Trait-based sensitivity of large mammals to a catastrophic tropical cyclone: DNA metabarcoding data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Extreme weather events perturb ecosystems and increasingly threaten biodiversity1. Ecologists emphasize the need to forecast and mitigate the impacts of these incidents, which requires knowledge of how risk is distributed among species and environments, but the scale and unpredictability of extreme events complicates assessment1–4. These challenges are compounded for large animals (‘megafauna’), which play crucial ecological roles but are hard to study5. Traits such as body size, dispersal ability, and habitat affiliation are among the hypothesized determinants of animals’ vulnerability to natural hazards1,6,7. However, it has rarely been possible to test these propositions or, more generally, to link short- and longer-term effects of weather-related disturbance8,9. Here, we show how large herbivores and carnivores in Mozambique responded to Intense Tropical Cyclone Idai, the deadliest storm on record in Africa, across scales ranging from individual decisions in the hours after landfall to community-level responses nearly 20 months later. Animals occupying low-elevation habitats exhibited strong spatial responses to rising floodwaters. Body size predicted species’ subsequent numerical responses: small-bodied species exhibited the greatest population declines. We trace this sensitivity to limited mobility, which increased likelihood of death during the flood and constrained animals’ capacity to withstand food shortages afterward. Our results identify potentially general trait-based mechanisms underlying animal responses to severe weather and may help to inform strategies for wildlife conservation in a volatile climate. Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press. Cambridge University Press, Cambridge, UK and New York, NY, USA, (2022). Smith, M. An ecological perspective on extreme climatic events: A synthetic definition and framework to guide future research. J. Ecol. 99, 656-663 (2011). Ummenhofer, C. C., & Meehl, G. A. Extreme weather and climate events with ecological relevance: a review, Phil. Trans. R. Soc. B. 372, 20160135 (2017). Jentsch, A., Kreyling, J., & Beierkuhnlein, C. A new generation of climate-change experiments: events, not trends. Front. Ecol. Environ. 5, 365-374 (2007). Pringle, R. M., et. al. Impacts of large herbivores on terrestrial ecosystems. Current Biology 33, R584-R610 (2023). Spiller, D. A., Losos, J. B., & Schoener, T. W. Impact of a catastrophic hurricane on island populations. Science 281, 695-697 (1998). Schoener, T. W., & Spiller, D. A. Nonsynchronous recovery of community characteristics in island spiders after a catastrophic hurricane. PNAS 103, 2220-2225 (2006). Pruitt, N., Little, A. G., Majumdar, S. J., Schoener, T. W., & Fisher, D. N. Call-to-Action: A global consortium for tropical cyclone ecology. TREE 34, 588-590 (2019). Lin, T. C., Hogan, J. A., & Chang, C. T. Tropical cyclone ecology: a scale-link perspective. TREE 35, 594-604 (2020).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.018 |
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 it