The geography of the Anthropocene differs between the land and the sea
Bibliographic record
Abstract
Abstract Climate change and other anthropogenic drivers of biodiversity change are unequally distributed across the world. The geographic patterns of different drivers, and the spatial overlap among these drivers, have important implications for the direction and pace of biodiversity change, yet are not well documented. Moreover, it is unknown if the geographic patterns of drivers differ between the terrestrial and marine realm, as expected due to marked differences in how humans interact with the land and ocean. We compiled global gridded datasets on climate change, land-use, resource exploitation, pollution, species invasions, and human population density. We used multivariate statistics to examine the spatial relationships among the datasets and to characterize the typical combinations of drivers experienced by different parts of the world. We found stronger positive correlations among drivers in the terrestrial than in the marine realm, leading to areas of high intensities of multiple drivers on land. Climate change tended to be negatively correlated with other drivers in the terrestrial realm (e.g., in the tundra and boreal forest with high climate change but low human use and pollution) whereas the opposite was true in the marine realm (e.g., in the Indo-Pacific with high climate change and high fishing). We show that different regions of the world can be defined by anthropogenic threat complexes (ATCs), distinguished by different sets of drivers with varying intensities. The ATCs can be used to test hypothesis about the pattern of biodiversity change, especially the joint effects of multiple drivers. More generally, our global analysis highlights the broad conservation priorities needed to mitigate the effects of anthropogenic change on biodiversity responses, with different priorities emerging on land and in the ocean, and in different parts of the world.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".