Ecohydrology and ecosystem services of a natural and an artificial bofedal wetland in the central Andes
Bibliographic record
Abstract
High-altitude wetlands of the Central Andes, locally known as bofedales, provide important ecosystem services, particularly carbon storage, forage provisioning, and water regulation. Local communities have artificially expanded bofedales by irrigating surrounding grasslands to maximise areas for alpaca grazing. Despite their importance, biophysical processes of both natural and artificial bofedales are still poorly studied, which hinders the development of adequate management and conservation strategies. We analyse and compare the vegetation composition, hydrological variables, groundwater chemistry, and soil characteristics of a natural and an artificial bofedal of at least 10 years old in southern Peru, to understand their interrelations and the consequences for ecosystem service provisioning. We do not find statistically significant differences in the soil, water, and vegetation characteristics. Soil organic carbon (SOC) content, which we use as a proxy for carbon storage, is negatively correlated to dissolved oxygen, pH, and soil water temperature. In addition, Non-Metric Multidimensional Scaling analysis shows a positive relation between plant community composition, SOC content, and water electric conductivity. Our results suggest a three-way interaction between hydrological, soil, and vegetation characteristics in the natural bofedal, which also holds for the artificial bofedal. Vegetation cover of two of the most highly nutritious species for alpaca, Lachemilla diplophylla and Lilaeopsis macloviana with 19-22% of crude protein, are weakly or not correlated to environmental variables, suggesting grazing might be obscuring these potential relationships. Given the high economic importance of alpaca breeding for local communities, expanding bofedales artificially appears an effective strategy to enhance their ecosystem services with minimal impact on the ecohydrological properties of bofedales.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".