Determining Tcrit, T50, and
T95 heat tolerances
At the end of the study period, we collected random leaves from each
focal individual and brought them to nearby laboratory facilities at the
University of Miami. Depending on the size of the leaves, between 3 and
66 leaves were collected from each individual and used to determine the
heat tolerances. Random leaflets from different leaves were sampled if
species had compound leaves. Once in the lab, we used a hole punch to
cut ~1.9 cm diameter disks from the leaves. We placed
six leaf disks from each individual in Miracloth fabric to prevent
anaerobiosis during heat treatments (Krause et al. 2010); one
layer of Miracloth was placed on the abaxial leaf surface and three
layers of Miracloth were placed on the adaxial leaf surface. We then
placed the Miracloth-enclosed leaves into waterproof plastic bags with
air removed and submerged in water baths maintained at room temperature
(~23˚C), 38, 40, 42, 44, 46, 48, 50, 52, 54, or 60˚C
with circulating heaters. Immediately following 15-minutes of heat
treatment, we removed the leaf pieces from water baths, placed them into
petri dishes lined with moist paper towels, and allowed them to recover
for 24 hours at room temperature under low light (~1μmol
photons m-2 s-1). Following this
recovery period, we dark-adapted the leaf pieces for 20 minutes before
measuring their maximum quantum yield
(FV/FM) with an OS30p+handheld fluorometer (Opti-Science, Hudson, NH USA).
To estimate each species’ Tcrit and T50,
we modeled the relationship of FV/FMversus treatment temperature for each plant using the ‘nls’ function in
base R’s ‘stats’ package (Core 2020). We calculated
Tcrit by finding the temperature where the slope of the
Fv/Fm vs. temperature relationship
reached 15% of its most extreme value. We calculated
T50 and T95 by predicting the
temperature that caused a 50% or 95% reduction in
Fv/Fm compared to the control treatment
as:
\(heat\ tolerance\ =\frac{log(\frac{\theta_{a}}{x}-\theta_{b})}{\theta_{c}}\)(eq. 3)
where \(\theta_{a}\) is the asymptote of the heat treatment-response
variable relationship, \(\theta_{b}\) is a constant, xrepresents 50% or 95% reduction in
Fv/Fm compared to control treatments,
and \(\theta_{c}\) is the decay parameter. The \(\theta\) parameters
were optimized and fit to the temperature-response relationship using
R’s ‘nls’ function following
\(y=\frac{\theta_{a}}{1+e^{-(\theta_{b}+\theta_{c}T)}}\) (eq. 4)
where T is the heat treatment temperature (R Core Team, 2018). We
generated bootstrapped means for Tcrit,
T50, and T95, by randomly resampling
data and fitting a new model for each species 100 times (Fig.
1b) . We present the mean bootstrapped values for Tcrit,
T50, and T95.