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Abstract Ensuring the sustainability of forest ecosystems requires understanding the mechanisms underlying tree growth and predicting their relative influence across taxa and environments.Functional ecology posits that variation in tree growth is related to individual differences in functional traits, which serve as proxies for resource acquisition and investment strategies. However, studies of trait–growth relationships have produced inconsistent results, likely due to unaccounted factors like interspecific interactions, ontogeny, differing leaf habit strategies, and variation in resource acquisition and allocation.We investigated the utility of key functional traits as predictors of tree height growth rates in common garden experiments in the absence of interspecific interactions. We posit that trait–growth relationships vary with age and between two groups relating to leaf habit: deciduous and evergreen species.Using data from 38 tree species planted in monoculture plots across seven sites of the International Diversity Experiment Network with Trees (IDENT) in North America and Europe, we compiled height growth rates over 9 years post‐germination. We modelled growth using a Bayesian hierarchical generalized linear model incorporating four above‐ground functional traits related to resource acquisition and investment: specific leaf area (SLA), wood density (WD), leaf dry matter content (LDMC) and seed mass (SM). Improvements in predictive power due to the variation of trait effects with age and leaf habit were evaluated via alternative hypothesis‐driven models, using the Expected Log Pointwise Predictive Density (ELPD) as a performance measure.Trait effects on growth varied with age and leaf habit, shifting between positive and negative effects, reflecting changes in resource acquisition and investment strategies. The relationships between traits and growth were strongest during the first three growing seasons for deciduous species and during the seventh to the ninth for evergreen species. Accounting for age and leaf habit substantially improved predictive power.Synthesis.Traits are not consistently associated with tree growth rates but instead reflect dynamic resource acquisition and investment strategies over time and between deciduous and evergreen species. Despite this variability, our findings confirm the utility of functional traits to predict tree growth rates, especially when trait effects are considered to vary with age and leaf habit.
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On the modelling of tropical tree growth: the importance of intra-specific trait variation, non-linear functions and phenotypic integration
Abstract Background and Aims The composition and dynamics of plant communities arise from individual-level demographic outcomes, which are driven by interactions between phenotypes and the environment. Functional traits that can be measured across plants are frequently used to model plant growth and survival. Perhaps surprisingly, species average trait values are often used in these studies and, in some cases, these trait values come from other regions or averages calculated from global databases. This data aggregation potentially results in a large loss of valuable information that probably results in models of plant performance that are weak or even misleading. Methods We present individual-level trait and fine-scale growth data from >500 co-occurring individual trees from 20 species in a Chinese tropical rain forest. We construct Bayesian models of growth informed by theory and construct hierarchical Bayesian models that utilize both individual- and species-level trait data, and compare these models with models only using individual-level data. Key Results We show that trait–growth relationships measured at the individual level vary across species, are often weak using commonly measured traits and do not align with the results of analyses conducted at the species level. However, when we construct individual-level models of growth using leaf area ratio approximations and integrated phenotypes, we generated strong predictive models of tree growth. Conclusions Here, we have shown that individual-level models of tree growth that are built using integrative traits always outperform individual-level models of tree growth that use commonly measured traits. Furthermore, individual-level models, generally, do not support the findings of trait–growth relationships quantified at the species level. This indicates that aggregating trait and growth data to the species level results in poorer and probably misleading models of how traits are related to tree performance.
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- PAR ID:
- 10207865
- Date Published:
- Journal Name:
- Annals of Botany
- ISSN:
- 0305-7364
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/aob/mcaa085
