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Evaluation of different approaches to individual tree growth and survival modelling using data collected at irregular intervals-a case study for Pinus patula in Kenya

Rita Juma  Timo Pukkala  Sergio de-Miguel  Mbae Muchiri  
【摘要】:Background:The minimum set of sub-models for simulating stand dynamics on an individual-tree basis consists of tree-level models for diameter increment and survival.Ingrowth model is a necessary third component in uneven-aged management.The development of this type of model set needs data from permanent plots,in which all trees have been numbered and measured at regular intervals for diameter and survival.New trees passing the ingrowth limit should also be numbered and measured.Unfortunately,few datasets meet all these requirements.The trees may not have numbers or the length of the measurement interval varies.Ingrowth trees may not have been measured,or the number tags may have disappeared causing errors in tree identification.Methods:This article discussed and demonstrated the use of an optimization-based approach to individual-tree growth modelling,which makes it possible to utilize data sets having one or several of the above deficiencies.The idea is to estimate all parameters of the sub-models of a growth simulator simultaneously in such a way that,when simulation begins from the diameter distribution at the first measurement occasion,it yields a similar ending diameter distribution as measured in the second measurement occasion.The method was applied to Pinus patula permanent sample plot data from Kenya.In this dataset,trees were correctly numbered and identified but measurement interval varied from 1 to 13 years.Two simple regression approaches were used and compared to the optimization-based model recovery approach.Results:The optimization-based approach resulted in far more accurate simulations of stand basal area and number of surviving trees than the equations fitted through regression analysis.Conclusions:The optimization-based modelling approach can be recommended for growth modelling when the modelling data have been collected at irregular measurement intervals.

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