Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Recent progress toward the application of process-based models in forestmanagement includes the development of evaluation and parameter estimation methods suitable for models with causal structure, and the accumulation of data that can be used in model evaluation. The current state of the art of process modeling is discussed in the context of forest ecosystem management. We argue that the carbon balance approach is readily applicable for projecting forest yield and productivity, and review several carbon balance models for estimating stand productivity and individual tree growth and competition. We propose that to develop operational models, it is necessary to accept that all models may have both empirical and causal components at the system level. We present examples of hybrid carbon balance models and consider issues that currently require incorporation of empirical information at the system level. We review model calibration and validation methods that take account of the hybrid character of models. The operational implementation of process-based models to practical forest management is discussed. Methods of decision-making in forest management are gradually moving toward a more general, analytical approach, and it seems likely that models that include some process-oriented components will soon be used in forestry enterprises. This development is likely to run parallel with the further development of ecophysiologically based models.

Coniferous trees growing in the boreal and temperate zones have a clear annual cycle of photosynthetic activity. A recent study demonstrated that the seasonal variation in photosynthetic capacity of Scots pine (Pinus sylvestris L.) could be attributed mainly to the light response curve of photosynthesis. The magnitude of the light response curve varied over the season while its shape remained constant, indicating that the two physiological parameters quantifying the curve—the quantum yield per unit internal carbon dioxide concentration and the corresponding light-saturated rate—remained proportional to each other. We now show, through modeling studies, that the quantum yield (and hence the light-saturated rate) is related to the annual cycle of temperature through a delayed dynamic response. The proposed model was tested by comparing model results with intensive measurements of photosynthesis and driving variables made from April to October in three shoots of Scots pine growing near the northern timberline. Photosynthetic capacity showed considerable acclimation during the growing season. A single model describing photosynthetic capacity as a reversible, first-order delay process driven by temperature explained most of the variation in photosynthetic capacity during the year. The proposed model is simpler but no less accurate than previous models of the annual cycle of photosynthetic capacity.

Thinning is a routine forest management operation that changes tree spacing, number, and size distribution and affects the material flows between vegetation and the atmosphere. Here, using direct micrometeorological ecosystem‐scale measurements, we show that in a boreal pine forest, thinning decreases the deposition velocities of fine particles as expected but does not reduce the carbon sink, water vapor flux, or ozone deposition. The thinning decreased the all‐sided leaf area index from 8 to 6, and we suggest that the redistribution of sources and sinks within the ecosystem compensated for this reduction in foliage area. In the case of water vapor and O₃, changes in light penetration and among‐tree competition seem to increase individual transpiration rates and lead to larger stomatal apertures, thus enhancing also O₃ deposition. In the case of CO₂, increased ground vegetation assimilation and decreased autotrophic respiration seem to cancel out opposite changes in canopy assimilation and heterotrophic respiration. Current soil‐vegetation‐atmosphere transfer models should be able to reproduce these observations.