Food web differences between two neighboring tropical high mountain lakes and the influence of introducing a new top predator
Bibliographic record
Abstract
High mountain lakes (HMLs) are considered unique and comparable ecosystems for monitoring global climate change. The food web structure can indicate the response of these ecosystems to ecological threats, such as fish introduction, by analyzing the trophic dynamics. Nonetheless, the food webs of tropical HMLs are less well-studied than temperate HMLs. The present study assessed the food webs of two neighboring (600 m apart) tropical HMLs, El Sol and La Luna, inside the crater of the Nevado de Toluca volcano, Mexico. It used stable isotopes (δ13C and δ15N) and Bayesian mixing models with different trophic discrimination factors and priors to assess the impacts of introduced rainbow trout, persisting only in the larger lake, El Sol. The food web in Lake El Sol was more complex than in Lake La Luna, mainly due to its larger size, extensive vegetated littoral zone, and being fueled by autochthonous primary production. In contrast, the smaller and fishless Lake La Luna has a reduced and bare littoral zone that harbored a simple food web substantially sustained by allochthonous carbon inputs. The persistence of introduced rainbow trout in Lake El Sol but not in Lake La Luna accentuated the differences between the lakes. The models suggested that rainbow trout fed on key consumers of littoral macroinvertebrates (70-80%) and pelagic zooplankton (20-30%), increasing the linkage between sub-networks. In both tropical HMLs, the species richness and herbivorous fraction were elevated compared with temperate HMLs, while the linkage density and omnivorous fraction were lower. Basal nodes dominated these tropical HMLs, and the vegetated littoral zone of Lake El Sol had more intermediate (omnivore) nodes. Our results showed the convenience of food web analysis to compare the effects of introduced fish in originally fishless lakes in different latitudes.
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 | 0.000 |
| 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) | 0.001 | 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".