Comparison and Parallel Implementation of Alternative Moving-Window Metrics of the Connectivity of Protected Areas Across Large Landscapes
Bibliographic record
Abstract
Abstract Context. A variety of metrics can be used to measure connectivity of protected areas. Assumptions about animal movement and mortality vary among metrics. There is a need to better understand what to use and why, and how much conclusions depend on the choice of metric. Objectives. We compare selected raster-based moving-window metrics for assessing the connectivity of protected areas to natural habitat in the surrounding area, and develop tools to facilitate calculation of these metrics for large landscapes. Methods. We developed parallel implementations of distance-weighted sum and Spatial Absorbing Markov Chain methods in R packages to improve their useability for large landscapes. We investigated correlations among metrics for Canadian protected areas, varying background mortality, cost of movement, mean displacement, dispersal kernel shape, distance measure used, and the treatment of natural barriers such as water, ice, and steep slopes. Results. At smaller spatial scales (2–5 km mean displacement), correlations among metric variants are high, suggesting that any of the metrics we investigated will give similar results and simple metrics will suffice. Differences among metrics are most evident at larger spatial scales (20–40 km mean displacement) in moderately disturbed regions. Assumptions about the impact of natural barriers have a large impact on outcomes. Conclusions. In some circumstances different metrics give similar results, and simple distance-weighted metrics likely suffice. At large spatial scales in moderately disturbed regions there is less agreement among metrics, implying that more detailed information about disperser distribution, behaviour, and mortality risk is required for assessing connectivity.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".