Petroleum hydrocarbon contamination in boreal forest soils: a mycorrhizal ecosystems perspective
Bibliographic record
Abstract
The importance of developing multi-disciplinary approaches to solving problems relating to anthropogenic pollution is now clearly appreciated by the scientific community, and this is especially evident in boreal ecosystems exposed to escalating threats of petroleum hydrocarbon (PHC) contamination through expanded natural resource extraction activities. This review aims to synthesize information regarding the fate and behaviour of PHCs in boreal forest soils in both ecological and sustainable management contexts. From this, we hope to evaluate potential management strategies, identify gaps in knowledge and guide future research. Our central premise is that mycorrhizal systems, the ubiquitous root symbiotic fungi and associated food-web communities, occupy the structural and functional interface between decomposition and primary production in northern forest ecosystems (i.e. underpin survival and productivity of the ecosystem as a whole), and, as such, are an appropriate focal point for such a synthesis. We provide pertinent basic information about mycorrhizas, followed by insights into the ecology of ecto- and ericoid mycorrhizal systems. Next, we review the fate and behaviour of PHCs in forest soils, with an emphasis on interactions with mycorrhizal fungi and associated bacteria. Finally, we summarize implications for ecosystem management. Although we have gained tremendous insights into understanding linkages between ecosystem functions and the various aspects of mycorrhizal diversity, very little is known regarding rhizosphere communities in PHC-contaminated soils. This makes it difficult to translate ecological knowledge into environmental management strategies. Further research is required to determine which fungal symbionts are likely to survive and compete in various ecosystems, whether certain fungal - plant associations gain in ecological importance following contamination events, and how PHC contamination may interfere with processes of nutrient acquisition and exchange and metabolic processes. Research is also needed to assess whether the metabolic capacity for intrinsic decomposition exists in these ecosystems, taking into account ecological variables such as presence of other organisms (and their involvement in syntrophic biodegradation), bioavailability and toxicity of mixtures of PHCs, and physical changes to the soil environment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".