Multi‐scale analysis of plant species richness in Serengeti grasslands
Notice bibliographique
Résumé
Abstract Aim To assess scale dependence between environmental factors and plant species richness. Additionally, we aimed to identify the scales at which niche relations and habitat heterogeneity, as hypothesized by A. Shmida & M.V. Wilson (1985) Journal of Biogeography , 12 , 1–20, operate in the savanna grasslands that were the focus of this study. Location Savanna grassland plant communities of Serengeti National Park, Tanzania. Methods Plant species richness was sampled in 102 modified Whittaker plots and tested for associations with two climate factors, mean annual rainfall (MAP) and potential evapotranspiration (PET), and two landscape variables, plot aspect (ASP) and topographic variation (TOPO), using multiple regressions. Scale dependence was assessed by conducting regressions after altering three aspects of spatial scale: grain, extent and focus. Grain was altered by analysing plant richness at 1, 10, 10 2 and 10 3 m 2 ; extent was investigated by restricting the maximum distance between samples to 75, 100, 125 and 150 km; and focus was manipulated by averaging samples spatially according to geographical land regions. Within the context of our data, we assumed that niche relations were represented by climate factors and habitat heterogeneity by landscape factors. Results Across all 102 plots, plant species richness between 1 and 10 2 m 2 had a negative relation to PET and a weak positive relation to MAP. Plant species richness at 10 3 m 2 had a positive association with TOPO and weaker associations with climate factors. ASP stayed in the model between grains of 10 and 10 3 m 2 , but had a very weak positive association with richness. When the focus was changed to land regions, associations between plant species richness and explanatory variables strengthened, but were not qualitatively different. At spatial extents of 75 and 100 km, PET was the strongest correlate of plant species richness across all spatial grains. At spatial extents ≥ 125 km, PET explained the majority of the model variance at spatial grains ≤ 10 2 m 2 , whereas TOPO explained equal amounts or more of the model variance at spatial grains of 10 3 m 2 . Main conclusions Both climate and topographic variation explained plant species richness in Serengeti grasslands, but specific patterns depended on grain, extent and, to a lesser degree, focus. Consistent with the ideas of Shmida & Wilson (1985) , determinants of plant species richness shifted from niche relations to habitat heterogeneity between spatial grains of 1 and 10 3 m 2 , although this occurred only at relatively large spatial extents (≥ 150 km). Finally, the signs, strength and shape of plant species richness relationships in Serengeti closely match those that describe macro‐scale patterns of woody plant species richness across the entire African continent.
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 | 0,001 | 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 ».