Support for the social buffering hypothesis, especially under unpredictable precipitation regimes

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Support for the social buffering hypothesis, especially under unpredictable precipitation regimes

Authors

Salguero-Gomez, R.; Santos, G.; Brent, L.; Gascoigne, S.; Bonsall, M. B. F.; Firth, J. A.; Albery, G.; Mondal, R.; Kajin, M.; Davis, K. J.

Abstract

Sociality is hypothesised to buffer populations against environmental stochasticity by stabilising key vital rates, yet this has rarely been tested across diverse taxa. We compiled demographic time series from 87 populations for 66 animal species spanning 12 taxonomic classes to test whether sociality promotes demographic buffering and whether this relationship depends on environmental context. Using stochastic elasticities and phylogenetically comparative analyses, we find broad support for the social buffering hypothesis: more social species exhibit lower sensitivity of population growth to vital rate variance (lower stochastic elasticity to vital rate variances, Tsigma), particularly in juvenile survival and reproduction. Contrary to expectations, juvenile vital rates, but not adult survival, showed the strongest buffering patterns in social taxa, suggesting life-stage-specific social mechanisms. Furthermore, we show that social species invest more in maximising the average performance of adult survival and reproduction (higher stochastic elasticity to vital rate means, Tmu), consistent with adaptive strategies favouring canalisation of fitness-related traits. This buffering benefit is especially relevant in highly unpredictable environments: in climates with erratic precipitation, social species have greater demographic advantages, exhibiting even lower Tsigma values. Together, these findings provide support for the social buffering hypothesis, especially in highly stochastic environments. Social traits confer some demographic stability under moderate climatic conditions, but may become more relevant under extreme stochasticity, an insight particularly important for understanding population resilience in a rapidly changing world.

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