DOI: 10.5061/dryad.d2547d80k
title: Public opinion about management strategies for a low-profile Species across multiple jurisdictions: whitebark pine in the northern Rockies

description: 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
description: 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
 (H<sub>1</sub>-H<sub>2</sub>) 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.
description: * 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)