Effects of Landscape Compositional and Configurational Heterogeneity on Biodiversity in Eastern Ontario Farmland
Bibliographic record
Abstract
As agriculture intensifies worldwide, there is interest in determining ways to improve current farmland landscape heterogeneity in a manner that will benefit many different species.It has been previously established in eastern Ontario farmland that a decrease in mean field size would benefit biodiversity (Fahrig et al., 2015).This thesis aimed to determine whether other landscape heterogeneity characteristics are also related to biodiversity in a manner which could be applied to farmland management strategies.Landscape metrics were used in a random forest regression analysis, and were related to abundance and alpha, beta, and gamma diversity of bees, birds, butterflies, plants, syrphids, spiders and carabids sampled in 2011 and 2012 at 93 1 km by 1 km study landscapes, which were mapped with detailed field verification.In order to test the relevance of these findings when applied to readily available data on agricultural lands for eastern Ontario, the landscape metric calculation and random forest regression approach were also used with Agriculture and Agri-Food Canada Annual Crop Inventory maps.The AAFC Annual Crop Inventory had an overall accuracy of 71.3% when compared with the highly detailed data for this project.Both map sources were assessed in terms of metrics of importance to each taxon and important metrics across taxa.It was found that mean field size remained the most consistent predictor, with a negative biodiversity response.However, the percentage of like adjacencies (while less consistent) often had a stronger negative response.In addition, of the four landscape metrics found to have the most consistent biodiversity response, the percentage of like adjacencies was found to be the most important metric for 20 of the 27 biodiversity variables.If biodiversity conservation is a concern for farmland management in the region, fields should not only be smaller, but field cover types should also be variably distributed wherever possible.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".