We applied spatial interpolation to understand spatial patterns of human dimensions data, with focus on proximity and scale of attitudes metrics within the context of utility vegetation management of roadside forests. Complex levels of spatial heterogeneity existed, such that attitudes toward vegetation management varied across space, and likely are associated with location-specific characteristics. Preceding analyses suggested that respondents had generally favorable attitudes toward vegetation management across all study areas, interplay of social and landscape factors affected individual attitudes (Hale and Morzillo 2020), and variation in social variables influenced attitudes among study areas (DiFalco and Morzillo 2021). In our analysis, maximum spatial autocorrelation distances for the three attitudes variables among the four study areas supports multiscalar spatial variation of people’s attitudes toward vegetation management, which may hinder success of a state-level one-size-fits-all vegetation management policy. To focus the discussion, we describe two underlying social phenomena that may contribute to the observed spatial heterogeneity among locations and offer direction for further analysis.
Supporting our first hypothesis that social phenomena closer together are more likely to be related than those further away, clustering of similar attitude scores existed for three of the 12 attitude-study area pairings (AttSafety-Northwest, AttSafety-Northeast, and AttTradeoff-Southeast). Contrasting the same hypothesis were two pairings with negative autocorrelation (AttProfessional- Northeast, and AttTradeoff-Southwest) and seven with no autocorrelation. Spatial clustering of favorable attitudes toward natural resources has been observed in other natural resources contexts. Attitudes toward tigers in Nepal were clustered based on human cultural factors, educational achievement, and experience with tiger attacks (Carter et al. 2014). Attitudes toward the desert were spatially clustered within neighborhoods of similar social and landscape characteristics; more favorable attitudes existed in high-income areas closer to preserved desert parks (Andrade et al. 2019). However, results from elsewhere in our project generally did not suggest socioeconomic factors to be strongly associated with spatial clustering of attitudes towards vegetation management (DiFalco and Morzillo 2021). Urban-rural distinctions in attitudes also may occur, such as clusters of favorable attitudes towards carnivores (e.g., wolves and black bears) more likely to be near urbanized areas (Morzillo et al. 2007; Behr et al. 2017), and rodent control behavior more likely among households closer to rather than further from natural areas (Morzillo and Schwartz 2011). However, ancillary evidence from elsewhere in our analysis (DiFalco and Morzillo 2021) did not suggest clustering based on self-selected urban-rural residential designation (LocReside; Table 1), or landscape characteristics surrounding respondent homes, except for AttTradeoff-Northwest which was positively associated with a greater percentage of tree cover (DiFalco and Morzillo 2021). Areas of negative spatial autocorrelation of attitudes demonstrates diversity of attitudes among proximate individuals, and potentially that processes observed in one area are influenced by neighboring areas (Griffith and Arbia 2010). It is possible that vegetation management actions completed by homeowners may affect and be affected by attitudes of their neighbors (e.g., Belaire et al. 2016), but support for this conclusion is beyond the scope of our data. Collectively, the mixed results from our study exemplified the heterogeneity and complexity of social processes that occur within exurban land use (e.g., Hiner 2014; Bauer et al. 2017).
Supporting our second hypothesis, varying autocorrelation distances among attitude-study location pairings illustrated that attitudes toward vegetation management existed at different spatial scales in different locations. Multi-scale governance processes may contribute to this observed heterogeneity, as observed elsewhere. For example, Morzillo et al. (2016) observed variation in resident preferences for natural resource-based amenities between two different cities, with additional inter-city differences detected at the property, neighborhood, and metropolitan scales. Although vegetation management regulations operate at the statewide level (McCarthy 2014; Public Utilities Regulatory Authority 2014), decisions about trees are influenced by individual property-scale preferences (Kloster et al. 2021) and neighborhood norms (Grove et al. 2006). Additionally, Chowdhury et al. (2011) suggested that municipal- and state-level land-use zoning affected household and neighborhood vegetation structure in the city of Baltimore, where a policy was implemented to increase urban tree canopy. Findings from that study suggested that most tree plantings would need to occur on private residential properties, thus relying on property-scale decision-making to achieve municipal goals, and demonstrating the importance of multi-scale processes in policy outcomes (Chowdhury et al. 2011).
Besides zoning, other governance structures such as town ordinances present additional layers of complexity to governance and therefore spatial distribution of tree plantings and removals on both public and private properties (Johnson et al. 2020). Comments from our survey suggested overall confusion about jurisdictional coordination of vegetation management:
We are perplexed by the randomness of activity in roadside tree removal/trimming. Is there an overall state or town plan for a comprehensive and methodical approach?
In Connecticut, some towns have specific ordinances in place that delay or prohibit implementation of utility practices related to state vegetation management guidelines. For example in the Northeast study area, the towns of Mansfield and Coventry encourage maintaining a closed forest canopy to preserve the aesthetic quality of forested scenic roadways (Town of Mansfield 1995; Town of Coventry 1997). However, in the same study area, the adjacent towns of Bolton and Andover do not have analogous regulations. Another commonly mentioned suggestion is to bury powerlines underground, which is now required in some towns for new housing developments (e.g., Town of Avon 2007). Despite potential aesthetic benefits (Navrud et al. 2008), undergrounding utilities often are considered cost-prohibitive because of complex regulations and high implementation costs (Cieslewicz and Novembri 2004; Campbell 2012), and do not ameliorate for outages between substations and the location of underground utilities. Ultimately, inter-town differences in tree-related governance at multiple scales complicates utility ability to perform consistent vegetation management along inter-town stretches of power lines.
Diversity in respondent attitudes toward vegetation management revealed by interpolation results also may reflect differences in regional culture across the state. Others have reported direct relationships between greater household incomes and a greater likelihood to plant trees in neighborhoods that sustain greater tree cover (Conway et al. 2011; Nitoslawski et al. 2016). In our study, the distribution of incomes varied among study areas (Income; Table 1); tree canopy cover was numerically lowest in the Northeast (average percentage: Northeast = 47.6, Southwest = 54.0, Northwest = 55.4, and Southeast = 52.1; DiFalco and Morzillo 2021). Respondent comments on the survey alluded to linkages among where people lived, the importance of tree cover, and experiences with vegetation management:
I think where we live in _________ [Northwest study area] we have so many rural/suburban communities - all surrounded by trees, state parks trails etc. so many of us live here because nature is abundant, it surrounds us, we hike, camp, live within nature. But realistically we need to work and live safely- not have power outages for weeks where we can't live or work. We need to find balance. Nature and animal’s homes are our homes.
Tree management to reduce power outages is important to us since we are one of the last areas to have power restored [after storm events] because of our sparse population. We live in a rural setting. This management should be done in an environmentally responsible way.
Maintaining the aesthetic character of an area also was expressed by respondents as an important outcome and existed concurrently with respondent understanding of the necessity for vegetation management:
Trees are so important but tree health is just as important. The woods, land preserved and forests should remain untouched. Trees in residential areas should be maintained for health and resident safety. I would happily allow the utilities to remove the tall trees in my front yard that stand tall enough to fall on the lines but also appreciate the other trees around my property.
This parallels previous research from New England on the topic of developing strategies for maintaining the rural character of a town while balancing economic growth and development (Zabik and Prytherch 2013). Our results also suggested opportunity for integrating homeowner preferences into management plans, such as incorporating desired visual outcomes of tree trimming and potentially replace taller trees with shorter statured species less likely to interfere with powerlines, echoing findings by Flowers and Gerhold (2000).
Limitations in this study offer opportunities for better integration of social and ecological data in landscape ecology. First, the geographic assumption that points closer together are more similar (Tobler 1970; ESRI 2019) applies well to particular ecological phenomena, such as the amount of precipitation in a given location (Camera et al. 2014) or weather conditions occurring across a country (Kim et al. 2010). However, as our results demonstrated, social processes within exurban areas may not follow this assumption, as human dimensions characteristics are heterogeneous and unequally distributed. Second, distribution of different land uses varied among and within the four study areas. For example, in the Northwest study area, interspersed protected open space covers about 20% of the towns of Avon and Simsbury, but only nine percent of the adjacent town of Canton (DEEP 2010). Our analysis did not exclude areas of non-residential land use and, therefore, spatial calculations included forested public lands and protected open space, which both confine and facilitate clustering of residential development. Such patterns of interspersion are further influenced by local development planning ordinances beyond those associated with trees and vegetation management. For example, in the Southeast study area, planning regulations for the town of Montville require subdivision projects to maintain a designated amount of area as permanent open space (Town of Montville 2020). Therefore, determining the most appropriate scale to address social phenomena in such contexts requires consideration of multiple spatial scales, concurrent and competing land uses, and ecological processes simultaneously (Vogt et al. 2002).
Differences in model performance among attitude-study area pairing also alluded to opportunities to improve landscape analytics for assessing social phenomena. For example, Gong et al. (2014) found that interpolations to assess contamination of groundwater wells across Texas were more accurate when data were divided into regional districts (i.e. specific aquifers or areas of the state) rather than pooled for the whole state - i.e., adjustments made to level of analysis. Bhowmik (2012) concluded that performance between climate prediction models and actual weather conditions improved as additional meteorological stations were added over subsequent years through increasing the number of data points. In our analysis, interpolated surfaces were more likely to predict the actual attitude score in portions of our study areas where the distance between respondents was smaller, suggesting that additional data points or oversampling among locations where households are further apart may be useful in areas of heterogeneous land use and development density, such as exurban landscapes. Future studies may also consider experimenting with mismatches between scale of data collection and social processes (Robinson et al. 2019) to better align methodological design of data collection to the spatial scale of the social process being assessed.
Results of this analysis supported the creation of multi-scalar strategies for roadside vegetation management that consider not only landscape-level decision-making but also local-level stakeholder heterogeneity. Collaborative management decisions by organizations working at different scales are critical components of such linkages, and between ecological and social components of forest management (e.g., Martins et. al 2022; Paletto et. al 2010; Madeira and Gartner 2018). In Connecticut and elsewhere this means landscape-level coordination among the forest management community, public engagement to improve awareness and understanding of the importance of the vegetation management process, recognition of general interest in maintaining the visual aesthetics of their communities, and outreach strategies and protocols that reflect and respond to both regional and local variation.