RAPID CONSUMPTION OF KELP CRAB: IMPLICATIONS FOR SEA OTTERS IN WASHINGTON STATE
Bibliographic record
Abstract
As animal populations approach environmental carrying capacity, competition for food increases, generally leading to decreased individual energy intake rate. Energy-intake rate can therefore be used as one metric of population status relative to carrying capacity. Focal observations of Sea Otter (Enhydra lutris) foraging behavior have been used throughout their range to estimate energy-intake rates and infer population status. In Washington State, previous research has demonstrated that handling times for Kelp Crabs (Pugettia spp.) by Sea Otters are 1.5 to 2 times faster than those observed in California and British Columbia, resulting in higher energy-intake rate estimates for Sea Otters in Washington. We investigated potential causes for the difference in handling time by: (1) comparing Sea Otter handling times of Kelp Crab and non-Kelp Crab prey items in Washington, California, and British Columbia; (2) comparing the handling times of Kelp Crabs by a subset of Sea Otters in California, which are Kelp Crab specialists (2003–2012, n = 244 Kelp Crab captures) to those of Sea Otters in Washington (2015–2018, n = 541 captures) and British Columbia (2013–2017, n = 359 captures); and (3) comparing the biomass-to-width ratios of Kelp Crabs from Washington and California. We did not observe consistent differences between regions in Sea Otter handling times of non-Kelp Crab prey. Mean Sea Otter handling time of small Kelp Crabs (carapace ≤1 Sea Otter paw width) in Washington (32.7 s) was significantly faster than in British Columbia (52.0 s, P < 0.0001) and all of California (40.6 s, P < 0.0001), but was not significantly different from that of Kelp Crab-specialist Sea Otters in California (31.7 s, P = 0.313). Mean Sea Otter handling time of large Kelp Crabs (≥1 Sea Otter paw) in Washington (64.7 s) was significantly faster than in British Columbia (87.7 s, P= 0.003), in all of California (104 s, P < 0.0001), and in the subset of Kelp Crab-specialist Sea Otters in California (91.6 s, P = 0.007). Kelp Crabs in Washington had a larger biomass-to-width ratio than Kelp Crabs in California: in Washington, a Kelp Crab with a 20-mm maximum carapace width had a 3.8% greater predicted biomass than a Kelp Crab in California of the same width, and a 27.1% greater biomass for a 60-mm carapace. Our results suggest that Sea Otters in Washington are Kelp Crab specialists with behavioral differences allowing them to consume Kelp Crabs faster, a difference that may affect the inference of Sea Otter population status from energy-intake rates in Washington.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".