applied to previous questions centered on these datasets as is appropriate (Cobb et al. 2010, 2020, Simler et al. 2018). All data analysis and modeling will be conducted in R.Goal 1 analysis: The Big Sur and Sonoma plot networks will be surveyed in 2021 and 2022 for Big Sur, and 2022 for Sonoma. The rugged landscape of Big Sur demands two years to complete a full plot census while the relatively forgiving terrain of Sonoma will allow for plot censusing in one year. Field efforts will be split between Big Sur and the SDSF and SPR sites in year one (2021, see below) and Big Sur, Sonoma, and any wrap-up measurements at SDSF/SPR in year two (2022).Additional efforts focused on goal 2: A new set of monitoring plots will be established in conjunction with current monitoring efforts at SPR and SDSF to 1) determine accuracy of existing datasets for comparison to forest structure measurements made in the Big Sur and Sonoma plot networks and 2) to quantify whole-forest experiments which are under preparation at the Soquel Demonstration State Forest. Plots established at Swanton Pacific Ranch (SPR) will be located with existing forest inventory plots and focused on stands where salvage harvests are being planned. The SPR plot network will be leveraged to contrast post-fire conditions in forests representative of the nearby SDSF site as well as to assess post-fire management efficacy on stand structure, fuels, and carbon (including soils) in the event post-fire restoration treatments such as salvage logging are completed during the project period. Pre-fire fuels mitigation experiments will be conducted at SDSF in 2021 and consist of mastication, mastication with prescribed burning, and thinning focused on bay laurel - a critical driver of P. ramorum spread and impacts -using a replicated set of 2 ac treatments established in ten treatment blocks. This will create ten replicates of each treatment with a paired reference plot (N = 60). At Swanton a similar plot network will be established in a set of ten 4 ac blocks where salvage harvest treatments are being planned and these will be paired reference plots (burned - no salvage harvesting; N = 20). The large number of treatment blocks and area are aimed at assessing additional forest management for ceanothus and replanting, potential treatments currently being discussed as part of long-term management at Swanton. Additionally, a large consortium of land managers is working to apply fuels treatments at scale (~1000 ac) in neighboring landholdings, particularly the San Vicente Redwoods property, which provides flexibility to address our central study questions at alternative by nearby and analogous sites. We aspire to provide treatment evaluation in as many of these sites as possible and will prioritize those which can most rapidly apply post-fire restoration treatments at our study design thresholds (N = 10 for each factor level). Restoration treatments at Swanton or neighboring sites hold great potential value for coast range forests from Big Sur to Humboldt County where severe wildfire impacts have been widespread over the last decade but post-fire restoration treatments have been neglected by researchers and managers.Goal 2 analysis: Whole forest experimental analysis will also be conducted in R using mixed models and general linear modeling as appropriate for specific parameter analysis. We also aim to employee counterfactual deterministic modeling of disease dynamics and fire to better identify the expected benefits of treatments over timescales longer than the study. We have used counterfactual modeling extensively as a tool for designing field experiments, identifying critical but weak datasets, and to determine expectations for forest-level experiments over the longer-term (Cobb et al., 2019; Cobb et al., 2017; Filipe et al., 2012). This approach is gaining traction as an adaptive management tool as barriers to deterministic modeling have been lowered with advances in R and other analysis platforms (Cobb et al., 2017; Gaydos et al., 2019). This will aid in dissemination of our research results beyond the scope of disease and fire challenges in California.