Rapid evaluation of habitat connectivity change to safeguard multispecies persistence in human-transformed landscapes
Bibliographic record
Abstract
Abstract Following the adoption of the Post-2020 Kunming-Montreal Global Biodiversity Framework (KM-GBF) of the Convention on Biological Diversity (CBD) there is a clear science-policy need to protect habitat connectivity and track its change over time to safeguard biodiversity and inform conservation planning. In response to this need we describe an analytical, multi-indicator and multispecies approach for the rapid assessment of habitat connectivity at fine spatial grain and at the extent of an entire ecoregion. Out of 68 connectivity indicators we found through a literature review, we identified a key-set of six indicators that align with the Essential Biodiversity Variables framework and are suitable to guide rapid action for connectivity and conservation targets in the KM-GBF. Using these selected indicators, we mapped and evaluated connectivity change from 2011 to 2021 across the ecoregion of the St-Lawrence Lowlands in Quebec (~ 30,000 km 2 ) for seven ecoprofile species representing regional forest habitat needs. For most of these species, trends over the last decade indicate a decline in effective connected area and metapopulation carrying capacity, via a division of large contiguous habitat into smaller fragments, whereas on average, habitat area slightly increased. These results highlight that temporal changes in habitat area and connectivity are not necessarily correlated and the urgent need to conserve and restore connectivity to meet targets under the KM-GBF. We provide an R-tool to support our general approach, which enables a comprehensive evaluation of connectivity for regional spatial planning for biodiversity in regions with moderate to high human disturbance.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".