Multipurpose habitat networks for short‐range and long‐range connectivity: a new method combining graph and circuit connectivity
Bibliographic record
Abstract
Summary Biodiversity conservation in landscapes undergoing climate and land‐use changes requires designing multipurpose habitat networks that connect the movements of organisms at multiple spatial scales. Short‐range connectivity within habitat networks provides organisms access to spatially distributed resources, reduces local extinctions and increases recolonization of habitat fragments. Long‐range connectivity across habitat networks facilitates annual migrations and climate‐driven range shifts. We present a method for identifying a multipurpose network of forest patches that promotes both short‐ and long‐range connectivity. Our method uses both graph‐theoretic analyses that quantify network connectedness and circuit‐based analyses that quantify network traversability as the basis for identifying spatial conservation priorities on the landscape. We illustrate our approach in the agroecosystem, bordered by the Laurentian and Appalachian mountain ranges, that surrounds the metropolis of Montreal, Canada. We established forest conservation priorities for the ovenbird, a Neotropical migrant, sensitive to habitat fragmentation that breeds in our study area. All connectivity analyses were based on the same empirically informed resistance surface for ovenbird, but habitat pixels that facilitated short‐ and long‐range connectivity requirements had low spatial correlation. The trade‐off between connectivity requirements in the final ranking of conservation priorities showed a pattern of diminishing returns such that beyond a threshold, additional conservation of long‐range connectivity had decreased effectiveness on the conservation of short‐range connectivity. Highest conservation priority was assigned to a series of stepping stone forest patches across the study area that promote traversability between the bordering mountain ranges and to a collection of small forest fragments scattered throughout the study area that provide connectivity within the agroecosystem. Landscape connectivity is important for the ecology and genetics of populations threatened by climate change and habitat fragmentation. Our method has been illustrated as a means to conserve two critical dimensions of connectivity for a single species, but it is designed to incorporate a variety of connectivity requirements for many species. Our approach can be tailored to local, regional and continental conservation initiatives to protect essential species movements that will allow biodiversity to persist in a changing climate.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".