10.5061/DRYAD.D2547D80K
Shanahan, Elizabeth
0000-0002-1074-5570
Montana State University
Raile, Eric
Montana State University
Naughton, Helen
University of Montana
Wallner, Michael
TechLink
Houghton, Kendall
University of Oregon
Public opinion about management strategies for a low-profile Species
across multiple jurisdictions: whitebark pine in the northern Rockies
Dryad
dataset
2020
Greater Yellowstone System (GYE)
Forests
management strategies
New Environmental Paradigm
public land management
Public opinion
whitebark pine
human dimensions
cognitive hierarchy
public lands
North Central Climate Science Center of the United States Department of
the Interior *
G13AC00394
National Science Foundation RII Track-1 with the Montana Institute on
Ecosystems **
EPS-1101342
National Institute of General Medical Sciences of the National
Institutes of Health*
P20GM103474
National Science Foundation RII Track-1 with the Montana Institute on
Ecosystems *
OIA-1443108
2020-04-27T00:00:00Z
2020-04-27T00:00:00Z
en
70241 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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
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.
* 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)