Operationalizing ecological connectivity in spatial conservation planning with Marxan Connect
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Globally, protected areas are being established to protect biodiversity and to promote ecosystem resilience. The typical spatial conservation planning process leading to the creation of these protected areas focuses on representation and replication of ecological features, often using decision support tools such as Marxan. Yet, despite the important role ecological connectivity has in metapopulation persistence and resilience, Marxan currently requires manual input or specialized scripts to explicitly consider connectivity. ‘Marxan Connect’ is a new open source, open access Graphical User Interface (GUI) tool designed to assist conservation planners with the appropriate use of data on ecological connectivity in protected area network planning. Marxan Connect can facilitate the use of estimates of demographic connectivity (e.g. derived from animal tracking data, dispersal models, or genetic tools) or structural landscape connectivity (e.g. isolation by resistance). This is accomplished by calculating metapopulation‐relevant connectivity metrics (e.g. eigenvector centrality) and treating those as conservation features or by including the connectivity data as a spatial dependency amongst sites in the prioritization process. Marxan Connect allows a wide group of users to incorporate directional ecological connectivity into conservation planning with Marxan. The solutions provided by Marxan Connect, combined with ecologically relevant post‐hoc testing, are more likely to support persistent and resilient metapopulations (e.g. fish stocks) and provide better protection for biodiversity.
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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 | 0.001 | 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) | 0.000 | 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 it