Habitat loss, climate change, and emerging conservation challenges in Canada<sup>1</sup>This review is part of the virtual symposium “Flagship Species – Flagship Problems” that deals with ecology, biodiversity and management issues, and climate impacts on species at risk and of Canadian importance, including the polar bear (<i>Ursus maritimus</i>), Atlantic cod (<i>Gadus morhua</i>), Piping Plover (<i>Charadrius melodus</i>), and caribou (<i>Rangifer tarandus</i>).
Bibliographic record
Abstract
In Canada, habitat loss has pushed many more species to the brink of extinction than expected in a region with extensive wilderness. However, species richness gradients depend strongly on climate, so species are concentrated in southern regions, where agricultural and urban land uses are both intensive and extensive. Agricultural pesticide use is associated with increasing rates of species endangerment in the south, but long-range transport of persistent organic pollutants is an emerging issue in remote northern regions. Because their distributions reflect climate so strongly, climate change threatens species throughout Canada. Evidence indicates that species’ distributions, phenologies, and interactions with pests and diseases are changing more rapidly in response to climate change than global mean values. Nevertheless, climate change is expected to impose dispersal requirements that surpass species’ maximum rates. Habitat losses may interact with climate change to impair species’ dispersal still further, creating the potential for widespread disruption of biological systems in the most diverse areas of Canada. New research is urgently needed to address questions, and the ethics, around species translocation, ecosystem engineering to anticipate future environmental conditions, and strategies to facilitate the persistence of rare species in landscapes dominated by human activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".