Lasting Effects of Different Scaled Mass Mortality Events on Soil Microbial Communities
Bibliographic record
Abstract
Death is a natural process present in all ecosystems; however, mass mortality events are instances of larger than average numbers of animals dying in a relatively short period of time. These events are increasing in frequency and magnitude, and the effects of mass mortalities — especially their long-term effects — are understudied. To better understand the long-term effects of mass mortalities in terrestrial ecosystems, we conducted experimental mass mortality events to determine if key ecosystem properties remained affected after 4 years. The experiment crossed three types of input treatments (control, carrion, and nutrient additive) with scavenger access (open plots versus fenced plots). To evaluate how increasing carrion biomass affected the ecosystem, sites were randomly assigned biomass (25, 59, 182, 363, 726 kg total (20m 2 plots)). Biomasses consisted of feral swine carcasses or the equivalent amount of N, phosphorus, and K nutrients. After 4 years, we found that while soil N did not differ among treatments, soil K and Ca significantly increased with biomass. Microbial communities significantly differed at the 182 kg biomass treatments compared to others and indicated significant effects between carrion and nutrient additive treatments. These results demonstrate that large die-offs, such as mass mortality events, can have long-lasting effects on soil composition through increased soil nutrients and alter soil microbial community (i.e., reduced Bacilliaceae, etc.). These long-lasting impacts can permanently alter the soil community, which can lead to cascading bottom-up effects that can alter the entire ecosystem structure.
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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 | 0.000 | 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 | 0.001 |
| 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".