Representation of multistanza life histories in Ecospace models for spatial organization of ecosystem trophic interaction patterns
Bibliographic record
Abstract
The Ecospace model for spatial organization of trophic interactions has seen limited use for evaluation of policies such as marine protected areas, partly because of concern about representing key indicator populations only by spatial biomass distributions. The software has been improved to include spatial representation of age structure for such species, by means of the Ecosim population submodel, which assumes similar diet compositions, predation risk, and vulnerability to fishing over blocks or stanzas of fish ages. A computationally efficient version of Ecospace now preserves the multistanza age structure over spatial habitat and ecosystem biomass maps, evaluating body growth and mortality rates as spatial averages weighted by relative biomass use of each model spatial cell. A more computationally intense version divides each multistanza population into spatial packets (an individual-based model approach) for more precise analysis of how movement patterns and movement histories over mosaics of trophic opportunities and risks affect population performance and variability. The two approaches give surprisingly similar predictions of abundance patterns over both time and space, agreeing well in case-study applications to the Gulf of Mexico and California coast with each other and with nonspatial Ecosim predictions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".