Return of the Salish Sea Harbor Porpoise, Phocoena phocoena: Knowledge Gaps, Current Research, and What We Need to Do to Protect Their Future
Bibliographic record
Abstract
The harbor porpoise ( Phocoena phocoena ) is one of the most abundant coastal cetacean species in the Northern Hemisphere with differential levels of regional knowledge. Gaps are particularly evident for the Pacific subspecies Phocoena phocoena vomerina . In the Salish Sea (a transboundary body of water spanning between Washington, United States and British Columbia (BC), Canada), there is a dearth of information on many aspects of the biology, ecology, behavior, sociality, and regionally specific threats. Here we present a case study of the Salish Sea harbor porpoise, combining historical and current research, from both BC and Washington, to provide a more holistic view of this species’ status, the knowledge continuum and gaps, risks from identified threats and what current research and collaborations are revealing about this enigmatic species. The Salish Sea harbor porpoise was abundant to the 1940s and 1950s, but by the 1990s their numbers were greatly reduced, and all but absent in some areas. By the early 2000s, numbers had resurged, and harbor porpoise are now once again found throughout much of the Salish Sea. Despite this, studies focused on Salish Sea harbor porpoises have been limited until recently. Current long-term research has been conducted from vessels and land in both Canada and the United States. Multi-faceted work using techniques including photo-identification (photo-ID), behavioral visual observations, acoustics, commercial fishery surveys, sighting reports, citizen science and other ecological data have provided insight into the seasonal variation in density and abundance, site fidelity, reproduction, by-catch rates, foraging and the identification of important habitats that are used intra- and inter-annually in this region. These may represent culturally and biologically significant habitats for Salish Sea harbor porpoise. Collaborations within and outside of the Salish Sea have revealed consistencies and dissimilarities between different communities or populations; indicating that some aspects are more uniform for the species, while others may be community or population specific. The importance of long-term broad and fine-scale research is highlighted, as well as recommendations to further close the knowledge gaps and reduce the known human threats within the Salish Sea.
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 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.000 | 0.001 |
| 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".