Claim · model-written summary
Adding plant and site variables improved model performance little.
- Outcome
- model performancee0
- Direction
- little improvemente0
- Comparator
- other plant variables (height, bole wood density), or site characteristics (climate, age, management)e0
Not reportedTaxonMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier
Verbatim
e0“with little improvement in model performance when other plant variables (height, bole wood density), or site characteristics (climate, age, management) were included”
Title
Keryn I. Paul; Stephen H. Roxburgh; Jerome Chave; et al. Global Change Biology 2015. doi:10.1111/gcb.13201
Other claims from this paper (8)
- Observation Biomass–stem diameter relationships were generic across ecoregions for five plant functional types.
- Method / resource Simple power-law models explained 84–95% of biomass variation.
- Method / resource Generalized models lost less than 1% prediction efficiency compared with species-specific models.
- Method / resource Generalized multispecies models showed no significant biomass prediction bias for 92% of 53 species.
- Method / resource Stand-level biomass prediction efficiency was 99%.
- Method / resource Mean absolute prediction error was 13%.
- Recommendation The authors recommend generic plant-functional-type allometric models for cost-effective biomass prediction.
- Recommendation The authors recommend new species-specific models only when stand prediction accuracy gains are high.