Quantifying inter-annual variability on the space-use of parental Northern Gannets (Morus bassanus) in pursuit of different prey types
Bibliographic record
Abstract
Spatial planning for marine areas of multi-species conservation concern requires in-depth assessment of the distribution of predators and their prey. Northern Gannets Morus bassanus are generalist predators that predate several different forage fishes depending on their availability. In the western North Atlantic, gannets employ different dive tactics while in pursuit of different prey types, performing deep, prolonged U-shaped dives when foraging on capelin (Mallotus villosus), and rapid, shallow, V-shaped dives when foraging on larger pelagic fishes. Therefore, much can be inferred about the distribution and abundance of key forage fishes by assessing the foraging behaviour and space-use of gannets. In this study, we aimed to quantify space-use and to determine areas of suitable foraging habitat for gannets in pursuit of different prey types using habitat suitability models and kernel density utilization distributions. We deployed 25 GPS/Time-depth recorder devices on parental Northern Gannets at Cape St. Mary's, Newfoundland, Canada from 2019 to 2021. To assess the influence of environmental variables on gannets foraging for different prey types, we constructed three different habitat suitability models: a U-shaped dive model, and two V-shaped dive models (early and late chick-rearing). Suitable foraging habitat for capelin, deduced by the U-shaped dive model, was defined by coastal, shallow waters with flat relief and sea surface temperatures (SST) of 11-15° C. Suitable habitat for early V-shaped dives was defined by shallow and coastal waters with steep slope and SST of 12-15°C and ~18°C, likely reflecting the variability in environmental preferences of different prey species captured when performing V-shaped dives. Suitable habitat for late V-shaped dives was defined by shallow coastal waters (<100m depth), as well as waters deeper than 200 m, and by SST greater than 16°C. We show that space-use by gannets can vary both within and between years depending on environmental conditions and the prey they are searching for, with consequences for the extent of potential interaction with anthropogenic activities. Further, we suggest regions defined as suitable for U-shaped dives are likely to be critical habitat of multi-species conservation concern, as these regions are likely to represent consistent capelin spawning habitat.
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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 | 0.000 | 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) | 0.001 | 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".