The ghosts of ecosystem engineers: Legacy effects of biogenic modifications
Bibliographic record
Abstract
Abstract Ecosystem engineers strongly influence the communities in which they live by modifying habitats and altering resource availability. These biogenic changes can persist beyond the presence of the engineer, and such modifications are known as ecosystem engineering legacy effects. Although many authors recognize ecosystem engineering legacies, and some case studies quantify the effects of legacies, few general frameworks describe their causes and consequences across species or ecosystem types. Here, we synthesize evidence for ecosystem engineering legacies and describe how consideration of key traits of engineers improves understanding of which engineers are likely to leave persistent biogenic modifications. Our review demonstrates that engineering legacies are ubiquitous, with substantial effects on individuals, communities and ecosystem processes. Attributes that may promote the persistence of influential legacies relate to an engineer's traits, including its body size, life span and living strategy (individual, conspecific group or collection of multiple co‐occurring species). Additional lines of inquiry, such as how the recipients respond (e.g. density or richness) or the mechanism of engineering (e.g. burrowing or structure building), should be included in future ecosystem engineering legacy research. Understanding patterns of these persistent effects of ecosystem engineers and evaluating the consequences of losing them is an important area of research needed for understanding long‐term ecological responses to global change and biodiversity loss. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".