Predicting individual tree growth using stand-level simulation, diameter distribution, and Bayesian calibration

Key message

We propose a methodology to develop a preliminary version of a growth model when tree-level growth data are unavailable. This modelling approach predicts individual tree growth using only one-time observations from temporary plots. A virtual dataset was generated by linking the whole stand and diameter distribution models. The individual tree model was parameterized using Bayesian calibration and a likelihood of diameter distributions.


Context A key component of tree-level growth and yield prediction is the diameter increment model that requires at least two different points in time with individual tree measurements. In some cases, however, sufficient inventory data from remeasured permanent or semitemporary plots are unavailable or difficult to access.
Aims The purpose of this study was to propose a three-stage approach for modelling individual tree diameter growth based on temporary plots.
Methods The first stage is to predict stand dynamics at 5-year intervals based on stand-level resource inventory data. The second stage is to simulate diameter distribution at 5-year intervals using a Weibull function based on tree-level research inventory data. The final stage is to improve the reliability of individual tree diameter estimates by updating parameters with Bayesian calibration based on a likelihood of diameter distributions.
Results The virtual-data-based diameter increment model provided logical patterns and reliable performances in both tree- and stand-level predictions. Although it underestimated the growth of suppressed trees compared with tree cores and remeasurements, this bias was negligible when aggregating tree-level simulations into stand-level growth predictions.
Conclusion Our virtual-data-based modelling approach only requires one-time observations from temporary plots, but provide reliable predictions of stand- and tree-level growth when validated with tree cores and whole-stand models. This preliminary model can be updated in a Bayesian framework when growth data from tree cores or remeasurements were obtained.

Diameter increment, Growth and yield, Sparse data, Data aggregation, Parametric uncertainty

Tian, X., Sun, S., Mola-Yudego, B. et al. Predicting individual tree growth using stand-level simulation, diameter distribution, and Bayesian calibration. Annals of Forest Science 77, 57 (2020).

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Data availability
The datasets generated or analyzed during the current study are available from the corresponding author on reasonable request.

Topical Collection
This article is part of the Topical Collection “Frontiers in Modelling Future Forest Growth, Yield and Wood Properties

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