The pattern of litterfall
The rate of litter production exhibited pronounced variation spatially and temporally within the study area. Specifically, the months of January (239.62 gm/m2), March (210.96 gm/m2), and May (216.05 gm/m2) demonstrated the highest accumulation of fallen litter, whereas July (239.62 gm/m2) registered the lowest return of litter. The sample points litter production rate was 114.23 gm/m2 to 131.55 gm/m2. Sample point 2 (131.55 gm/m2) emerged as a prominent contributor to the overall litter production (Fig. 5).
The turnover rate represents the proportion of litter that decomposes or is removed from the sample point over a specified period (Fig. 6). These turnover rates indicate the variability in litter decomposition and turnover across different sample points and months. The turnover rates varied noticeably between sample locations and months, pointing to diverse processes of litter breakdown. Sample points 4 and 5 had much greater turnover rates in May (11.70% and 11.85%, respectively), indicating the litter was quickly turning into organic matter. The turnover rate for sample point 6 was lower for the same month, at 5.58%. July's sample point 5 turnover rate was the lowest, at 2.88%, indicating a more gradual decomposition process. Contrarily, sample point 6 showed a comparatively higher turnover rate of 5.61% for May. As September approached, sample point 5 recorded the highest turnover rate at 6.34%, indicating an increased pace of litter decomposition. Similarly, in March, sample point 6 experienced the highest turnover rate of 25.83%, highlighting a significant transformation of litter. Notably, July to November consistently demonstrated the most favorable turnover rates, implying more efficient litter breakdown processes in these months.
Decomposition rates and nutrient return dynamic
The decomposition rate refers to the speed at which organic material breaks down and transforms into simpler compounds, contributing to nutrient cycling and ecosystem processes. In May, sample point 6 exhibited the most rapid decomposition rate at 20.37%, indicating a substantial conversion of litter into organic matter. Sample point 7 also displayed a notable decomposition rate of 16.94% this month. As July arrived, sample point 5 exhibited a decomposition rate of 15.72%, highlighting a significant transformation of litter material. Sample point 6 is closely followed with a decomposition rate of 11.61%. In September, sample point 6 maintained its position with the highest decomposition rate at 28.7%, reinforcing its role as a hotspot for litter transformation. Sample point 7 also exhibited a considerable decomposition rate of 30.8% during this period. In November, sample point 6 sustained a high decomposition rate of 11.61%, while sample point 7 showed a rate of 10.84%, suggesting ongoing decomposition processes. In January, sample points 6 and 7 had decomposition rates of 6.38% and 7.58%, respectively, reflecting a comparatively slower decomposition phase during this month. Lastly, in March, sample point 1 displayed the highest decomposition rate at 4.71%, while samples 2, 3, 4, and 5 displayed no measurable decomposition (Fig. 7).
The decomposition constant (k) represents the rate at which leaf litter breaks down over time, with higher values indicating faster decomposition (Table 2). Sample point 1, characterized by a decomposition constant (k) of 0.06, portrays a moderate pace of decay. This is substantiated by a high R-squared (R2) value of 0.96, indicating a robust fit of the exponential decay model to the empirical data. The residence time of leaf litter (Rt) for this specific location is calculated to be 15.39 months, implying that 15.39 months are required for half of the initial leaf litter to undergo decomposition. Correspondingly, the half-life period (t 0.5) is determined to be 10.67 months, marking the time for the initial amount of litter to decrease by half.
Moving to sample point 2, the decomposition constant (k) escalates to 0.10, reflecting a swifter rate of disintegration compared to sample 1. The commendable R-squared (R2) value of 0.95 reaffirms the accuracy of the exponential decay model. Here, the residence time of leaf litter (Rt) dwindles to 9.61 months, resulting in a half-life period (t 0.5) of 6.66 months. Sample point 3 reveals a decomposition constant (k) of 0.13, hinting at a more accelerated decay process. Although the R-squared (R2) value stands at 0.94, indicating a sound model fit, leaf litter (Rt) residence time further contracts to 7.98 months. Consequently, the half-life period (t 0.5) is calculated at 5.53 months, underscoring the swift pace of change.
Sample points 4, 5, and 6 boast decomposition constants (k) spanning from 0.08 to 0.11, encapsulating a range of decomposition rates. The R-squared (R2) values for these points all exceed 0.94, with a residence time of leaf litter (Rt) extending from 9.24 to 13.06 months, yielding half-life periods (t 0.5) oscillating between 6.40 and 9.05 months. Lastly, sample point 7 had a decomposition constant (k) of 0.07, indicating a gradual decay rate. The notable R-squared (R2) value of 0.97 with a residence time of leaf litter (Rt) stretches to 13.85 months, yielding a half-life period (t 0.5) of 9.60 months.
Table 2
Leaf litter decay rate coefficient (k), half-life (t0.5), and residence time for various sample points.
Sample plot | Decomposition constant (k) | Y = ae− kt | Residence time of leaf litter (Rt) | Half-life period (t 0.5) |
| Month− 1 | R2 | Month | Month |
Sample point 1 | 0.06 | 0.96 | 15.39 | 10.67 |
Sample point 2 | 0.10 | 0.95 | 9.61 | 6.66 |
Sample point 3 | 0.13 | 0.94 | 7.98 | 5.53 |
Sample point 4 | 0.11 | 0.96 | 9.24 | 6.40 |
Sample point 5 | 0.11 | 0.94 | 9.41 | 6.52 |
Sample point 6 | 0.08 | 0.98 | 13.06 | 9.05 |
Sample point 7 | 0.07 | 0.97 | 13.85 | 9.60 |
Nutrient Use Efficiency (NUE) offers valuable insights into the effectiveness of nutrient utilization and allocation strategies (Fig. 8). NUE values demonstrate a diverse range, highlighting differential carbon use efficiency across the ecosystem. Sample point 1 displayed a distinctive pattern, with the NUE percentage escalating from May to January and reaching 50.92%, suggesting a more efficient carbon utilization during these months. Sample point 2 exhibited notable variability, with higher NUE percentages observed in July and September, indicating potential optimization of carbon use during these periods. Sample points 3, 4, and 5 portrayed consistent NUE patterns, with fluctuations within a certain range across different months. Sample point 7 showed an intriguing trend, with NUE percentages fluctuating significantly between January and March, indicating varying degrees of carbon use efficiency. The absence of NUE values in March for most sample points suggests a potential limitation in carbon utilization.
Sample point 7 is particularly intriguing, with notably high NUE percentages in September and November, suggesting an efficient utilization of Nitrogen during these periods. However, this efficiency contrasts sharply with November for sample point 1, where NUE drops to zero, potentially indicating a shift in nitrogen dynamics or availability. Sample points 2 to 6 exhibit varying NUE percentages over the months, with differing levels of nitrogen utilization efficiency. The absence of NUE values for January and March implies potential limitations or shifts in nitrogen utilization during these months.
Sample point 1 demonstrates a distinct pattern, with NUE percentages progressively increasing from May to January and subsequently surging to 53.85%, suggesting an enhanced phosphorous utilization during these months. Similarly, sample point 7 showcases a remarkable increase in NUE from January to March, potentially indicating a shift in phosphorous dynamics. Sample points 2 through 6 exhibits varying NUE percentages over the months, reflecting differences in phosphorous utilization efficiency. The absence of NUE values in March suggests potential limitations or shifts in potassium utilization during that period.
Sample point 1 experiences a gradual increase in NUE percentages from May to January, potentially indicating an improved potassium utilization strategy. Similarly, sample point 7 displays a remarkable surge in NUE from January to March, suggesting a shift in potassium dynamics. Conversely, sample points 2 to 6 exhibit fluctuating NUE percentages over the months, highlighting diverse potassium utilization strategies. The absence of NUE values in March implies potential limitations or changes in potassium utilization.
Nutrient Accumulation Index (NAI) provides a comprehensive perspective on the accumulation and distribution of carbon resources over time (Fig. 9). Sample points 3, 4, 5, and 6 demonstrate consistent increases in NAI percentages, suggesting a progressive accumulation of carbon content during the study period. Conversely, sample point 1 displays a fluctuating pattern, while sample point 7 showcases fluctuations followed by a decline in March.
Sample points 3 and 6 exhibit consistent increases in NAI percentages, indicating a continuous buildup of nitrogen content throughout the study period. In contrast, sample points 4 and 5 display more modest variations in NAI percentages, suggesting stable nitrogen accumulation. Sample points 1, 2, and 7 present unique profiles with fluctuating NAI values and, in some cases, minimal accumulation during certain months.
Sample points 6 and 7 exhibited consistent increases in NAI percentages, indicating a continuous buildup of phosphorus content throughout the study period. In contrast, sample points 1, 2, and 3 show fluctuating NAI values, suggesting variations in phosphorus accumulation influenced by environmental factors and plant nutrient uptake dynamics. Sample points 4 and 5 display stable NAI percentages, indicating a consistent phosphorus accumulation pattern.
Examining the sample points, distinct trends in NAI values emerge, highlighting diverse strategies for potassium accumulation. Sample points 1, 4, and 7 exhibit upward trends in NAI percentages, suggesting a consistent potassium accumulation over the study period. Conversely, sample points 2, 3, 5, and 6 show fluctuating patterns, potentially reflecting environmental and plant nutrient uptake dynamics variations.
Nutrient Retranslocation Efficiency (NRE) represents a critical aspect of ecosystem nutrient cycling, shedding light on how efficiently nutrients are remobilized from senescent leaves and reabsorbed before leaf shedding (Fig. 10). For Carbon among the sample points, NRE values ranged from 8.52–18.51%, displaying diverse nutrient retranslocation efficiencies. Sample point 7 exhibited the highest NRE of 18.51% in May, indicating substantial reabsorption of nutrients from litter fall. Conversely, sample point 3 displayed the lowest NRE of 8.52%, implying a comparatively lower efficiency in nutrient remobilization.
Figure no 10: Nutrient retranslocation efficiency (NRE) %
The NRE percentages, ranging from 15.98–57.98%, unveil dynamic variations in the nutrient retranslocation efficiency of Nitrogen. Sample point 5 stands out with notably high Nitrogen NRE values, reaching 85.48% in November, indicating efficient nitrogen remobilization from senescent leaves before shedding. In contrast, sample point 3 displayed relatively lower NRE values, starting from 18.77% in May and gradually increasing to 74.47% in November. Sample point 2 and sample point 7 showcased consistent NRE patterns, with values ascending from 53.75% and 56.07% in May, respectively, to nearly 100% in subsequent months, suggesting efficient nutrient reabsorption during later stages of decomposition. Sample point 1 displayed consistent 100% NRE values for January and March, suggesting complete nitrogen reabsorption during these months.
Among the sample points, NRE values exhibited a wide range from around 7.70–50.71%, demonstrating diverse strategies for phosphorus retranslocation. Sample point 4 stood out with notably high NRE values, reaching 50.71% in July, indicating efficient phosphorus remobilization from senescent leaves. In contrast, sample point 7 displayed relatively lower NRE values, particularly in January and March, suggesting less efficient phosphorus reabsorption during these months.
Sample points 2, 3, 4, and 6 are notably high NRE percentages, indicating efficient potassium remobilization. In contrast, sample point 1 displayed negative NRE values in May, indicating a potential potassium loss during that period. Sample point 7 showcased interesting NRE patterns, fluctuating over the months. Notably, in January, NRE dropped to 60.74%, suggesting less efficient potassium reabsorption during that month.
Abiotic factors and their relationship with leaf litter dynamics
Water holding capacity (0.73), moisture content (0.757), soil carbon content (0.766), soil nitrogen content (0.761), and humidity (0.726) showed a correlation with Decomposition rate in Pearson correlation analysis. The pH of the soil was investigated for its potential influence on several factors, and the F-statistics were employed to determine the significance of these effects. The results showed that soil pH had a significant impact on several parameters. Notably, there were significant associations between soil pH and the factors "Decomposition rate and "soil carbon content" (F = 4.204 and F = 7.796, respectively; all p < 0.01). This suggests that variations in soil pH levels correlate with parameter changes.
The initial eigenvalues for the components reflect their respective abilities to explain variance independently (Fig. 11). The first principal component exhibited the highest eigenvalue of 10.77, signifying its substantial impact in capturing the dataset's variability. The subsequent components, with eigenvalues of 6.66, 4.09, and 1.21, contributed successively less to the total variance. The extraction sums of squared loadings illustrate the variance captured by each principal component after transformation. The first component retained a massive portion of the variance at 39.90%, with subsequent components explaining 24.66%, 15.16%, and 4.48%, respectively. Together, these components cumulatively accounted for 84.22% of the total variance. Orthogonal rotation was applied to enhance interpretability, resulting in the rotation sums of squared loadings. The first component retained its dominance in explaining variance, contributing 39.90% of the total after rotation. The second, third, and fourth components accounted for 24.66%, 15.16%, and 4.48% of the variance after rotation (Table 3).
Table 3
Total Variance Explained for PCA analysis.
PCA | Eigen Value | % of Variance | Cumulative % |
1 | 10.773 | 39.902 | 39.902 |
2 | 6.661 | 24.669 | 64.570 |
3 | 4.093 | 15.161 | 79.731 |
4 | 1.212 | 4.489 | 84.220 |
The Principal Component Analysis (PCA) results indicate key relationships between soil and environmental parameters and the primary PCA component. PC1 showed strong positive correlations with mass loss % (0.863), NRE potassium (0.744), and soil potassium (0.814). PC2 was correlated with decomposition rate (KL) (0.858), humidity (0.865), water holding capacity (0.802), moisture content (0.802), soil carbon (0.763), and soil nitrogen (0.722). Rotated component matrices are essential to understand the underlying structure of complex datasets. The rotated component matrix obtained from the PCA analysis provides valuable insights into the relationships between variables and the underlying structure of the data (Table 4). Principal Component 1 (PC1) is influenced by variables such as Decomposition Rate (KL), pH, Water holding capacity, Moisture content, Soil organic carbon, soil carbon, soil nitrogen, soil phosphorous, soil potassium, and humidity. PC2 reflects contributions from Decomposition Rate (KL), NUE carbon, NUE phosphorous, NUE potassium, NAI carbon, NAI phosphorous. PC3 reflects contributions to mass loss %, NRE carbon, NRE nitrogen, NRE potassium. The result indicates that pH (0.812), water holding capacity (0.924), moisture content (0.944), soil organic carbon (0.772), soil carbon (0.893), soil nitrogen (0.857), soil phosphorous (0.847), and soil potassium (0.726) and humidity (0.836) significantly influence Litter Decomposition in Sal Forest ecosystem.
Table 4
PCA component correlation with litter dynamics of Sal Forest.
Parameters | Component Matrix Correlation | Rotated Component Matrix |
Mass loss % | 0.863 | 0.945 |
NRE (Carbon) | | 0.784 |
NRE (Nitrogen) | | 0.910 |
NRE (Potassium) | 0.744 | 0.735 |
Decomposition rate (KL) | 0.858 | 0.865 |
NUE (Carbon) | | 0.974 |
NUE (Nitrogen) | | 0.966 |
NUE (Potassium) | | 0.883 |
NAI (Carbon) | | 0.970 |
NAI (Potassium) | | 0.954 |
pH | | 0.812 |
Water Holding Capacity | 0.803 | 0.924 |
Moisture Content | 0.802 | 0.944 |
Soil Organic Carbon | | 0.772 |
Soil Carbon | 0.763 | 0.893 |
Soil Nitrogen | 0.722 | 0.857 |
Soil Phosphorous | | 0.847 |
Soil Potassium | 0.814 | 0.726 |
Humidity | 0.865 | 0.836 |