Passive restoration and non-invasive monitoring of soft-sediment ecosystems on the North Coast of British Columbia, Canada
Bibliographic record
Abstract
Soft-sediment ecosystems can be degraded through anthropogenic development, leading to reduced habitat suitability for biological communities. On the North Coast of British Columbia, Canada, intensive industrial activity and coastal development has occurred, specifically around the Skeena and Kitimat River Estuaries. In addition to current development, both regions have the potential for further development, while also undergoing passive restoration from historical disturbances. Therefore, I aimed to broaden our understanding of passive restoration and non-invasive monitoring of intertidal soft-sediment ecosystems, by carrying out experiments at mudflats in both estuaries during the summer of 2017. Specifically, I aimed to expand the use of a non-invasive population assessment technique to novel species in soft-sediment ecosystems. Relationships between burrowing decapod abundance and burrow openings have been successfully used to estimate population sizes, but this technique has yet to be applied to large burrowing polychaetes, bivalves, or in regions of high macrofaunal diversity. As such, I assessed mudflats in regions of low (n = 1 species) and high (n = 8 species) biodiversity to determine if macrofauna abundances could be estimated from burrow openings on the sediment surface. Where only one burrowing bivalve species was present, a relationship between burrow openings and population abundance was not feasible, but burrow openings were useful in estimating total macrofaunal community abundance at a high diversity mudflat. This suggests that monitoring through burrow opening counts has the ability to detect overall changes in population abundance. Next, I examined the infaunal community, sediment conditions, and nutrient availability at one intertidal mudflat in the Skeena River Estuary following the cessation of heavy industrial activities (i.e. a salmon cannery and pulp mill) to determine the capacity for passive restoration. Sediment conditions varied spatiotemporally, and nutrient availability showed temporal variation but trends were difficult to relate to historical or current potential disturbances. The legacy of past development is still apparent on the infaunal community in the form of patchy distributions of disturbance-indicating taxa, but the mudflat appears to be in an overall healthy state with a diverse and functioning food web, indicating community recovery from historical activities. Results from these studies indicate passive restoration can be appropriate for estuarine soft-sediment ecosystems, while monitoring population abundance through burrow openings could be a method of detecting disturbances or tracking recovery of macrofaunal populations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".