National-scale multispecies connectivity models represent movements for a majority of species tested
Bibliographic record
Abstract
Abstract Context Ideally, connectivity models would be developed using animal movement data because connectivity is fundamentally specific to species and movement processes. However, it can take years to collect sufficient data for all species of interest. Generalized multispecies connectivity models developed from expert opinion might help in the meantime. Objectives We aimed to evaluate how well two common types of circuit theory-based generalized multispecies connectivity models (park-to-park and omnidirectional) predict areas important for animal movement for many species and movement processes. Methods Using GPS locations from 3525 individuals belonging to 17 species from 46 study areas across Canada and five tests, we assessed connectivity model prediction accuracy against movement processes measured at different scales, from within home range to presumed dispersal. Results Areas important for movement were accurately predicted for 52 to 78% of the datasets and movement processes. Prediction accuracy was lower for fast movements. The omnidirectional model was slightly better at predicting areas important for multiple movement processes. Both models were more accurate for species known to be more averse to human disturbance (72–78% of tests were accurate) compared to species less averse to human disturbance, steep slopes, and/or high elevations (38–41% of tests were accurate). Conclusions Our study demonstrates that both park-to-park and omnidirectional multispecies connectivity models can predict areas important for various movements for many species and can be used for time-sensitive projects aimed at landscape-scale connectivity conservation. However, because the models were less accurate for some species and faster movements, species-specific connectivity models may be required for informing land management decisions.
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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 | 0.000 |
| 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".