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Enregistrement W2219121751 · doi:10.1642/auk-15-151.1

Phenological Synchrony and Bird Migration: Changing Climate and Seasonal Resources in North America<b>Phenological Synchrony and Bird Migration: Changing Climate and Seasonal Resources in North America</b>edited by Eric M. Wood and Jherime L. Kellermann. 2015. CRC Press, Boca Raton, Florida, USA. xiv + 228 pages, 8 color and 53 black-and-white illustrations. $116.96 (hardcover). ISBN 978-1-4822-4030-6.

2015· article· en· W2219121751 sur OpenAlexaff
Ann E. McKellar

Notice bibliographique

RevueThe Auk · 2015
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueSpecies Distribution and Climate Change
Établissements canadiensEnvironment and Climate Change Canada
Organismes subventionnairesnon disponible
Mots-clésPhenologyBird migrationGeographyClimate changeEcologyBiology

Résumé

récupéré en direct d'OpenAlex

The study of phenology has received renewed interest in recent years, in large part due to concern over species' abilities to adapt to a changing climate. Nowhere is this more apparent than in the study of bird migration. Some bird species have adjusted their timing of migration along with climate change, whereas others have not, and an additional issue is that even those that adjust may not be able to do so quickly enough to keep up with phenological shifts in the resources upon which they depend. Such phenological “mismatches” are predicted to have negative fitness consequences for migratory birds (Visser et al. 1998). Phenological Synchrony and Bird Migration tackles these issues from a slightly new angle. While most research to date has been focused on phenology at stationary breeding or nonbreeding areas, this volume is focused almost entirely on stopover phenology. This is a major strength of the book, but it also creates some shortcomings, which I'll discuss below. Also novel is that the book is specific to North America, whereas much past research on migration phenology has taken place in Europe. The book is divided into four sections. The first concerns conservation and management issues related to climate change and bird migration, including some theoretical models that describe how species and ecosystems are predicted to respond to projected climate change. Although somewhat dry, this section is highlighted by a chapter on the National Phenology Network's citizen-science program “Nature's Notebook,” a must-read for anyone who wants to know how the data generated by this network could be incorporated into their own research. I found the authors' argument for the value of phenological monitoring, over and above the more typical assessment of relatively static vegetation resources, especially compelling: Phenological monitoring of vegetation and other resources relevant to birds allows for a more mechanistic understanding of bird habitat selection and, thus, the potential drivers of changes in bird phenology. The next section, “Migratory Connectivity,” includes two chapters that examine relationships between spring arrival phenology and weather experienced by birds on the wintering grounds. These studies add to a growing body of research demonstrating that the conditions experienced by migratory birds across the phases of their annual cycle can influence their performance, including arrival timing, during subsequent phases. As such, this section might have been more appropriately titled “Carryover Effects of Weather” rather than “Migratory Connectivity,” which has more to do with the degree to which individuals from specific breeding populations are connected to specific wintering populations and vice versa (Webster et al. 2002). In any case, the section serves to showcase, once again, the potential utility of citizen science for informing phenology studies, as demonstrated by a study that makes use of a large dataset from Project FeederWatch to examine variation in arrival of short-distance migrants in relation to winter weather. The final two sections, “Spring Migration” and “Fall Migration,” are arguably the meat of the book: seven chapters that aim to understand variation in the phenology of birds and their resources (and potential mismatches between the two) at stopover areas. They include studies of poorly understood phenomena such as trophic cascades and the (mal)adaptive significance of phenological synchrony, in addition to the more rarely studied period of fall migration. However, these sections are where the shortcomings inherent in the relatively young field of stopover phenology play out, because many of the chapters include data collected over only two or three seasons at only a handful of study sites. Thus, it may be difficult to generalize the findings to the context of long-term phenological changes over large spatial scales. This is not to say that these chapters are not useful—on the contrary, they provide a necessary framework for future work. The exception to the problem of small sample size is the final chapter, which provides an in-depth study of fall migration phenology of 37 landbird species over 44 years at the Manomet Center for Conservation Sciences. Interestingly, the findings highlight the significant variability in the ways that different species are responding to a changing climate, serving to underscore the importance of understanding this variation if we are to achieve species-specific conservation goals. Overall, Phenological Synchrony and Bird Migration would make a useful contribution to the personal libraries of researchers and graduate students who study bird migration and resource phenology in North America. In fact, the book should be especially valuable for new graduate students who are developing their own projects and want to get a feel for what has been done and where the gaps remain. Thus, the major contribution of this book is likely to be the foundation it will provide for future research into climate change, phenology, and bird migration.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,037
Score d'incertitude au seuil0,074

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0170,003

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.

Tête enseignante Opus0,021
Tête enseignante GPT0,225
Écart entre enseignants0,204 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2015
Routes d'admission1
Résumé présentnon

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