A multi-scale investigation of species richness, productivity and nutrient relationships in herbaceous plant communities of the grasslands of Southern Interior British Columbia
Notice bibliographique
Résumé
Philip Grime's humped-back model predicts that species richness reaches a maximum at intermediate productivity and drops off at low and high productivities. Soil nutrient availability is known to influence primary productivity and plant species richness. In general, productivity increases with increasing soil nutrient availability, and experiments that increase soil nutrients often show increases in biomass with corresponding decreases in species richness; as such, certain patterns in soil nutrients are expected to coincide with the humped-back model. Scale is also an important factor, as the relationship between species richness and productivity may vary with scale. Objectives of this work were (1) to test the explicit humped-back model prediction that the relationship between species richness and productivity is unimodal; (2) to test implicit humped-back model predictions that species richness is related to nutrients that are tightly associated with productivity, such as carbon and nitrogen, by a unimodal relationship and identify patterns between soil nutrients, leaf nutrients, productivity and species richness; and (3) to evaluate the influence of increasing scales from 1 m2 to 64 m2 on relationships found between species richness, productivity and nutrients. This was done using biomass and litter collections and species richness counts from 14 multi-scale grids (8 m × 8 m grids containing 64, 1 m2 plots), soil samples from a subset of four of the 14 multi-scale grids, and leaf samples from a subset of nine adjacent plots in each of the four multi-scale grids that were sampled for soil nutrients. Total carbon and nitrogen contents in soil and leaf samples were analyzed with a CE-440 Elemental Analyzer. Total aluminum, boron, calcium, copper, iron, magnesium, manganese, sodium, phosphorus, potassium, sulphur, and zinc were quantified in soil samples prepared by very high-pressure closed vessel microwave acid digestion in a Milestone “Ultrawave” single reaction chamber followed by analysis with a Teledyne/Leeman Labs “Prodigy” dual view inductively coupled plasma-optical emission spectrometer. In principal component analyses of soil nutrient data, the first and second principal components appeared to represent productivity and species richness, respectively; and carbon, nitrogen, phosphorus, potassium, boron, iron, magnesium and manganese were identified as important soil nutrients. Bivariate regressions of the first principal component with total biomass (R2 = 0.76) and the second principal component with species richness (R2 = 0.35) returned strong R2 values. Species richness was related to total biomass (R2 = 0.11), soil carbon (R2 = 0.15), soil nitrogen (R2 = 0.14) and iron (R2 = 0.29) by concave down relationships, to boron (R2 = 0.17) and magnesium (R2 = 0.49) by concave up relationships, to phosphorus (R2 = 0.27) and manganese (R2 = 0.25) by positive linear relationships and to potassium (R2 = 0.14) by a negative linear relationship. Scaling results were inconclusive but suggested that patterns in the literature may be partly due to sample size. Results generally support the humped-back model and that nutrient availability is an important factor correlated with herbaceous plant productivity and species richness. Nutrient availability is likely an important driver of plant productivity and herbaceous plant diversity.
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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 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».