Small Changes in Climate Can Profoundly Alter the Dynamics and Ecosystem Services of Tropical Crater Lakes
Bibliographic record
Abstract
African tropical lakes provide vital ecosystem services including food and water to some of the fastest growing human populations, yet they are among the most understudied ecosystems in the world. The consequences of climate change and other stressors on the tropical lakes of Africa have been informed by long-term analyses, but these studies have largely focused on the massive Great Rift Valley lakes. Our objective was to evaluate how recent climate change has altered the functioning and services of smaller tropical lakes, which are far more abundant on the landscape. Based on a paired analysis of 20 years of high-resolution water column data and a paleolimnological record from a small crater lake in western Uganda, we present evidence that even a modest warming of the air (∼0.9°C increase over 20 years) and changes in the timing and intensity of rainfall can have significant consequences on the dynamics of this common tropical lake type. For example, we observed a significant nonlinear increase (R(2) adj = 0.23, e.d.f. = 7, p<0.0001) in thermal stability over the past 20 years. This resulted in the expansion of anoxic waters and consequent deterioration of fish habitat and appears to have abated primary production; processes that may impair ecosystem services for a vulnerable human population. This study on a system representative of small tropical crater lakes highlights the far-reaching effects of global climatic change on tropical waters. Increased research efforts into tropical aquatic ecosystem health and the development of sound management practices are necessary in order to strengthen adaptive capabilities in tropical regions.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".