Food Web Structure and Trophic Dynamics of a Fish Community in an Ephemeral Floodplain Lake
Bibliographic record
Abstract
In Africa, wetlands such as shallow, ephemeral lakes provide ecosystem services such as water purification, food supply and flood control but are subject to dyanmic flooding/drying cycles which vary in duration from years to decades. The stochastic nature of drying events subjects ephemeral lake fauna to persistent disturbance regimes, therefore understanding how biota respond to flooding and drying events is essential for their conservation and management. Primary production sources supporting consumer biomass in the shallow ephemeral Lake Liambezi (upper Zambezi Ecoregion), were investigated using stable isotope analysis, mixing models and stomach content analysis to investigate the following hypotheses: (1) algal primary production supports a higher consumer biomass than aquatic macrophytes; (2) the lake food chain is short, because the majority of fish fauna are detritivorous/herbivorous cichlids that are consumed by top predators; (3) fish community trophic structure will be similar between years; and (4) with short food chains and stochastic resource availability, there will be substantial competition for food among fish species. Results showed that phytoplankton production supported substantial consumer biomass in Lake Liambezi, with important contributions from macrophytes and associated detritus and/or periphyton. While particulate organic matter (POM) contributed substantially to the diet of herbivorous/detritivorous tilapiine cichlids (the backbone of Lake Liambezi’s commercial fishery), considerable dietary carbon was likely also derived from aquatic plants and associated detritus and/or periphyton compared to other fishes. Three major food chains were identified in the lake. The phytoplankton-based pelagic food chain was longest, involving up to four trophic transfers. The benthic food chain based primarily on detritus of planktonic origin (but may also include macrophyte associated detritus/periphyton) was characterised by high levels of omnivory and involved up to three trophic transfers. The macrophytic detritus-based food chain was shortest, involving just two trophic transfers. Predators fed across all three food chains, but predominantly on the two benthic food chains. A combination of dietary overlap (amongst piscivores/predators, amongst insectivores), dietary specialisation (tilapiine cichlids, alestids), the integration of multiple food chains and behavioural adaptation to changing dietary resources underpins the ability of Lake Liambezi’s fish community to thrive under the stochastic nature of ephemeral lake ecosystems.
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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 | 0.000 |
| 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 | 0.000 |
| 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".