Modeling the spatial autocorrelation of pelagic fish abundance
Bibliographic record
Abstract
The relationship between pelagic fish and ocean temperature is cited in many studies, the majority of which investigate correlations of pelagic species and sea surface temperatures (SST). While appropriate for surface-associated species, this may not be accurate for deep-diving fishes. A different way to examine this relationship is to model spatial autocorrelation of fish species and temperatures at an appropriate range of depths. Spatial autocorrelation, the distance at which data are interdependent, is a potential descriptor of the patch size of an organism. Here we modeled spatial autocorrelation for 5 pelagic species that inhabit different depths in the Gulf of Mexico: dolphinfish Coryphaena hippurus, wahoo Acanthocybium solandri, yellowfin tuna Thunnus albacares, swordfish Xiphias gladius, and bigeye tuna Thunnus obesus. Additionally, we modeled spatial autocorrelation for ocean temperatures at the surface, at 200, and 400 m. We hypothesized that autocorrelation distances will be greater for deeper water temperatures and for species that live at deeper depths due to greater homogeneity of deep waters over greater spatial ranges. Results show average distances of autocorrelation on the order of 55 to 60 km for wahoo and dolphinfish, 90 km for yellowfin tuna, and 135 to 145 km for swordfish and bigeye tuna; the same data for temperature were 75, 135, and 300 km for SST, and at 200 and 400 m depth, respectively. Autocorrelation distances of dolphinfish, wahoo, and yellowfin were correlated with that of of SST, while the autocorrelation distance of swordfish and bigeye were correlated with that of temperatures at 200 m. Results suggest that autocorrelation distances may be useful as a proxy for habitat delineation.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".