Research and Publications
View article.
Sagebrush shrublands in the Great Basin, USA, are experiencing widespread increases in wildfire size and area burned resulting in new policies and funding to implement fuel treatments. However, we lack the spatial data needed to optimize the types and locations of fuel treatments across large landscapes and mitigate fire risk. To address this, we developed treatment response groups (TRGs)—sagebrush and pinyon-juniper vegetation associations that differ in resilience to fire and resistance to annual grass invasion (R&R) and thus responses to fuel treatments.
View article.
Many nations administer national forest inventory programs for unbiased estimation of forest attributes over broad spatial and temporal extents. However, management and conservation decisions often demand reliable estimates for finer spatiotemporal domains. In the western US, wildfire activity is expanding and postfire regeneration must contend with a warmer, drier climate. We evaluate the potential of K nearest neighbor (KNN) strategies for estimation of stocking across postfire measurements of Forest Inventory & Analysis plots in 11 western US states, and subsequently for model-assisted (MA) estimation of stocking over domains defined by aggregations of burned areas within individual states and 4-year periods. In particular, we develop and evaluate a form of constrained KNN that allows for unbiased MA domain estimation under simple random sampling by drawing only on measurements external to a domain of interest. KNN strategies based on geographically, radiometrically, and climatically proximate measurements are found to provide more accurate estimates of stocking at the plot level than domain means. Applying the selected external KNN strategy also reduced standard errors of MA domain estimates by 16% over direct domain estimators, but bias correction introduces substantial variability over synthetic estimates. Further applications of the external constraint imposed on KNN are discussed.
View white paper.
Like many communities across the western United States, the greater Flagstaff area in northern Arizona has experienced multiple wildfires in recent years that have resulted in postfire flooding. The 2019 Museum Fire provides a case study for better understanding how the cascading disturbances of wildfire and postfire flooding, which can be further compounded by adjacent disturbances like monsoon-related flooding, impacted Flagstaff residents, and how they were informed of, perceive, and respond to these risks. In 2022, we conducted a survey in Flagstaff after 2021 flooding associated with the Museum Fire burn scar and monsoonal events to better understand attitudes “before” and “after” flooding. This resulted in findings in eight thematic areas: 1) respondent demographics; 2) geographic distribution of respondents in 2022; 3) experiences with recent flooding events; 4) communication during flood events; 4) flood risk perceptions; 6) flood insurance coverage; 7) mitigating flood risk; and 8) managing flood risk, wildfires, and forest management. This work builds upon a survey we completed in 2019 immediately following the Museum Fire that evaluated respondents’ experience with the fire and evacuation, communication of fire emergency information, and opinions regarding forest management.
View article.
Wildfire regimes are changing dramatically across North American deserts with the spread of invasive grasses. Invasive grass fire cycles in historically fire-resistant deserts are resulting in larger and more frequent wildfire. This study experimentally compared how single and repeat fires influence invasive grass-dominated plant fuels in the Great Basin, a semi-arid, cold desert, and the Mojave, a hyper-arid desert. Both study sites had identical study designs. In the summer of 2011, we experimentally burned half of each experimental block, the other half remaining as an unburned control. Half of the burned plots were reburned 5 years later to simulate increasing burn frequency. We estimated non-woody plant biomass, cover, and density in plots from 2017 to 2020.
View article.
Pyrodiversity may affect biodiversity by diversifying available ecological niches, stabilizing community networks and/or supporting diverse species pools available for post-fire colonization. Further, pyrodiversity’s effects on biodiversity vary across different spatial, temporal and organismal scales depending on the mobility and other life history traits of the organisms in question and
may be mediated by regional eco-evolutionary factors such as historical fire regimes. Developing a generalizable understanding of pyrodiversity effects on biodiversity has been challenging, in part because pyrodiversity can be quantified in various ways.
View brief.
Imagine being able to take a bird’s eye view of the forest: you could see the forest structure, how the trees are grouped, the height and size of each tree in a matter of moments as you cruise over. You could fly over the stand today, then again next year and examine the effects of a treatment or a wildfire or an insect outbreak. Uncrewed aerial systems (UAS – aka drones) are starting to allow managers to do just that.
View article.
Mapped representations of species−habitat relationships often underlie approaches to prioritize area-based conservation strategies to meet conservation goals for biodiversity. Generally a single surrogate species is used to inform conservation design, with the assumption that conservation actions for an appropriately selected species will confer benefits to a broader community of organisms. Emerging conservation frameworks across western North America are now relying on derived measures of intactness from remotely sensed vegetation data, wholly independent from species data. Understanding the efficacy of species-agnostic planning approaches is a critical step to ensuring the robustness of emerging conservation designs. We developed an approach to quantify ‘strength of surrogacy’, by applying prioritization algorithms to previously developed species models, and measuring their coverage provided to a broader wildlife community. We used this inference to test the relative surrogacy among a suite of species models used for conservation targeting in the endangered grasslands of the Northern Sagebrush Steppe, where careful planning can help stem the loss of private grazing lands to cultivation. In this test, we also derived a simpler surrogate of intact rangelands without species data for conservation targeting, along with a measure of combined migration representative of key areas for connectivity. Our measure of intactness vastly outperformed any species model as a surrogate for conservation, followed by that of combined migration, highlighting the efficacy of strategies that target large and intact rangeland cores for wildlife conservation and restoration efforts.
View article.
Adequate numbers of replicated, dispersed, and random samples are the basis for reliable sampling inference on resources of concern, particularly vegetation cover across large and heterogenous areas such as rangelands. Tools are needed to predict and assess data precision, specifically the sampling effort required to attain acceptable levels of precision, before and after sampling. We describe and evaluate a flexible and scalable process for assessing the sampling effort requirement for a common monitoring context (responses of rangeland vegetation cover to post-fire restoration treatments), using a custom R script called “SampleRange.” In SampleRange, vegetation cover is estimated from available digital-gridded or field data (e.g., using the satellite-derived cover from the Rangeland Assessment Platform). Next, the sampling effort required to estimate cover with 20% relative standard error (RSE) or to saturate sampling effort is determined using simulations across the environmental gradients in areas of interest to estimate the number of needed plots (“SampleRange quota”). Finally, the SampleRange quota are randomly identified for actual sampling. A 2022 full-cycle trial of SampleRange using the best available digital and prior field data for areas treated after a 2017 wildfire in sagebrush-steppe rangelands revealed that differences in the predicted compared with realized RSEs are inevitable. Future efforts to account for uncertainty in remotely sensed−based vegetative products will enhance tool utility.
View article.
The sagebrush biome is a dryland region in the western United States experiencing rapid transformations to novel ecological states. Threat-based approaches for managing anthropogenic and ecosystem threats have recently become prominent, but successfully mitigating threats depends on the ecological resilience of ecosystems. We used a spatially explicit approach for prioritizing management actions that combined a threat-based model with models of resilience to disturbance and resistance to annual grass invasion. The threat-based model assessed geographic patterns in sagebrush ecological integrity (SEI) to identify core sagebrush, growth opportunity, and other rangeland areas. The resilience and resistance model identified ecologically relevant climate and soil water availability indicators from process-based ecohydrological models. The SEI areas and resilience and resistance indicators were consistent – the resilience and resistance indicators showed generally positive relationships with the SEI areas. They also were complementary – SEI areas provided information on intact sagebrush areas and threats, while resilience and resistance provided information on responses to disturbances and management actions. The SEI index and resilience and resistance indicators provide the basis for prioritizing conservation and restoration actions and determining appropriate strategies. The difficulty and time required to conserve or restore SEI areas increase as threats increases and resilience and resistance decrease.
View article.
Using data on the patterns of participation of 10,199 individual stakeholders in 837 community wildfire protection plans (CWPPs) within the western U.S., we document the emergence of a locally clustered but spatially extensive wildfire risk governance network. Our evaluation of factors that contribute to connectivity within this network indicates that risk interdependence (e.g., joint exposure to the same fires) between planning jurisdictions increases the prospects for linkages between planning processes, and that connectivity is also more likely among planning processes that are more proximate and similar to one another. We discuss how our results advance understanding of how changing hazard conditions prompt risk mitigation policy networks to reorganize, which in turn affects risk outcomes at multiple spatial scales.