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Abstract Light, temperature, water, and nutrient availability influence how plants grow to maximize access to resources. Axial growth, the linear extension of tissues by coordinated axial cell expansion, plays a central role in these adaptive morphological responses. Using Arabidopsis (Arabidopsis thaliana) hypocotyl cells to explore axial growth control mechanisms, we investigated WAVE-DAMPENED2-LIKE4 (WDL4), an auxin-induced, microtubule-associated protein and member of the larger WDL gene family shown to modulate hypocotyl growth under changing environmental conditions. Loss-of-function wdl4 seedlings exhibited a hyper-elongation phenotype under light conditions, continuing to elongate when wild-type Col-0 hypocotyls arrested and reaching 150% to 200% of wild-type length before shoot emergence. wdl4 seedling hypocotyls showed dramatic hyper-elongation (500%) in response to temperature elevation, indicating an important role in morphological adaptation to environmental cues. WDL4 was associated with microtubules under both light and dark growth conditions, and no evidence was found for altered microtubule array patterning in loss-of-function wdl4 mutants under various conditions. Examination of hormone responses showed altered sensitivity to ethylene and evidence for changes in the spatial distribution of an auxin-dependent transcriptional reporter. Our data provide evidence that WDL4 regulates hypocotyl cell elongation without substantial changes to microtubule array patterning, suggesting an unconventional role in axial growth control. 

Abstract Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss.