update: Emerging research opportunities in global urban ecology
Notice bibliographique
Résumé
ISSN 1948‐6596 References Beck J., & Sieber, A. (2010) Is the spatial distribution of mankind’s most basic economic traits deter‐ mined by climate and soil alone? PLoS ONE 5(5): e10416. Burke, M., Miguel, E., Satyanath, S., Dykema, J. & Lo‐ bell, D. (2009) Warming increases risk of civil war in Africa. Proceedings of the National Acad‐ emy of Sciences USA, 106, 20670–20674. Diamond, J. (2005) Collapse: how societies choose to fail or succeed. Viking. Hsiang, S.M., Meng, K.C. & Cane, M.A. (2011) Civil con‐ flicts are associated with the global climate. Na‐ ture, 476, 438–411. Samson, J., Berteaux, D., McGill, B.J., Humphries, M.M. (2011) Geographic disparities and moral hazards in the predicted impacts of climate change on human populations. Global Ecology and Bio‐ geography, 20, 532–544. news and update Zhang, D.D., Lee, H.F., Wang, C., Lie, B., Pei, Q., Zhang, J. & An, Y. (2011) The causality analysis of cli‐ mate change and large‐scale human crisis. Pro‐ ceedings of the National Academy of Sciences USA, 108, 17296–17301. Zhang, D.D., Brecke, P., Lee, H.F., He, Y.‐Q. & Zhang, J. (2007) Global climate change, war and popula‐ tion decline in recent human history. Proceed‐ ings of the National Academy of Sciences USA, Edited by Richard Ladle update Emerging research opportunities in global urban ecology Biogeographers have examined how human activi‐ ties have affected patterns of biological diversity from a variety of perspectives, with special atten‐ tion often given to oceanic islands. With the cur‐ rent accelerating pace of environmental change, these effects are increasingly evident at global scales. Human industry, commerce, agriculture and transportation all have the potential now to affect natural systems globally through an assort‐ ment of drivers; primary among these are land‐ use change, species introductions and climate change. Human activities and their consequences come to a unique focus in urban areas, an expand‐ ing form of land use that is attracting increasing research attention from ecologists (Grimm et al. 2008). Urban areas contain similar environmental conditions worldwide and act as a focal point for species introductions and extinctions. These hu‐ man‐dominated environments offer unique op‐ portunities to investigate the broad‐scale dynam‐ ics of human‐mediated biotic interchange (La Sorte et al. 2007), its consequences for β diversity (La Sorte et al. 2008) and the regional factors and biological traits associated with native species ex‐ tinctions (Hahs et al. 2009, Duncan et al. 2011). Urban areas typically contain spatially heteroge‐ neous collections of native and non‐native species (McKinney 2008); these unique assemblages can be examined based on their compositional (Niemela et al. 2002) and phylogenetic structures (Ricotta et al. 2009). Three nested sampling ap‐ proaches are currently used to investigate urban systems at broad spatial scales: urban plots or transects, the entire urban matrix and the urban matrix embedded within a regional context (Werner 2011). Each sampling approach provides a unique inferential basis, although the third al‐ lows for more refined interpretation, controlling for regional differences. A recent study in Global Ecology and Bio‐ geography adopts a novel perspective and exam‐ ines how avian assemblages sampled within plots of intact vegetation in urban and semi‐natural ar‐ eas differ based on several common mac‐ roecological relationships. Pautasso et al. (2011) compiled data on species composition and abun‐ dance from all around the globe, although the majority of the samples are from Europe and North America. A primary finding of the study was a lack of evidence for differences in the species– area, species–abundance or species–biomass rela‐ frontiers of biogeography 3.3, 2011 — © 2011 the authors; journal compilation © 2011 The International Biogeography Society
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».