Broad‐scale climate variation drives the dynamics of animal populations: a global multi‐taxa analysis
Bibliographic record
Abstract
Climate is a major extrinsic factor affecting the population dynamics of many organisms. The Broad-Scale Climate Hypothesis (BSCH) was proposed by Elton to explain the large-scale synchronous population cycles of animals, but the extent of support and whether it differs among taxa and geographical regions is unclear. We reviewed publications examining the relationship between the population dynamics of multiple taxa worldwide and the two most commonly used broad-scale climate indices, El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). Our review and synthesis (based on 561 species from 221 papers) reveals that population changes of mammals, birds and insects are strongly affected by major oceanic shifts or irregular oceanic changes, particularly in ENSO- and NAO-influenced regions (Pacific and Atlantic, respectively), providing clear evidence supporting Elton's BSCH. Mammal and insect populations tended to increase during positive ENSO phases. Bird populations tended to increase in positive NAO phases. Some species showed dual associations with both positive and negative phases of the same climate index (ENSO or NAO). These findings indicate that some taxa or regions are more or less vulnerable to climate fluctuations and that some geographical areas show multiple weather effects related to ENSO or NAO phases. Beyond confirming that animal populations are influenced by broad-scale climate variation, we document extensive patterns of variation among taxa and observe that the direct biotic and abiotic mechanisms for these broad-scale climate factors affecting animal populations are very poorly understood. A practical implication of our research is that changes in ENSO or NAO can be used as early signals for pest management and wildlife conservation. We advocate integrative studies at both broad and local scales to unravel the omnipresent effects of climate on animal populations to help address the challenge of conserving biodiversity in this era of accelerated climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".