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Public opinion about management strategies for a low-profile Species across multiple jurisdictions: whitebark pine in the northern Rockies

Citation

Shanahan, Elizabeth et al. (2020), Public opinion about management strategies for a low-profile Species across multiple jurisdictions: whitebark pine in the northern Rockies , Dryad, Dataset, https://doi.org/10.5061/dryad.d2547d80k

Abstract

1. As public land managers seek to adopt and implement conservation measures aimed at reversing or slowing the negative effects of climate change, they are looking to understand public opinion regarding different management strategies.

2. This study explores drivers of attitudes toward different management strategies (i.e., no management, protection, and restoration) for a low-profile but keystone tree species, the whitebark pine (Pinus albicaulis), in the Greater Yellowstone Ecosystem. Since the whitebark pine species has a range that traverses different federal land designations, we examine whether attitudes toward management strategies differ by jurisdiction (i.e., wilderness or federal lands more generally).

3. We conducted a web and mail survey of residents from Montana, Idaho, and Wyoming, with 1,617 valid responses and a response rate of 16%.

4. We find that active management strategies have substantially higher levels of support than does no management, with relatively little differentiation across protection and restoration activities or across different land designations. We also find that support for management strategies is not influenced by values (political ideology) but is influenced by beliefs (about material vs. post-material environmental orientation, global climate change, and federal spending for public lands) and some measures of experience (e.g., knowledge of threats).

5. This study helps land managers understand that support for active management of the whitebark pine species is considerable and nonpartisan and that beliefs and experience with whitebark pine trees are important for support

Methods

Our study employed a cross-sectional design with a survey methodology to test our hypotheses. We distributed the questionnaire initially to 9,000 randomly selected addresses in Montana, Wyoming, and Idaho, proportional to the population in each state. We made multiple efforts to increase response rates (Dillman, Smyth, & Christian, 2014). Two letters were sent in two-week increments to direct potential respondents to a web version of the survey into which they would enter an authentication code to prevent duplicate entries; a hard copy of the survey with a business reply envelope was sent to non-respondents after another two weeks. We then drew another random sample of 1,000 new addresses, again proportional to state population. In this round, we sent only a paper version of the survey, with no web option. For all 10,000 randomly selected residents, we also randomly assigned an incentive value ($0, $1, or $2), with corresponding response rates of 9.9%, 17.3%, and 21.7%.

We test our hypotheses primarily using Wilcoxon signed-rank tests for matched pairs (Wilcoxon, 1945) and ordered logistic regression analysis. We use the Wilcoxon tests in comparing attitudes across management strategies and land types, as the data for the ordinal variables are matched at the individual respondent level. These tests are appropriate as the hypotheses (H1-H2) deal with comparison of variable distributions rather than association between variables. However, we apply Chi-square tests in a follow-up analysis exploring relationships among the six ordinal management strategy variables in an attempt to clarify the substantive significance of the Wilcoxon signed-rank test findings. We employ ordered logistic regression to account for the ordinal nature of the dependent variables (Long & Freese, 2014) in testing the remainder of the hypotheses, some of which involve continuous independent variables. Ordered logistic regression permits the calculation of post-estimation statistics to assess the marginal influence of one variable on the other. In order to facilitate interpretation of the regression results, we calculate changes in predicted probabilities for the dependent variables taking on particular values as the independent variables change values. Predicted probabilities are a common way to demonstrate marginal effects with ordinal dependent variables, as the regression coefficients can be difficult to interpret otherwise. 

Data were cleaned and variables were recoded and relabeled in STATA 14.

Usage Notes

* REPLICATION FILE *

* "Public Opinion about Management Strategies for a Low-Profile Species across Multiple Jurisdictions:

* Whitebark Pine in the Northern Rockies"

* Shanahan, Raile, Naughton, Wallner, & Houghton

* April 20, 2020 *

 

*** CODEBOOK ***

* ident = ID code

* web = Web (=0) vs. paper (=1) completion

* state = State of respondent (1=MT, 2=WY, 3=ID)

* trec3 = Recreation by increasing categories of frequency

* noman2 = No management on federal lands (1=Strongly oppose, 5=Strongly support)

* protect2 = Protection on federal lands (1=Strongly oppose, 5=Strongly support)

* restore2 = Restoration on federal lands (1=Strongly oppose, 5=Strongly support)

* wnoman2 = No management in wilderness (1=Strongly oppose, 5=Strongly support)

* wprotect2 = Protection in wilderness (1=Strongly oppose, 5=Strongly support)

* wrestore2 = Restoration in wilderness (1=Strongly oppose, 5=Strongly support)

* women = Female (=1) indicator vs. other (=0)

* spend2 = Pro-government spending (0=Too much, 0.5=About right, 1=Too little)

* polview2 = Political ideology (1=Strongly conservative, 7=Strongly liberal)

* gccind2 = Global climate change index

* seeind2 = Whitebark experience index

* nepind2 = Post-material environmental orientation

 

*** TABLE 1 CONSTRUCTION: SURVEY DETAILS ***

tab state

tab web

 

 

*** TABLE 2 CONSTRUCTION: DEPENDENT VARIABLES ***

tab noman2 if noman2!=. & gccind2!=. & spend2!=. & polview2!=. & nepind2!=. & seeind2!=. ///

& trec3!=. & women!=.

 

sum noman2 if noman2!=. & gccind2!=. & spend2!=. & polview2!=. & nepind2!=. & seeind2!=. ///

& trec3!=. & women!=.

 

tab protect2 if protect2!=. & gccind2!=. & spend2!=. & polview2!=. & nepind2!=. & seeind2!=. ///

& trec3!=. & women!=.

 

sum protect2 if protect2!=. & gccind2!=. & spend2!=. & polview2!=. & nepind2!=. & seeind2!=. ///

& trec3!=. & women!=.

 

tab restore2 if restore2!=. & gccind2!=. & spend2!=. & polview2!=. & nepind2!=. & seeind2!=. ///

& trec3!=. & women!=.

 

sum restore2 if restore2!=. & gccind2!=. & spend2!=. & polview2!=. & nepind2!=. & seeind2!=. ///

& trec3!=. & women!=.

 

tab wnoman2 if wnoman2!=. & gccind2!=. & spend2!=. & polview2!=. & nepind2!=. & seeind2!=. ///

& trec3!=. & women!=.

 

sum wnoman2 if wnoman2!=. & gccind2!=. & spend2!=. & polview2!=. & nepind2!=. & seeind2!=. ///

& trec3!=. & women!=.

 

tab wprotect2 if wprotect2!=. & gccind2!=. & spend2!=. & polview2!=. & nepind2!=. & seeind2!=. ///

& trec3!=. & women!=.

 

sum wprotect2 if wprotect2!=. & gccind2!=. & spend2!=. & polview2!=. & nepind2!=. & seeind2!=. ///

& trec3!=. & women!=.

 

tab wrestore2 if wrestore2!=. & gccind2!=. & spend2!=. & polview2!=. & nepind2!=. & seeind2!=. ///

& trec3!=. & women!=.

 

sum wrestore2 if wrestore2!=. & gccind2!=. & spend2!=. & polview2!=. & nepind2!=. & seeind2!=. ///

& trec3!=. & women!=.

 

*** TABLE 3 CONSTRUCTION: INDEPENDENT VARIABLES ***

sum polview2 nepind2 gccind2 spend2 seeind2 trec3 women if wrestore2!=. & gccind2!=. & spend2!=. & polview2!=. ///

& nepind2!=. & seeind2!=. & trec3!=. & women!=.

 

 

*** TABLE 4 CONSTRUCTION: WILCOXON STRATEGY TESTS ***

signrank restore2 = protect2

signrank restore2=noman2

signrank protect2=noman2

signrank wrestore2=wprotect2

signrank wrestore2=wnoman2

signrank wprotect2=wnoman2

 

*** TABLE 5 CONSTRUCTION: WILCOXON LAND TYPE TESTS ***

signrank noman2=wnoman2

signrank protect2=wprotect2

signrank restore2=wrestore2

 

 

*** TABLE 6 CONSTRUCTION: CHI-SQUARE TESTS OF ASSOCIATION ***

tab noman2 wnoman2, cell chi2

tab protect2 noman2, cell chi2

tab protect2 wnoman2, cell chi2

tab noman2 wprotect2, cell chi2

tab wnoman2 wprotect2, cell chi2

tab protect2 wprotect2, cell chi2

tab restore2 noman2, cell chi2

tab restore2 wnoman2, cell chi2

tab restore2 protect2, cell chi2

tab restore2 wprotect2, cell chi2

tab wrestore2 noman2, cell chi2

tab wrestore2 wnoman2, cell chi2

tab wrestore2 protect2, cell chi2

tab wrestore2 wprotect2, cell chi2

tab wrestore2 restore2, cell chi2

 

*** TABLE 7 CONSTRUCTION: ORDERED LOGISTIC REGRESSION ***

ologit noman2 polview2 nepind2 gccind2 spend2 seeind2 trec3 women

ologit wnoman2 polview2 nepind2 gccind2 spend2 seeind2 trec3 women

ologit protect2 polview2 nepind2 gccind2 spend2 seeind2 trec3 women

ologit wprotect2 polview2 nepind2 gccind2 spend2 seeind2 trec3 women

ologit restore2 polview2 nepind2 gccind2 spend2 seeind2 trec3 women

ologit wrestore2 polview2 nepind2 gccind2 spend2 seeind2 trec3 women

 

 

*** FIGURE 2 CONSTRUCTION: CHANGES IN LIKELIHOOD ***

ologit noman2 nepind2 gccind2 spend2 seeind2 polview2 trec3 women

prchange, rest(mean)

 

ologit wnoman2 nepind2 gccind2 spend2 seeind2 polview2 trec3 women

prchange, rest(mean)

 

ologit protect2 nepind2 gccind2 spend2 seeind2 polview2 trec3 women

prchange, rest(mean)

 

ologit wprotect2 nepind2 gccind2 spend2 seeind2 polview2 trec3 women

prchange, rest(mean)

 

ologit restore2 nepind2 gccind2 spend2 seeind2 polview2 trec3 women

prchange, rest(mean)

 

ologit wrestore2 nepind2 gccind2 spend2 seeind2 polview2 trec3 women

prchange, rest(mean)

Funding

North Central Climate Science Center of the United States Department of the Interior , Award: G13AC00394

National Science Foundation RII Track-1 with the Montana Institute on Ecosystems *, Award: EPS-1101342

National Institute of General Medical Sciences of the National Institutes of Health, Award: P20GM103474

National Science Foundation RII Track-1 with the Montana Institute on Ecosystems , Award: OIA-1443108