Woody plant encroachment pervasive across three socially and ecologically diverse ecoregions
Bibliographic record
Abstract
Woody plant encroachment in grasslands represents one of the greatest challenges for global biodiversity conservation. Furthermore, this is a social-ecological problem, where human activity and behavior have resulted in significant changes in ecological processes that control woody plants, and failure to fully recognize the role of human activity has led to continued loss of grasslands worldwide. It is therefore critical that conservation professionals understand how ecological systems, settlement patterns, and fragmentation from anthropogenic development interact to influence rates of woody plant encroachment. Using annual estimates of tree cover derived from regionally available remote-sensing data, our objectives were, first, to describe rates of woody plant encroachment over the last 20 years (2000–2019) across three ecologically and socially diverse ecoregions in the Southern Great Plains of North America. Then, we examined how anthropogenic and biophysical variables influenced rates of encroachment (both directly and indirectly) within the region. Results indicate that, despite marked differences in social and ecological characteristics, all three ecoregions have experienced consistent increases in woody plant encroachment during the study period. This included the Flint Hills ecoregion of Kansas and Oklahoma, an area that experiences widespread and frequent fires. At the regional scale, rates of encroachment were directly and negatively related to the average area burned in a county, initial cover of trees, and fragmentation from row crops. Percent cover of development and row crop agriculture also indirectly alter rates of encroachment in a county by influencing initial tree cover and fire activity. The pervasive nature of woody plant encroachment, even in regions that experience frequent fires, suggests that many grasslands are being managed outside of critical ecological thresholds needed to maintain grasslands and limit woody encroachment, which can have significant implications for biodiversity. Our results show that anthropogenic or ecological factors do not act in isolation in their influence on woody plant encroachment and can form complex relationships that shape regional trends in woody plant encroachment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".