Indicators of the impact of climate change on migratory species
Bibliographic record
Abstract
The Bonn Convention on the Conservation of Migratory Species of Wild Animals adopted a Resolution in 2005 recognising the impacts of climate change on migratory species. It called on Contracting Parties to undertake more research to improve our understanding of these impacts and to implement adaptation measures to reduce foreseeable adverse effects. Given the large diversity of taxa and species affected by climate change, it is impossible to monitor all species and effects thereof. However, it is likely that many of the key ecological and physical processes through which climate change may impact wildlife could be monitored using a suite of indicators, each comprising parameters of species/populations or groups of species as proxies for wider assemblages, habitats and ecosystems. Herein, we identify a suite of 17 indicators whose attributes could reveal negative impacts of climate change on the global status of migratory species: 4 for birds, 4 for marine mammals, 2 for sea turtles, 1 for fish, 3 for land mammals and 3 for bats. A few of these indicators would be relatively straightforward to develop, but most would require additional data collation, and in many cases methodological development. Choosing and developing indicators of the impacts of climate change on migratory species is a challenge, particularly with endangered species, which are subject to many other pressures. To identify and implement conservation measures for these species, indicators must account for the full ensemble of pressures, and link to a system of alerts and triggers for action.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".