Leaf temperatures, traits, and rates

A grand challenge for humanity is predicting how climate change will alter the functioning of the biosphere and the services it provides. Current predictive models suffer from two main limitations. First, most models assume that plant physiology is driven by ambient macroclimate, but this is almost never accurate. Instead, plant traits operate via energy budgets to decouple macroclimate from the microclimates at which physiological processes occur. For example, plant leaves and forest canopies buffer variation in ambient temperature, which promotes maintenance of plant temperatures near metabolic optima and provides thermal refugia in variable climates. Second, most models are based on tissue, organ, or organismal physiology, so their predictions do not easily map to higher levels of organization (such as communities and ecosystems) that experience spatial variation in microclimate and physiological traits. For example, while climate has a well-established influence on rates of leaf-level carbon assimilation, it has a much weaker influence on rates of ecosystem-level carbon assimilation.

Predicting how spatially and biologically complex natural communities will respond to climate change requires a mechanistic mathematical framework for scaling plant-environment interactions across levels of organization, linking small scale climate and physiology with large scale ecosystem dynamics. We are synthetizing approaches from meteorology and scaling to develop novel integrative theory for predicting plant microclimates from macroclimate and forest structure. Microclimatic variation then drives variation in plant physiological rates, which “scale up” to control higher level processes such as primary productivity and evapotranspiration.

Key resources

Blonder B, Escobar S, Kapás RE, Michaletz ST. 2020. Low predictability of energy balance traits and leaf temperature metrics in desert, montane and alpine plant communities. Functional Ecology 34:1882-1897. Full text, Supporting Information

Blonder B, Michaletz ST. 2018. A model for leaf temperature decoupling from air temperature. Agricultural and Forest Meteorology 262:354-360. Full text, File S1, File S2, Mathematica code, R code, Data

Michaletz ST. 2018. Evaluating the kinetic basis of plant growth from organs to ecosystems. New Phytologist 219:37-44. Full text, Supporting Information, R code

Michaletz ST, Weiser MD, McDowell NG, Zhou J, Kaspari M, Helliker BR, Enquist BJ. 2016. The energetic and carbon economic origins of leaf thermoregulation. Nature Plants 2:16129. Full text, Supplementary Information

Michaletz ST, Weiser MD, Zhou J, Kaspari M, Helliker BR, Enquist BJ. 2015. Plant thermoregulation: Energetics, trait-environment interactions, and carbon economics. Trends in Ecology & Evolution 30:714-724. Full text, Supplementary Material

Michaletz ST, Johnson EA. 2006. Foliage influences forced convection heat transfer in conifer branches and buds. New Phytologist 170: 87-98. Full text, erratum

Forest MacroSystems network

The Forest MacroSystems (FMS) network is a set of long-term forest monitoring plots that provide data for analyzing and understanding drivers of global variation in forest diversity, demographics, and dynamics. It was established in 2011 in collaboration with the Enquist Macroecology Lab and several others. The FMS network currently comprises nine sites arrayed across a latitudinal climate gradient, from tropical seasonal forest in Panama (9° N) to temperate rainforest in British Columbia (50° N). The network is expanding; please contact Sean Michaletz if you are interested in installing a site!

We are currently working on several projects based on FMS network data. One project synthesizes radiative transfer theory and metabolic scaling theory to quantitatively predict energy non-equivalence in forest communities, and test this using FMS data. Another project is examining the influences of plant size, plant functional traits, and climate on global variation in forest mortality rates.

Key resources

Buzzard V, Michaletz ST, Deng Y, He Z, Ning D, Shen L, Tu Q, Van Nostrand J, Voordeckers JW, Wang J, Weiser MD, Kaspari M, Waide RB, Zhou J, Enquist BJ. 2019. Continental scale structuring of forest and soil diversity via functional traits. Nature Ecology & Evolution 3:1298-1308. Full text, Supplementary Information

Hogan J, McMahon S, Buzzard V, Michaletz ST, Enquist BJ, Thompson J, Swenson N, Zimmerman J. 2019. Drought and the interannual variability of stem growth in an aseasonal, everwet forest. Biotropica. Full text

Deng Y, Ning D, Qin Y, Xue K, Wu L, He Z, Yin H, Liang Y, Buzzard V, Michaletz ST, Zhou J. 2018. Spatial scaling of forest soil microbial communities across a temperature gradient. Environmental Microbiology 20:3504-3513. Full text

Zhou J, Deng Y, Shen L, Wen C, Qin Y, Xue K, Wu L, He Z, Voordeckers J, Buzzard V, Michaletz ST, Enquist BJ, Weiser MD, Kaspari M, Waide R, Yang Y, Brown JH. 2016. Temperature mediates continental-scale diversity of microbes in forest soils. Nature Communications 7:12083. Full text, Supplementary Material

Tu Q, Deng Y, Yan Q, He Z, Wu L, Buzzard V, Michaletz ST, Enquist BJ, Weiser MD, Kaspari M, Waide R, Brown JH, Shen L, Zhou J. 2016. Biogeographic patterns of soil diazotrophic communities across six forests in North America. Molecular Ecology 25:2937-2948. Full text

Plant Functional Traits Courses

We have helped develop, organize, deliver and teach a series of international Plant Functional Traits Courses (PFTC) since the very beginning. The PFTC unite scientists of all career stages from around the world to collect, analyze, and publish functional ecology data. We have held five courses since 2015, in China, Peru, and Svalbard. The next two courses will be held in Norway and South Africa.

During the PFTCs, our lab has focused on the role of leaf traits and temperature in leaf metabolism. Specifically, we have collected data on leaf energy balance traits and photosynthesis temperature response from diverse taxa spanning large elevational climate gradients at each site. These data characterize the kinetics of leaf metabolism, and are being used to understand how climate effects on leaf metabolism “scale up” to control higher level processes such as plant growth and ecosystem production.