Dynamics of litter production and its determinants in a subtropical mixed evergreen and deciduous broad-leaved forest in a karst ecosystem

doi:10.17520/biods.2024248

Abstract

Abstract:

Aims: Litter constitutes a crucial component of forest ecosystems, playing significant roles in biogeochemical cycling, energy flow, and nutrient balance. Nevertheless, understanding the mechanisms by which biotic and abiotic factors influence litter production in heterogeneous natural forest ecosystems remains contentious. This study aims to investigate the long-term dynamics and influential factors affecting litter production in karst forests within subtropical regions.

Methods: In this study, we set up 151 litter traps, and collected litter monthly from 2018 to 2022 in a 25-ha karst evergreen and deciduous broad-leaved mixed forest dynamics plot in the Mulun National Nature Reserve, Guangxi Province. All litter in each trap was dried, classified and weighed.

Results: The average annual litter production in karst forests was 5,946.55 ± 77.27 kg/ha, with significant inter-annual variability. The proportion of each litter component ranked as follows: leaves (63.26%) > debris (24.89%) > twigs (12.79%). Seasonal patterns for total litter and leaf litter production were similar, displaying a bimodal trend with peaks in spring (March-April) and fall (September-October). Monthly dynamics of branch litter followed a unimodal pattern, peaking in autumn (around October), while debris showed a bimodal pattern with peaks in spring (May) and fall (October), respectively. The species diversity, convexity, mean diameter at breast height (DBH), were the main driving factors of litter production within a 5 m radius neighborhood around the traps. The results of the structural equation model indicated that the species diversity, DBH, and convexity have a direct influence on the total litter production, while slope had an indirect influence on the total litter production through altering species diversity.

Conclusion: Five years of continuous monitoring have revealed significant seasonal variations in litter production within the karst evergreen and deciduous broad-leaved mixed forest. Both biotic and topographic factors collectively affected the spatial variability of litter production.

Key words: litter, biodiversity, topography, karst ecosystems, Mulun

CLC Number:

Song Xiong, Jiang Gan, Yanjun Xie, Xizhao Deng, Guole Qin, Wanxia Peng, Fuping Zeng, Zhili Zhan, Weining Tan, Guoqin Huang, Hu Du. Dynamics of litter production and its determinants in a subtropical mixed evergreen and deciduous broad-leaved forest in a karst ecosystem[J]. Biodiv Sci, 2024, 32(12): 24248.

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URL: https://www.biodiversity-science.net/EN/10.17520/biods.2024248

https://www.biodiversity-science.net/EN/Y2024/V32/I12/24248

Figures/Tables 7

Table 1 Average annual yield of each component of litter in 2018-2022 (mean ± SE)

组分 Components	凋落物产量 Litter production (kg/ha)	比例 Proportion (%)
叶 Leaf	3,761.67 ± 60.20^a	63.26
枝 Twig	760.54 ± 15.46^c	12.79
杂物 Debris	1,480.68 ± 26.3^b	24.89
总计 Total	5,946.55 ± 77.27	100

Fig. 1 Annual dynamics of the production of each component of litter in 2018-2022 (mean ± SE). Different letters indicate significant differences in the same components and different years.

Fig. 2 Monthly dynamics of the total and each component litter production in 2018-2022

Fig. 3 Pearson correlations between yield of litter components and biological and topographic factors. Richness, Species richness; Shannon, Species diversity; Pielou, Species evenness; DER, Significant ratio of deciduous evergreen species; Basal area, Average tree diameter at breast height; Soilthick, Soil thickness; Rockcov, Rock exposure rate; Total, Total litter production; Leaf, Leaf litter production; Twig, Twig litter production; Debris, Debris litter production.

Fig. 4 Relative importance of biotic and abiotic factors for the yield of each litter component. ns for not significant. * P < 0.05, ** P < 0.01, *** P < 0.001.

Fig. 5 Regression analysis of litter fraction yield and dominant factor

Fig. 6 Structural equation modelling of total litter production and its influencing factors. The solid line indicates that the action path is significant, and the dashed line indicates that the action path is not significant. The arrow width indicates the strength of the relationship. Values next to the arrows are the normalized pathway coefficients with the corresponding statistical significance. Comparative fit index (CFI) and root mean square error of approximation (RMSEA) were used as indicators of model goodness fit index. R2 represents the total change in the dependent variable explained by the combined independent variable. * P < 0.05, *** P < 0.001.

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