Impacts on tundra vegetation from heavy metal-enriched fugitive dust on National Park Service lands along the Red Dog Mine haul road, Alaska
Bibliographic record
Abstract
The DeLong Mountain Transportation System (DMTS) haul road links the Red Dog Mine-one of the world's largest zinc mines-with a shipping port on the Chukchi Sea in northwest Alaska, USA. The road traverses 32 km of National Park Service (NPS) lands managed by Cape Krusenstern National Monument (CAKR). Fugitive dusts from ore concentrate transport and mining operations have dispersed zinc (Zn), lead (Pb), cadmium (Cd), and metal sulfides onto NPS lands since the mine began operating in 1989. This study assessed the effects of metal-enriched road dusts on the diversity and community structure of lichens, bryophytes, and vascular plants in dwarf-shrub tundra within CAKR. In a Bayesian posterior predictions model, lichen species richness (LSR) was highly correlated to distance from the haul road and was distributed on the landscape consistently with the spatial patterns of Zn, Pb and Cd patterns published earlier in this journal. The mean modeled LSR of the 3000-4000 m distance class was 41.3, and LSR decreased progressively down to 9.4 species in the 0-50 m class. An ordination of 93 lichen species by 91 plots revealed strong community patterns based on distance from the haul road. The major community gradient was highly correlated (r = 0.99) with LSR and negatively correlated with Cd, Pb and Zn (-0.79 < r < -0.74). Ordinations of bryophyte classes showed less response than lichens to distance from the road and heavy metals values, and vascular plant ordination showed less still. Measures of bryophyte health such as the midrib blackening and frond width of Hylocomium splendens were positively correlated with distance from the haul road and negatively correlated with this same suite of elements. A total area of approximately 55 km2 showed moderate to strong impacts on lichens from fugitive dusts. This is equivalent to an area of almost 1 km on both sides of the haul road running 32 km through CAKR.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".