Assessing land-use legacy effects on soil physico-chemical properties and earthworm biodiversity in urban parks
Bibliographic record
Abstract
Human land-use alters soil properties and biodiversity differently depending on the intensity and type of use, often resulting in persistent temporal effects known as legacy effects. Cities are expected to be rich in legacy effects due to their development histories and complex socio-ecological landscapes. However, few urban ecological studies consider the role of history in shaping contemporary patterns. Therefore, we asked: do soil properties and biodiversity of our present-day urban greenspaces differ due to varied historical land-use? We surveyed 25 urban parks across the island of Montreal, Quebec, Canada with three former land-uses: forested (low intensity), agricultural (medium intensity), and industrial (high intensity). We measured soil bulk density, heavy metal concentrations, and carbon and nitrogen stocks, as well as earthworm abundance, biomass, species richness, and community composition. Most studied soil properties did not differ across historical land-uses. All properties except for heavy metal concentrations significantly increased with age, implying a legacy effect of recovery from disturbance and management post park establishment. Earthworm distribution was highest in forested sites whereas earthworm biodiversity was lower in previously agricultural sites. These findings suggest that aspects of soils in our urban greenspaces are minimally susceptible to legacy effects of historical human land-use. This demonstrates a certain effectiveness in developing municipal parks on a variety of past land-uses. This could allow for a focusing on current management choices and decisions which may have a greater influence on park ecosystem functioning.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".