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1. Linking leaf dark respiration to leaf traits and reflectance spectroscopy across diverse forest types

   https://doi.org/10.1111/nph.20267  

   Wu, Fengqi; Liu, Shuwen; Lamour, Julien; Atkin, Owen K; Yang, Nan; Dong, Tingting; Xu, Weiying; Smith, Nicholas G; Wang, Zhihui; Wang, Han; et al (April 2025, New Phytologist)

   Summary Leaf dark respiration (R_dark), an important yet rarely quantified component of carbon cycling in forest ecosystems, is often simulated from leaf traits such as the maximum carboxylation capacity (V_cmax), leaf mass per area (LMA), nitrogen (N) and phosphorus (P) concentrations, in terrestrial biosphere models. However, the validity of these relationships across forest types remains to be thoroughly assessed.Here, we analyzedR_darkvariability and its associations withV_cmaxand other leaf traits across three temperate, subtropical and tropical forests in China, evaluating the effectiveness of leaf spectroscopy as a superior monitoring alternative.We found that leaf magnesium and calcium concentrations were more significant in explaining cross‐siteR_darkthan commonly used traits like LMA, N and P concentrations, but univariate trait–R_darkrelationships were always weak (r² ≤ 0.15) and forest‐specific. Although multivariate relationships of leaf traits improved the model performance, leaf spectroscopy outperformed trait–R_darkrelationships, accurately predicted cross‐siteR_dark(r² = 0.65) and pinpointed the factors contributing toR_darkvariability.Our findings reveal a few novel traits with greater cross‐site scalability regardingR_dark, challenging the use of empirical trait–R_darkrelationships in process models and emphasize the potential of leaf spectroscopy as a promising alternative for estimatingR_dark, which could ultimately improve process modeling of terrestrial plant respiration. 

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2. A new dimension of leaf economic spectrum: temporal instability of relationships among genotypes

   https://doi.org/10.1111/nph.20191  

   Pahadi, Pratima; Wason, Jay; Annis, Seanna; McGill, Brian; Zhang, Yong‐Jiang (December 2024, New Phytologist)

   Summary Leaf economic spectrum (LES) relationships have been studied across many different plant lineages and at different organizational scales. However, the temporal stability of the LES relationships is largely unknown. We used the wild blueberry system with high genotypic diversity to test whether trait–trait relationships across genotypes demonstrate the same LES relationships found in the global database (GLOPNET) and whether they are stable across years.We studied leaf structure, photosynthesis, and leaf nutrients for 16 genotypes of two wild blueberry species semi‐naturally grown in a common farm in Maine, USA, across 4 yr.We found substantial variation in leaf structure, physiology, and nutrient traits within and among genotypes, as well as across years in wild blueberries. The LES trait–trait relationships (covariance structure) across genotypes were not always found in all years. The trait syndrome of wild blueberries was shifted by changing environmental conditions over the years. Additionally, traits in 1 yr cannot be used to predict those of another year.Our findings show that LES generally holds among genotypes but is temporally unstable, stressing the significant influence of trait plasticity in response to fluctuating environmental conditions across years, and the importance of temporal dimensions in shaping functional traits and species coexistence. 

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3. Deciphering the variability of leaf phosphorus‐allocation strategies using leaf economic traits and reflectance spectroscopy across diverse forest types

   https://doi.org/10.1111/nph.70219  

   Dong, Tingting; Wu, Fengqi; Tsujii, Yuki; Townsend, Philip A; Yang, Nan; Xu, Weiying; Liu, Shuwen; Swenson, Nathan G; Lamour, Julien; Han, Wenxuan; et al (August 2025, New Phytologist)

   Summary Allocation of leaf phosphorus (P) among different functional fractions represents a crucial adaptive strategy for optimizing P use. However, it remains challenging to monitor the variability in leaf P fractions and, ultimately, to understand P‐use strategies across diverse plant communities.We explored relationships between five leaf P fractions (orthophosphate P, P_i; lipid P, P_L; nucleic acid P, P_N; metabolite P, P_M; and residual P, P_R) and 11 leaf economic traits of 58 woody species from three biomes in China, including temperate, subtropical and tropical forests. Then, we developed trait‐based models and spectral models for leaf P fractions and compared their predictive abilities.We found that plants exhibiting conservative strategies increased the proportions of P_Nand P_M, but decreased the proportions of P_iand P_L, thus enhancing photosynthetic P‐use efficiency, especially under P limitation. Spectral models outperformed trait‐based models in predicting cross‐site leaf P fractions, regardless of concentrations (R² = 0.50–0.88 vs 0.34–0.74) or proportions (R² = 0.43–0.70 vs 0.06–0.45).These findings enhance our understanding of leaf P‐allocation strategies and highlight reflectance spectroscopy as a promising alternative for characterizing large‐scale leaf P fractions and plant P‐use strategies, which could ultimately improve the physiological representation of the plant P cycle in land surface models. 

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4. Nonstructural carbohydrate dynamics' relationship to leaf development under varying environments

   https://doi.org/10.1111/nph.19333  

   Blumstein, Meghan; Oseguera, Miranda; Caso‐McHugh, Theresa; Des Marais, David L. (October 2023, New Phytologist)

   NA (Ed.)

   Summary Leaf‐out in temperate forests is a critical transition point each spring and advancing with global change. The mechanism linking phenological variation to external cues is poorly understood. Nonstructural carbohydrate (NSC) availability may be key.Here, we use branch cuttings from northern red oak (Quercus rubra) and measure NSCs throughout bud development in branch tissue. Given genes and environment influence phenology, we placed branches in an arrayed factorial experiment (three temperatures × two photoperiods, eight genotypes) to examine their impact on variation in leaf‐out timing and corresponding NSCs.Despite significant differences in leaf‐out timing between treatments, NSC patterns were much more consistent, with all treatments and genotypes displaying similar NSC concentrations across phenophases. Notably, the moderate and hot temperature treatments reached the same NSC concentrations and phenophases at the same growing degree days (GDD), but 20 calendar days apart, while the cold treatment achieved only half the GDD of the other two.Our results suggest that NSCs are coordinated with leaf‐out and could act as a molecular clock, signaling to cells the passage of time and triggering leaf development to begin. This link between NSCs and budburst is critical for improving predictions of phenological timing. 

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5. Resolving the effects of functional traits on tree growth rates: The influence of temporal dynamics and divergent strategies by leaf habit

   https://doi.org/10.1111/1365-2745.70151  

   Déziel, Mégane; Schoenig, Daniel; Sousa‐Silva, Rita; Searle, Eric B; Parker, William C; Cavender‐Bares, Jeannine; Mereu, Simone; Scherer‐Lorenzen, Michael; Nock, Charles A; Messier, Christian; et al (November 2025, Journal of Ecology)

   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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