Human activities as a driver of spatial variation in the trophic structure of fish communities on Pacific coral reefs
Bibliographic record
Abstract
Anthropogenic activities such as land-use change, pollution and fishing impact the trophic structure of coral reef fishes, which can influence ecosystem health and function. Although these impacts may be ubiquitous, they are not consistent across the tropical Pacific Ocean. Using an extensive database of fish biomass sampled using underwater visual transects on coral reefs, we modelled the impact of human activities on food webs at Pacific-wide and regional (1,000s-10,000s km) scales. We found significantly lower biomass of sharks and carnivores, where there were higher densities of human populations (hereafter referred to as human activity); however, these patterns were not spatially consistent as there were significant differences in the trophic structures of fishes among biogeographic regions. Additionally, we found significant changes in the benthic structure of reef environments, notably a decline in coral cover where there was more human activity. Direct human impacts were the strongest in the upper part of the food web, where we found that in a majority of the Pacific, the biomass of reef sharks and carnivores were significantly and negatively associated with human activity. Finally, although human-induced stressors varied in strength and significance throughout the coral reef food web across the Pacific, socioeconomic variables explained more variation in reef fish trophic structure than habitat variables in a majority of the biogeographic regions. Notably, economic development (measured as GDP per capita) did not guarantee healthy reef ecosystems (high coral cover and greater fish biomass). Our results indicate that human activities are significantly shaping patterns of trophic structure of reef fishes in a spatially nonuniform manner across the Pacific Ocean, by altering processes that organize communities in both "top-down" (fishing of predators) and "bottom-up" (degradation of benthic communities) contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".