Mortality in an off-shore predator critical habitat during the Northeast Pacific marine heatwave
Bibliographic record
Abstract
Defining baseline mortality and trends in wildlife populations is imperative to understand natural and anthropogenic threats to overall population health and improve conservation measures for species, particularly in geographically confined habitats. The Guadalupe fur seal Arctocephalus townsendi (GFS) is a threatened pinniped that ranges throughout the west coast of Mexico with sporadic dispersion to higher latitudes. Their breeding habitat is restricted to Guadalupe Island, Mexico, which is vulnerable to periodic and cyclic warming of the Northeast Pacific Ocean. The impacts of environmental change on GFS health and reproductive success at Guadalupe Island are poorly defined and the aim of this study was to establish baseline pup (GFS n ) mortality rates and primary causes of death during the 2013-2016 breeding seasons at Guadalupe Island. Interannual mortality rates and causes of death were compared by year, breeding seasons and by geographic location. The highest mortality rate in GFS n was in 2015 (14.7%), followed by 2014 (8.2%), 2016 (6.7%) and 2013 (5.6%). The presumptive causes of mortality of GFS n were consistent with other published long term otariids health surveys and included: emaciation (49%), trauma (24%), infectious disease (8%), drowning (4%) stillbirth/perinatal mortality (4%) and undetermined (11%). However, in 2015 and coinciding with northeast Pacific marine heatwave in 2014-2016, emaciation accounted for 54% of GFS n mortality in contrast to 9% in 2013. For GFS n , terrestrial habitat may influence mortality rates and causes of mortality but like other marine predators, marine habitat features, such as an increase in sea surface temperature are associated with changes in maternal care, nutritional status and pups survival. Monitoring mortality rate and causes in GFS n at Guadalupe Island is crucial to establish baseline health trends, document potential impacts on species demographics and recruitment during marine heatwaves and potential consequences in population recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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 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".