Predicted impacts of climate change on wild and commercial berry habitats will have food security, conservation and agricultural implications
Bibliographic record
Abstract
Climate change is now a reality and is altering ecosystems, with Canada experiencing 2-4x the global average rate of warming. This will have a critical impact on berry cultivation and horticulture as well as wild, traditional and country food species. Enhancing our understanding of how wild and cultivated berries will be essential to mitigating impacts on ecosystems, culture and food security. Our objective was to predict the impact of climate change on habitat suitability of four berry producing Vaccinium species: two species with primarily northern distributions (V. uliginosum, V. vitis-idaea) and one species with a primarily southern distribution (V. oxycoccos) and the commercially cultivated V. macrocarpon. We used the maximum entropy (maxent) model and the CMIP6 shared socioeconomic pathways (SSPs) 126 and 585 projected to 2041-2060 and 2061-2080. Wild species showed a uniform northward progression and expansion of suitable habitat. Our modeling predicts that suitable growing regions for commercial cranberries is also likely to shift with some farms becoming unsuitable for the current varieties and other regions becoming more suitable for cranberry farms. Both V. macrocarpon and V. oxycoccos showed a high dependence on precipitation associated variables. V. vitis-idaea and V. uliginosum had a greater number of variables with smaller contributions which may improve their resilience to individual climactic events. Future competition between commercial cranberry farms and wild berries in protected areas could lead to conflicts between agriculture and conservation priorities. New varieties of commercial berries are required to maintain current commercial berry farms.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".