Influences of large-scale climatic variability on reindeer population dynamics: implications for reindeer husbandry in Norway
Bibliographic record
Abstract
There is increasing evidence that the globe is currently warming, with changes being more pronounced in northern latitudes. Understanding the ecological effects of climatic variability is therefore important. There is recent support for the idea that a large-scale atmospheric phenomenon, the North Atlantic Oscillation (NAO), through its effects on vegetation and regional weather conditions, influences several aspects of life histories and population dynamic processes of several mammal species, including reindeer Rangifer tarandus. However, patterns are inconsistent both between species and within species. Here, we focus on reindeer, a herbivore that inhabits an extremely seasonal environment. We review and discuss predicted patterns of global climatic change in Norway and assess potential consequences for reindeer husbandry. We argue that although it is clearly shown that local and global climate affect reindeer directly (e.g. increased energetic costs of moving through deep snow and in accessing forage through snow) and indirectly (e.g. effect on forage plant biomass and quality, level of insect harassment and associated parasitism), it is difficult to predict a general pattern of how future climate change will influence this species. It is especially difficult to predict how reindeer husbandry (an important economic and cultural activity for the Saami People) will be affected in Norway. Indeed, (1) patterns in life history traits and population parameters of reindeer vary over space and time, (2) both temperature and precipitation will increase in Norway, with greater changes in the North, i.e. the areas with reindeer husbandry, but the rate of increase will vary with space and seasons, (3) there are several indirect effects of global warming that can complicate the ecological response, especially involving the response of vegetation (e.g. forage on which reindeer depend), and (4) spatial variation, seasonality, complexity of the ecosystem functioning and nonlinearity of ecological processes make any firm prediction uncertain. Consequently, it is difficult to assess the practical and socio-economic implications for the reindeer husbandry industry.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".