Effects of past land use and landscape context on plant species composition and richness in woodlots of an agricultural landscape in Québec
Bibliographic record
Abstract
The forest transition occurring in developed countries, including northeastern North America and western Europe, led to an emergence of secondary forests that fueled research about past land-use legacies on biodiversity. Past land-use has been shown to lower plant species richness, especially that of forest herb species. A few studies have also considered landscape configuration around secondary forests, either in the present-day or during the early period of old field colonization. My study examines the effects of both past land-use and past landscape context on species composition and richness in a 2046 km2 landscape dominated by agriculture in the Montérégie in southwestern Québec. Using historical topographical maps from the 1860s and 1910s, and a forest map from the 1990s, I analyzed the evolution of forest cover across the landscape and used an Affinity Propagation algorithm to cluster forest patches with similar land-use and configuration history. I then tested the effects of past land use and landscape context (surrounding forest proportion) on species composition and richness in 52 study sites using nonmetric multidimensional scaling analysis and correlations. Results show that landscape context in the past, and especially in the 1910s within a 200m buffer around study sites, is correlated with both current species composition and richness. Hence, forest patches that had a low forest cover around them in the past have a lower total, endozoochorous and anemochorous species richness and greater epizoochorous species richness than forest patches that were surrounded by a greater amount of forest in the past. As for past land use, this variable does not have a strong effect on plant species composition and richness. Other variables like soils, surficial deposits or recent forest management intensity do not affect total species richness and composition in study sites except for tree species, likely because of maple syrup production. My conclusions can be useful for forest protection and conservation by helping to prioritize forest patches to protect based on past landscape context. The results also suggest that the quality of forest patches can be enhanced by creating ecological corridors connecting patches, thereby increasing the number of surrounding seed sources.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".