Role of Biodiversity in Ecological Calendars and Its Implications for Food Sovereignty: Empirical Assessment of the Resilience of Indicator Species to Anthropogenic Climate Change
Bibliographic record
Abstract
Ecological calendars are knowledge systems based on close observation of one's habitat to measure and give meaning to time, thereby providing anticipatory capacity for livelihood activities and contributing to food sovereignty. They rely upon seasonal indicators that integrate biophysical and ecological phenomena (e.g., first snowfall, last frost, blossoming of a tree species; seasonal appearance of an animal or plant) with locally grounded cultural meaning and value systems. These context-specific relationships have enabled Indigenous and rural societies to anticipate weather and other seasonal processes in their environment. However, anthropogenic climate change could undermine ecological calendars due to adverse impacts on specific indicators species, but this issue remains unexplored. We address this knowledge gap by examining how anthropogenic climate change might affect selected species (birds, fish, and mammals) that are seasonal and key to Indigenous food systems in two Western Arctic communities. We leverage existing dietary animal datasets to which we apply a novel methodology for assessing organismal vulnerability to climate change. The methodology uses intrinsic species traits such as physiological tolerances, genetic variability, and life history traits to generate an empirical and integrative assessment of vulnerability for any given species. Subsequently, an aggregate view of vulnerability across calendar species is achieved through comparative statistical analysis across species both within and between communities. This exercise permits the first quantitative assessment of the continued relevance and effective use of an ecological calendar, thus demonstrating that food sovereignty and livelihood security is enhanced by biodiversity of indicator species.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".