Morphological differentiation of Alnus pollen from western North America
Notice bibliographique
Résumé
Increasing the taxonomic resolution of fossil pollen identification is important for accurate paleoecological reconstructions. Here, an attempt is made to identify the critical morphological features that will permit differentiation of Alnus pollen in fossil records. Palynologists working in the Pacific Northwest often distinguish alder pollen into two morphotypes. However, no definitive method outlining the validity of species level identifications has been devised to date. To test and validate species-level identifications, the pollen morphology of the three main alder species (Alnus viridis subsp. sinuata, Alnus incana subsp. tenuifolia and Alnus rubra) that occur in westernNorth America is examined with the goal of identifying morphological characteristics with which to distinguish the pollen of these species in fossil records. Modern pollen samples were collected from 27-35 individual plants from across the range of each of the three alder species. Pollen grains (n=30) from each individual plant were examined using light microscopy at 1000´ magnification under oil immersion. For each individual pollen grain, six quantitative traits (pollen grain diameter, exine thickness, arci width, and annulus height, width and area), and three qualitative traits (pore protrusion, grain shape and arci strength) were measured. In total, 21,390 alder pollen were examined from 93 separate collections. In addition, the number of pores was determined for 200 pollen grains from each individual plant. Statistically significant differences between species were found for all quantitative traits when traits were compared via nested ANOVA. However, there is high variability in pollen morphology within each species and pollen morphology is best described as occurring along a morphological continuum. A single morphological trait is insufficient for precise identification of alder pollen to species. CART analysis, when used to derive a multi-trait classification model, is shown to be a useful tool in separating the pollen of A. rubra and A. viridis subsp. sinuata into two separate ‘morphotypes,’ analogous to species identification. The confounding intermediate morphology of A. incana subsp. tenuifolia precludes the possibility of distinguishing the pollen of all three species. CART modelling isolates A. rubra and A. viridis subsp. sinuata pollen based on annulus width, arci strength, diameter and exine thickness, traits that support the differences used by palynologists for separating alder pollen into ‘morphotypes.’ Sensitivity analysis shows clearly that the common practice of using small sample sizes (e.g. n=7 and n=15) for identifying critical morphological traits for pollen identification produces misleading and erroneous results. Regional differences in pollen morphology were also assessed by splitting the dataset into regions. Classification accuracy is diminished from over 70% to less than 20% when a CART model derived from pollen grains from one region is used to classify grains from a different region. This research underscores the importance of using large sample sizes from across species’ ranges when attempting to determine the diagnostic morphological features for accurate pollen identification.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| 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 | 0,001 | 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) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».