Desafios na conservação de vida selvagem: perspectivas presentes e futuras
Notice bibliographique
Résumé
The global environment is under increasing pressure from expanding human activities and climate changes. The change in global environment, including increase environmental temperature, marine pollution, ocean acidification among others, has resulted in significant loss in biodiversity. To dates, the International Union for Conservation of Nature (IUCN) has estimated that 41% of amphibians, 33% of corals, 26% of mammals and 13% of birds are threatened by extinction (International Union for Conservation of Nature - IUCN, 2015). Imminent extinction of wild species is often caused by multiple factors and may not always be due to failure of animals to breed. Nevertheless, reproductive sciences play critical roles in wildlife conservation, especially captive breeding program. A clear example of how reproductive biology contributes to species recovery program is the case of black footed ferret (Howard et al., 2003; Santymire et al., 2014), endemic to North America. In 1980s, the species underwent severe population decline with only 18 individuals remained in the wild which were brought into captivity. To-date, >150 ferret kits have been produced by artificial insemination (AI), including offspring produced from frozen founder sperm stored for as long as 20 years. Since the inception of the captive breeding program, 8,000 black footed ferrets have been produced, half of which have been reintroduced into 20 sites in eight US States, Canada and Mexico. Despite the success story of the black-footed ferret, the application of reproductive technologies to wildlife species is very limited. This is mostly due to the lack of basic knowledge on reproductive biology. As described in a review paper by Wildt et al. (2010), of 12,000 papers published in 10 leading reproductive journals, only 6% were dedicated to mammals (non-traditional species), 3% for fishes and <1% for amphibians, birds and reptiles. Without thorough understanding of reproductive biology, it will be very difficult to apply reproductive technologies to a given species on a regular basis. While there are some success in wild felids (Swanson, 2012), so far, there has not been a single example of embryo-based technologies being consistently utilized in species recovery program. More research on basic reproductive biology is needed for embryo technology can be incorporated into species recovery program. Furthermore, mechanisms for reproduction are as diverse as animals are in physical appearance, genotype or geographic origin (Wildt et al, 2010). Examples of reproductive diversity have been recently reviewed for carnivores by Jewgenow and Songsasen (2014). Basically, reproductive mechanisms of animals within the same taxon are not always the same. For example, female maned wolves, unlike other canid species, require the presence of a male conspecific for ovulation to occur (Johnson et al., 2014). Ovulation induction in this species is believed to be regulated by chemical signals (Kester et al 2015). Because of the high diversity in reproductive mechanisms among species, ones cannot directly apply protocol developed from one species to another. This presentation will provide an overview of global threats to wildlife, conservation challenges, specific examples of success and failure stories and future priorities for successful application of reproductive technologies to wildlife conservation.
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 | 0,013 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,008 |
| Communication savante | 0,011 | 0,012 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,007 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 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 ».