Community Activeness in Response to Forest Disturbance in Alaska

Society and Natural Resources, 20:431–450 Copyright # 2007 Taylor & Francis Group, LLC ISSN: 0894-1920 print/1521-0723 online DOI: 10.1080/08941920701211850

Community Activeness in Response to Forest Disturbance in Alaska COURTNEY G. FLINT Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois, USA

A.E. LULOFF Department of Agricultural Economics and Rural Sociology, The Pennsylvania State University, University Park, Pennsylvania, USA Community theorists have long grappled with the question of whether or not communities collectively respond to threats. Community, risk, disaster, and natural resource management theories all inform an understanding of community action. Here, a conceptual model of community activeness is empirically tested using survey data from six Kenai Peninsula, Alaska, communities. Data analysis revealed that socioeconomic and biophysical vulnerability, proximity to hazard, experience, risk perception, and local interactional capacity significantly influenced community activeness on the part of residents in response to forest disturbance associated with an outbreak of spruce bark beetles. Implications for theory, forest management and policy, and natural resource-based communities are advanced. Keywords community activeness, forest disturbance, risk perception, spruce bark beetles, survey research method

Forest-based communities, or those communities having close proximity and strong ties to forest resources, are often depicted as being vulnerable to environmental and social problems (Flint and Luloff 2005). This is often seen as the result of natural resource dependence (Humphrey 1990; Marchak 1983) and=or their close proximity to risks found at the wildland-urban interface (e.g., fire; Haight et al. 2004; Kumagi et al. 2004). Research on forest dependency has expanded to include the study of multiple values and interests. It is increasingly acknowledged that forests provide numerous social goods (beyond timber), including scenic and aesthetic integrity, nontimber forest products, recreation and tourism opportunities, and a sense of community Received 29 July 2005; accepted 17 July 2006. This research was supported by the USDA Forest Service, Pacific Northwest Research Station (project PNW 03-CA-11261975) and by the Pennsylvania Agricultural Experiment Station (project 3870). Many thanks to Gary Greenberg from the Kenai Peninsula Borough Spruce Bark Beetle Mitigation Program for the map and to the Journal of Forest Ecology and Management for map reprinting permission. The authors appreciate comments from the journal editor and anonymous reviewers. Address correspondence to Courtney G. Flint, Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana–Champaign, 1023 Plant Sciences Laboratory MC 634. 1201 S. Dorner Dr., Urbana, IL 61801, USA. E-mail: [email protected]

431

432

C. G. Flint and A. E. Luloff

identity or quality of life (cf. Clark et al. 1999). In addition, there is increasing appreciation of the heterogeneity of the socioeconomic structure of forest-based communities (Christensen and Donoghue 2001). Moreover, community-based actions have increasingly been the focus of attention in forest or ecosystem management (Gray et al. 2001). The emergence of community action in response to changing forest ecosystems, however, is not automatic. Some communities have greater capacities than others for community agency or the mobilization of collective resources in times of need (Luloff and Swanson 1995). Forest ecosystems are subject to disturbance from both management activities and natural processes such as fire or insect outbreaks. These disturbances create risks to both the biophysical and human attributes of forest-based communities. Local community responses can either facilitate or impede forest management (Flint 2004). Collaboration between forest managers and localities can best be supported by understanding those factors influencing community action in response to forestrelated risks. This article builds upon and evaluates a conceptual model of community action posed by Flint and Luloff (2005). It makes use of mail survey data drawn from a larger, mixed methods study of communities in the Kenai Peninsula, Alaska, where spruce bark beetles killed more than 1 million acres of trees in two decades.

Conceptual Model Communities provide context—a spatial and temporal setting—for experiencing and negotiating problems, events, and processes (Thrift 1983). They are situated at the interface between individuals and society as well as between the environment and society (Field and Burch 1988; Wilkinson 1991). When problems emerge that encompass both societal and environmental processes, communities, especially natural resource-based communities, are often at the front line in terms of impact, experience, and mitigation. Community actions are formal or informal collective actions taken by community residents to reduce the risk of harm from external threats (Tilly 1973). Such action is not automatic, nor is its range limited by external factors (Luloff 1990). A recent conceptual framework of risk-related community action in natural resourcebased communities outlined an array of factors influencing community action (see Appendix) (Flint and Luloff 2005). That framework posited that community action is influenced by: (1) a community’s biophysical and socioeconomic risk context; (2) a shared community perception or social construction of risk; and (3) local interactional capacity to work together on community issues and problems. This article evaluates this framework in the context of forest disturbance by bark beetles in Alaska. The risk context is a combination of socioeconomic and biophysical indicators of vulnerability. These indicators include structural characteristics based on socioeconomic and demographic data as well as biophysical hazard ratings based on environmental characteristics such as vegetative cover, fuel loading, topography, and the geographic distribution of settlements. Local social and environmental patterns and processes are often objectively measured through technical risk assessments to determine a community’s level of risk (Haynes and Cleaves 1999). Purely technical risk assessments are, however, insufficient (Hannigan 1995; Irwin 2001). Risk, like nature, is socially constructed. Subjective, shared risk perceptions play a key role in how risks are interpreted and framed as problems at the community level (Fitchen et al. 1987). Community risk perception exists when residents collectively

Community Activeness and Forest Disturbance

433

experience anxiety=concern about the emergence of threats in their community. Residents are more likely to participate in community actions when these risks are collectively perceived to be real. Three factors act as primary influences on community risk perception. Perceived proximity to hazard incorporates the importance of geographic and environmental factors in influencing the effects of risk (Savage 1993; Sorokin 1928). Second, the relationship between a community and land managers is important to risk perception and local participation or community action. Satisfaction and confidence in land management agencies has been shown to minimize concern about possible risks (Giddens 1994; Peters et al. 1997; Wynne 1996). Third, collective experience with environmental disturbances or crises contributes to shared community identity, solidarity, and increased awareness of environmental processes. Past experience with crises promotes shared, community risk perceptions and motivates collective, community action in times of need (Fitchen et al. 1987; Hannigan 1995). Beyond these factors, where higher levels of interactional capacity (or the ability to mobilize participation and local resources) are found, community residents are more likely to take action in response to risk situations (Luloff 1990; Luloff and Wilkinson 1979; Luloff and Swanson 1995). Participation in social organizations, community events and activities, community leadership, and governance are all components of community interaction. Such interaction promotes the continual reshaping and molding of collective identities and perceptions of problems while providing the means for motivating specific community actions in response to specific threats or risks.

The Kenai Peninsula Spruce Bark Beetle Outbreak The Kenai Peninsula is situated in south-central Alaska (Figure 1). Over the last two decades, a spruce bark beetle outbreak led to massive ecological change on the Kenai Peninsula, resulting in more than 90% mortality of white and Lutz spruce on more than 1 million acres (Ross et al. 2001). Spruce bark beetles are endemic to the region, with evidence of sporadic infestations over the past few centuries, but the recent outbreak was unprecedented in scope and intensity. The loss of forest cover from beetle activity led to numerous positive and negative impacts. These impacts included increased fire risk, falling trees, loss of fish and wildlife habitat and scenic=aesthetic integrity, the rise and fall of timber jobs and industry, greater ecological awareness, and increased availability of firewood (Flint 2006). The institutional setting for responding to forest management issues is complicated on the Kenai Peninsula due to its complex mosaic of land ownership—from the Chugach National Forest in the northwest to a mix of State of Alaska, Kenai Peninsula Borough (equivalent to a county administrative unit), native association, and private properties elsewhere. The most local-level formal management response to the spruce bark beetles came from the borough government in the form of timber sales, right-of-way clearing, and a research and mitigation office. Community-level involvement was driven by active residents on a volunteer basis. This is partly due to the unincorporated status of all but five communities on the Peninsula and little to no forest management presence in the incorporated cities. A large-scale social science study of the Kenai Peninsula spruce bark beetle outbreak was funded by the USDA Forest Service (State and Private Forestry in Alaska and the Pacific Northwest Research Station). Resource management questions were directed at the community level of analysis, in part, because of differential response

434


Figure 1. Map of the Kenai Peninsula, Alaska. Study communities indicated by solid black circles. Reprinted from Flint (2006, p. 208), with permission from Elsevier.

to the bark beetle disturbance and subsequent forest management by community. Community residents simply did not uniformly participate with forest managers in acknowledging or attempting to ameliorate forest risks. In some cases, people perceived some risks as outside the scope of most forest management attention, such as watershed quality and emotional loss. The range of responses reflected the


435

diversity of experiences, perceptions, and capacities to act found among the citizens of these communities. This divergence led to an impasse in forest management and frustration on the part of forest managers and community residents alike. Our objective was to provide a research vehicle to illuminate community concerns and perspectives and develop strategies for forest managers dealing with multiple community interests across changing landscapes.

Methods The conceptual model was evaluated using a mixed methods approach. Secondary analysis of socioeconomic, biophysical, and archival data provided a structural backdrop for further study. Key informant interviews, described elsewhere (Flint 2006; Flint and Haynes 2006), provided detailed narratives of community experiences and outlined the range of variation in concepts from the conceptual model. Building on the structural and qualitative data, mail surveys were constructed and administered in 2004 to residents in six Kenai Peninsula communities affected by the spruce bark beetle outbreak. Study Site Selection and Sampling Methods The study communities—Homer, Anchor Point, Ninilchik, Cooper Landing, Moose Pass, and Seldovia—were selected using a typological approach based on four criteria to assure representation of the larger regional context. The six communities represented various points on a timeline of spruce bark beetle activity, presence and absence of active Alaska Native associations, different land ownership or jurisdictional settings, and incorporated and unincorporated status. The survey sampling frame consisted of households identified using the Permanent Fund Dividend (PFD) database available from the State of Alaska. The PFD is a means of distributing a percentage of yearly oil revenue taxes to eligible Alaskans and is viewed as the most complete sampling frame for this state (Reed and Brown 2003). Individuals from the PFD database were identified in each of the study communities and aggregated to the household level using common addresses. Household populations obtained from the 2003 PFD database were as follows: Anchor Point 779; Ninilchik 439; Seldovia 196; Cooper Landing 157; and Moose Pass 102. All 1673 households in these five communities received surveys. The PFD database identified 3476 households in Homer. In order to statistically represent the population at a confidence level of .05, a sample of 346 was needed (Isaac and Michael 1997). In Homer, 800 randomly sampled households were identified in light of the recent trend of declining response rates (Luloff 1999; Connelly et al. 2003). Thus, in total, 2473 households from the six communities were identified and asked to participate in the study. The survey instrument specified that one individual 18 years old or older who had celebrated her=his birthday most recently should complete the survey in order to randomize the selection of respondents within households (Wulfhorst and Krannich 1999). Survey Administration The mail survey consisted of a 16-page printed, mail-back questionnaire. A tailored design method (Dillman 2000) was used to increase response rates. Given the mail

436


delay to Alaska from Pennsylvania and the high proportion of post office box addresses where inconsistent mail retrieval is common, the timing of contacts with recipients suggested by the tailored design method was extended. Attempts were made to increase local attention to the survey through articles in local newspapers, interviews on local radio stations, flyers in multiple community locations, and repeated contacts with key informants in each community (Flint 2004). Each survey was personalized and included a cover letter signed by the principal investigator and live postage, including a preaddressed return envelope (Luloff 1999). The initial mailing of cover letter and survey was followed after 2 weeks by a thank you=reminder postcard to all households. Four weeks later, a second modified letter and survey were sent to nonrespondents. After an additional 4 weeks, a third modified letter and survey were sent to nonrespondents. Following these efforts over 3 months, unreturned surveys were considered nonresponses. Nearly identical surveys were sent to each community except for minor differences in front cover, and specific community references to local events or experiences in an effort to make the instrument more relevant to respondents. Questions supporting the dependent and independent variables used were identical with one exception (differentiation between tsunami experience for coastal residents and avalanche experience for mountain residents).

Measurement of Variables Dependent Variable: Community Activeness in Response to Forest Risks. Community activeness was operationalized using eight community-related actions identified by key informants as having occurred in response to the spruce bark beetle outbreak (Flint 2004). Respondents were asked whether they had (1) participated in a neighborhood or community effort to clear trees; (2) participated in community cone or seed gathering; (3) attended an informational meeting; (4) attended meetings or took other actions to oppose timber sales on borough or state land; (5) attended meetings or taken other actions to support timber sales on borough or state land; (6) cleared public trails; (7) consulted with public officials or foresters; and (8) participated in efforts to preserve natural forests. Responses were coded ‘‘0’’ for no participation and ‘‘1’’ for participation. A composite dependent variable was created by summing responses to the eight actions. Based on results from exploratory factor analysis,1 all eight items were included in the composite measure of community activeness in response to forest risks from the spruce bark beetle outbreak (alpha reliability coefficient ¼ .63).2 Biophysical and Socioeconomic Vulnerability.3 Two biophysical community control variables were included: (1) the number of fire starts from 1980 to 2002 and (2) vegetative cover condition based on the percentage of spruce mortality within the census designated area around each community. Two control variables were used to measure community socioeconomic vulnerability. The first measure was an index of socioeconomic vulnerability constructed from data obtained from the Alaska Community and Economic Development Office indicating community status on three conditions: (a) poverty rate relative to the borough average; (b) presence of a local high school within the community; and (c) incorporated status (alpha reliability coefficient ¼ .76). The second socioeconomic vulnerability measure was the community dependency ratio relative to the borough average, which reflects


437

the proportion of the population above retirement age and below working age compared to the working population between ages 18 and 65 years (Beckley and Bonnell 2003). Sociodemographic Characteristics. A number of sociodemographic variables were included in the analysis to allow for an evaluation of whether selected characteristics of respondents influenced community activeness in response to forest risks and helped to confirm the representativeness of the sample and the generalizability of the research findings (Babbie 1998). The sociodemographic variables included in this study were age, gender, years lived in community, Alaska Native status, household income, and education. Sociodemographic variables from the aggregate survey data are shown in Table 1. Independent Variables. Three variables measured perceived proximity to dead spruce trees and the severity of the resulting hazard. Respondents were asked to describe tree mortality on their personal property and around their community. A third question addressed the perceived amount of regrowth of spruce trees in and around the respondent’s community. Relationship with land managers was measured by respondents’ levels of satisfaction with nine land management entities (responses ranged from ‘‘1’’ very dissatisfied to ‘‘5’’ very satisfied). Based on exploratory factor analysis, two factors emerged: (1) satisfaction with government forest management entities (Kenai Peninsula Borough, State Forestry, the U.S. Forest Service, and State Parks), and (2) satisfaction with local forest management entities (private individuals and landowners, city government or local community groups, Native associations, local fire department, and private logging companies). Composite index variables were created for the two categories (alpha reliability coefficient ¼ .91 for government and .71 for local). Respondents were also asked to respond to a series of statements reflecting attitudes about forest management (responses ranged from ‘‘1’’ strongly disagree to ‘‘5’’ strongly agree). These questions replicated the Clearwater Forest Area Survey (McFarlane and Stedman 2003). Following exploratory factor analysis, nine statements were included in a composite measure of faith in forest industry (alpha reliability coefficient ¼ .83): (1) forests should be managed to meet as many human needs as possible; (2) forests should have the right to exist for their own sake, regardless of human concerns and uses; (3) forests should exist mainly to serve human needs; (4) forests should be left to grow, develop, and succumb to natural forces without being managed by humans; (5) forests that are not used for the benefit of humans are a waste of our natural resources; (6) forest regrowth will be greater and faster if land is first cleared by logging; (7) the present rate of logging is too great to sustain our forests in the future; (8) the economic benefits from logging usually outweigh any negative consequences; and (9) forestry practices generally produce few long-term negative effects on the environment. Experience with emergencies was measured in the survey by asking respondents to identify whether or not they had personal knowledge of the following emergencies: falling trees, wildland fire, volcanic ash from eruption, flooding, earthquake, tsunami or destructive high tide (in coastal communities) or avalanche (in mountain communities), severe winter storm or blizzard, and toxic contamination (e.g., oil or gas spill, chemical exposure). An index was created based on the sum of experience responses (alpha reliability coefficient of .69).

438


Table 1. Socioeconomic characteristics for the aggregate data set Demographic characteristics Age (n ¼ 1052) 19–29 30–39 40–49 50–59 60–69 70–79 80–89 Gender (n ¼ 1063) Female Male Years in community (n ¼ 1086) 0–5 6–10 11–25 26–50 51 or more Native Alaskan (n ¼ 1040) Non Native Native Total household income (n ¼ 959) Less than $15,000 $15,000 to $24,999 $25,000 to $34,999 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 $100,000 to $149,999 $150,000 or more Education (n ¼ 1062) Less than a high school degree High school degree or GED Some college or post high school training Two-year technical or associate’s degree Four-year college degree Advanced degree

Mean

Survey %

Standard deviation

Range

52.50 2.9 11.5 27.8 30.2 17.0 8.1 2.5

12.64

19–89

13.24

1–86

1.86

1–8

1.41

1–6

44.3 55.7 17.59 17.2 21.6 38.8 20.0 2.4 90.3 9.7 4.17 11.8 9.6 12.4 18.8 24.9 11.9 7.3 3.3 3.63 4.5 17.9 32.5 12.9 20.0 12.2

Risk perception following the bark beetle outbreak was measured by questions asking how concerned the respondent was about the following forest risks for his or her community: (1) forest fire; (2) grass fire; (3) falling trees; (4) decline in fish and wildlife habitat; (5) increased erosion and runoff; (6) loss of community identity tied to the forest; (7) loss of forest as an economic resource; and (8) loss of scenic= aesthetic quality (responses ranged from ‘‘1’’ not concerned to ‘‘5’’ extremely concerned). Factor analysis revealed two factors among the variables—one for


439

immediate threats to property or safety (forest fire, grass fire, and falling trees) and one for broader threats to community and ecological well-being (decline in habitat, increased erosion and runoff, loss of community identity, loss of economic resource, and loss of scenic=aesthetic quality). Composite index variables were created for both risk categories (alpha reliability coefficient ¼ .74 for immediate property=safety safety threats and .83 for broader well-being threats). Interactional capacity was measured by two indicators. The first measured whether or not respondents participated in general community activities in the previous 12 months, including: (1) participation in a local community event (like a school concert, community parade, or craft fair); (2) contacting a public official about some issue (for example, about the management of public lands or provision of services like fire protection); (3) working with others in the community to try and deal with some community issue or problem; (4) attending any public meeting in the community (like a school board meeting or a planning meeting); (5) serving as an officer in a community organization; or (6) serving on a local government or advisory commission, committee, or board. Based on factor analysis, responses were summed as a composite variable (alpha reliability coefficient ¼ .74). The second measure of interactional capacity was a composite variable of two items designed to assess the level and quality of communication in the respondent’s community (responses ranged from ‘‘1’’ very poor to ‘‘5’’ excellent) (alpha reliability coefficient ¼ .88). Bivariate correlations were examined to determine whether the strength and direction of relationships among these variables were consistent with the conceptual model. Multivariate analysis, using ordinary least squares (OLS) regression, was used to empirically evaluate the conceptual model.

Results Response Rates For this study, 2473 surveys were mailed to Kenai Peninsula community residents. After adjusting for undeliverable surveys due to incorrect addresses (108), the completed surveys (1088) yielded an aggregate response rate of 46%. While not a high response rate in general, this rate was considerably higher than the norm in Alaska, which is renowned for low response rates (Brown 2005). Mail surveys by Brown (2005) and Reed and Brown (2003) using the PFD database as a sampling frame had response rates ranging of 18% to 32%. We attribute the higher response rate in our study to five factors: (1) saliency of the bark beetle issue; (2) project publicity generated by local newspapers, radio stations, and posted flyers; (3) local attention focused on the study by word of mouth and communication through key informants from earlier interviews; (4) survey administration January to April to avoid busy summer months, when response rates are typically lower; and (5) contrary to other surveys using the PFD database, addresses were aggregated to the household level, thus avoiding surveys sent to children and multiple household members (Brown 2005; Reed and Brown 2003). Also, the use of the mixed methods in this study facilitated local contact between the researchers and the larger community populations under investigation. This contact generated increased awareness and receptiveness to the mail survey among households in the study communities. Survey data on sociodemographic characteristics (Alaska native, income, and education) were

440


Table 2. Bivariate correlations among the model variables (n ¼ 1088) Variables 1. Community activeness 2. Number of fire starts 3. Vegetation cover 4. Dependency ratio 5. Socioeconomic vulnerability 6. Age 7. Gender 8. Years in community 9. Native Alaskan 10. Total household income 11. Education 12. Describe trees (property) 13. Describe trees (community) 14. Regrowth 15. Average satisfaction with local managers 16. Average satisfaction with gov’t managers 17. Faith in forest industry 18. Personal experience with emergencies 19. Direct risk perception 20. Indirect risk perception 21. Community participation 22. Community communication Mean Standard deviation

1

2

3

4

5

6

7

8

9

.05 .12 .06 .02 .04 .04 .12 .06 .07 .15 .07 .06 .06 .07

.57 .60 .04 .62 .07

.08

.10 .09 .08 .07 .05 .07 .00 .03 .10 .06 .06 .09 .04 .31 .06 .15 .01 .19 .08 .03 .04 .08 .06 .12 .09 .06 .04

.19 .01 .12

.17 .05 .04 .16

.19 .17 .03 .01

.16

.34

.11 .00 .07 .04

.01

.04

.00

.11

.32

.03

.11 .22 .01 .07 .31 .05

.01

.02

.09 .10 .01

.01

.07

.12

.14 .07 .09 .04 .04 .00

.09 .04

.17

.10

.04

.19

.04 .03 .05 .03 .01

.05

.03

.06 .04 .09 .03

.14

.04

.19

.11

.01

.12

.04

.09

.02 .02

.13 .08

.02

.05

.07

.15

.07

.10

.03

.13

.09

.07

.14

.02

.03

.04

.10

.08

.42

.01

.11

.01

.10

.00

.04

.08

.01

.08

.12 .04

.08

.02

.05

.06

.56 .50

17.59 13.24

1.49 1.59

66.44 181.00

.08 .17

2.73 .58

Note. Statistical significance: at the .05 level and

.74 .44

1.53 1.22

52.50 12.64

at the .01 level.

.10 .30


10

11

12

13

14

15

16

17

18

19

441

20

21

22

.19 .01 .06 .01 .00 .05

.01

.11 .02 .04

.07

.30 .03 .02

.10 .00 .10

.12 .12 .12

.46

.12 .23

.02

.14 .02

.22 .12

.09

.13

.07

.07

.04

.15

.27

.13 .07

.09

.09

.04

.13

.23 .19 .06 .23

.09

.47 .04

.04 .06

.08

.03 .09

.04

.12

.04

.25

.30 .00

.01

.02

.03

.11 .04

.24

.02

.06

.02

.02

.12

.20

.20 .03

.02

.05

.01

4.17 1.86

3.63 1.41

.78 .41

3.92 .87

3.00 1.06

5.23 1.86

3.77 1.02

3.24 1.03

3.07 .89

2.70 1.05

2.93 .86

.09 .14 3.23 1.76

3.17 .91

442


compared to available census data for Kenai Peninsula Borough communities, revealing no substantial differences, thereby reducing concerns about representativeness associated with a response rate under 50%. While education levels were slightly higher among respondents than the borough census data suggested (more respondents with 4-year and advanced degrees), income levels and the percentage of Alaska natives among survey respondents were similar to census data. Based on this comparison, the community samples were seen as being sufficient to talk about community-level attributes. Bivariate Correlations Aggregate level Pearson’s correlations are shown in Table 2. Of the community control variables, only vegetation cover was statistically significant in its bivariate relationship with community activeness (r ¼ .12, p < .01). Of the sociodemographic characteristics, years in community (r ¼ .12, p < .01), total household income (r ¼ .07, p < .05), and education (r ¼ .15, p < .01) were statistically significant in their bivariate relationships with community activeness (though very weakly for income). Moreover, at least one variable from each construct in the conceptual model had a statistically significant bivariate relationship with community activeness. Community Activeness Model Multiple regression analysis, using ordinary least squares procedures and a sevenblock regression model, was used in this study and the results are shown in Table 3. The F statistic for the final model regressing community activeness on the independent variables was 33.11 (22=977 degrees of freedom, p < .001). The adjusted (adj.) R2 value for the explained variance was .25 and multiple parameter estimates in the model were statistically significant. Thus, the model fit was good and the amount of variation explained fell well within the range of most published social science research. However, we were not interested in maximizing the accounted for variation in this model; instead, our concern was with testing a theoretical framework and attempting to discern whether each of the constructs therein played a role in the aggregate response of Kenai Peninsula residents. Models for each community revealed substantial differences, which are discussed briefly at the end of this article and in more depth elsewhere (Flint 2006; Flint and Haynes 2006). In the first model, community control variables relating to the number of fire starts, dependency ratio, and socioeconomic vulnerability had significant relationships with community activeness. Fire starts and the socioeconomic vulnerability index were negatively related, indicating that communities with fewer fires and lower socioeconomic vulnerability were more likely to have higher participation levels in community action. The dependency ratio was positively related to community activeness, suggesting that communities with lower proportions of working-aged individuals had higher levels of community activeness. This model explained 2.5% of the variance in the dependent variable (F ¼ 7.68, p < .001). Model 2 added sociodemographic characteristics to the regression analysis. The community control variables remained statistically significant in their relationship to community activeness, while age, years in community, and education became statistically significant predictors of community activeness. Age was negatively related to

443

Relationship with land managers Average satisfaction with local land managers Average satisfaction with government land managers Faith in forest industry Personal experience with emergencies

Proximity Describe trees (property) Describe trees (community) Regrowth

Sociodemographic variables Age Gender (males ¼ 1) Years in community Native Alaskan (Native ¼ 1) Total household income Education

Community control variables Number of fire starts Vegetation cover Dependency ratio Socioeconomic vulnerability .453 .111 .301 .268

Model 1

.082 .033 .168 .065 .024 .190

.447 .125 .299 .214

Model 2

.079 .099 .050

.066 .022 .153 .062 .022 .193

.495 .101 .302 .279

Model 3

.040 .253

.001

.017 .029

.040

.051 .078 .034

.026 .001 .103 .058 .001 .172

.473 .129 .273 .284

Model 5

.052

.079 .094 .045

.071 .017 .149 .066 .014 .202

.491 .101 .303 .284

Model 4

.062 .237

.003

.053

.052 .064 .057

.041 .008 .113 .054 .005 .167

.483 .120 .278 .294

Model 6

.050 .171

.043

.062

.044 .052 .050

.045 .025 .080 .045 .061 .077

.483 .152 .295 .274

Model 7

(Continued)

.191

.073 .061

.067

.479 .155 .283 .259

Reduced model

Table 3. Comparison of seven multivariate models on community activeness in response to forest risks for aggregate data, given as standardized regression coefficients

444

Model 2

Model 3

Model 4

at the .01 level, and

.005 .110

Model 6

.145 .153 9.08 8.73 812 812

Model 5

at the .001 level.

.025 .075 .090 .088 7.68 8.36 7.92 5.92 1022 911 911 812

Model 1

Note. Boldface indicates statistical significance: at the .05 level,

Interactional capacity Community participation Community communication R2 adjusted F value Cases

Risk perception Direct risk perception Indirect risk perception

Table 3. Continued

.067

Reduced model

.351 .353 .015 .252 .248 14.02 33.11 812 977

.006 .086

Model 7


445

community activeness, meaning that younger individuals were more likely to be active in community efforts related to the spruce bark beetle, though this relationship was very weak. Years in community and education were positively related to community activeness meaning those with longer residence and more education had higher levels of community activeness. This model accounted for 7.5% of the variation in community activeness and significantly increased the explanatory power of the regression model over the previous model (F change ¼ 9.13, p < .001). When the proximity variables were added (model 3), the community control variables remained unaffected, but age was no longer statistically significant in its relationship with community activeness. Describing trees for both property and community was positively and statistically significant (but very weak) in its relationship to community activeness. Those who perceived a higher degree of tree mortality on their property and around their community had higher levels of community activeness. This model increased the explanatory value of the regression model (F change ¼ 5.98, p < .001, adj. R2 ¼ .090). Thus, the conceptual block relating to proximity to hazard contributed to our ability to explain the dependent variable. Model 4 added relationship with land mangers to the regression analysis. None of these indicators had a statistically significant relationship with community activeness in this model, but all model 3 variables remained significant. The inclusion of these variables resulted in the loss of 99 cases and did not significantly change the explanatory power of the model. Model 5 added personal experience with emergencies to the analysis. All variables except describing trees on personal property remained statistically significant in their relationship with community activeness. Personal experience with emergencies was positively related to community activeness. The model that incorporated experience significantly increased the explanatory value of the regression model and contributed conceptually to understanding community activeness (F change ¼ 53.49, p < .001, adj. R2 ¼ .145). Model 6 added risk perception variables to the analysis. Variables with statistically significant relationships with community activeness from the previous model remained except for describing trees in the community. Risk perception regarding immediate threats to property and safety was not statistically significant in relation to community activeness, but risk perception regarding broader threats to community and ecological well-being was positive and statistically significant in its relationship with community activeness. Model 6, which added the conceptual block related to experience, significantly contributed to the explanation of community activeness (F change ¼ 4.93, p < .01, adj. R2 ¼ .153). Model 7 added the interactional capacity variables. With their inclusion, vegetation cover joined the other three community control variables as statistically significant in their relationship with community activeness. All other previously significant variables remained significant. General community participation was positive and statistically significant in its relationship with community activeness. The inclusion of interactional capacity variables significantly increased the explanatory power of the regression model (F change ¼ 53.31, p < .001, adj. R2 ¼ .252). The inclusion of the conceptual block related to interactional capacity provided the strongest contribution to explaining community activeness. Finally, a reduced model was estimated by systematically eliminating nonsignificant variables. The final reduced model included all four community vulnerability control variables; years in community; describing community trees; perceived re-growth; personal experience with emergencies; risk perception of broader threats

446


to community and ecological well-being; and general community participation. The final reduced model accounted for 25% of the variation in the model (adj. R2 ¼ .248). The strongest relationships with community activeness were found between the community control variables, personal experience with emergencies, and interactional capacity. Other relationships, while statistically significant, were very weak.

Discussion A major goal of this study was to empirically evaluate the conceptual model of community action proposed by Flint and Luloff (2005). This analysis showed that with the exception of relationship with land managers, at least one variable from each construct in the conceptual model had a statistically significant influence on participation in community activeness in response to forest-related risks (albeit a weak relationship in some cases). These results confirm the hypothesis that risk perception and interactional capacity increase understanding of community activeness beyond the biophysical and socioeconomic risk context. Further, factors suggested as having an affect on risk perception, such as proximity to hazard and experience, also significantly influenced community activeness. While relationship with land managers was not found to influence community activeness, additional analyses revealed the presence of a strong relationship between such relationships and risk perception when the latter was treated as a dependent variable. This suggests the potentially important role relationships with land managers plays in the coalescence of community risk perception. It is possible that this study’s combining of different land management entities to gauge public satisfaction masked important differences in local or individual attitudes about different land managers. The relationship between local residents and land managers deserves more attention in order to establish the role of this construct in influencing how communities respond to forest risks. The analysis suggested that the ramifications of forest disturbance and forestrelated risks were more complicated than previously envisioned. Indicators of vulnerability, both biophysical and socioeconomic in nature, explained very little of the variation in participation in community activeness in response to forest risk situations on the Kenai Peninsula (3%). By adding risk perception variables, proximity and experience, and interactional capacity, a more complete understanding of what influences community activeness was gained. Interactional capacity had by far the strongest influence on whether or not individuals participated in bark beetlerelated community activities. Those who were more active in their community in general were more likely to participate in community activities in response to the specific forest risk situation. This suggested that community response to threats or risks should not be assumed to be automatic. Instead, targeted community activeness is influenced by the degree of general interaction within the community prior to the emergence of a specific threat. The same OLS regression analysis was conducted for each study community separately. While full discussion of these results is beyond the scope of this article, a summary provides context for interpreting aggregate results. In the community analyses, no two OLS regression models were identical. The only conceptual factor found to be consistently strong and statistically significant in its relationship with community activeness in all communities was interactional capacity. Almost all of


447

the other independent variables were significant in some community models but not others. Proximity to hazard, weakly statistically significant in the aggregate data analysis, was not significant in its influence on community activeness in any of the community models. Relationship with land managers, while not contributing to community activeness in the aggregate data, emerged as having a statistically significant influence in Homer and Anchor Point. In one case (Moose Pass) the explanatory power of the model was 50%, while others mostly ranged from 25 to 30%. It appears that the factors found to influence community activeness in the aggregate data were likely affected by community contexts. Survey data revealed that residents of Moose Pass had the highest levels of overall community activeness related to the spruce bark beetle outbreak followed by Cooper Landing, Homer, Seldovia, Anchor Point, and Ninilchik residents though the specific nature of activities varied substantially. Further analysis in other papers explores community variation in more depth (Flint and Haynes 2006; Flint 2006).

Implications The general support of the conceptual model of community action found in this analysis has implications for our understanding of communities, for natural resource management and policy, and for communities themselves. Where people actively care about each other and the place they live, communities are more likely to mobilize collective resources in response to threats. Such community action is not automatic as suggested by earlier community theory (Tilly 1973), but is the function of many factors, especially a community’s interactional capacity. An interactional theory of community facilitates our understanding of community processes and their relationship with environmental change and resource management. In addition, the influence of risk perception on community action suggests that people are less likely to act if they aren’t concerned about potential for further problems. Further, all four biophysical and socioeconomic control variables in the reduced model add significantly and independently to our ability to explain community activeness. This means that community context matters. This analysis responds to recent calls for greater understanding of the relationship between perception and action at the community scale of analysis regarding environmental hazards and vulnerability (Cutter 2001; Lowenthal 2000). The theoretical implication of this finding supports claims about the importance of social constructions of risk in adding to, but not replacing, the explanatory power of technical risk assessments (Hannigan 1995; Irwin 2001). This research has implications for natural resource managers as they assess and mitigate risk. Assessing community risk perceptions and action capacities in addition to technical risk assessments highlights areas of agreement about management priorities and mitigation strategies. Such a broader community risk assessment would highlight potential conflict areas and issues prior to the expenditure of substantial time and resources. Because local actions can impede mitigation plans if they do not resonate with community concerns and values, it is essential that such an assessment be undertaken early in the implementation of any resource management plan. The failure to consider local perceptions and capacities contributes to increased strains on relationships between communities and land managers. Natural resource policies often address concerns about immediate threats to property and safety. The findings of this study suggests that broader threats to community and ecological well-being are more likely to motivate local involvement and

448


action than immediate threats, even when such concerns are elevated (Flint and Haynes 2006). Therefore, decision making at multiple scales regarding natural resources should address the larger picture of human environment interactions. These findings also suggest that policies focused on the development of community interaction (Summers 1986; Wilkinson 1991) are integral to building local capacities to respond to risk and mobilize local participation in risk mitigation in times of need. Finally, this study has implications for communities. Working to appreciate the local context of natural resource issues and environmental change increases public involvement, gives voice to local concerns, and builds relationships and trust with resource managers. It focuses attention on the need to develop community opportunities for interaction both in general and regarding specific risks that emerge across forest landscapes.

Notes 1. All exploratory factor analyses referred to in this article were accomplished using principal components factoring with varimax rotation (Kim and Mueller 1978). 2. We are aware that .63 is below the normally accepted range for reliability coefficients (.65– .70). We use it because it allows us to keep all eight indicators in the model and reflects the exploratory nature of this research. 3. Due to substantial risk of fire and blowdown, the condition of vegetation and fire experience was critical to the biophysical context of this study. Our variables of socioeconomic variability follow existing literature. Since response to risk has been shown to depend on community capacity, information on local poverty levels, schools, incorporation status, and age structure was included.

References Babbie, E. 1998. The practice of social research. Belmont, CA: Wadsworth. Beckley, T. M. and B. Bonnell. 2003. After the fall: Perceptions of forest management in Western Newfoundland. In Two paths toward sustainable development: Public values in Canada and the United States, eds. B. A. Shindler, T. M. Beckley, and M. C. Finley, 149–167. Corvallis: Oregon State University Press. Brown, G. 2005. Mapping spatial attributes in survey research for natural resource management: Methods and applications. Society Nat. Resources 18(1):17–39. Christensen, H. H. and E. M. Donoghue. 2001. A research framework for natural resourcebased communities in the Pacific Northwest. General technical report. PNW-GTR-515. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Clark, R. N., G. H. Stankey, P. J. Brown, J. A. Birchfield, R. W. Haynes, and S. R. McCool. 1999. Toward an ecological approach: Integrating social, economic, cultural, biological, and physical considerations. In Ecological stewardship: A common reference for ecosystem management,vol. III, eds. W. T. Sexton, A. J. Mark, R. C. Szaro, and N. C. Johnson, 297–318. Kidlington, Oxford, UK: Elsevier Science. Connelly, N. A., T. L. Brown, and D. J. Decker. 2003. Factors affecting response rates to natural resource-focused mail surveys: Empirical evidence of declining rates over time. Society Nat. Resources 16(6):541–549. Cutter, S. 2001. The geography of hazard events and losses. Nat. Hazards Observer 24(1):3–4. Dillman, D. A. 2000. Mail and Internet surveys: The tailored design method. New York: John Wiley and Sons. Field, D. R. and W. R. Burch, Jr. 1988. Rural sociology and the environment. Middleton, WI: Social Ecology Press.


449

Fitchen, J. M., J. S. Heath, and J. Fessenden-Raden. 1987. Risk perception in community context: A case study. In The social and cultural construction of risk, eds. B. B. Johnson and V. T. Covello, 31–54. Boston: Reidel. Flint, C. G. 2006. Community perspectives on spruce beetle impacts on Alaska’s Kenai Peninsula. J. For.t Ecol. Manage. 227(3):207–218. Flint, C. G. 2004. Community response to forest disturbance on Alaska’s Kenai Peninsula. PhD dissertation, Pennsylvania State University, University Park. Flint, C. G. and R. Haynes. 2006. Managing forest disturbances and community responses: Lessons from the Kenai Peninsula, Alaska. J. For. 104(5):269–275. Flint, C. G. and A. E. Luloff. 2005. Natural resource-based communities, risk, and disaster: An intersection of theories. Society Nat. Resources 18(5):399–412. Giddens, A. 1994. Living in a post-traditional society. In Reflexive modernization: Politics, tradition and aesthetics in the modern social order, eds. U. Beck, A. Giddens, and S. Lash, 56–109. Stanford, CA: Stanford University Press. Gray, G. J., M. J. Enzer, and J. Kusel. 2001. Understanding community-based forest ecosystem management. Binghamton, NY: Food Products Press. Haight, R. G., D. T. Cleland, R. B. Hammer, V. C. Radeloff, and T. S. Rupp. 2004. Assessing fire risk in the wildland–urban interface. J. For. 102(7):41–48. Hannigan, J. A. 1995. Environmental sociology: A social constructionist perspective. London: Routledge. Haynes, R. W. and D. Cleaves. 1999. Uncertainty, risk, and ecosystem management. In Ecological stewardship: A common reference for ecosystem management, vol. III, eds. W. T. Sexton, A. J. Malk, R. C. Szaro, and N. C. Johnson, 413–429. Kiddlington, Oxford, UK: Elsevier Science. Humphrey, C. R. 1990. Timber dependent communities. In American rural communities, eds. A. E. Luloff and L. E. Swanson, 34–60. Boulder, CO: Westview Press. Irwin, A. 2001. Sociology and the environment. Cambridge, UK: Polity Press Isaac, S. and W. Michael. 1997. Handbook in research and evaluation. San Diego, CA: EdITS. Kim, J. and C. W. Mueller. 1978. Factor analysis: Statistical methods and practical issues. Beverly Hills, CA: Sage. Kumagai, Y., M. S. Carroll, and P. Cohn. 2004. Coping with interface wildfire as a human event: Lessons from the disaster=hazards literature. J. For. 102(6):28–32. Lowenthal, D. 2000. A historical perspective on risk. In Risk in the modern age: Social theory, science and environmental decision-making, ed. M. J. Cohen, 251–257. New York: St. Martin’s Press. Luloff, A. E. 1990. Communities and social change: How do small communities act? In American rural communities, eds. A. E. Luloff and L. E. Swanson, 214–227. Boulder, CO: Westview Press. Luloff, A. E. 1999. The doing of rural community development research. Rural Society 9(1):313–327. Luloff, A. E. and L. E. Swanson. 1995. Community agency and disaffection: Enhancing collective resources. In Investing in people: The human capital needs of rural America, eds. L. J. Beaulieu and D. Mulkey, 351–372. Boulder, CO: Westview Press. Luloff, A. E. and K. P. Wilkinson. 1979. Participation in the national flood insurance program: A study of community activeness. Rural Sociol. 44(1):137–152. Marchak, M. P. 1983. Green gold: The forest industry in British Columbia. Vancouver, BC, Canada: University of British Columbia Press. McFarlane, B. and R. Stedman. 2003. Influencing public attitudes toward forest management in Alberta: An example of tours of Sunpine Forest Products mills and woodlands. Canadian Forest Service Northern Forestry Centre Report NOR-X-02–13-1R, Edmonton, Alberta, Canada: Canadian Forest Service.

450


Peters, R. G., V. T. Covello, and D. B. McCallum. 1997. The determinants of trust and credibility in environmental risk communication: An empirical study. Risk Anal. 17(1): 43–54. Reed, P. and G. Brown. 2003. Public land management and quality of life in neighboring communities: The Chugach National Forest planning experience. For. Sci. 49(4): 479–498. Ross, D. W., G. E. Daterman, J. L. Boughton., and T. M. Quigley. 2001. Forest health restoration in south-central Alaska: A problem analysis. General technical report. PNWGTR-523. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Savage, I. 1993. Demographic influences on risk perceptions. Risk Anal. 13(4):413–420. Sorokin, P. A. 1928. Contemporary sociological theories: Through the first quarter of the twentieth century. New York: Harper and Brothers. Summers, G. 1986. Rural community development. Annu. Rev. Sociol. 12:341–371. Thrift, N. 1983. On the determination of social action in space and time. Environ. Plan. D 1:23–57. Tilly, C. 1973. Do communities act? Sociol. Inquiry 43(3–4):209–240. Wilkinson, K. P. 1991. The community in rural America. Westport, CT: Greenwood Press. Wulfhorst, J. D. and R. S. Krannich. 1999. Effects on collective morale from technological risk. Society Nat.l Resources 12(1):1–18. Wynne, B. 1996. May the sheep safely graze? A reflexive view of the expert–lay knowledge divide. In Risk, environment, and modernity: Towards a new ecology, eds. S. Lash, B. Szerszynski, and B. Wynne, 44–83. London: Sage.

Appendix: Conceptual Framework for Community Action in Response to Risk

Note: This is not a fully identified model – it is a conceptual model. We recognize that each of the endogenous factors have direct and indirect effect on community action in response to risk. We portray this model in this format to ease interpretation. Risk context and interactional capacity are portrayed as being exogenous to the model of community action in response to risk.