A new set of tools to explore, analyze, and communicate animal movements with environmental and anthropogenic context
Notice bibliographique
Résumé
The Yellowstone to Yukon Conservation Corridor (Y2Y) is North America's largest nature corridor and connectivity project for wildlife. The 2,000-mile swath of land between Wyoming, USA and the Yukon Territory of Canada is one of the last remaining intact mountain ecosystems on Earth, and home to many endangered and at-risk species. The Y2Y is a mosaic of protected and unprotected land including Canadian and US national/state/provincial/territory parks, federally/state managed wildland and national forests, Indigenous territories, and privately managed conservation easements. We are developing a collaborative animal-movement archive for the Y2Y and research tools to study and communicate the effectiveness of protected areas, drivers of migration, and movement connectivity. These tools are applied by end users throughout the Y2Y to support decision making and land and wildlife management.Our Movebank-based archive of in situ animal location observations provides a uniform data format and QA protocol for conducting large-scale, long-term, and multi-species analyses in support of wildlife management efforts in the region. These data will contribute to biodiversity assessments related to climate and other regional and global changes, and provide a baseline against which to detect early signals of local or large-scale ecosystem changes. We have developed an array of interactive tools for preparing and analyzing movement data using the MoveApps platform, a GUI-based App-development environment for data processing and analysis tools. These tools facilitate the integration of contextual environmental data from remote sensing and weather data products, and additional local environmental data layers. We have developed Apps to detect and quantify events of interest, particularly road crossings, parturition events and kill clusters, and are developing additional Apps to conduct resource and step-selection analyses using data from multiple studies at varying resolutions. To facilitate data exploration and data-based outreach and communication, we have developed ECODATA – a set of data preparation and visualization software packages in MATLAB and Python for building custom animated maps of animal movements along with contextual land management and environmental data layers.MoveApps and ECODATA are general tools that can be applied to any animal movement dataset. Initial research questions and applications, catered to the decision-making needs of our end users in the Y2Y project, include: How are protected lands utilized by mammals throughout the Y2Y? How is connectivity between conservation areas influenced by current and predicted future environmental characteristics and anthropogenic disturbances (roads in particular)? Continuous joint development and application of tools with active collaboration with our end users guarantee that the research tools we develop answer the management and research needs of end users, while answering new and exciting questions about environmental drivers of movement in the Y2Y.
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 | 0,003 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,011 |
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 ».