Seed germination traits and dormancy classification of 27 species from a degraded karst mountain in central Yunnan–Guizhou Plateau: seed mass and moisture content correlate with germination capacity

Abstract

In degraded karst ecosystem, vegetation restoration efforts almost exclusively rely on planted seedlings, but this is not effective to maintain community diversity and resilience. As seed functional traits, seed dormancy and germination are key to community assembly. Unfortunately, these elements are commonly overlooked in restoring degraded ecosystems.• This work classifies seed dormancy of 27 species with different life forms that are common on a degraded karst mountain. We examined the effects of temperature regime and light conditions on percentage germination and assessed the relationships between seed traits and germination index using a partial least squares regression (PLSR).• Approximately 48% of the investigated species had physiological dormancy, 37% were non-dormant, 7% had morphophysiological dormancy, 4% had morphological dormancy and 4% had physical dormancy. We found that 94% (15 out of 16) species had maximum germination in warm temperature regimes (20/13 and 25/18 °C), while the remaining species required cool temperatures (10/4 °C). PLSR analysis indicated a significant positive correlation between seed mass and T 50m (time to 50% final germination), and a negative correlation between seed moisture content and percentage germination.• Our findings indicate that seed traits are important factors in seed-based restoration practice. F. esculentum, O. opipara, P. fortuneana and S. salicifolia are recommended for direct seeding during the early rainy season to restore seriously degraded lands in subtropical karst regions.

Defect detection method for key area guided transmission line components based on knowledge distillation

“…And give better performing teacher models a greater say in instruction. Literature (Wang et al, 2022) proposes an attention mechanism based on the feature map quality evaluation algorithm (IQE). The knowledge distillation method based on the IQE attention mechanism uses the IQE method to identify important knowledge in the pretrained SAR target recognition deep neural network.…”