Objective and perceived wildfire risk and its influence on private forest landowners’ fuel reduction activities in Oregon’s (USA) ponderosa pine ecoregion

Abstract

Abstract. Policymakers seek ways to encourage fuel reduction among private forest landowners to augment similar efforts on federal and state lands. Motivating landowners to contribute to landscape-level wildfire protection requires an understanding of factors that underlie landowner behaviour regarding wildfire. We developed a conceptual framework describing landowners' propensity to conduct fuel reduction as a function of objective and subjective factors relating to wildfire risk. We tested our conceptual framework using probit analysis of empirical data from a survey of non-industrial private forest landowners in the ponderosa pine (Pinus ponderosa) region of eastern Oregon (USA). Our empirical results confirm the conceptual framework and suggest that landowners' perceptions of wildfire risk and propensity to conduct fuel treatments are correlated with hazardous fuel conditions on or near their parcels, whether they have housing or timber assets at risk, and their past experience with wildfire, financial capacity for conducting treatments and membership in forestry and fire protection organisations. Our results suggest that policies that increase awareness of hazardous fuel conditions on their property and potential for losses in residential and timber assets, and that enhance social networks through which awareness and risk perception are formed, could help to encourage fuel reduction among private forest landowners.Additional keywords: fuel treatments, landscape models, non-industrial private forest landowners, wildfire risk, wildland-urban interface.

Examining the influence of biophysical conditions on wildland–urban interface homeowners’ wildfire risk mitigation activities in fire-prone landscapes

“…We examined homeowner mitigation behavior using a conceptual framework informed by the literature, particularly Fischer et al (2014) and Champ et al (2013), as well as Protection Motivation Theory (Rogers 1983). Synthesizing this work, we hypothesized that homeowners' perceived wildfire risk is a function of factors such as hazardous fuel conditions near the home site, as well as the homeowners' past experiences with wildfire, and the social context (or networks) in which homeowners' beliefs, attitudes, and norms about wildfire are formed and diffused (Fig.…”

“…We use the word modeled as others have http://www.ecologyandsociety.org/vol22/iss1/art21/ used the word "objective" in prior research efforts-to describe risk factors that are based on modeled biophysical variables (e.g., Fischer et al 2014). Specifically, we (1) identify biophysical and socioeconomic factors that influence homeowners' wildfire risk perceptions and their likelihood to mitigate risk, and (2) demonstrate how such information can be used to anticipate where homeowners are mostly likely to conduct risk mitigation activities.…”

Examining fire-prone forest landscapes as coupled human and natural systems

“…Social networks might play a role in explaining landowner inclination to conduct vegetation management. Initial research results support the notion of a positive relationship between fuel treatment efforts and connections with conservation and fire organizations (Fischer et al 2014). …”

Integrating social science into empirical models of coupled human and natural systems

“…Thus, the sociologist and economists who examined individual landowners used mail surveys to investigate these actor groups. Grounded in conceptual framing developed by Fischer et al (2014), their analyses relied on logistic regression to describe the statistical likelihood that individual homeowners and family forest landowners conducted various management and wildfire risk mitigation activities as a function of explanatory variables that represented key biophysical and socioeconomic influencing factors (e.g., ).…”

“…We based the noncommercial fuel reduction activities of federal and tribal actors on annual treatment targets, combined at each model timestep with stand characteristics that were identified by managers as indicating suitable treatment opportunities, including priorities that concerned particular forest values (e.g., habitat), timber productivity, fuel loads, WUI designation, and transportation infrastructure (Table 3). We based our decision rules for individual private landowners on logistic regression analysis of specific survey questions that pertained to family forest owners' harvest and fuel reduction activities and homeowners' structure and landscape fire-proofing activities , following Fischer et al (2014). We used the resulting regression equations in the ABM to compute probabilities that family forest owners and homeowners would conduct particular activities at each modeling time-step as a function of key biophysical and socioeconomic variables (Table 3).…”