Article

The pace of life for forest trees

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Abstract

Tree growth and longevity trade-offs fundamentally shape the terrestrial carbon balance. Yet, we lack a unified understanding of how such trade-offs vary across the world’s forests. By mapping life history traits for a wide range of species across the Americas, we reveal considerable variation in life expectancies from 10 centimeters in diameter (ranging from 1.3 to 3195 years) and show that the pace of life for trees can be accurately classified into four demographic functional types. We found emergent patterns in the strength of trade-offs between growth and longevity across a temperature gradient. Furthermore, we show that the diversity of life history traits varies predictably across forest biomes, giving rise to a positive relationship between trait diversity and productivity. Our pan-latitudinal assessment provides new insights into the demographic mechanisms that govern the carbon turnover rate across forest biomes.

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... Altogether our findings suggest that trees exhibit a consistent reduction in tree lifespan in response to increases in growth regardless of the type of species or forest, climate, or geographical location. These findings are consistent with demographic studies showing growth-survival tradeoffs across species in tropical forests (Russo et al. 2020;Wright et al. 2010), temperate forests (Fan, Zhang, and Zhao 2022; Kunstler, Coomes, and Canham 2009;Zhu et al. 2017), and within continents (Bialic-Murphy et al. 2024). ...
... Introduction of a greater diversity of species may thus contribute to weakening trade-offs within communities. Consistent with this several studies show that growth-mortality trade-offs are not necessarily universally applicable (Bialic-Murphy et al. 2024;Russo et al. 2020). To further understand these patterns future investigations should focus on species functional trait diversity. ...
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1. Matrix models are popular tools for plant demographic studies, but their application to long-lived, slow-growing species is hampered by the fact that (i) model output is highly sensitive to category width and (ii) growth variation between individuals can only be partially accounted for. Integral Projection Models (IPMs) – an extension of matrix models – offer a solution to these problems. 2. Here, we introduce a new method to parameterize IPMs for trees – the ‘integration method’– which allows constructing IPMs for long-lived, slow-growing species. This approach is more suitable than the ‘midpoint rule’, which is customarily used. 3. We built IPMs for six tree species from Vietnamese (sub)tropical forests. For four of these species, population growth rate (λ) was highly sensitive to the number of categories in the transition matrix. Population growth stabilized for IPMs with 100–1000 categories, corresponding to categories of 0.1–1 cm in trunk diameter. This preferred width is much narrower than the 10-cm-wide categories customarily used in tree models. 4. The distribution of elasticity values over transition types (stasis, progression to next and further categories) is also highly sensitive to matrix dimension in IPMs. In addition, elasticity distribution is influenced by including or excluding growth variation. 5. Age estimates obtained from IPMs were also highly sensitive to matrix dimension: an IPM with 1000 size categories yielded 2–4 times higher age estimates for large trees than one with 10 size categories. Observed ages obtained from tree ring analyses for four of the study species allowed validating these estimates. IPMs with 10 categories strongly underestimated age, while those with 1000 categories yielded slight age overestimates. Underestimating age in small matrices is caused by the occurrence of unrealistically fast pathways through the life cycle and is probably widespread among tree models with broad categories. Overestimating ages in IPMs with narrow categories may be due to temporally autocorrelated growth or errors in fitting growth curves. 6. Synthesis. IPMs are highly suitable tools to analyse tree demography. We recommend that tree IPMs (and classical matrix models) apply narrow diameter categories (0.1–1 cm width) to obtain reliable model output.
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