Four steps to strengthen connectivity modeling
Bibliographic record
Abstract
Maintaining and restoring ecological connectivity is considered a global imperative to help reverse the decline of biodiversity. To be successful, practitioners need to be guided by connectivity modeling research that is rigorous and reliable for the task at hand. However, the methods and workflows within this rapidly growing field are diverse and few have been carefully scrutinized. We propose four steps that should be consistently undertaken in connectivity modeling studies in order to improve rigour and utility: 1) describe the type of connectivity being modeled, 2) assess the uncertainty and sensitivity of model parameters, 3) validate the model outputs, ideally with independent data and 4) make non‐sensitive raw data and code openly available to enhance computational reproducibility. We reviewed the literature to determine the extent to which studies included these four steps. We focused on studies that generated novel landscape connectivity outputs using circuit theory and restricted our assessment to studies concerning terrestrial mammals. Among 181 studies meeting our search criteria, 39% communicated the type of connectivity being modeled, 18% conducted some form of sensitivity or uncertainty analysis (or both), 18% of studies attempted to validate their connectivity model outputs and only 7% used fully independent data to do so. Lastly, 13% of the studies made all raw data available, 2% provided all required code and only two studies provided both. Our findings highlight a clear need and opportunity to improve the reliability, reproducibility and utility of connectivity modeling research. We provide a checklist that researchers can consult and include with outputs. This will help practitioners make more informed decisions and ensure limited resources for connectivity conservation and restoration are allocated appropriately.
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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 | 0.185 | 0.380 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".