Using Ecopath with Ecosim to Explore Nekton Community Response to Freshwater Diversion into a Louisiana Estuary
Bibliographic record
Abstract
Abstract Current methods to restore Louisiana's estuaries include the reintroduction of Mississippi River water through freshwater diversions to wetlands that are hydrologically isolated from the main channel. The reduced salinities associated with freshwater input are likely affecting estuarine nekton, but these effects are poorly described. Ecopath with Ecosim was used to simulate the effects of salinity changes caused by the Caernarvon freshwater diversion on species biomass distributions of estuarine nekton. A base model was first created in Ecopath from 5 years of monitoring data collected prior to the opening of the diversion (1986–1990). The effects of freshwater discharge on food web dynamics and community composition were simulated using a novel application of Ecosim that allows the input of salinity as a forcing function coupled with user-specified salinity tolerance ranges for each biomass pool. The salinity function in Ecosim not only reveals the direct effects of salinity (i.e., increases in species biomass at their optimum salinity and decreases outside the optimal range) but also the indirect effects resulting from trophic interactions. Through multivariate analyses we determined that the simulated species biomass distributions in the estuary change significantly with distance from the diversion. However, the simulations do not show a significant difference estuarywide between the modeled distributions (with pooling of the data from different distances) before and after the opening of the diversion. This indicates a redistribution of species in the estuary rather than the replacement of species within the estuarine nekton community. The salinity function is a useful addition to the Ecopath with Ecosim software for estuarine ecologists and fisheries managers. Received January 31, 2011; Acceptdate November 30, 2011
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".