Effect of drought on productivity in a Costa Rican tropical dry forest
Bibliographic record
Abstract
Climate models predict that precipitation patterns in tropical dry forests (TDFs) will change, with an overall reduction in rainfall amount and intensification of dry intervals, leading to greater susceptibility to drought. In this paper, we explore the effect of drought on phenology and carbon dynamics of a secondary TDF located in the Santa Rosa National Park (SRNP), Costa Rica. Through the use of optical sensors and an eddy covariance flux tower, seasonal phenology and carbon fluxes were monitored over a four-year period (2013–2016). Over this time frame, annual precipitation varied considerably. Total precipitation amounts for the 2013–2016 seasons equaled 1591.8 mm (+14.4 mm SD), 1112.9 mm (+9.9 mm SD), 600.8 mm (+7.6 mm SD), and 1762.2 mm (+13.9 mm SD), respectively. The 2014 and 2015 (ENSO) seasonal precipitation amounts represent a 30% and 63% reduction in precipitation, respectively, and were designated as drought seasons. Phenology was affected by precipitation patterns and availability. The onset of green-up was closely associated with pre-seasonal rains. Drought events lead to seasonal NDVI minimums and changes in phenologic cycle length. Carbon fluxes, assimilation, and photosynthetic light use efficiency were negatively affected by drought. Seasonal minimums in photosynthetic rates and light use efficiency were observed during drought events, and gross primary productivity was reduced by 13% and 42% during drought seasons 2014 and 2015, respectively. However, all four growth seasons were net carbon sinks. Results from this study contribute towards a deeper understanding of the impact of drought on TDF phenology and carbon dynamics.
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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 | 0.000 | 0.000 |
| 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 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".