Low predictability of energy balance traits and leaf temperature metrics in desert, montane, and alpine plant communities

Benjamin Blonder, Sabastian Escobar, Rozália E. Kapás, Sean T. Michaletz

Read an interview with the author here.

Leaf temperatures influence numerous resource fluxes and biochemical processes in plants. They are important for predicting water use rates, carbon uptake rates, and mortality risk. However, leaf temperature can often differ from air temperature by large amounts, making it difficult to predict how plants will respond to environmental change. 

This study focused on better predicting leaf temperature variation, as well as measuring the properties of plants (functional traits) and the environment that potentially could predict this variation. We wanted to know whether there were easy ways to predict leaf temperature from weather data, without the need for detailed and expensive direct measurements of temperature.

We carried out a study in a range of habitats in the Colorado Rocky Mountains. We monitored leaf temperature for dozens of species over day-long intervals using infrared cameras, then simultaneously measured weather conditions and functional traits. The work involved many days of carrying heavy and delicate equipment up mountains, like the one shown in the companion photograph.

We found, contrary to our expectation, that leaf temperature variation was very difficult to predict from proxies. Despite this, we also documented high heterogeneity in temperature across species and time. Overall the study indicates that accurate empirical prediction of leaf temperature decoupling from air temperature remains an important and unresolved frontier.

Read the paper in full here.

Read an interview with the author here.

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