Agricultural Land‐Use Change in Prairie Canada: Implications for Wetland and Waterfowl Habitat Conservation
Bibliographic record
Abstract
Wetlands and waterfowl in the prairie pothole region (PPR) of North America are inextricably linked to agriculture. Government and private agencies must therefore understand agricultural land‐use change to implement effective conservation. We develop a land‐use model to predict the proportion of land in eight agricultural uses in Alberta, Manitoba, and Saskatchewan. We then use the model to simulate future land use to better understand the potential impacts of agricultural land‐use change on wetland and waterfowl habitat. We also simulate acreage‐based subsidy programs to investigate their potential effectiveness as conservation payment programs. Last, we compare predicted subsidy impacts across space with waterfowl densities to highlight the potential for spatial targeting to increase conservation cost‐effectiveness. Our results indicate that agricultural expansion and intensification are likely to continue, with a predicted increase of over 10 million acres of intensive spring crops. Predicted conversion of pasture land (which contains the most productive wetlands and waterfowl habitat) is heterogeneous across Canada's pothole region, suggesting the potential for spatially targeted conservation programs. Simulations of alternative conservation targeting strategies indicate that limited conservation dollars should be targeted toward high‐quality habitat that is at relatively low risk of converting. Les milieux humides et la sauvagine dans la région des cuvettes des Prairies d’Amérique du Nord sont inextricablement liés à l’agriculture. Les organismes gouvernementaux et privés doivent comprendre les changements dans l’utilisation des terres agricoles afin de mettre en place des mesures de conservation efficaces. Nous avons élaboré un modèle d’utilisation des terres pour prédire la proportion de terres qui seront affectées à huit usages agricoles en Alberta, au Manitoba et en Saskatchewan. Nous avons ensuite utilisé le modèle pour simuler l’utilisation future des terres afin de mieux comprendre les répercussions éventuelles des changements dans l’utilisation des terres agricoles sur les milieux humides et l’habitat de la sauvagine. Nous avons également simulé des programmes de subvention établis en fonction des superficies pour évaluer leur efficacité potentielle en tant que programmes de paiements pour encourager la conservation. Nous avons finalement comparé les répercussions prévues des subventions dans toutes les régions avec la densité de sauvagine pour montrer le potentiel du ciblage spatial comme moyen d’accroître le rapport coût‐efficacité de la conservation. D’après nos résultats, l’expansion et l’intensification de l’agriculture risquent de se poursuivre avec une augmentation prévue de plus de dix millions d’acres consacrées aux cultures de printemps. La conversion prévue de pâturages (où les milieux humides et les habitats de sauvagine sont les plus productifs) est hétérogène à la grandeur des cuvettes des Prairies canadiennes, ce qui laisse entrevoir la possibilité d’instaurer des programmes de conservation spatialement ciblés. Les simulations de différentes stratégies de conservation ont indiqué que, compte tenu des ressources financières limitées, les efforts devraient cibler les habitats de grande qualité qui risquent peu d’être convertis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".