Does Mitigation Achieve Conservation? Evaluating the effectiveness of freshwater mussel species-at-risk translocations in southwestern Ontario
Bibliographic record
Abstract
Freshwater mussels (Unionidae) serve as critical structural and functional links for aquatic food webs and are effective bioindicators, but large numbers of species are declining globally, with many in Canada federally listed as species-at-risk of extinction (SAR). Restricted in dispersal ability due to their sessile nature, Unionidae are incredibly vulnerable to human activities such as river infrastructure projects like bridge construction, culvert replacements, and earth moving activities adjacent to waterbodies. Therefore, translocation efforts involving freshwater mussel populations are commonly conducted as a mitigation response under the federal Fisheries and Species at Risk Act which protects freshwater mussels. Since publication of the Mackie protocol in 2008, practitioners have been required to follow standard practices to ensure translocation success, however little to no follow-up has been done to evaluate the effectiveness of this practice. To begin to assess translocation success, we have received privileged access to several translocation reports spanning 15 years from which we have conducted a data synthesis. In addition, multiple sites of previous translocations in the Grand and Thames River watersheds located in southern Ontario were surveyed during the 2022 field season. Findings indicate that mussel communities do not fully recover following translocation, negatively affecting the population density and biodiversity of communities instead of conserving and protecting them. Moreover, it appears that critical habitats do not fully recover, even 15 years post impact. We offer data and insights to inform changes to the practice of translocation to hopefully improve conservation of the species-at-risk and restoration of their critical habitats.
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 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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".