DOI: 10.5061/dryad.k6djh9wd6
title: Interests, beliefs, experience, and perceptions shape tolerance towards impacts of recovering predators

description: The modification of landscapes is increasing the interface between humans
 and wildlife, while conflicts concerning predator impacts on human
 activities persist. Some previously persecuted but now protected predator
 species are experiencing recovery and range expansion. Tolerance is
 considered essential for achieving coexistence between humans and
 wildlife; however, its conceptualisation remains unresolved. Little is
 known about tolerance in the context of recovering predators, particularly
 which drivers are relevant to all or specific species and human interests.
 Using an online questionnaire survey shared with members of organisations
 with interests in rural land-based activities, we collected data on
 interests and beliefs, and attitudes, perceptions, experience, and
 management preferences for six recovering vertebrate predators in the UK
 (n=819). We created a species tolerance score representing the management
 choices of the respondents in different conflict scenarios, which differed
 in the degree of impact on the predator population. Our species tolerance
 score was characterized by a complex combination of the interests and
 beliefs of the respondents about wildlife management, perceptions and
 experience of that species (perceived benefits, population trend, positive
 and negative experience, indirect negative experience) and negative
 experience of other recovering predators.  We found a tolerance
 gradient between interest groups with notable overlap between groups with
 primary interests in wildlife conservation, shooting, farming, and
 fishing. Although higher perceived benefits consistently corresponded to
 higher tolerance, having a negative experience of the species dampened the
 effect of perceived benefits on tolerance. When both negative personal and
 indirect experiences were reported, tolerance was dramatically reduced.
 The classification of species from least to most tolerated was consistent
 between interest groups.  The application of our species
 tolerance score as the normative dimension (i.e., acceptability) in
 Brenner and Metcalf’s (2020) Social Tolerance of Wildlife Framework
 highlights that tolerance (negative attitude-high acceptability) is
 potentially rare and more positive attitudes must be achieved before
 acceptance of the impacts of species can increase.  Our findings
 highlight that considering only primary interests may hinder debates
 concerning recovering predators. Strategies to reduce negative experiences
 or change how they are perceived could significantly increase tolerance in
 combination with increasing positive experiences.
description: This dataset is used in "Interests, beliefs, experience, and
 perceptions shape tolerance towards impacts of recovering predators"
 by Hobson K J, Stringer A, Gill R, MacPherson J, Lambin X, in People and
 Nature. Corresponding author: Professor Xavier Lambin
 (University of Aberdeen) x.lambin@abdn.ac.uk The data is dervived from a quesitonnaire survey for our study which was approved by the Ethics Review Committee of the School of Psychology of the University of Aberdeen (Application No. PEC/4220/2019/5). Informed consent to participate in the study was obtained from all respondents. The dataset contains data collected using an online survey (using SNAP 11 Professional software, www.snapsurveys.com) shared with stakeholders with interests in rural land-based activities between June 2019 to February 2020. The questionnaire gathered information on people’s interests and how they value wildlife, and their attitudes, perceptions, and experience of six recovering predator species: otter, pine marten, polecat, buzzard, golden eagle, and red kite, in the UK. Using varying hypothetical conflict scenarios (e.g., species impacting livestock, game, pets, and native species) we asked each respondent to choose the management strategy they deemed most appropriate. From this, we created a tolerance score for each respondent about each species. The dataset contains different versions of the variables used in the study including raw data, as well as the final variables used to model tolerance to recovering predators using linear mixed models with Gaussian distributions and a random effect of the respondent (see paper and supplementary materials for the final 23 variables used).  The dataset contains data from the 819 respondents that completed the questionnaire. Respondents or observations (a respondent's answers about one of the species) were removed from the analysis if there were missing data for any of the final variables or if the respondent could not be assigned to an interest group. The final data used in the analysis is noted in the column "Data_used_mixed_model" as "final_data". Structure - the data has multiple observations/lines per respondent, one for each species. If a species was not recognised by the respondent then the questions were not asked about this species.  See tables in paper and supplementary materials for full details on variables and data manipulation.   Data derived from other questions in the questionnaire are not included in the dataset, only the data that was used in the anaylsis for this study. 
description: #
 Data_questionnaire_Hobson_et_al_2023_Stakeholder_tolerance_recovering_predators_UK [Access this dataset on Dryad](https://doi.org/10.5061/dryad.k6djh9wd6) Hobson, Keziah et al. (Forthcoming 2023). Interests, beliefs, experience, and perceptions shape tolerance towards impacts of recovering predators [Dataset]. Dryad. https://doi.org/10.5061/dryad.k6djh9wd6 Article DOI: 10.1002/pan3.10560 This dataset is used in "Interests, beliefs, experience, and perceptions shape tolerance towards impacts of recovering predators" by Hobson K J, Stringer A, Gill R, MacPherson J, Lambin X, in People and Nature. Corresponding author: Professor Xavier Lambin (University of Aberdeen) x.lambin@abdn.ac.uk The dataset contains data collected using an online survey (using SNAP 11 Professional software, www.snapsurveys.com) shared with stakeholders with interests in rural land-based activities between June 2019 to February 2020. The questionnaire gathered information on people’s interests and how they value wildlife, and their attitudes, perceptions, and experience of six recovering predator species: otter, pine marten, polecat, buzzard, golden eagle, and red kite, in the UK. Using varying hypothetical conflict scenarios (e.g., species impacting livestock, game, pets, and native species) we asked each respondent to choose the management strategy they deemed most appropriate. From this we created a tolerance score for each respondent about each species. The dataset contains different versions of the variables used in the study including raw data, as well as the final variables used to model tolerance to recovering predators using linear mixed models with Gaussian distributions and a random effect of the respondent (see paper and supplementary materials for the final 23 variables used). ## Description of the data and file structure Data dervived from a quesitonnaire survey for our study which was approved by the Ethics Review Committee of the School of Psychology of the University of Aberdeen (Application No. PEC/4220/2019/5). Informed consent to participate in the study was obtained from all respondents. The dataset contains data from the 819 respondents that completed the questionnaire. Respondents or observations (a respondent's answers about one of the species) were removed from the analysis if there were missing data for any of the final variables or if the respondent could not be assigned to an interest group. The final data used in the analysis is noted in the column "Data_used_mixed_model" as "final_data". Structure - the data has multiple observations/lines per respondent, one for each species. If a species was not recognised by the respondent then the questions were not asked about this species. "NA" - Missing data code - "S.C" in column name are for numeric variables that have been standardised (mean=0) and scaled (standard deviation 1). "[redacted] - data removed for the date the questionnaire was completed (this is following Dryad's policy that specific dates relating to human participants should be removed for datasets). Columns names and data: ID (raw) - code assigned to a respondent (type: integer) species (raw) - questions asked about this species (type: factor) ID.data (raw) - date the respondent completed the questionnaire (type: date) Q1.1 (raw) - Question asking for consent (type: factor) Q19.1 (raw) - How old are you? (type: factor) Q19.2 (raw) - What is your gender (type: factor) Q1.3 (raw) - Question: Did you have a mostly urban, rural or suburban upbringing? 3 levels: "Rural", "Urban", "Suburban" (type: factor) upb.rural (manip) - (type: factor) answers to Q1.3 mainpulated - merging of levels "urban" and "suburban" to "Sub.urban", 2 levels: "Sub.urban", "Rural" Q1.7 - Q1.8. Please indicate the extent to which you agree or disagree with the following statements (type: all ordinal /ordered factors): Q1.7a (raw) Humans.should.manage.wild.animals.ben.of.humanity Q1.7b (raw) I.object.to.shooting.any.animals.as.it.violates.their.right.to.exist Q1.7c (raw) Opportunity.to.see.wildlife.is.one.reason.I.take.trips.to.live.in.a.particular.area Q1.7d (raw) It.is.important.for.humans.to.manage.pops.of.wild.animals Q1.7e (raw) Needs.of.people.always.more.important.than.any.rights.of.wild.animals Q1.7f (raw) Shooting.helps.people.enjoy.the.outdoors.in.a.positive.way Q1.7g (raw) It.is.acceptable.to.kill.an.indiv.animal.as.long.as.its.pop.is.not.jeopardised Q1.7h (raw) I.enjoy.watching.wildlife.when.I'm.outdoors Q1.8 Please indicate the extent to which you agree or disagree with the following statements: Q1.8a (raw) It.is.important.that the.UK.always.has.abundant.wildlife.inc.predator.species Q1.8b (raw) Rights.of.people.and.rights.of.animals.are.equally.important Q1.8c (raw) Shooting.helps.people.appreciate.wildlife.and.natural.processes Q1.8d (raw) I.enjoy.learning.about.wildlife Q1.8e (raw) Whether.or.not.I.get.out.to.see.wildlife.as.much.as.I'd.like.its.important.to.know.it.exists.in.UK Q1.8f (raw) I.enjoy.seeing.wildlife.around.my.home Q1.8g (raw) Shooting.is.cruel.and.inhumane.to.animals Q1.8h (raw) Having.wildlife.incl.predator.species.around.my.home.is.importTant.to.me Further info: Q1.7a.reord:Q1.8h.reord, and Q1.7a.z:Q1.8h.z (type: ordinal) are questions Q1.7a:Q1.8h recoded based on questions and whether agreement or disagreement were indicate a more utilitarian response or not. See article and supplementary materials (Table S6) for more details. Q1.7a, Q1.7d, Q1.7g, Q1.8c, Q1.7f, Q1.7e - Reverse coded: 2 (strongly disagree) to -2 (strongly agree). Q1.8g, Q1.7b, Q1.8b - Item response scales range from -2 (strongly disagree) to 2 (strongly agree). Lower scores indicate a more utilitarian response. BB.Shoot.avg (type: numeric): Indicators used for the Shooting basic belief dimension. Calculated as average of Q1.8g.reord, Q1.7g.reord, Q1.8c.reord, Q1.7b.reord, Q1.7f.reord BB.W.rights.avg (type: numeric):- Indicators used for the Equality between people and wildlife/wildlife rights basic belief dimension. Calculated as average of Q1.8b.reord and Q1.7e.reord. BB.W.mgt.avg (type: numeric):- Indicators used for the Equality between people and wildlife/wildlife rights basic belief dimension. Calculated as average of Q1.7a.reord and Q1.7d.reord Q18.2 (raw) (type: factor) Please rank your interests by dragging them to the grey box. Please put your main interest at the top (1st position) and only rank interests selected in the last question Q18.2a Farming Q18.2b Fishing Q18.2c Forestry Q18.2d Shooting Q18.2e Tourism Q18.2f Red squirrel conservation Q18.2g Wildlife conservation primary (manip) (type: factor) -using answer to Q18, interest chosen as 1st. secondary (manip) (type: factor) - using answer to Q18, interest chosen as 2nd. tertiary (manip) (type: factor) - using answer to Q18, interest chosen as 3rd. cluster.old (manip) (type: factor) - Based on primary, secondary, and tertiary interests, each respondent assigned to an interest group using rules - see article and supplementary materials Table S4. Initial names used for groups. cluster (manip) (type: factor)- renaming of interest groups, and final vairable used in the analysis. recog_y_n (type: factor) - recognised species of not (Yes, No) recognised (type: factor) - name of species recognised informed - (type: ordinal/ordered factor) answer to How informed do you feel about the following species? 5 levels informed.fac (manip)(type: ordinal/ordered factor) - informed answer with "Dont.recog" for species that the respondent did not recognise. informed.fac_3lev (type: ordinal/ordered factor)- informed.fac with factor levels merged. Not at all/slightly combined, and very/extremely combined, Moderately, Dont.recog (Don't recognise species). Columns CC, CD, CE: answer to question - In your view, how would you describe "species name" with respect to the following attributes? column CC: unatt.att.fac - Unattractive (1) to Attractive (5) (Type: ordinal) column CD: charisma.fac - Dull (1) to Charismatic (5) (Type: ordinal) column CE: harm_ben.fac - Harmful (1) to Beneficial (5) (Type: ordinal) harm_ben.num.S.C.new - (type: numeric) - numeric version of harm_ben.fac that has been standardised (mean=0) and scaled (standard deviation 1) unatt.att.num.S.C.new - (type: numeric)- numeric version of unatt.att.fac that has been standardised (mean=0) and scaled (standard deviation 1) charisma.num.S.C.new - (type: numeric) - numeric version of charisma.fac that has been standardised (mean=0) and scaled (standard deviation 1) attitude - (type: ordinal) - Answer to question: In your view, how would you describe "species name" with respect to the following attributes? Negative attitude to towards (1) to Positive attitude towards (5) live.with.species - (type: factor) Answer to question: Select the species that you know are present in your current area (value - species names selected) live.with.fac - (type: factor) - "live.with.species" variable manipulated to make a binary variable of yes or no. personal.exp - (type: factor) - personal experience of the species, 3 level factor: Yes, No, Not_sure per.exp.y.n - (type: factor) - "personal.exp" manipulated into binary variable (Yes, No) personal.exp.binary (type: binary) - "personal.exp" manipulated into binary variable (Yes 1, No 0) personal.exp.freq - (type:ordinal/ordered factor) - Answer to How often have you had a personal experience with "insert species"? 5 level factor per.exp.freq.fac - (type:ordinal/ordered factor) - "personal.exp.freq" manipulated so that species not recognised by the respondents are recorded as "Dont.recog" - not recognised. per.exp.freq.4lev - (type: ordinal/ ordered factor) "personal.exp.freq" manipulated into 4 levels "very rarely" and "rarely" merged to form rarely, and "frequently" and "very frequently" merged to create "frequently", other levels: Occasionally, No.exp (No experience of species). neg.exp (type: factor) - Answer to question "Have you had any particularly negative experience with *insert species name*? - Levels: Yes or No. neg.exp.binary - (Type: binary) Answer to question "Have you had any particularly negative experience with *insert species name*? - Binary: 1 or 0. neg.exp.overall - (Type: factor) - "neg.exp.binary" manipulated so that respondents with no negative experience or no experience of species assigned as "No.neg.no.exp", and respondents reporting a negative experience as "Yes" pos.exp - (Type: factor) Answer to question "Have you had any particularly positive experience with *insert species name*? - Levels: Yes or No. pos.exp.binary (type: binary) - Answer to question "Have you had any particularly positive experience with *insert species name*? - binary: 1 (yes) or 0 (no). pos.exp.overall (type: factor)- "pos.exp.binary" manipulated so that respondents with no positive experience or no experience of species assigned as "No.pos.no.exp", and respondents reporting a positive experience as "Yes". Col CV, DA, DC: Answer to Have you ever been impacted by *insert species* in the following ways, and if so how frequently has it happened Col CV: inconvenience - Caused inconvenience - (type:ordinal/ ordered factor) levels: Never, Once, 2 to 4 times, 5 to 10 times, 11 to 20 times, 21 to 50 times, More than 50 times. Col DA: damage - Damage to equipment or property - (type:ordinal/ ordered factor) levels: Never, Once, 2 to 4 times, 5 to 10 times, 11 to 20 times, 21 to 50 times, More than 50 times. Col DC: predation - Injuring or killing livestock, game birds, pets or other animals been killed or injured (type:ordinal/ ordered factor) levels: Never, Once, 2 to 4 times, 5 to 10 times, 11 to 20 times, 21 to 50 times, More than 50 times. incon.binary - (type: binary) "inconvenience" manipulated to binary variable (0 - no inconvenience reported, 1 - inconvenience reported) damage.binary - (type: binary) "damage" manipulated to binary variable (0 - no damage reported, 1 - damage reported) predation.binary - (type: binary) "predation" manipulated to binary variable (0 - no predation reported, 1 - predation reported) impact.overall - (type: factor) Impacted overall included respondents that reported being impacted (predation, damage, and/or inconvenience) i.e. a respondent that reported predation, and a respondent that reported damage and inconvenience would both be recorded as "Yes" for impact.overall. Col DF, DG, DH, DI - (type: factor) Answer to Have you heard of any neighbours, local acquaintances, or colleagues working in the same geographical area having a negative or positive experiences with *insert species* Col DF - local.people.exp.neg - if Negative selected, recorded as "Negative_" Col DG - local.people.exp.pos - if Positive selected, recorded as "Positive" Col DH - local.people.exp.no - if No selected, recorded as "No" Col DI - local.people.exp.not.sure - if Not sure selected, recorded as "Not_sure" hearsay.loc - (type: factor) -col DF, DG, DH, DI used to create factor with 4 levels: negative (only negative reported), positive (only positive report), No.hearsay (No for local.people.exp.no), and pos_neg (positive and negative reported) hearsay.loc.pos - (type: factor) - 3 level factor: Yes (positive reported), No (no positive reported), Dont.recog (respondent did not recognise species) hearsay.loc.neg - (type: factor) - 3 level factor: Yes (negative reported), No (no negative reported), Dont.recog (respondent did not recognise species) Col DM, DN, DO, DP - (type: factor) Answer to Have you heard of anyone that does not live near you having negative or positive experiences with *insert species* Col DM - other.people.exp.neg - if Negative selected, recorded as "Negative_" Col DN - other.people.exp.pos - if Positive selected, recorded as "Positive" Col DO - other.people.exp.no - if No selected, recorded as "No" Col DP - other.people.exp.not.sure - if Not sure selected, recorded as "Not_sure" hearsay.wider - (type: factor) col DM, DN, DO, DP used to create factor with 4 levels: negative (only negative reported), positive (only positive report), No.hearsay (No for other.people.exp.no), and pos_neg (positive and negative reported) hearsay.wider.pos - (type: factor) 3 level factor: Yes (positive reported), No (no positive reported), Dont.recog (respondent did not recognise species) hearsay.wider.neg - (type: factor) 3 level factor: Yes (negative reported), No (no negative reported), Dont.recog (respondent did not recognise species) hearsay.all - (type: factor) 4 level factor data for hearing information from local and other people: negative (only negative reported), positive (only positive report), No.hearsay (No for other.people.exp.no and local.people.exp.no), and pos_neg (positive and negative reported) hearsay.all.pos (type: binary, but factor with level Dont.recog included) - binary variable created using hearsay.all: 1 (positive), 0 (negative only, or no), or Dont.recog (Don't recognise species). hearsay.all.neg - (type: binary, but factor with level Dont.recog included) - binary variable created using hearsay.all: 1 (negative), 0 (positive only, or no), or Dont.recog (Don't recognise species). nat.pop.status - (type: factor) Answer to question Do you think the UK populations of these species are, 4 levels: Decreasing, Stable, Increasing, Don't know (Dont_know). nat.pop.should - (type: factor) Answer to question Do you think the UK populations of these species should: 4 levels: Decrease, Remain the same (Remain_the_same), Increase, No opinion (No_opinion). nat.pop.should.ord - (type: ordinal) "nat.pop.should" manipulated to ordinal: 1 Decrease, 2 Remain the same, 3 Increase. nat.pop.st.fac - (type: factor) "nat.pop.status" manipulated to include "Dont.recog" for don't recognise species. Neg.exp.oth.pred (type: factor)- Negative experience with any of the other species included in the survey, binary (Yes, No) Impact.oth.pred (type: factor) - Impacted by any of the other species included in the survey, binary (Yes, No) Col EC:EH (type: factor) - Question asked, Under the following circumstances, which strategy do you think is the most acceptable for mitigating the negative impacts of Buzzards if all strategies were legal under UK law: Predation of livestock and game species (including pheasants) or pets by *insert species". Choices of strategy: Do nothing, Mitigation (e.g. fences, deterrents), Financial compensation, Trap and move individual, Trap and kill individual, Control population. Col EC - conflict.sc.1: One of event where 1 to a few individuals are killed Col ED - conflict.sc.2: One of event where 10+ individuals are killed Col EE - conflict.sc.3: Regular event where 1 to a few individuals are killed Col EF - conflict.sc.4: Regular event where 10+ individuals are killed Col EG - conflict.sc.5: Injures pet Col EH - conflict.sc.6: Kills pet Col EI:EJ (type: factor) - Question asked, Under the following circumstances, which strategy do you think is the most acceptable for mitigating the negative impacts of Buzzards if all strategies were legal under UK law: Impact on native species. If there is strong evidence that *insert species name*. Choices of strategy: Do nothing, Mitigation (e.g. fences, deterrents), Financial compensation, Trap and move individual, Trap and kill individual, Control population. Col EI - conflict.sc.7: Reduce the abundance of endangered or rare native species Col EJ - conflict.sc.8: Reduce the abundance of other native species conflict.sc.2.ord.5lev (type: ordinal) - "conflict.sc.2" manipulated to ordinal varible with 1 (Control population), 2 (Trap and kill individual), 3 (Trap and move individual), 4 (Mitigation (e.g. fences, deterrents) and Financial compensation), 5 (Do nothing) conflict.sc.4.ord.5lev - (type: ordinal)"conflict.sc.4" manipulated to ordinal varible with 1 (Control population), 2 (Trap and kill individual), 3 (Trap and move individual), 4 (Mitigation (e.g. fences, deterrents) and Financial compensation), 5 (Do nothing) conflict.sc.6.ord.5lev - (type: ordinal) "conflict.sc.6" manipulated to ordinal varible with 1 (Control population), 2 (Trap and kill individual), 3 (Trap and move individual), 4 (Mitigation (e.g. fences, deterrents) and Financial compensation), 5 (Do nothing) conflict.sc.7.ord - (type: ordinal) "conflict.sc.7" manipulated to ordinal varible with 1 (Control population), 2 (Trap and kill individual), 3 (Trap and move individual), 4 (Mitigation (e.g. fences, deterrents) and Financial compensation), 5 (Do nothing) tol_EBM - the tolerance response variable (score) (type - numeric) calculated using a Confirmatory factor analysis and empirical Bayes method to create a separate tolerance score for each respondent for each predator species. Indicators used: conflict.sc.2.ord.5lev , conflict.sc.4.ord.5lev , conflict.sc.6.ord.5lev , conflict.sc.7.ord as indicators. See Figure 2, and S2 of article. Data_used_mixed_model - (factor) data used in the final analysis in the article coded as "final_data" *** See article and supplementary materials for full details on variables and data manipulation. Data derived from other questions in the questionnaire are not included in the dataset, only the data that was used in the anaylsis for this study. ## Code/Software Analysis of the data was undertaken in R (v.4.0.2,R Development Core Team, 2020).