Fish community responses to habitat alteration: Interactions, biomass shifts, and the value of imperfect data
Bibliographic record
Abstract
Recent ecological research has emphasized the value of “big data,” but small or imperfect datasets can still yield valuable insights when analyzed rigorously. To demonstrate this, we conducted a case study examining how habitat variable relationships and interspecific interactions function together to shape fish communities under conditions of habitat alteration. Despite extensive theoretical discussions on this topic, empirical studies remain limited due to data collection challenges. We analyzed electrofishing data and 15 habitat variables from three sampling events across four experimental sites. Habitat conditions and species composition varied among the sites. Correlation analyses identified 30 statistically significant species-habitat variable relationships and 20 statistically significant interspecific relationships (p < 0.05, rho < − 0.60 or rho > 0.60). The vast majority of these relationships were supported by previously conducted research and the species life history. A biomass-per-volume analysis showed no significant differences in overall biomass capacity among the four sites (p > 0.05). These findings suggest that habitat change drives species-specific shifts in biomass, with some species increasing at the expense of others, influenced by habitat suitability and interspecific interactions. The results highlight how habitat modification drives biomass redistribution within fish communities and emphasize that complex community level responses can be detected even with imperfect data.
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 | 0.022 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".