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Articles containing the keyword 'multilevel models'
Category : Research article
article id 504,
category
Research article
(2003).
Modeling mortality of individual trees in drained peatland sites in Finland.
Silva Fennica
vol.
37
no.
2
article id 504.
https://doi.org/10.14214/sf.504
Keywords:
Pinus sylvestris;
Betula pubescens;
simulation;
peatlands;
mortality;
generalized linear mixed models;
multilevel models
Abstract |
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Author Info
Multilevel logistic regression models were constructed to predict the 5-year mortality of Scots pine (Pinus sylvestris L.) and pubescent birch (Betula pubescens Ehrh.) growing in drained peatland stands in northern and central Finland. Data concerning tree mortality were obtained from two successive measurements of the National Forest Inventory-based permanent sample plot data base covering pure and mixed stands of Scots pine and pubescent birch. In the modeling data, Scots pine showed an average observed mortality of 2.73% compared to 2.98% for pubescent birch. In the model construction, stepwise logistic regression and multilevel models methods were applied, the latter making it possible to address the hierarchical data, thus obtaining unbiased estimates for model parameters. For both species, mortality was explained by tree size, competitive position, stand density, species admixture, and site quality. The expected need for ditch network maintenance or re-paludification did not influence mortality. The multilevel models showed the lowest bias in the modeling data. The models were further validated against independent test data and by embedding them in a stand simulator. In 100-year simulations with different initial stand conditions, the models resulted in a 72% and 66% higher total mortality rate for the stem numbers of pine and birch, respectively, compared to previously used mortality models. The developed models are expected to improve the accuracy of stand forecasts in drained peatland sites.
- Jutras, Département des sciences du bois et de la forêt, Université Laval, Ste-Foy, Québec, G1K 7P4, Canada E-mail: sj@nn.ca
-
Hökkä,
Finnish Forest Research Institute, Rovaniemi Research Station, P.O. Box 16, FIN 96301 Rovaniemi, Finland
E-mail:
hannu.hokka@metla.fi
- Alenius, Finnish Forest Research Institute, Rovaniemi Research Station, P.O. Box 16, FIN 96301 Rovaniemi, Finland E-mail: va@nn.fi
- Salminen, Finnish Forest Research Institute, Rovaniemi Research Station, P.O. Box 16, FIN 96301 Rovaniemi, Finland E-mail: hs@nn.fi
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