Extreme heat events are becoming increasingly common, and the short-term acclimation of photosynthesis will have a large impact on plant performance. Trees in lowland tropical forests, which are hypothesized to have limited abilities to tolerate rising temperatures, may need to rely on short term acclimation of their more plastic traits, like photosynthetic thermal tolerance, to persist in the face of increasingly variable climates. Here we investigated seasonal acclimation of thermal tolerances in plant species of the moist Amazon. Specifically, we measured the photosynthetic thermal tolerances of six common woody Amazonian species at the beginning and at the end of the dry season to determine the species’ abilities to acclimate to intra-annual changes to climate. In addition, we used the natural thermal gradient present at our research site to test the acclimation of individual plants to maximum air temperatures not currently observed elsewhere in the moist lowland Amazon (up to ~43 °C). Between seasons, there were significant overall increases in the thermal tolerances of three species (i.e., higher thermal tolerances in the hotter dry season), suggesting that leaf megathermy is prominent in these species. Also, three species acclimated their thermal tolerances to microsite-level differences in seasonal temperature maxima, suggesting closer fidelity between leaf and air temperatures (i.e., limited homeothermy) for these species. Our results show that some woody species from the moist Amazon can acclimate their thermal tolerances over short time scales, although acclimation is likely insufficient to overcome thermal stress during extreme temperature events. Some species may therefore be more sensitive to heatwaves than others, which could impact survival and composition of tropical lowland forests into the future.

We collected individual-level photosynthetic thermal tolerance data for six woody species of the lowland Amazon (Boiling River, Huánuco Department, Peru) following the methods described in the associated manuscript. We remeasured the same individuals (n_trees = 8-12 per species) at the beginning and end of the local dry season (in June and October, respectively) to test for seasonal acclimation of thermal tolerance. To measure thermal tolerance, we recorded the maximum quantum efficiency of photosystem II (F_v/F_m) on leaf discs 24 hours after being treated in 15-minute temperature-controlled water baths (n_temperatures = 9). We then fit a four-parameter logistic regression to each individual in each season and calculated thermal tolerance metrics of interest: T5 (5% decline in photosystem II functioning), T50 (50% decline in photosystem II functioning), T95 (95% decline in photosystem II funcitoning), DW (difference between T95 and T5), and Tcrit (initial decline in photosystem II functioning).

Associated seasonal air temperature maxima for each sampled individual were obtained by interpolating the seasonal maximum air temperature recorded by 13 TOMST TMS-4 dataloggers deployed across the study site with a clear Landsat image taken in each respective season. Interpolation was done with linear regression between the TOMST dataloggers and Landsat imagery to continuously predict seasonal maximum air temperature across the study site.

Please see associated publication for further details.