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

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 US: 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
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 US, 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.