Divergent trends in structural landscape connectivity from historic and potential future grassland conversion in Alberta, Canada
Bibliographic record
Abstract
Grasslands across the Canadian prairies are crucial for maintaining biodiversity and ensuring landscape connectivity. In Alberta, a large portion of natural grasslands has been converted to agricultural cropland or other land uses, while the remaining natural grasslands are mainly used as rangeland. However, with increasing crop demand and food security concerns, there is a potential risk of further grassland conversion to cropland, particularly in areas where climate change may enhance suitability for farming. Here, we (1) quantified the impact of the present state of grasslands on maintaining landscape permeability; and (2) determined how the conversion of remaining grasslands to croplands could affect structural landscape connectivity at multiple spatial scales. We simulated four progressive scenarios of grassland conversion to cropland, starting with grasslands identified as most suitable for farming. Our results revealed that structural landscape connectivity, quantified as mean normalized current density with resistance values based on naturalness, decreased by up to 43% in southwestern and central areas of the Parkland and Grassland regions with higher rates of conversion. Conversion scenarios introduced new areas with notably constrained ecological flow in the Grassland region in the southeastern part of the province. Conversely, increased current density was observed in the Rocky Mountain and Boreal regions, which appear to act as alternative pathways for redirected ecological flow. Future grassland conversion is expected to further shift current flow from the grasslands westward through the foothills of the Rocky Mountain and northward into the Parkland and Boreal regions. These findings underscore the critical role of grasslands in maintaining structural landscape connectivity across Alberta, which is essential for supporting biodiversity and gene flow among species. Simulated changes in connectivity were most pronounced at the finer spatial scale, revealing key areas of past and future permeability shifts. Incorporating local land management decisions is crucial for improving landscape permeability and effective connectivity planning province-wide.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".