Native American Land-Use Impacts on a Temperate Forested Ecosystem, West Central New York State
Bibliographic record
Abstract
Land survey records (LSRs) describing forest species composition prior to extensive European American settlement are critical sources of information on past environmental controls of forest dynamics in eastern North America. Embedded within these historical data sources is evidence of prior Native American land use. This study expands on previous LSR-based analyses of Seneca and Iroquoian populations’ impacts on the temperate forests of west central New York State. We use an enhanced array of geospatial LSR vegetation data beyond conventional bearing tree data and implement, for the first time, combined indirect ordination of vegetation data along major environmental gradients and numerical classification of discrete upland vegetation communities. Nonmetric multidimensional scaling revealed three main drivers of vegetation dynamics in the study area: (1) fire frequency (53.7 percent of total variance); (2) soil productivity (22.6 percent variance); and (3) Native American land use (15.9 percent variance). Agglomerative hierarchical clustering reinforced the primacy of these gradients by delineating two major forest types differentiated primarily by fire frequency and secondarily by soil productivity. Seneca and Iroquoian agricultural villages were preferentially concentrated within fire-tolerant dry upland forests on high-productivity soils within the interior portion of the Lake Ontario Lowland. Fire-tolerant, dry upland forests on low-productivity soils were situated on the adjacent Appalachian Plateau, which was likely used by indigenous populations for silvicultural land-use activities. Native American disturbance of temperate forested ecosystems likely varied across the diverse culture areas of eastern North America, with the Seneca and Iroquois representing an extreme end-member within a broad continuum of anthropogenic disturbance. Key Words: forest composition, land survey records, land-use history, Native Americans, vegetation disturbance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".