Climate and land‐use change impacts on cultural use berries: Considerations for mitigative stewardship
Bibliographic record
Abstract
Societal Impact Statement Cultural use berries are prized foods and medicines across the United States and Canada, with almost 200 different species used by Indigenous Peoples. Berries are increasingly being impacted by environmental and land‐use change. Berry habitats, how and when berry plants reproduce, and the volume of berries available for harvest each year are shifting widely. These changes are impacting access to, availability of, and consumption of berries. Biocultural stewardship practices, like low‐intensity fire, transplanting, and thinning, can be used in response to these stressors to support berry plant health and productivity as well as a sustained relationship with this important food. Summary Almost 200 different species of berries are used for food and medicine by Indigenous Peoples, with unparalleled nutritional and cultural significance among plant foods. Environmental and land‐use change is increasingly compromising access to, availability of, and consumption of berries. In this review, I consider (a) how climate and land‐use change are impacting cultural use berries across species and places, as documented by Indigenous Peoples and in the scientific literature, and (b) how stewardship practices are being applied to promote resilience and sustainability in berrying landscapes experiencing stress. Climate impacts on Arctic and subarctic berry species include earlier ripening, changes in taste, or increased variability in abundance. These same regions are experiencing a proliferation of shrubs, while forests throughout the lower 48 and Canada are suffering from suffocating fuel loads and stand densities that are not conducive to berry habitat for many species. In the Pacific West, berries are influenced by prolonged droughts and increasing spring and summer temperatures. Climate change impacts are amplified by shifts in land use for forestry and agriculture. Biocultural stewardship practices, like low‐intensity fire, thinning, transplanting, and cultural care, can be used to mitigate these impacts and promote berry microclimate habitats. There is opportunity for intertribal networking and knowledge sharing around berry stewardship practices that will support local and regional climate change responses.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".