Omnidirectional and omnifunctional connectivity analyses with a diverse species pool
Bibliographic record
Abstract
Abstract Connectivity among habitat patches in both natural and disturbed landscapes needs to be accounted for in conservation planning for biodiversity maintenance. Yet methods to assess connectivity are often limited, because simulating the dispersal of many species is computationally prohibitive, and current simulations make simplifying assumptions about movement that are potentially erroneous. Here we show how these limits can be circumvented and propose a novel framework for the assessment of omnifunctional and omnidirectional connectivity in a 28000 km2 area in the Laurentian region of Québec, Canada. Our approach relies on (i) the use of Omniscape , an improved version of Circuitscape which allows omnidirectional simulations that better emulate animal movement and (ii) the synthesis of large volume of species-level dispersal simulations through a posteriori clustering of the current intensity. Our analysis reveals that the movement of 93 species evaluated can be clustered into three functional dispersal guilds, corresponding to mostly aquatic species, terrestrial species able to use aquatic environments, and strictly terrestrial species. These functional guilds do not share connectivity hotspots, suggesting that corridor planning would need to account for the multiplicity of dispersal strategies. Although this approach requires a large volume of computing resources, it provides richer information on which landscape features are critical to maintain or need to be regenerated for broader biodiversity maintenance goals.
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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 | 0.001 |
| 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 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".