Notice bibliographique
Résumé
General opinion in the contemporary literature is that disturbance strongly influences patterns of species diversity, and that diversity is greatest at intermediate levels of disturbance. However, empirical evidence is equivocal, and quantitative predictions about the strength of diversity-disturbance relationships are lacking. Using Markov models of dynamics of real communities, I derived predicted changes in diversity when communities are subjected to quantified disturbance gradients. I also derived predictions regarding effects of sampling intensity and species selectivity of disturbance on diversity-disturbance relationships. My models predict peaked relationships should be relatively rare, variation in diversity over disturbance gradients typically should be low, and relationship shape varies with sampling intensity. These results are broadly consistent with a review of published diversity-disturbance relationships. A meta-analysis of 197 published diversity-disturbance relationships was performed to determine how frequently observed relationships are peaked; how strong, in general, relationships are; and whether various attributes of a study influence the observed shape and strength of a relationship. Non-significant relationships were the most common, and peaked responses were reported in only 16% of cases. Variation in diversity explained by disturbance was variable, but averaged ∼50%. Results suggest that strong and/or peaked relationships may arise from procedural artifacts. I conclude that there is little evidence to support the belief that disturbance should have a consistently important role in determining patterns of diversity, or that diversity-disturbance relationships are typically peaked. Anthropogenic habitat loss is often cited as the most important cause of recent species' extinctions. Recent studies identifying hot spots of imperiled species have suggested that habitat loss is the primary factor threatening the survival of imperiled species. However, interpretation of such hot spots is not practical without knowledge of where imperiled species have been lost. I determined distributions of richness and losses of imperiled species in Canada, and statistically examined the relationship between species' losses and various landuse variables. Several hot spots of losses were identified in southern regions of Canada. The combination of frequent and intensive insecticide applications, routine herbicide use, and habitat loss due to agricultural development appears to be the most important threat to imperiled species.
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 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».