Tropical fish community does not recover 45 years after predator introduction
Bibliographic record
Abstract
Predation is considered to be an important factor structuring natural communities. However, it is often difficult to determine how it may influence long-term, broad-scale, diversity patterns, particularly in diverse tropical systems. Biological introductions can provide powerful insight to test the sustained consequences of predation in natural communities, if pre-introduction data are available. Half a century ago, Zaret and Paine demonstrated strong and immediate community-level effects following the introduction of a novel apex predator (peacock bass, Cichla monoculus) into Lake Gatun, Panama. To test for long-term changes associated with this predator introduction, we followed up on their classic study by replicating historical sampling methods and examining changes in the littoral fish community at two sites in Lake Gatun 45 years post-introduction. To broaden our inference, we complemented this temporal comparison with a spatial analysis, wherein we compared the fish communities from two lakes with and one lake without peacock bass. Comparisons with historical data revealed that the peacock bass remains the most abundant predator in Lake Gatun. Furthermore, the collapse of the littoral prey community observed immediately following the invasion has been sustained over the past 45 years. The mean abundance of native littoral fish is now 96% lower than it was prior to the introduction. Diversity (rarefied species richness) declined by 64% post-introduction, and some native species appear to have been locally extirpated. We observed a similar pattern across invaded and uninvaded lakes: the mean abundance of native fishes was 5-40 times lower in lakes with (Gatun, Alajuela) relative to the lake without peacock bass (Bayano). In particular, small-bodied native fishes (Characidae, Peociliidae), which are common prey of the peacock bass, were more than two orders of magnitude (307 times) less abundant in Gatun and one order of magnitude (28 times) less abundant in Alajuela than in Bayano. However, total native fish diversity did not differ significantly across lakes, suggesting that while many native species have declined in abundance, few have been completely extirpated. Introduced predators can have strong effects on community structure and functional diversity, even in highly diverse tropical communities, and these effects can persist over multiple decades.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".