Morphological differentiation of Alnus pollen from western North America
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".