Using Google Trends to Determine Current, Past, and Future Trends in the Reptile Pet Trade
Notice bibliographique
Résumé
Reptiles are one of the most popular exotic pets in the world, with over a third of all described species currently being traded. However, the most commonly available reptiles are typically non-threatened, captive-bred, and/or domestically obtained, which means they are also largely unregulated and unmonitored, resulting in a large portion of the reptile pet trade remaining unknown. In this study, the past, current, and future trends of the most popular reptiles in the pet trade were examined. Google Trends was used to determine the global popularity of the most popular pets from 2004 to 2020 and compared to the results from an online survey sent to individuals involved in the reptile trade. The most popular pets from the previous five years were also compared globally across regions and countries. The results determined that the most popular reptile species during the last decade is by far bearded dragons, followed by ball pythons and leopard geckos. Although the survey results were similar when asked what the top reptiles were, most respondents named ball pythons as the most popular reptile. However, when asked what reptiles had lost the most popularity during the previous decade, the survey respondents named green iguanas, Burmese pythons, chameleons, red-eared sliders, and green anoles, concurring with what was found with Google Trends. The reptiles thought to be more popular in the upcoming decade by the survey participants were blue-tongued skinks, tegus, uromastyx, crested geckos, and ball pythons-most of which did indeed show an increase in popularity during the last decade, as indicated with Google Trends. The results from Google Trends demonstrated that ball pythons and crested geckos have increased their popularity more than any other reptile in the last two decades. Reptile popularity also differed between countries, with bearded dragons the most popular reptile in Australia, Western Europe, the U.S., and Canada. Leopard geckos were the most popular reptile in Italy and Turkey, and ball pythons were the reptile of choice in Mexico, Indonesia, and India. The general finding of this study is that the reptiles declining in popularity were mostly wild-caught or restricted due to regulations, while current and future species were captive-bred and available in many varieties or morphs. The most popular species were also docile, medium-sized, and easy to handle, with relatively simple care requirements. This study demonstrates that Google Trends can be a useful tool for determining relative popularity among reptiles, or any other pet group, with results closely mirroring those obtained through direct surveying of people involved in the pet trade. However, unlike surveys, this analysis is quick, quantifiable, and can show what is popular and in-demand not only at the global level but at much finer scales. Thus, Google Trends can be a valuable tool in many research applications, especially in topics that may otherwise be difficult to monitor and quantify.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,010 | 0,010 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».