A study of crown development mechanisms using a shoot-based tree model and segmented terrestrial laser scanning data

Background and Aims: Functional-structural plant models (FSPMs) allow simulation of tree crown development as the sum of modular (e.g. shoot-level) responses triggered by the local environmental conditions. The actual process of space filling by the crowns can be studied. Although the FSPM simulations are at organ scale, the data for their validation have usually been at more aggregated levels (whole-crown or whole-tree). Measurements made by terrestrial laser scanning (TLS) that have been segmented into elementary units (internodes) offer a phenotyping tool to validate the FSPM predictions at levels comparable with their detail. We demonstrate the testing of different formulations of crown development of Scots pine trees in the LIGNUM model using segmented TLS data. Methods: We made TLS measurements from four sample trees growing in a forest on a relatively poor soil from sapling size to mature stage. The TLS data were segmented into intenodes. The segmentation also produced information on whether needles were present in the internode. We applied different formulations of crown development (flushing of buds and length of growth of new internodes) in LIGNUM. We optimized the parameter values of each formulation using genetic algorithms to observe the best fit of LIGNUM simulations to the measured trees. The fitness function in the estimation combined both tree-level characteristics (e.g. tree height and crown length) and measures of crown shape (e.g. spatial distribution of needle area). Key Results: Comparison of different formulations against the data indicates that the Extended Borchert- Honda model for shoot elongation works best within LIGNUM. Control of growth by local density in the crown was important for all shoot elongation formulations. Modifying the number of lateral buds as a function of local density in the crown was the best way to accomplish density control. Conclusions: It was demonstrated how segmented TLS data can be used in the context of a shoot-based model to select model components.Peer reviewe

A steady-state stomatal model of balanced leaf gas exchange, hydraulics and maximal source–sink flux

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Environmental control of growth variation in a boreal Scots pine stand : a data-driven approach

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Diurnal patterns in Scots pine stem oleoresin pressure in a boreal forest

Coniferous tree stems contain large amounts of oleoresin under positive pressure in the resin ducts. Studies in North-American pines indicated that the stem oleoresin exudation pressure (OEP) correlates negatively with transpiration rate and soil water content. However, it is not known how the OEP changes affect the emissions of volatile vapours from the trees. We measured the OEP, xylem diameter changes indicating changes in xylem water potential and monoterpene emissions under field conditions in mature Scots pine (Pinus sylvestris L.) trees in southern Finland. Contrary to earlier reports, the diurnal OEP changes were positively correlated with temperature and transpiration rate. OEP was lowest at the top part of the stem, where water potentials were also more negative, and often closely linked to ambient temperature and stem monoterpene emissions. However, occasionally OEP was affected by sudden changes in vapour pressure deficit (VPD), indicating the importance of xylem water potential on OEP as well. We conclude that the oleoresin storage pools in tree stems are in a dynamic relationship with ambient temperature and xylem water potential, and that the canopy monoterpene emission rates may therefore be also regulated by whole tree processes and not only by the conditions prevailing in the upper canopy.Peer reviewe

Reliability of temperature signal in various climate indicators from northern Europe

We collected relevant observational and measured annual-resolution time series dealing with climate in northern Europe, focusing in Finland. We analysed these series for the reliability of their temperature signal at annual and seasonal resolutions. Importantly, we analysed all of the indicators within the same statistical framework, which allows for their meaningful comparison. In this framework, we employed a cross-validation procedure designed to reduce the adverse effects of estimation bias that may inflate the reliability of various temperature indicators, especially when several indicators are used in a multiple regression model. In our data sets, timing of phenological observations and ice break-up were connected with spring, tree ring characteristics (width, density, carbon isotopic composition) with summer and ice formation with autumn temperatures. Baltic Sea ice extent and the duration of ice cover in different watercourses were good indicators of winter temperatures. Using combinations of various temperature indicator series resulted in reliable temperature signals for each of the four seasons, as well as a reliable annual temperature signal. The results hence demonstrated that we can obtain reliable temperature information over different seasons, using a careful selection of indicators, combining the results with regression analysis, and by determining the reliability of the obtained indicator.Peer reviewe

Finnish Centre of Excellence in Physics, Chemistry, Biology and Meteorology of Atmospheric Composition and Climate Change : summary and outlook

Peer reviewe

Reliability of temperature signal in the temperature indicator series, followed by 95% confidence intervals (in parentheses).

<p>Reliability of temperature signal in the temperature indicator series, followed by 95% confidence intervals (in parentheses).</p

Temperature indicator series and weather data used in this study (see S2 Fig), along with sub-division of tree ring series to regional series (from South to North).

<p>Temperature indicator series and weather data used in this study (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180042#pone.0180042.s003" target="_blank">S2 Fig</a>), along with sub-division of tree ring series to regional series (from South to North).</p

Time-series plot of predicted and measured seasonal (A-D) and annual (E) temperatures.

<p>Red line denotes the measured temperature, black line the predicted temperature, based on the best models: single variable for winter and LARS for spring, summer, autumn, and annual. All predictions were obtained using cross-validation. Missing predictions are due to lack of values in the indicator variables.</p