Variation in leaf traits is critical for carbon gains and losses during leaf life and drives litter carbon and nutrient losses via decomposition. Accurately quantifying litter decomposition parameters is essential for assessing ecosystem carbon and nutrient dynamics. Leaf litterbags have commonly been employed to measure effects of environmental drivers, decomposers, and plant traits on decomposition rates. There has been much debate regarding the suitability of substituting senesced dead leaves with fresh (green) leaves in litterbags, which has been common practice for mimicking green leaf fall or for practical reasons. Therefore, we tested the null hypothesis that replacement of dead leaves with fresh leaves in litterbag experiments is justified, based on similarities in structural and chemical traits between fresh and dead leaves across plant species and growth forms. We conducted a paired litterbag decomposition experiment with both fresh and dead leaves of 26 common species in subtropical China, in each of five contrasting ecosystems. While fresh leaves generally decomposed faster than dead leaves, this deviation varied among species and growth forms, based on their traits. Overall, there was significant but rather weak correlation between dead leaf decomposition rate k and fresh leaf k, across species and ecosystem types; the deviation between fresh and dead leaf k was larger for fast-decomposing, mostly herbaceous species. The different decomposition patterns for fresh versus dead leaves were underpinned by key underlying traits integrated in leaf resource economics spectra (LES) for fresh and dead leaves. The dead leaf LES exhibited a greater predictive capability for dead leaf k while the fresh leaf LES had higher explanatory value for the fresh leaf k values. Our findings partly reject the null hypothesis and ask for caution in inferring leaf litter decomposition rates based on green leaf litterbags or traits data. We suggest follow-up research on substituting senesced roots and stems with fresh ones in decomposition experiments. Synthesis. Human activities and extreme weather events are leading to increasing pulse inputs of fresh plant parts and our study contributes to knowledge on how they contribute to overall decomposition rates besides senesced litter inputs.

The study was carried out in Zhejiang Province, East China, which has a subtropical monsoon climate with a mean annual temperature of 16.2°C and a mean annual precipitation of 1375 mm. Five different ecosystems were selected for the study including subtropical broadleaf evergreen forest, coniferous forest, deciduous forest, shrublands, and grasslands. Fresh mature leaves and senescent dead leaves of selected 26 plant species representing different ecosystems were collected from August to December 2021, and the collected leaves were air-dried in the laboratory. Decomposition experiments of litter were carried out using nylon mesh belts with a mesh diameter of 1 mm and each litter bag contained 2 grams of air-dried leaves, totaling 3,120 litter bags. Three replicate plots measuring 10 m × 10 m were established in each ecosystem, and prepared litter bags were placed in the sample plots. Litter bag incubation began in late March 2022 and was harvested at 3-month intervals for a total of four times during the year. After each harvest, litter bags were recovered, washed, dried, and weighed to determine dry mass.

1. For litter decomposition rate data:

   We used the negative exponential model to calculated the decomposition rate k of fresh and dead leaves of different species based on Olson (1963) as y_R=ae^-kt , where t and y_R are the decomposition time and litter mass remaining at time t, and a is the initial litter mass.

2. For leaf traits data:

   For measurement of fresh and dead leaf traits representing the fresh LES and dead LES and relevant for the species’ decomposability, random sub-samples of each litter sample were stored in sealed plastic bags immediately after collection in the field, i.e., before the decomposition experiment, and kept cool until being brought back to the laboratory. Subsequently, both fresh and dead leaves were moistened and placed in sealed bags to ensure the same treatment. These bags were then stored in a refrigerator at 4 °C for 24 hours. After this period, the softened dead leaves were spread out. We scanned both the fresh and dead leaves using a leaf area meter (LI-3100C, Li-Cor, USA) to determine their mean leaf area, following Pérez-Harguindeguy et al. (2013). Next, we dried the leaf samples at 75 °C in an oven for 48 hours to determine their dry mass, and to calculate specific leaf area (SLA; fresh area/ dry mass)

3. For leaf economics spectrum data: 

   We used a principal component analysis (PCA) to construct the “fresh” leaf economics spectrum (fresh-LES) and the “dead” leaf economics spectrum (dead-LES), respectively. Traits in including Carbon, Nitrogen, Phosphorus, Lignin, Cellulose, and SLA. Because of the relatively high proportion of species variance explained by the first PCA axis, we used the PC1 scores to represent a synthetic variable incorporating multiple trait variables as an index of the fresh-LES and dead-LES in the subsequent analyses.