DOI: 10.5061/dryad.5mkkwh7cx
title: Results and analysis script from a discrete choice experiment assessing public preferences for rewilding in the Oder Delta

description: 1. Rewilding is an emerging paradigm in restoration science, and is
 increasingly gaining popularity as a cost-effective ecosystem restoration
 option. A rewilding framework was recently proposed that contains three
 integral components: restoring trophic complexity, allowing for stochastic
 disturbances, and enhancing species’ potential to disperse. However, as of
 yet, there has been limited quantitative analysis looking at public
 preference for rewilding and each of its elements. 2. We used a discrete
 choice experiment approach to determine public preference for rewilding in
 the Oder Delta. The unique geographical context of the Oder Delta,
 spreading evenly across two countries, allowed us to analyze differences
 between the German (n = 1,005) and Polish (n = 1,066) samples. 3. In both
 countries, we found respondents were willing to pay for rewilding
 interventions when compared against a status quo option. Notably,
 preferences were strongest for restoring trophic complexity through
 promoting the comeback of large mammals. 4. In addition, we found
 respondents living locally to the study region had significantly different
 preferences than the nationwide samples, exhibiting negative willingness
 to pay for the restoration of natural flooding regimes and the presence of
 large predator species.
description: <strong>Choice experiment set-up</strong>
 To determine respondent WTP for rewilding interventions in the
 Oder Delta, we conducted a discrete choice experiment. The survey was
 conducted online in both Germany and Poland, with respondents selected by
 professional online survey companies to provide a representative sample of
 the respective national populations. The survey consisted of three
 sections. The first section contained standard socio-demographic questions
 and also studied respondents’ prior use of the Oder Delta. The second
 section contained the choice experiment along with follow up questions
 assessing respondent comprehension and motivation, and the third section
 included novel motivational questions assessing respondents’ relationship
 to the Oder Delta and nature more broadly. We prepared
 final choice sets for our study using an efficient Bayesian design
 optimized for Mixed Logit models, using the software Ngene 1.3. Prior
 values were obtained from a pilot study conducted on a sample of 250
 respondents; in the pilot study an optimal in difference design was used
 (Street and Burgess, 2007). A total of 48 choice situations were
 generated, which were subsequently grouped into 4 blocks, with each block
 comprising 12 choice situations. Respondents were presented with a total
 of twelve different choice situations, each including three alternatives,
 with one fixed as a “status quo” option (no change in the current
 management, leading to intensification by 2050). Each scenario was
 described using two stochastic disturbance attributes, the dispersal
 attribute, one trophic complexity attribute, and a cost attribute.
 Attributes levels and icons were carefully selected to be understandable
 and credible for respondents. As part of this process, we consulted in
 detail with local experts (including members of the Rewilding Oder Delta
 local practitioners’ association) and rewilding experts. Focus groups
 formed part of an iterative process, in which the attributes and icons
 were consistently refined in response to feedback. Before the choice
 tasks, respondents first carried out a simple ranking exercise using
 photographs of the different landscape levels. This element of the survey
 was designed to help familiarize respondents with attribute
 levels.  Cost was defined as a new annual obligatory
 tax paid by all citizens in the respondents’ country for the foreseeable
 future. It was explained to the respondent that the tax would be used to
 help finance the landscape management interventions alongside supporting a
 local rewilding fund that would offset any associated adverse economic
 impact on local residents of the proposed changes. Care was taken to
 ensure that the survey design followed the state of the art
 recommendations of Johnston et al., (2017), presenting respondents with
 “an incentive-compatible valuation exercise that involves a plausibly
 consequential decision”. Notably, efforts were made to mitigate against
 hypothetical bias (Carson et al., 2014) such that the respondent perceived
 the payment vehicle as binding (if the proposed change goes ahead) and
 that their decisions could plausibly lead to changes in the attributes
 being valued (Carson and Groves, 2007). The master
 version of the survey was written in English and translated into German
 and Polish by native speakers. <strong>Choice
 experiment data collection</strong> The primary
 test for Germany and Poland was conducted in August and September 2022,
 resulting in a total of 1,657 completed surveys collected for Germany and
 1,514 for Poland. Of these, the survey company collected approximately 500
 respondents from each country residing within or neighboring the national
 state containing the Oder Delta region (e.g., Mecklenburg-Vorpommern in
 Germany and the West Pomerania Province in Poland). The aim of this was to
 try and collect respondents living locally to the Oder Delta.
 Quality control questions were included in the survey to ensure
 respondents were reading the questions and responding accurately. The
 collected data was then cleaned to remove respondents identified as
 speeders (those below 50% of the median time), those that failed the
 quality control questions, or those identified to have given protest
 responses. After cleaning, there were 1,005 completed surveys for Germany
 and 1,066 for Poland.  Given the nature of this study,
 and in compliance with university guidelines at the time, ethical approval
 for this study was granted by the lead supervisor responsible for the
 project. Care was taken to ensure that all data collected was anonymized,
 that no personal or sensitive data was collected and all participants were
 over the age of 18. Individual respondents were only identifiable in the
 results through a unique numerical ID. Participants took part in the
 survey voluntarily and by submitting the survey gave consent for their
 anonymized data to be used. They could back out at any time before
 submitting. <strong>Choice experiment
 analysis</strong> We used the data from our DCE
 to estimate respondents’ WTP for rewilding interventions in the Oder
 Delta. In a DCE, respondents are presented with a set of available
 alternatives and asked to choose their preferred option. Discrete choice
 models are founded in the theories of economic value (Lancaster, 1966) and
 random utility theory (McFadden, 1974). We employed the
 mixed logit (MIXL) model to analyze the data. MIXL models were estimated
 for the individual rewilding attribute levels of the pooled populations in
 Germany and Poland and for respondents living locally (rural, within
 100km) to the Oder Delta in both countries. For the MIXL model, we used
 the panel specification proposed by Revelt and Train (1998). 
 We asked respondents to choose between management alternatives in
 the Oder Delta in 12 different choice situations. Following cleaning of
 the data to remove speeder surveys and individuals that failed the
 comprehension questions, MIXL models were estimated from a representative
 sample of 1,005 respondents in Germany and 1,066 respondents in Poland. Of
 these, 108 were classed as local in Germany, and 103 in Poland.
description: # Results and analysis script from a discrete choice experiment assessing
 public preferences for rewilding in the Oder Delta **Paper title**: Public
 preference for the rewilding framework: a choice experiment in the Oder
 Delta  **Authors**: Rowan Dunn-Capper a,b, Marek Giergiczny a,c, Néstor
 Fernández a,b, Fabian Marder a, Henrique M. Pereira a,b,d  a German Centre
 for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig,
 Puschstrasse 4, 04103, Leipzig, Germany b Institut für Biologie,
 Martin-Luther-University Halle-Wittenberg, Halle, Germany c Faculty of
 Economic Science, University of Warsaw, ul Długa 44/50 00-241, Warsaw,
 Poland d CIBIO (Research Centre in Biodiversity and Genetic
 Resources)–InBIO (Research Network in Biodiversity and Evolutionary
 Biology), Universidade do Porto, Vairão, Portugal **Summary**: ·      File
 count – 5 files ·      File formats - .xls, .mat, .md ·      File sizes –
 108-115 KB **Dataset overview**: ·      MXL_d_results_pooledGER18.1.24 –
 .xls file containing the results from respondents to the choice experiment
 residing in Germany. Results show the estimations from a mixed logit
 model. Description of variables can be found below. More information on
 the methodology and interpretation of the results can be found in the
 manuscript. ·      ger_data18.1.24.mat – choice experiment data from
 respondents residing in Germany stored as a matlab structure. Columns show
 the outcomes of individuals choice tasks. Respondents are assigned
 anonymous ids. More information on analysing choice experiment data is
 readily available online e.g., see Train, K 2009. ·     
 MXL_d_results_pooledPOL18.1.24 – .xls file containing the results from
 respondents to the choice experiment residing in Poland. Results show the
 estimations from a mixed logit model. Description of variables can be
 found below. More information on the methodology and interpretation of the
 results can be found in the manuscript. ·      pl_data18.1.24.mat – choice
 experiment data from respondents residing in Poland stored as a matlab
 structure. Columns show the outcomes of individuals choice tasks.
 Respondents are assigned anonymous ids. **Detailed file information**
 ·      ger_data18.1.24.mat & pl_data18.1.24.mat List of variables:
 ·      id – anonymized respondent id from survey ·      cs – choice
 situation number, there are 12 cs per respondent ·      alt – alternative
 number, there are 3 alt per person ·      choice – dummy coding choice
 (takes 1 if alt was chosen) ·      sq – dummy coding status quo alt (=1)
 ·      forest - forest variable o   takes 4 levels. 1 = SQ level, 2 =
 lowest rewilding level (intensified land use), 3 = middle rewilding level,
 4 = highest rewilding level (deadwood on floor) ·      river – river
 variable o   takes 4 levels. 1 = SQ level, 2 = lowest rewilding level
 (intensified land use), 3 = middle rewilding level, 4 = highest rewilding
 level (natural flooding regimes) ·      agri – agriculture variable o  
 takes 4 levels. 1 = SQ level, 2 = lowest rewilding level (intensified land
 use), 3 = middle rewilding level, 4 = highest rewilding level (land
 abandonment) ·      connect – connectivity variable o   takes 4 levels. 1
 = SQ level, 2 = lowest rewilding level (intensified land use), 3 = middle
 rewilding level, 4 = highest rewilding level (eco bridges and road
 removal) ·      lc – large carnivore variable o   takes 4 levels. 1 = SQ
 level, 2 = lowest rewilding level (intensified land use), 3 = middle
 rewilding level, 4 = highest rewilding level (both large carnivores
 present) ·      lh – large herbivore variable o   takes 4 levels. 1 = SQ
 level, 2 = lowest rewilding level (intensified land use), 3 = middle
 rewilding level, 4 = highest rewilding level (both large herbivores
 present) ·      cost – cost in EUR of choice alternative ·      booster –
 0 = national representative sample, 1 = booster sample on local residents
 *More detail on the attribute levels and rationale for specification can
 be found in the paper* ·      MXL_d_results files List of variables:
 ·      SQ – status quo alternative ·      Forest2 – second first level
 (compared against level 1 forest – intensification) ·      Forest3 – third
 first level (compared against level 1 forest – intensification) ·     
 Forest4 – fourth first level (compared against level 1 forest –
 intensification) ·      *Same for *River2, 3, 4 (compared against level 1
 river – intensification) ·      *Same for *Agri2, 3,4 (compared against
 level 1 agri – intensification) ·      *Same for *Connect2, 3, 4 (compared
 against level 1 river – intensification) ·      Lynx – presence of lynx
 (compared against level 1, no lynx or wolf) ·      Wolf – presence of wolf
 (compared against level 1, no lynx or wolf) ·      LC_both – presence of
 lynx and wolf (compared against level 1, no lynx or wolf) ·      Elk –
 presence of elk (compared against level 1, no elk or bison) ·      Bison –
 presence of bison (compared against level 1, no elk or bison) ·     
 LH_Both – presence of elk and bison (compared against level 1, no elk or
 bison) ·      -Cost/100(EUR) – cost variable Model diagnostics – measures
 model fit LL – log-likelihood *Detailed description and analysis of
 results found in paper*