Evaluating the effectiveness of joint species distribution modeling for freshwater fish communities within large watersheds
Bibliographic record
Abstract
Accurately predicting species’ distributions is critical for the management and conservation of fish and wildlife populations. Joint species distribution models (JSDMs) account for dependencies between species often ignored by traditional species distribution models. We evaluated how a JSDM approach could improve predictive strength for stream fish communities within large watersheds (the Chesapeake Bay Watershed, USA), using a cross-validation study of JSDMs fit to data from over 50 species. Our results suggest that conditional predictions from JSDMs have the potential to make large improvements in predictive accuracy for many species, particularly for more generalist species where single species models may not perform well. For some species there was no added explanatory effect from conditional information, most of which already exhibited strong marginal predictive ability. For several rare species there were significant improvements in occurrence predictions, while the results for two invasive species considered did not show the same improvements. Overall, the optimal number of species to condition upon, as well as the effects of conditioning upon an increasing number of species, varied widely among species.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".