Forest Age Rivals Climate to Explain Reproductive Allocation Patterns in Forest Ecosystems Globally

Ward, Rachel E. and Zhang‐Zheng, Huanyuan and Abernethy, Kate and Adu‐Bredu, Stephen and Arroyo, Luzmilla and Bailey, Andrew and Barlow, Jos and Berenguer, Erika and Chesini‐Rossi, Liana and Cho, Percival and Dahlsjö, Cecilia A. L. and das Neves, Eder Carvalho and de Oliveira Sales, Bianca and Farfan‐Rios, William and Ferreira, Joice Nunes and Freitag, Renata and Girardin, Cécile and Huaraca Huasco, Walter and Joly, Carlos A. and Malhi, Yadvinder and Marimon, Beatriz and Marimon Junior, Ben Hur and Morel, Alexandra C. and Muller‐Landau, Helene C. and Peixoto, Karine da Silva and Reis, Simone and Riutta, Terhi and Salinas, Norma and Seixas, Marina and Silman, Miles R. and Kueppers, Lara M. (2025) Forest Age Rivals Climate to Explain Reproductive Allocation Patterns in Forest Ecosystems Globally. Ecology Letters, 28 (8): e70191. ISSN 1461-023X

Abstract

Forest allocation of net primary productivity (NPP) to reproduction (carbon required for flowers, fruits, and seeds) is poorly quantified globally, despite its critical role in forest regeneration and a well‐supported trade‐off with allocation to growth. Here, we present the first global synthesis of a biometric proxy for forest reproductive allocation (RA) across environmental and stand age gradients from a compiled dataset of 824 observations across 393 sites. We find that ecosystem‐scale RA increases ~60% from boreal to tropical forests. Climate shows important non‐linear relationships with RA, but is not the sole predictor. Forest age effects are comparable to climate in magnitude (MAT: ß = 0.24, p = 0.021; old growth forest: ß = 0.22, p < 0.001), while metrics of soil fertility show small but significant relationships with RA (soil pH: ß = 0.07, p = 0.001; soil N: ß = −0.07, p = 0.001). These results provide strong evidence that ecosystem‐scale RA is mediated by climate, forest age, and soil conditions, and is not a globally fixed fraction of positive NPP as assumed by most vegetation and ecosystem models. Our dataset and findings can be used by modellers to improve predictions of forest regeneration and carbon cycling.