Effects of habitat loss in a Canadian deciduous forest: Analyses of understory plant and insect relationships
Bibliographic record
Abstract
This thesis is a study to investigate the effects of habitat loss in a Canadian deciduous forest. The analyses involved relationships of insects and understory plants in the Carolinian forest zone. The methodological approaches of landscape ecology based on a Geographic Information System (GIS) were used to reveal the effects of habitat loss (forest loss) on the structures and functions of the plants and insects. Three aspects were principally investigated. First, the structure of insect and plant communities and their pollination interactions in relation to habitat loss was investigated. Especially generalization and specialization shifts in the interactions of spring flowering plants and their potential insect pollinators were looked at. The results indicated a significant relationship between the increase of habitat loss and the shift to generalization in insect, but not in plant, communities. These suggest that, in plant-pollinator interactions, insect communities are more sensitive and/or quicker than plant communities to respond to the effects of habitat loss. Second, the effects of forest loss on the function of communities of pollinator insects and the reproductive successes of insect-pollinated woodland plants were investigated. The spring flowering plants, 'Maianthemum canadense' and 'Erythronium americanum,' with local bee communities, were used as model organisms. The results of the two model cases showed that forest loss negatively affected reproductive success of these plants. In addition, negative impacts of forest loss were shown on bee communities (potential pollinators), which also contributed to the lower reproductive success of 'E. americanum.' Third, the effects of both forest loss at a landscape scale level and local conditions at a local habitat scale on the community structure of cavity-nesting wasps were investigated. Trap-nests were used to collect the wasps. The results indicated that the wasp communities were influenced more by the local habitat conditions at the smaller scale than by forest loss at the landscape scale. The results of these three aspects suggest that, to investigate the effects of habitat loss, it is important to consider various scales of habitats in the choice of cases. These might be attributed to different organisms, seasons, ecosystems and the interactions of those factors.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".