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Wolves are back: Sociopolitical identity and opinions on management of Canis lupus

Citation

Hartter, Joel; Hamilton, Lawrence (2020), Wolves are back: Sociopolitical identity and opinions on management of Canis lupus, Dryad, Dataset, https://doi.org/10.5061/dryad.wm37pvmhr

Abstract

In 2010 an interdisciplinary team of social and natural scientists began a project to study society–environment interactions in northeast Oregon. At first, the Communities and Forests in Oregon (CAFOR) project focused on Baker, Union and Wallowa Counties. Subsequently the project’s scope expanded to cover Crook, Grant, Umatilla and Wheeler Counties. One part of the CAFOR research involved a series of telephone surveys carried out in four stages over 2011 to 2018. The surveys employed consistent methods with landline or cell telephone interviews of independent random samples conducted by trained personnel from the Survey Center of the University of New Hampshire. Questions covered a range of topics related to environment and community change. Some questions were repeated with identical wording on two, three or four surveys, watching for continuity and change in public opinion. The survey design involved stratification with oversampling of smaller-population counties, and in some cases also of forest landowners. To adjust for both design and response bias, probability weights (inverse of the probability of selection) are applied for all graphs and statistical analyses in this paper. Effects of this weighting on variables of interest here are not large, but make the results more representative with respect to county populations and age/sex distributions. Geographic coverage varied across the four surveys. The initial stage in 2011 sampled residents from three counties: Baker, Union and Wallowa. Subsequently, the 2014 and 2015 surveys expanded to seven contiguous counties, including those three. The project’s final survey in 2018 refocused on the original three. A total of 5,085 interviews had been conducted, 3,782 of them involving residents of Baker, Union or Wallowa County. To maintain comparability, our analysis in this dataset concerns only that three-county subset, although findings remain broadly similar in analyses using the complete seven-county dataset.

Methods

Survey Questions.  The return of wolves has been controversial in this region, particularly with ranchers and hunters. To measure views among the general public, we asked on all four CAFOR surveys:  Which of the following four statements about wolves in eastern Oregon comes closest to your personal beliefs?”. Respondents could choose eliminating wolves from the region, limited hunting, or no hunting with or without compensation for livestock losses. Telephone interviewers rotated the order of these response choices. The simple, un-nuanced choices by no means exhaust the possible views on wolf management, but only three percent of our respondents were unable to choose between them. As will be seen, responses were highly structured in terms of individual characteristics and location, and highly replicable across survey years—suggesting high validity despite or because of the question’s simplicity.

Data Selection. Four CAFOR surveys conducted over 2011 to 2018 interviewed a combined total of more than 5,000 northeast Oregon respondents. The surveys employed similar methods and asked many of the same questions, so in principle they could be integrated into one dataset. In practice, however, secondary variations in questions asked or geographic coverage restrict us to subsets where these factors are consistent, for purposes of a particular analysis. Three such subsets are employed in this paper. (1) Interviews from all four survey years (2011, 2014, 2015, 2018; n = 3,782) from Baker, Union and Wallowa counties. A simple 3-party political question was asked in 2011; all later surveys asked two political questions, permitting construction of a 4-party indicator as described by Hamilton & Saito (2015), but to maintain consistency with 2011 we use only the 3-party version for analysis of this subset. (2) Interviews from 2018 only, also in Baker, Union and Wallowa counties (n = 1,097). The 2018 survey for the first time asked about political identification of the respondent’s friends, so its effects are tested only with these data. The more detailed 4-party indicator also can be used for this analysis; in terms of respondents, the 2018 survey comprises a subset of the 2011–2018 dataset. (3) About 1,300 respondents in four additional counties (Crook, Grant, Umatilla and Wheeler) were interviewed in 2014 or 2015 only. These geographically distinct data and non-overlapping interviews are not mixed into the main three-county analyses here, but provide an independent replication for several if its key findings, as mentioned in the discussion.

Statistical Methods. Statistical analyses were performed using Stata version 16, applying probability weights consistently for percentages, graphs and modeling. Models shown involve weighted logit regression, a method appropriate for binary dependent variables such as elimination/no elimination of wolves. The original wolves survey question offered respondents four choices, so an alternative approach could use multinomial logit regression. The corresponding multinomial models, however, are three times more complicated (i.e., three parameters are estimated for each predictor variables, against a reference value), while reaching substantially the same conclusions. Other coding choices specified in Table 2, for use in modeling, similarly provide the most clear, replicable and robust results. Political party is treated as an ordinal predictor due to its consistently monotonic and close to linear effects on environmental topics, in the present datasets and many others (for examples see Hamilton and Saito 2015; Hamilton et al. 2018a, 2018b).

 

Usage Notes

The Communities and Forests in Oregon (CAFOR) project is supported by the Agricultural and Food Research Initiative, U.S. Department of Agriculture (2014-68002-21782 and 2010-67023-21705). Any opinions, findings, and conclusions or recommendations derived from these data are those of the authors and do not necessarily reflect the views of the US Department of Agriculture. For further questions about the data, please contact authors.

Funding

U.S. Department of Agriculture, Award: 2010-67023-21705

U.S. Department of Agriculture, Award: 2014-68002-21782