MOVING THE GOALPOSTS: MIGRATORY BIRDS IN A CHANGING WORLD
Notice bibliographique
Résumé
Billions of birds undertake migratory movements each year, traveling distances that range from several hundreds to tens of thousands of kilometers. Migratory birds must be flexible enough to cope with the fluctuating conditions they encounter during these journeys and at their destinations. However, humans are rapidly and dramatically changing the environment across all portions of migratory species’ ranges through habitat destruction and conversion, introduction of invasive species, climate change, and other alterations. My dissertation research seeks to understand the constraints and threats facing birds during two understudied phases of the annual cycle: migration and the non-breeding stationary period. In Chapter 1, I explore how human activities may nonlethally affect birds during migration. I reviewed the scientific literature for evidence of nonlethal effects and of interacting threats that may compound fitness costs to migrating birds. In general, I found that scientific understanding of nonlethal effects during migration lags behind research on direct mortality. Because birds migrate through increasingly anthropogenic landscapes and airspaces, I identify this knowledge gap as a hindrance to effective conservation of migratory birds. In Chapter 2, I investigate if individual songbirds adjust the rate and timing of spring migration based on the vegetation phenology they encounter within North America which may allow them to keep pace with advancing spring phenology under climate change. In the spring, migrating birds must quickly reach their breeding grounds to secure territories and mates ahead of the competition, but individuals that arrive too early may encounter inclement weather or food shortages. Using the Motus automated radio telemetry network, I tracked individual songbirds as they traveled from the southern U.S. towards their breeding areas in spring. I used estimates of spring onset timing at different points on their migration routes to determine if birds traveled in sync with the “green wave” of emerging vegetation or if they used a different strategy. I found that birds migrating from their non-breeding areas arrived in the southern U.S. well after local spring onset, but were able to catch up to the wave of emerging spring vegetation as they traveled northwards, following a “catching up” strategy rather than a “surfing” one. In Chapter 3, I examine how individual songbirds respond to the threat of predation during migratory stopover, when they must balance conflicting demands of refueling and avoiding predators. Migrating birds must contend with both native avian predators such as hawks (Accipiter sp.) and abundant introduced predators such as free-roaming domestic cats (Felis catus), yet their behavioral responses to cats have been little studied during migration. Using an aviary experiment, I exposed wild Gray Catbirds Dumetella carolinensis to either a hawk or a domestic cat and observed their behaviors before and after exposure to determine if they responded appropriately to the threat posed by each predator. When compared with a control group, Catbirds responded differently to both types of predators in the short term, but I detected no differences in their behavior after release. This study provides novel insights into the possible nonlethal effects of introduced predators that birds may encounter during migration. In Chapter 4, I shift focus to explore the threat that free-roaming domestic cats pose to birds in the Caribbean within a Neotropical city. Urban regions are increasingly recognized to provide valuable wildlife habitat but may also contain hazards such as introduced predators, and we currently lack information on the effects of free-roaming cats on migratory and resident bird species during non-breeding seasons. I designed a camera trapping project in San Juan, Puerto Rico to estimate free-roaming cat densities across a gradient of urbanization as a step towards understanding their potential impacts on wildlife. I deployed cameras across 16 trapping grids at three levels of urbanization and used photographic captures of cats to build spatial capture-recapture models. Estimated cat densities ranged from 48 8 (SE) cats/km2 in exurban areas to 473 40 cats/km2 in the most heavily urbanized parts of the city. These data may prove useful for conservation practitioners in San Juan deciding where to target cat management efforts for the benefit of urban wildlife and public health.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,009 | 0,007 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».