Stem diameter variations as a versatile research tool in ecophysiology

High-resolution stem diameter variations (SDV) are widely recognized as a useful drought stress indicator and have therefore been used in many irrigation scheduling studies. More recently, SDV have been used in combination with other plant measurements and biophysical modelling to study fundamental mechanisms underlying whole-plant functioning and growth. The present review aims to scrutinize the important insights emerging from these more recent SDV applications to identify trends in ongoing fundamental research. The main mechanism underlying SDV is variation in water content in stem tissues, originating from reversible shrinkage and swelling of dead and living tissues, and irreversible growth. The contribution of different stem tissues to the overall SDV signal is currently under debate and shows variation with species and plant age, but can be investigated by combining SDV with state-of-the-art technology like magnetic resonance imaging. Various physiological mechanisms, such as water and carbon transport, and mechanical properties influence the SDV pattern, making it an extensive source of information on dynamic plant behaviour. To unravel these dynamics and to extract information on plant physiology or plant biophysics from SDV, mechanistic modelling has proved to be valuable. Biophysical models integrate different mechanisms underlying SDV, and help us to explain the resulting SDV signal. Using an elementary modelling approach, we demonstrate the application of SDV as a tool to examine plant water relations, plant hydraulics, plant carbon relations, plant nutrition, freezing effects, plant phenology and dendroclimatology. In the ever-expanding SDV knowledge base we identified two principal research tracks. First, in detailed short-term experiments, SDV measurements are combined with other plant measurements and modelling to discover patterns in phloem turgor, phloem osmotic concentrations, root pressure and plant endogenous control. Second, long-term SDV time series covering many different species, regions and climates provide an expanding amount of phenotypic data of growth, phenology and survival in relation to microclimate, soil water availability, species or genotype, which can be coupled with genetic information to support ecological and breeding research under on-going global change. This under-exploited source of information has now encouraged research groups to set up coordinated initiatives to explore this data pool via global analysis techniques and data-mining.