Impacts of dust on plant health, survivorship and plant communities in semi‐arid environments
Bibliographic record
Abstract
Abstract There is a general perception that dust accumulation on plant surfaces causes negative impacts to plants. Consequently, it is common for environmental regulatory agencies to apply vegetation monitoring requirements to oil, gas and mining developments. We use two independent, medium‐term monitoring studies in semi‐arid Australia to examine this relationship at two scales: plant health and survivorship of a threatened subspecies (Tetratheca paynterae paynterae: Elaeocarpaceae) at Windarling Range between 2003 and 2014; and changes in plant health and floristic composition on Barrow Island between 2009 and 2014. Accumulation of dust decreased rapidly with distance from source. At Windarling Range, even at the site with the highest dust load, there was no significant impact on Tetratheca paynterae paynterae compared with the less dusty sites for 10 years. Similarly, there was no significant effect between distance from the source of dust and floristic composition on Barrow Island for 5 years. The probability of plants transitioning to a lower health condition between one year and the next did not appear to be related to dust load. This is further supported by comparing the same site before and after paving the road (removal of dust source), which showed no clear trends. Trends in plant health are likely to be driven more by the variability of cumulative rainfall in the preceding 5 months than dust load. The observed temporal variation in the mean dust load may also be related to variation in rainfall. In conclusion, in these case studies from semi‐arid Australia, we find no evidence to support the perception that, under the observed climatic condition and dust deposition rates up to 20 or 77 g m−2 per month at Windarling Range and Barrow Island, respectively, dust accumulation on plants causes negative impacts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".