Changes in freshwater macroinvertebrate richness due to river impoundment in the United States
Bibliographic record
Abstract
ABSTRACT Whether it is for water supply, flood control or hydropower uses, the transformation of a river into a reservoir can impact freshwater ecosystems and their biodiversity. Using the National Lake Assessment (NLA; 148 reservoirs) and the National Rivers and Streams Assessment (NRSA; 2121 rivers and streams) of the United States Environmental Protection Agency (USEPA), we evaluated the impacts of river impoundment on macroinvertebrate biodiversity at three spatial scales ( i.e. , reservoir, ecoregion and country scale). We used a space-for-time substitution approach to model the impact of impoundment ( i.e. , we used rivers and streams as the before-impoundment conditions, and reservoirs as the after-impoundment conditions). We expressed the impact on biodiversity in terms of potentially disappeared fraction of species (PDF) to be used in the life cycle assessment (LCA) framework. To understand the role of regionalization, and some potentially influential variables, on changes in macroinvertebrate richness following impoundment in the United States, we used analyses of variance (ANOVAs) as well as variation partitioning, and developed empirical predictive models. Overall, 26% of macroinvertebrate taxa disappeared following impoundment in the United States, and PDFs followed a longitudinal gradient across ecoregions ( i.e. , higher PDFs in the western part of the country, lower PDFs in the eastern part). We also observed that large and oligotrophic reservoirs, located in high elevation had high PDFs. This study provides the first empirical PDF values for macroinvertebrates to be used as characterization factors (CFs) by LCA practitioners. We also provide strong support for regionalization and a simple predictive model to be used by LCA modellers.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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 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".