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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Italian Society of Silviculture and Forest Ecology (SISEF)
2015-06-01
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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 |
_version_ |
1725209133199654912 |