Short‐term Costs and Benefits of Habitat Complexity for a Territorial Fish
Bibliographic record
Abstract
Abstract An increase in habitat complexity is thought to decrease visibility and the territory size of visually oriented animals. Hence, the addition of physical structure has been viewed as a useful restoration technique to increase the density of territorial species, particularly in stream fishes. However, a decrease in territory size may have a negative effect on the fitness of individual organisms. We attempted to quantify some of the positive and negative effects of increasing habitat structure on the behaviour and growth rate of wild young‐of‐the‐year (YOY) Atlantic salmon. Fish were exposed to one of two habitat treatments in mesh enclosures in Catamaran Brook, New Brunswick: a gravel substrate (low complexity) or a gravel substrate with boulders added (high complexity). Wild‐caught individuals were tagged, weighed and measured before being stocked at densities of 1·m−2 for 7‐d trials. While fish from high‐complexity treatments benefited from lower rates of aggression, they also had lower foraging rates and smaller territories compared to those in low‐complexity treatments. Specific growth rate, however, did not differ significantly between treatments. While the addition of structure to a habitat may be beneficial at the population level in terms of an increase in population density, our results suggested that individual fish may pay some short‐term costs in these environments. Further research is needed to evaluate the longer term costs and benefits of adding structure to improve the habitat quality for stream salmonids.
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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 | 0.000 | 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) | 0.003 | 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".