Environmental DNA as a valuable and unique source of information about ecological networks in Arctic terrestrial ecosystems
Bibliographic record
Abstract
Arctic terrestrial ecosystems are particularly vulnerable to the effects of ongoing and predicted climate changes. The current states of environmental biodiversity and ecological networks in the Arctic need to be known and understood to monitor how they change and how these changes may influence the particular components of the ecosystem. Despite the fact that the Arctic tundra is generally poor in nutrients, it has a surprisingly high biodiversity, especially of invertebrates and microorganisms. Besides macroclimatic features, there may be local factors influencing biodiversity, such as microclimate, water availability, or large seabird colonies depositing guano. This last can have a substantial impact on the soil’s physicochemical features, and consequently the distribution, number, and diversity of tundra-associated plants and animals in the vicinity of the colony. Changes in the Arctic biodiversity and the functioning of the ecosystem at all trophic levels are difficult to investigate using traditional methods. In this review, we discuss how modern molecular techniques, including next generation sequencing, influence our ability to investigate and understand this ecosystem at both the micro- and the macroscale and how they can complement the more traditional approaches to studying ecological networks in the Arctic.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".