Exurban development is the fastest growing land use across the United States (US). Its prevalence on the East Coast is susceptible to natural disaster events such as hurricanes and nor’easters. However, the socio-ecological processes related to disaster mitigation within exurban areas remain understudied. Our objective was to integrate social and landscape data to compare resident attitudes towards utility roadside vegetation management across four areas in the state of Connecticut, US. We collected data from residents using two mail surveys completed in 2017 and 2019 (n = 1962). From the survey questions, three attitude variables measured perceptions of the utility vegetation management process, and tradeoffs between protecting trees and maintaining reliable power. Across all locations, respondents with more favorable attitudes toward vegetation management were more likely to have greater knowledge about trees, and beliefs that trees should be used for human benefit; land cover characteristics and sociodemographic variables were less strongly associated with attitudes scores. Respondents differed among study areas in their preferences for aesthetics of roadside trees and their basic beliefs regarding the importance of trees. The results suggested that social processes within the exurban landscapes are spatially heterogeneous. Therefore, local variation in residential preferences for vegetation management may influence support for natural disaster management policy.

Context: Knowledge about spatial patterns of human dimensions data within landscape ecology is nascent despite its importance in natural resources management decision-making. We explored this topic within the context of utility roadside forest vegetation management, a complex situation involving ecological, cultural, and aesthetic aspects of forests and reliable power.Objectives We applied spatial interpolation to investigate patterns of human attitudes toward exurban roadside vegetation management data across an exurban landscape.Methods Mail surveys (n = 1962) were used to collect social science data from residents in four areas of Connecticut, USA. For each area, three attitudes variables were evaluated for spatial autocorrelation using Moran's I statistic. Based on identi ed autocorrelation distance or scale, attitudes were interpolated using inverse distance weighting. Model validation of interpolated surfaces was completed using root mean square error.Results: Statistically signi cant spatial autocorrelation was present for ve of 12 study area-attitude pairings at variable distances. Accuracy of interpolations also varied among study areas, suggesting that the choice of spatial scale of analysis in uenced model results.Conclusions: Social processes within the exurban landscape were spatially heterogeneous and multiscalar for the same variables in different locations, exemplifying the complexity of social processes within exurban land use. Interpolation assumptions often applied toward ecological studies did not work well for social processes studied in this analysis. Results demonstrated the importance of understanding spatial dimensions at which social processes operate and, therefore, may in uence ecological outcomes of the roadside forest within the context of state-level natural resources management and policy.

Context: Knowledge about spatial patterns of human dimensions data within landscape ecology is nascent despite its importance in natural resources management decision-making. We explored this topic within the context of utility roadside forest vegetation management, a complex situation involving ecological, cultural, and aesthetic aspects of forests and reliable power.Objectives We applied spatial interpolation to investigate patterns of human attitudes toward exurban roadside vegetation management data across an exurban landscape.Methods Mail surveys (n = 1962) were used to collect social science data from residents in four areas of Connecticut, USA. For each area, three attitudes variables were evaluated for spatial autocorrelation using Moran’s I statistic. Based on identified autocorrelation distance or scale, attitudes were interpolated using inverse distance weighting. Model validation of interpolated surfaces was completed using root mean square error.Results: Statistically significant spatial autocorrelation was present for five of 12 study area-attitude pairings at variable distances. Accuracy of interpolations also varied among study areas, suggesting that the choice of spatial scale of analysis influenced model results. Conclusions: Social processes within the exurban landscape were spatially heterogeneous and multi-scalar for the same variables in different locations, exemplifying the complexity of social processes within exurban land use. Interpolation assumptions often applied toward ecological studies did not work well for social processes studied in this analysis. Results demonstrated the importance of understanding spatial dimensions at which social processes operate and, therefore, may influence ecological outcomes of the roadside forest within the context of state-level natural resources management and policy.