| Literature DB >> 35235582 |
Lauren Margaret Smith1, Rupert Quinnell1, Alexandru Munteanu2, Sabine Hartmann2, Paolo Dalla Villa3,4, Lisa Collins1.
Abstract
Free-roaming dog population management is conducted to mitigate risks to public health, livestock losses, wildlife conservation, and dog health and welfare. This study aimed to determine attitudes towards free-roaming dogs and their management and describe dog ownership practices in three European countries. We distributed an online questionnaire comprising questions relating to dog ownership practices and attitudes towards free-roaming dogs using social media. We used logistic regression and ordinal probit models to determine associations between demographic and other factors with ownership practices and attitudes towards free-roaming dogs. This study found that most surveyed respondents wanted to see a reduction in free-roaming dog numbers, and felt that this should be achieved through sheltering, catch-neuter-release, and by controlling owned dog breeding. We identified significant associations between both attitudes and ownership practices with gender, religious beliefs, age, education level, reason for dog ownership, previous experience with free-roaming dogs, and country of residence. Respondents who identified as: (i) male, (ii) holding religious beliefs, (iii) owning dogs for practical reasons, (iv) being young, or (v) having no schooling or primary education had a lower probability of neutering and a higher probability of allowing dogs to roam. Respondents who identified as: (i) female, (ii) feeling threatened by free-roaming dogs, (iii) older, or (iv) having more education had a higher probability of answering that increases in free-roaming dog numbers should be prevented. These findings can help to inform future dog population management interventions in these countries. We emphasise the importance of considering local attitudes and dog ownership practices in the development of effective dog population management approaches.Entities:
Mesh:
Year: 2022 PMID: 35235582 PMCID: PMC8890656 DOI: 10.1371/journal.pone.0252368
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Response and predictor variables (self-reported responses to questions) included in the statistical analyses and their levels.
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* Ordinal predictor variables analysed as continuous variables in statistical models.
Fig 1Ownership practices of respondents in Bulgaria, Italy, and Ukraine.
The percentage of respondents who answered each of the answer options regarding (A) acquisition of dog, (B) reasons for not preventing breeding, (C) the outcome of the dog, and (D) reason for relinquishment. * Multi answer question: Percentage of respondents who selected each answer option (i.e. 100% would indicate that all respondents chose this option).
Fig 2Attitudes of respondents towards free-roaming dogs in Bulgaria, Italy and Ukraine.
The percentage of respondents who answered each of the answer options regarding (A) observation of free-roaming dogs and (B) agreement with statement “I do not like free-roaming dogs being present around my home or work”.
Fig 3Attitudes of respondents towards dog population management in Bulgaria, Italy, and Ukraine.
The percentage of respondents who answered each of the answer options for: (A) who should be responsible for dog population management? and (B) how should free-roaming dogs be reduced? These were multi answer questions: Percentage of respondents who selected each answer option (i.e. 100% would indicate that all respondents chose this option).
Associations between predictor and outcome variables on the probability scale.
| Predictor variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
|---|---|---|---|---|---|---|
| Probability of neutering (95% CI) | Probability of answering | Probability of answering | Probability of answering | Probability of answering | ||
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| Male |
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| Female |
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| Religious |
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| NI | NI | NI |
| Non-religious |
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| NI | NI | NI | |
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| Practical |
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| NI | NI | NI |
| Non-practical |
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| NI | NI | NI | |
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| Attacked | NI | NI |
| 0.98 (0.98–0.99) |
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| Not attacked | NI | NI |
| 0.98 (0.98–0.99) |
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| Bitten | NI | NI |
| 0.98 (0.98–0.99) |
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| Not bitten | NI | NI |
| 0.98 (0.98–0.99) |
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| Yes | NI | NI | 0.15 (0.15–0.16) | 0.98 (0.98–0.99) |
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| No | NI | NI | 0.15 (0.14–0.16) | 0.98 0.98–0.99) |
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| Yes | NI | NI | 0.15 (0.15–0.16) | 0.98 (0.98–0.99) |
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| No | NI | NI | 0.15 (0.14–0.15) | 0.98 (0.98–0.99) |
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| 18–24 |
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| 25–34 |
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| 35–44 |
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| 45–54 |
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| 55–64 |
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| 65–74 |
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| 75+ |
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| None |
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| 0.14 (0.12–0.16) |
| 0.55 (0.51–0.60) |
| Primary |
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| 0.14 (0.13–0.16) |
| 0.57 (0.54–0.60) | |
| Secondary |
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| 0.15 (0.14–0.16) |
| 0.58 (0.56–0.60) | |
| Tertiary |
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| 0.15 (0.15–0.16) |
| 0.60 (0.58–0.60) | |
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| Strongly disagree | NI | NI |
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| Disagree | NI | NI |
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| Neutral | NI | NI |
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| Agree | NI | NI |
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| Strongly agree | NI | NI |
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| Bulgaria |
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| 0.99 (0.98–0.99) |
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| Italy |
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| 0.98 (0.98–0.99) |
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| Ukraine |
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| 0.98 (0.98–0.98) |
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Significant results are highlighted in bold. NI = predictor variable not included in the model.