Multilevel mixed-effect models to predict wood volume in a hyperdiverse Amazon forest
ABSTRACT Accurate wood volume predictions are critical in hyperdiverse forests because each species has specific size and shape traits. Although generic models at a multispecies level were widely used in Amazonian managed forests, they are subject to more significant bias due to interspecific variab...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Instituto Nacional de Pesquisas da Amazônia
2024-01-01
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| Series: | Acta Amazonica |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0044-59672024000100500&lng=en&tlng=en |
| _version_ | 1797361171148308480 |
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| author | Vinicius Costa CYSNEIROS Allan Libanio PELISSARI Rodrigo Geroni Mendes NASCIMENTO Sebastião Amaral MACHADO |
| author_facet | Vinicius Costa CYSNEIROS Allan Libanio PELISSARI Rodrigo Geroni Mendes NASCIMENTO Sebastião Amaral MACHADO |
| author_sort | Vinicius Costa CYSNEIROS |
| collection | DOAJ |
| description | ABSTRACT Accurate wood volume predictions are critical in hyperdiverse forests because each species has specific size and shape traits. Although generic models at a multispecies level were widely used in Amazonian managed forests, they are subject to more significant bias due to interspecific variability. We used an extensive database of wood volume collected in managed forests to test the hypothesis that generic models violate the independence assumption due to that predictions vary with species-specific size. Our hypothesis was proved as residuals of the generic model were conditioned to species and specific size. The multilevel models were more accurate both in fitting and validation procedures, and accounted for variance derived from species and specific size, providing a more reliable prediction. However, we found that the size-specific models have a similar predictive ability to species-specific models for new predictions. This implies more practical estimates in hyperdiverse forests where fitting species-specific models can be complex. The findings are crucial for sustainable forest management as they allow for more reliable wood volume estimates, leading to less financial uncertainty and preventing damage to forest stocks through under or over-exploitation. |
| first_indexed | 2024-03-08T15:50:06Z |
| format | Article |
| id | doaj.art-c3e47257b059433bb0857a195c7a86d9 |
| institution | Directory Open Access Journal |
| issn | 0044-5967 |
| language | English |
| last_indexed | 2024-03-08T15:50:06Z |
| publishDate | 2024-01-01 |
| publisher | Instituto Nacional de Pesquisas da Amazônia |
| record_format | Article |
| series | Acta Amazonica |
| spelling | doaj.art-c3e47257b059433bb0857a195c7a86d92024-01-09T07:41:32ZengInstituto Nacional de Pesquisas da AmazôniaActa Amazonica0044-59672024-01-0154110.1590/1809-4392202302081Multilevel mixed-effect models to predict wood volume in a hyperdiverse Amazon forestVinicius Costa CYSNEIROShttps://orcid.org/0000-0002-1365-9360Allan Libanio PELISSARIRodrigo Geroni Mendes NASCIMENTOSebastião Amaral MACHADOABSTRACT Accurate wood volume predictions are critical in hyperdiverse forests because each species has specific size and shape traits. Although generic models at a multispecies level were widely used in Amazonian managed forests, they are subject to more significant bias due to interspecific variability. We used an extensive database of wood volume collected in managed forests to test the hypothesis that generic models violate the independence assumption due to that predictions vary with species-specific size. Our hypothesis was proved as residuals of the generic model were conditioned to species and specific size. The multilevel models were more accurate both in fitting and validation procedures, and accounted for variance derived from species and specific size, providing a more reliable prediction. However, we found that the size-specific models have a similar predictive ability to species-specific models for new predictions. This implies more practical estimates in hyperdiverse forests where fitting species-specific models can be complex. The findings are crucial for sustainable forest management as they allow for more reliable wood volume estimates, leading to less financial uncertainty and preventing damage to forest stocks through under or over-exploitation.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0044-59672024000100500&lng=en&tlng=enforest managementvolume predictionmultispecies generic modelmodel improvement |
| spellingShingle | Vinicius Costa CYSNEIROS Allan Libanio PELISSARI Rodrigo Geroni Mendes NASCIMENTO Sebastião Amaral MACHADO Multilevel mixed-effect models to predict wood volume in a hyperdiverse Amazon forest Acta Amazonica forest management volume prediction multispecies generic model model improvement |
| title | Multilevel mixed-effect models to predict wood volume in a hyperdiverse Amazon forest |
| title_full | Multilevel mixed-effect models to predict wood volume in a hyperdiverse Amazon forest |
| title_fullStr | Multilevel mixed-effect models to predict wood volume in a hyperdiverse Amazon forest |
| title_full_unstemmed | Multilevel mixed-effect models to predict wood volume in a hyperdiverse Amazon forest |
| title_short | Multilevel mixed-effect models to predict wood volume in a hyperdiverse Amazon forest |
| title_sort | multilevel mixed effect models to predict wood volume in a hyperdiverse amazon forest |
| topic | forest management volume prediction multispecies generic model model improvement |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0044-59672024000100500&lng=en&tlng=en |
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