| Literature DB >> 35081132 |
Katherine A Cronin1, Maureen Leahy1, Stephen R Ross2, Mandi Wilder Schook3, Gina M Ferrie3, Andrew C Alba3.
Abstract
The trade and private ownership of non-domesticated animals has detrimental effects on individual animals and their wild populations. Therefore, there is a need to understand the conditions that motivate and dissuade interest in non-domesticated pet ownership. Past research has demonstrated that the way in which non-domesticated animals are portrayed in images influences the public's perception that they are suitable as pets. We conducted an online survey of people residing in the United States to investigate how viewing images that could be realistically captured in the zoo and broader tourism industries impact the degree to which people report interest in having that animal as a pet. We focused on two species, reticulated pythons (Malayopython reticulatus) and two-toed sloths (Choloepus hoffmanni), and presented each species in six different visual contexts. After viewing an image, respondents reported interest in pet ownership on a four-point Likert scale. Each species was studied separately in a between-subjects design and results were analyzed using ordinal logistic regression models. Thirty-nine percent of respondents reported interest in sloth pet ownership, and 21% reported interest in python pet ownership. However, contrary to our hypotheses, we found that viewing these species in different visual contexts did not significantly affect survey respondents' reported interest in having either species as a pet. Generation was a significant predictor of interest in both sloth and python pet ownership, with younger generations reporting more interest in having these species as pets. Male respondents reported more interest in python pet ownership, whereas there were no significant differences between genders regarding interest in sloth ownership. We consider how modern media exposure to animals in unnatural contexts may relate to the generational effect and discuss priorities for future research to better understand the development of individual interests in non-domesticated pet ownership.Entities:
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Year: 2022 PMID: 35081132 PMCID: PMC8791465 DOI: 10.1371/journal.pone.0262208
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 1Experimental conditions, visual stimuli, and sample size per condition.
Conditions presented in the online survey in a between-participants design, and number of subjects in each condition. An additional condition was run for internal evaluation of a Lincoln Park Zoo program; details are in Supporting Information.
Proportion of responses by level of agreement with the statement, “I would like to have a sloth/python as a pet”.
| Strongly Agree | Agree | Disagree | Strongly Disagree | |
|---|---|---|---|---|
| Sloth: All generations | 0.112 | 0.276 | 0.353 | 0.260 |
| Sloth: Gen Z | 0.054 | 0.137 | 0.327 | 0.482 |
| Sloth: Millennial | 0.046 | 0.198 | 0.261 | 0.495 |
| Sloth: Gen X | 0.053 | 0.157 | 0.225 | 0.564 |
| Sloth: Boomers II | 0.019 | 0.075 | 0.182 | 0.723 |
| Sloth: Boomers | 0.000 | 0.045 | 0.104 | 0.851 |
| Sloth: Post War | 0.000 | 0.000 | 0.000 | 1.000 |
| Python: All generations | 0.044 | 0.165 | 0.245 | 0.546 |
| Python: Gen Z | 0.134 | 0.282 | 0.401 | 0.183 |
| Python: Millennial | 0.133 | 0.305 | 0.332 | 0.229 |
| Python: Gen X | 0.089 | 0.256 | 0.379 | 0.275 |
| Python: Boomers II | 0.038 | 0.197 | 0.357 | 0.408 |
| Python: Boomers | 0.043 | 0.085 | 0.383 | 0.489 |
| Python: Post War | 0.000 | 0.167 | 0.333 | 0.500 |
Proportions of response selections per species and generation, collapsing across genders.
Likelihood ratio test results for ordinal logistic regression models.
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| Fixed Factors | |||
| Context | 3.872 | 5 | 0.5680 |
| Gender | 4.459 | 2 | 0.1076 |
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| Fixed Factors | |||
| Context | 5.897 | 5 | 0.3163 |
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Results of the likelihood ratio tests isolating the contributions of the fixed effects in both the sloth and python models are shown in the table. The factors in bold are significant predictors of interest in pet ownership.
Ordinal logistic regression results for the sloth experiment.
| Predictor | Coefficient | Lower-95 | Upper-95 | S.E. | Odds Ratio |
|---|---|---|---|---|---|
| Context_Naturalistic Zoo Habitat | -0.0323 | -0.3280 | 0.2633 | 0.1509 | 0.9682 |
| Context_Keeper Contact | -0.1580 | -0.4526 | 0.1363 | 0.1502 | 0.8538 |
| Context_Educational Perch | 0.0639 | -0.2317 | 0.3595 | 0.1508 | 1.0660 |
| Context_Educational Perch with Visitor Contact | 0.1100 | -0.1854 | 0.4055 | 0.1507 | 1.1163 |
| Context_Public Setting (Yoga) | -0.0346 | -0.3314 | 0.2621 | 0.1514 | 0.9660 |
| Gender_Male | 0.0119 | -0.1595 | 0.1833 | 0.0875 | 1.0120 |
| Gender_Other | -1.0316 | -2.035 | -0.0639 | 0.4962 | 0.3565 |
| Generation_ Millennial | -0.0298 | -0.3454 | 0.2857 | 0.1609 | 0.9707 |
| Generation_ Gen X | -0.3772 | -0.7194 | -0.0355 | 0.1744 | 0.6858 |
| Generation_Boomers II | -0.9426 | -1.3571 | -0.5305 | 0.2108 | 0.3896 |
| Generation_Boomers I | -1.3510 | -1.9704 | -0.7457 | 0.3116 | 0.2590 |
| Generation_Post War | -1.3556 | -3.0201 | 0.1433 | 0.7812 | 0.2578 |
The reference value for visual context was the control condition, the reference value for gender was female, and the reference value for generation was Gen Z. Original coefficients are scaled in terms of logs and we provide the exponentiated odds ratios as well.
Ordinal logistic regression results for the python experiment.
| Predictor | Coefficient | Lower-95 | Upper-95 | S.E. | Odds Ratio |
|---|---|---|---|---|---|
| Context_Naturalistic Zoo Habitat | -0.1501 | -0.4694 | 0.1691 | 0.1629 | 0.8606 |
| Context_Keeper Contact | -0.0696 | -0.3870 | 0.2477 | 0.1619 | 0.9327 |
| Context_Educational Perch | 0.1709 | -0.1359 | 0.4776 | 0.1565 | 1.1863 |
| Context_Educational Perch with Visitor Contact | 0.0059 | -0.3061 | 0.3179 | 0.1592 | 1.0060 |
| Context_Public Setting (Tourist Photo) | 0.1555 | -0.1548 | 0.4658 | 0.1583 | 1.1682 |
| Gender_Male | 0.4722 | 0.2882 | 0.6562 | 0.0939 | 1.6035 |
| Gender_Other | 1.0356 | 0.2167 | 1.8545 | 0.4178 | 2.8168 |
| Generation_ Millennial | 0.0168 | -0.2882 | 0.3219 | 0.1556 | 1.0170 |
| Generation_ Gen X | -0.1529 | -0.4916 | 0.1858 | 0.1728 | 0.8582 |
| Generation_Boomers II | -0.9140 | -1.3586 | -0.4693 | 0.2269 | 0.4009 |
| Generation_Boomers I | -1.6722 | -2.4033 | -0.9411 | 0.3730 | 0.1878 |
| Generation_Post War | -13.1193 | -495.2670 | 469.0285 | 245.9937 | 0.000 |
The reference value for visual context was the control condition, the reference value for gender was female, and the reference value for generation was Gen Z. Original coefficients are scaled in terms of logs and we provide the exponentiated odds ratios as well.
Fig 2A-C. Predictor Effect Plots Showing the Role of Each Predictor on Interest in Sloth Ownership. Predictor effect plots provide graphical summaries for fitted regression models by averaging and conditioning the other predictor variables to summarize the role of a selected focal predictor in a fitted regression model (Fox & Weisberg, 2019). (Note: Figures sized to span two columns).
Fig 3A-C. Predictor Effect Plots Showing the Role of Each Predictor on Interest in Python Ownership. Predictor effect plots provide graphical summaries for fitted regression models by averaging and conditioning the other predictor variables to summarize the role of a selected focal predictor in a fitted regression model (Fox & Weisberg, 2019). (Note: Figures sized to span two columns).