Pop‐off data storage tags reveal niche partitioning between native and non‐native predators in a novel ecosystem
Bibliographic record
Abstract
Abstract Niche partitioning might be predicted to be particularly dynamic in ‘novel ecosystems’ characterized by human‐altered environmental conditions and biological invasions. Restoration efforts for native species in such systems can be informed by detailed characterization of niche partitioning. In Lake Ontario, fishery management agencies have been engaged in a long‐term struggle to restore native top predators including lake trout (Salvelinus namaycush). Meanwhile, management agencies continue to stock non‐native species like Chinook salmon (Oncorhynchus tshawytscha) into the lake to support a recreational fishery and to help control the abundance of a non‐native forage fish, the alewife (Alosa pseudoharengus). We used pop‐off data storage tags to study fine scale (9.1M lines of data from 22 animals) behaviour and habitat use by lake trout (native) and Chinook salmon (non‐native) in Lake Ontario in terms of depth and temperature, recorded at ≤70 s intervals for periods of up to 12 months. Chinook salmon occupied warmer and shallower waters during summer than did lake trout, and their niche breadth was wider. They achieved greater niche breadth in part because they were much more active vertically, cumulatively traveling 103 ± 1 m/hour during summer (model‐estimated median), whereas most lake trout were relatively inactive vertically (7 ± 1 m/hour). In each of our analyses, there was more inter‐individual variation among lake trout than among Chinook salmon, driven by some lake trout that spent considerable time making forays into warmer, shallower waters. Synthesis and applications. Our results illustrate the different foraging tactics used by two species in the Great Lakes and reflect their distinct life histories. Physical niche partitioning between Chinook salmon and lake trout helps to explain how these species can co‐exist in a multi‐species fishery even while having overlap in diet. The diversity of behaviours exhibited here by native lake trout have likely helped them persist during dramatic changes to the forage base in recent decades; that flexibility could help underlie their long‐term prospects for restoration during future changes to the ecosystem.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".