Modeling stand mortality using Poisson mixture models with mixed-effects

Stand mortality models play an important role in simulating stand dynamic processes. Periodic stand mortality data from permanent plots tend to be dispersed, and frequently contain an excess of zero counts. Such data have commonly been analyzed using the Poisson distribution and Poisson mixture mode...

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Main Authors: Zhang X-Q, Lei Y-C, Liu X-Z
Format: Article
Language:English
Published: Italian Society of Silviculture and Forest Ecology (SISEF) 2015-06-01
Series:iForest - Biogeosciences and Forestry
Subjects:
Online Access:https://iforest.sisef.org/contents/?id=ifor1022-008
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spelling doaj-f00e348b62bd46dcba1fee1ac08037a52020-11-25T01:01:29ZengItalian Society of Silviculture and Forest Ecology (SISEF)iForest - Biogeosciences and Forestry1971-74581971-74582015-06-018133333810.3832/ifor1022-0081022Modeling stand mortality using Poisson mixture models with mixed-effectsZhang X-Q0Lei Y-C1Liu X-Z2Key Laboratory of Tree Breeding and Cultivation, State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091 (China)Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091 (China)Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091 (China)Stand mortality models play an important role in simulating stand dynamic processes. Periodic stand mortality data from permanent plots tend to be dispersed, and frequently contain an excess of zero counts. Such data have commonly been analyzed using the Poisson distribution and Poisson mixture models, such as the zero-inflated Poisson model (ZIP), and the Hurdle Poisson model (HP). Based on mortality data obtained from sixty Chinese pine (Pinus tabulaeformis) permanent plots near Beijing, we added the random-effects to the Poisson mixture models. Results showed that the random-effects in the ZIP model was not convergent, and HP mixed-effects model performed better in modeling stand mortality than the Poisson fixed-effects model, the Poisson mixed-effects model, the ZIP fixed-effects model and the HP fixed-effects model. Moreover, the HP model accounts for two sources of dispersion, the first accounting for extra zeros and the second accounting to some extent for the dispersion due by individual heterogeneity in the positive set. We also found that stand mortality was negatively related to stand arithmetic mean diameter and positively related to dominant height.https://iforest.sisef.org/contents/?id=ifor1022-008Hurdle ModelMixed ModelPoisson ModelStand MortalityZero Inflated Model
collection DOAJ
language English
format Article
sources DOAJ
author Zhang X-Q
Lei Y-C
Liu X-Z
spellingShingle Zhang X-Q
Lei Y-C
Liu X-Z
Modeling stand mortality using Poisson mixture models with mixed-effects
iForest - Biogeosciences and Forestry
Hurdle Model
Mixed Model
Poisson Model
Stand Mortality
Zero Inflated Model
author_facet Zhang X-Q
Lei Y-C
Liu X-Z
author_sort Zhang X-Q
title Modeling stand mortality using Poisson mixture models with mixed-effects
title_short Modeling stand mortality using Poisson mixture models with mixed-effects
title_full Modeling stand mortality using Poisson mixture models with mixed-effects
title_fullStr Modeling stand mortality using Poisson mixture models with mixed-effects
title_full_unstemmed Modeling stand mortality using Poisson mixture models with mixed-effects
title_sort modeling stand mortality using poisson mixture models with mixed-effects
publisher Italian Society of Silviculture and Forest Ecology (SISEF)
series iForest - Biogeosciences and Forestry
issn 1971-7458
1971-7458
publishDate 2015-06-01
description Stand mortality models play an important role in simulating stand dynamic processes. Periodic stand mortality data from permanent plots tend to be dispersed, and frequently contain an excess of zero counts. Such data have commonly been analyzed using the Poisson distribution and Poisson mixture models, such as the zero-inflated Poisson model (ZIP), and the Hurdle Poisson model (HP). Based on mortality data obtained from sixty Chinese pine (Pinus tabulaeformis) permanent plots near Beijing, we added the random-effects to the Poisson mixture models. Results showed that the random-effects in the ZIP model was not convergent, and HP mixed-effects model performed better in modeling stand mortality than the Poisson fixed-effects model, the Poisson mixed-effects model, the ZIP fixed-effects model and the HP fixed-effects model. Moreover, the HP model accounts for two sources of dispersion, the first accounting for extra zeros and the second accounting to some extent for the dispersion due by individual heterogeneity in the positive set. We also found that stand mortality was negatively related to stand arithmetic mean diameter and positively related to dominant height.
topic Hurdle Model
Mixed Model
Poisson Model
Stand Mortality
Zero Inflated Model
url https://iforest.sisef.org/contents/?id=ifor1022-008
work_keys_str_mv AT zhangxq modelingstandmortalityusingpoissonmixturemodelswithmixedeffects
AT leiyc modelingstandmortalityusingpoissonmixturemodelswithmixedeffects
AT liuxz modelingstandmortalityusingpoissonmixturemodelswithmixedeffects
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