A pre‐European settlement pollen–climate calibration set for Minnesota, USA: developing tools for palaeoclimatic reconstructions
Notice bibliographique
Résumé
Abstract Aim The bias in modern North American pollen assemblages by landscape disturbance from Euro‐American settlement has long been overlooked in the construction of pollen–climate transfer functions. Our aim is to examine this problem and to develop an unbiased pre‐settlement pollen–climate transfer function, and to test its performance and inference power in comparison with commonly used techniques. Location Minnesota, USA, is of palaeoclimatic interest because within the state are located two continental‐scale ecotones, controlled by temperature and available moisture. Shifts of these ecotones can be tracked using palaeoecological techniques. Methods Using a data set of pre‐settlement pollen assemblages from 133 lakes, which were coupled to climate data from the earliest instrumental records (i.e. 1895–1924), a pre‐settlement pollen–climate data set was developed that lacked the influence of anthropogenic landscape disturbance. A corresponding modern pollen data set (from lake sediment core tops) and a modern climate (i.e. 1961–90) data set were also developed. The two pollen sets were compared to demonstrate the effects of landscape disturbance from human activities. Ordination (redundancy analysis with Monte Carlo permutation tests) and regression techniques (generalized linear modelling) were used to establish the relationships between the early instrumental climate variables and pre‐settlement pollen assemblages and individual taxa, respectively. Transfer functions for the most suitable climate variables (i.e. those forming a minimal set of non‐collinear climate variables that explained the greatest amount of pollen variance) were developed from the pre‐settlement data set using bootstrapping. Results Comparison of pre‐settlement pollen and modern pollen showed an over‐representation of Ambrosia , Chenopodiaceae and Poaceae, and an under‐representation of arboreal taxa (e.g. Pinus , Quercus , Ostrya ) in the modern assemblages. Not surprisingly, ordination and regression techniques showed a strong relationship between the early instrumental climate variables and pre‐settlement pollen assemblages and taxa. Transfer functions were developed for May and February mean temperature and available moisture. Pre‐settlement transfer functions substantially improved the root mean squared error by 37–72% in comparison with modern transfer functions inferring pre‐settlement conditions, suggesting that the modern transfer functions have poorer predictive abilities. Main conclusions For climatic reconstructions, there can be a serious distortion of inferences based solely on modern pollen–climate data sets in regions where anthropogenic landscape disturbance has occurred. By using historical climate data, coupled with pre‐disturbance pollen assemblages, robust transfer functions for temperature and effective moisture were developed.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 ».