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New tree-level temperature response curves document sensitivity of tree growth to high temperatures across a US-wide climatic gradient.

Josephine Gantois
Published in: Global change biology (2022)
Temperature is a key climate indicator, whose distribution is expected to shift right in a warming world. However, the high-temperature tolerance of trees is less widely understood than their drought tolerance, especially when it comes to sub-lethal impacts of temperature on tree growth. I use a large data set of annual tree ring widths, combined with a flexible degree day model, to estimate the relationship between temperature and tree radial growth. I find that tree radial growth responds non-linearly to temperature across many ecoregions of the United States: across temperate and/or dry ecoregions, spring-summer temperature increases are beneficial or mostly neutral for tree growth up to around 25-30°C in humid climates and 10-15°C in dry climates, beyond which temperature increases suppress growth. Thirty additional degree days above the optimal temperature breakpoint lead to an average decrease in tree ring width of around 1%-5%, depending on ecoregions, seasons, and inclusion or exclusion of temperature-mediated drought impacts. High temperatures have legacy effects across a 5-year horizon in dry ecoregions, but none in the temperate-humid South-East or among temperature-sensitive trees. I find limited evidence that trees acclimatize to high temperatures within their lifetime: local variation in early exposure to high temperatures, which stems from local variation in the timing of tree birth, does not significantly impact the response to high temperatures, although temperature-sensitive trees acquire some heightened sensitivity from early exposure. I also find some evidence that trees adapt to high temperatures in the long run: across humid ecoregions of the United States, high temperatures are 40% less harmful to tree growth, where their average incidence is one standard deviation above average. Overall, these results highlight the strength of a new methodology which, applied to representative tree ring data, could contribute to predicting forest carbon uptake potential and composition under global change.
Keyphrases
  • climate change
  • machine learning
  • heat stress
  • big data
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  • artificial intelligence
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