Monitoring Tallgrass Prairie Restoration Performance Using Floristic Quality Assessment
Bibliographic record
Abstract
Floristic Quality Assessment (FQA), a tool that allows botanists to quickly and effectively determine a site's natural quality, has primarily been used to identify and rank areas of remnant natural quality. In this study FQA was employed for long-term monitoring of the Upland Prairie, an ecological restoration project in Grant County, Indiana. In 1993, the year of planting, permanent transects were established to monitor community development as well as the effects of nitrogen enrichment and intermittent seasonal flooding. FQA was applied to species cover data collected nine times from 1993 through 2006. Analysis revealed that mean conservatism (MC) and floristic quality index (FQI) values rose with increasing site age as species dominance shifted from native and exotic weeds to native prairie grasses and forbs. Quadrat level metrics were more valuable for elucidating trends because transect level metrics were easily affected by slight differences in species composition year to year. FQA monitoring confirmed the impact of nitrogen enrichment reported in previous, intensive studies of the site. Areas prone to intermittent flooding scored lower MC and FQI scores because flooding inhibited the establishment of most prairie species. Mean wetness scores for these transects indicated that the vegetation was more representative of a wet meadow than mesic tallgrass prairie. This research determined that FQA is a useful, cost-effective tool for examining trends and responses to treatments and disturbances in prairie restorations. Prior to the establishment of European agriculture on the Great Plains, prairie was the dominant biome in the United States, covering much of the expanse from western Ohio to the Rockies and from southern Canada to Texas (Samson & Knopf 1994). Within the last 200 years, tallgrass prairie of the eastern part of the prairie biome has experienced losses greater than that of any other major ecosystem in this country-as much as 82% in Kansas and over 99% in Indiana, Illinois, Iowa, North Dakota, and Wisconsin. These losses are due largely to cultural practices including agricul ture, control or elimination of native grazers, and prolonged fire suppression (Howe 1994; Samson & Knopf 1994).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".