Changing Species Richness and Composition in Canadian National Parks
Bibliographic record
Abstract
Abstract: Canada's national parks and their surrounding areas differ markedly in size, climate, vegetation, and extent of human development. We tested the extent to which total species richness, native species richness, and the number of extirpations and introductions of terrestrial vertebrates were correlated with each of these factors. To do this, we used surveys of park fauna from the present and from the time of park establishment. Richness, extirpations, and introductions were all strongly related to climate. After we controlled for climate, smaller parks had higher rates of species loss than larger parks. Land‐use patterns (forest cover and fragmentation, roads, etc.) within parks were strongly correlated with land use in the regions surrounding the parks, showing that parks have not been isolated from regional development. Richness and extirpations within parks were generally more strongly related to regional characteristics than to the characteristics of the parks themselves. Species richness and numbers of introduced species were higher in parks found in landscapes with greater fragmentation. Frequencies of extirpations were less clearly related to human‐influenced habitat characteristics. Introductions and extinctions most often involved game species or species directly associated with human activities. There is little evidence of subtle ecological effects being responsible for species loss. Our results suggest that management should focus on direct human interventions, such as hunting, introduction of game species, and habitat fragmentation, in parks and surrounding areas.
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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 | 0.002 | 0.002 |
| 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".