Linking Marine Ecosystem Response to Shoreline Armor Removal and Large Dam Removals in the Elwha River and Nearshore, Washington, USA
Bibliographic record
Abstract
Shaffer, J.A.; Oxborrow, B.; Parks, D.S.; Maucieri, D.G., and Michel, J., 2025. Linking marine ecosystem response to shoreline armor removal and large dam removals in the Elwha River and nearshore, Washington, USA. Journal of Coastal Research, 41(1), 1–15. Charlotte (North Carolina), ISSN 0749-0208. Large in-river dams and shoreline armor have a significant negative effect on coastal hydrodynamic and ecosystem processes. Armor removal (AR) is a well-documented shoreline restoration tool, and removal of large dams is proving to be an extremely effective tool to restore riverine ecosystem processes. However, nearshore ecosystem restoration associated with dam removals (DRs) is incomplete when shoreline impediments, including shoreline armoring and lower river alterations, remain, and linkages between dam and shoreline ARs are not well understood. In this study, nearshore ecosystem processes and function restoration response to large DRs and shoreline AR are assessed. Two nearly century-old large dams in the Elwha River watershed in the NW United States were removed during 2011–14, which liberated upward of 18 million tonnes (Mt) or approximately ∼9 million m3 of silt, sand, and gravel to sediment-starved, armored, and unarmored shorelines. Within 1 year of the initiation of DR, unarmored shorelines in the drift cell broadened, flattened, sediment fined, and large woody debris (LWD) volumes significantly increased. Armored shorelines continued to be steep and coarse grained. In 2016–17, approximately 4700 m3 of large riprap (shoreline armor) was removed from more than 650 m of the armored Elwha River east delta reach drift cell. Following AR, previously eroding shorelines broadened, sediment fined, LWD volumes increased significantly, and beach wrack metrics resembled non-armored beaches. These changes followed AR and did not occur at unarmored DR or control treatments. Invertebrate communities also responded to dam and armor removal (DAR) and showed increasing trends every year for 3 years after the project. It is concluded that only partial nearshore ecosystem restoration occurs from large DR when shoreline armoring that impairs nearshore hydrodynamic processes remains and that full ecosystem restoration of the nearshore associated with large DRs is obtained by restoring impaired shorelines along with DRs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".