Invited Review: Ecosystem services provided by grasslands in the Southeast United States
Bibliographic record
Abstract
This review describes ecosystem services (ES) obtained from grasslands within the southeastern United States. In addition, future direction and the importance of these ES to sustain productive agroecosystems are de- scribed. Results from published studies investigating various ES provided by grasslands within the southeastern United States are summarized in this article. Ecosystem services can be classified into 4 categories: provisioning, regulating, supporting, and cul- tural. Grasslands in the southeastern United States range from wet prairies in Florida, to transitional grasslands emerging from tallgrass prairie and longleaf pine ecosys- tems into coastal marshes in Texas. Provisioning ES from grasslands include animal products, timber, fruits, pods, and medicinal products. Supporting and regulating ES in- clude nutrient cycling, biological nitrogen fixation, water catchment and purification, recharge of aquifers, climate regulation, primary productivity, habitat for wildlife and pollinators, and biodiversity. Grasslands are also impor- tant for aesthetic and cultural ES, including hunting leases and recreational parks. Grasslands in the southeastern United States have decreased due to urban- ization, rising livestock production costs, and decreases in seed resources. Providing ES assessments will be impor- tant to assign value to grassland ecosystems, especially to increase adoption of novel management practices that may enhance delivery of ES. Remote sensing, machine learning, and artificial intelligence are promising tools to scale up the measurement of ES at landscape and watershed levels. In the future, ES will likely be a more prominent component of agroecosystems, and payment mechanisms will become more common to compensate landowners for the benefit they provide for the entire society.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".