Abstract: Global climate change intensifies temperature extremes, yet empirical insights into forest species’ post-heat resilience mechanisms remain scarce. Climate extremes are projected to increase in frequency and intensity, making species resilience a critical determinant of forest ecosystem stability and biodiversity conservation under global change scenarios. Following the unprecedented 2022 Chongqing heatwave, we investigated resilience across 79 woody species along an altitudinal gradient. Given that reproductive investment strategies may be crucial for post-disturbance recovery, we hypothesized that the trade-offs between reproductive and vegetative investment may better predict species resilience under extreme heat conditions. Integrating Bayesian structural equation modeling-an advanced statistical approach that enables simultaneous analysis of multiple causal pathways and accounts for measurement uncertainty through probabilistic inference, we analyzed how vegetative traits (height, wood density and specific leaf area) and reproductive traits (seed mass, output and allocation) influence resilience. The results reveal: (1) specific leaf area (SLA) and seed mass negatively influenced species’ resilience, and seed mass was a particularly strong predictor of resilience across 79 species; (2) in 46 evergreen species, reproductive allocation at twig level enhanced resilience, while vegetative-reproductive trade-offs collectively explained resilience patterns; and (3) evergreen species adopted a bet-hedging strategy, dynamically adjusting trait allocations to balance survival and regeneration under thermal stress. This study advances community-scale resilience understanding by identifying seed mass as a key reproductive trait governing post-heat recovery, highlights seed size ecology under climate extremes, and informs post-disaster forest recovery strategies. In ecological afforestation, the findings may underscore the urgency of integrating trait-based ecology into forest management. Small-seeded species may be prioritized in post-heat stress recovery due to their stronger resilience capacity; evergreen species require balanced vegetative-reproductive resource allocation, while deciduous species with small SLA be favored. Long-term and multi-generational effects following extreme heat events require further exploration, and detailed technical specifications and operational guidelines represents the necessary next step for practical application.

Key words: Bet-hedging strategy, Bayesian structural equation modeling, Extremely high temperature, SLA, Trait combination, Vegetative-reproductive investment