Tropical montane cloud forests (TMCFs) inhabit regions rich in biodiversity that play an important role in the local and regional water cycle. Canopy plants such as epiphytes and hemiepiphytes are an important component of the biodiversity in the TMCF and therefore play a significant role in the carbon, nutrient, and water cycles. With only partial or no access to resources on the ground, canopy plants may be vulnerable to changes in climate that increase canopy temperatures and decrease atmospheric humidity or precipitation inputs. Despite their importance in the TMCF, little is known about variation in functional strategies relating to drought avoidance or drought tolerance of canopy plants. In this study, we quantified variation in a number of functional traits in 11 species of epiphytes and hemiepiphytes in a Costa Rican TMCF. We also generated pressure–volume and xylem vulnerability curves that we used as indicators of drought tolerance. In addition, we hand‐sectioned fresh leaves and examined cross sections under a microscope to quantify leaf thickness, mesophyll thickness and the thickness of water storage cell layers (i.e., hydrenchyma), if present. Lastly, we determined the capacity for foliar water uptake in the laboratory and measured whole‐plant transpiration in the field. A trade‐off was found between traits that confer relative drought resistance and foliar water uptake capacity vs. traits that confer leaf capacitance and relative drought avoidance. This trade‐off may represent an additional axis of the leaf economics spectrum that is unique to epiphytes. We also found that all species had the capacity for foliar uptake of water and that this process contributed substantially to their water balance. On average, foliar uptake of water contributed to the reabsorption of 70% of the water transpired over a relatively wet, 34‐day study period. Our results indicate that canopy plants can mitigate water loss substantially via internal water storage or that they can directly utilize cloud water to offset losses. Our results indicate that species that rely on foliar uptake of water may be more vulnerable to projected changes in climate than species that buffer the effects of drought via internal water storage.

Partial least squares regression (PLSR) modelling is a statistical technique for correlating datasets, and involves the fitting of a linear regression between two matrices. One application of PLSR enables leaf traits to be estimated from hyperspectral optical reflectance data, facilitating rapid, high-throughput, non-destructive plant phenotyping. This technique is of interest and importance in a wide range of contexts including crop breeding and ecosystem monitoring. The lack of a consensus in the literature on how to perform PLSR means that interpreting model results can be challenging, applying existing models to novel datasets can be impossible, and unknown or undisclosed assumptions can lead to incorrect or spurious predictions. We address this lack of consensus by proposing best practices for using PLSR to predict plant traits from leaf-level hyperspectral data, including a discussion of when PLSR is applicable, and recommendations for data collection. We provide a tutorial to demonstrate how to develop a PLSR model, in the form of an R script accompanying this manuscript. This practical guide will assist all those interpreting and using PLSR models to predict leaf traits from spectral data, and advocates for a unified approach to using PLSR for predicting traits from spectra in the plant sciences.

By measuring this proxy of canopy VPD, TMCF researchers will better understand differences in microclimate and plant community composition across TMCF sites. Incorporating such information in comparative studies will allow for more meaningful comparisons across TMCFs and will further conservation and management efforts in this ecosystem.

Stomata play a central role in surface–atmosphere exchange by controlling the flux of water and CO2 between the leaf and the atmosphere. Representation of stomatal conductance (gsw) is therefore an essential component of models that seek to simulate water and CO2 exchange in plants and ecosystems. For given environmental conditions at the leaf surface (CO2 concentration and vapor pressure deficit or relative humidity), models typically assume a linear relationship between gsw and photosynthetic CO2 assimilation (A). However, measurement of leaf‐level gsw response curves to changes in A are rare, particularly in the tropics, resulting in only limited data to evaluate this key assumption. Here, we measured the response of gsw and A to irradiance in six tropical species at different leaf phenological stages. We showed that the relationship between gsw and A was not linear, challenging the key assumption upon which optimality theory is based—that the marginal cost of water gain is constant. Our data showed that increasing A resulted in a small increase in gsw at low irradiance, but a much larger increase at high irradiance. We reformulated the popular Unified Stomatal Optimization (USO) model to account for this phenomenon and to enable consistent estimation of the key conductance parameters g0 and g1. Our modification of the USO model improved the goodness‐of‐fit and reduced bias, enabling robust estimation of conductance parameters at any irradiance. In addition, our modification revealed previously undetectable relationships between the stomatal slope parameter g1 and other leaf traits. We also observed nonlinear behavior between A and gsw in independent data sets that included data collected from attached and detached leaves, and from plants grown at elevated CO2 concentration. We propose that this empirical modification of the USO model can improve the measurement of gsw parameters and the estimation of plant and ecosystem‐scale water and CO2 fluxes.