1. During rainfall, plant litter interception regulates overland flow with an impact on water runoff generation and sediment displacement. Besides the rainfall characteristics, the effects of litter mass, thickness, storage and drainage properties on rainfall interception are reasonably well understood. In contrast, less is known about the influence of leaf traits, which we hypothesized to affect interception, soil hydrology and conservation via litter structure assembly.
2. We measured the runoff and soil loss generation as determined by litter layer structural and hydraulic properties of 16 coexisting tropical woody species with wide-range morphological leaf traits in a rainfall simulator experiment.
3. Our results show that litter produced by coexisting species can differ in precipitation interception, thereby influencing runoff and soil loss. This is because there is important interspecific variation in litter water storage and drainage, which are negatively affected by leaf area. Leaf water repellency positively affected litter water storage. Moreover, leaf area also negatively affected litter layer density. Litter density, in turn, increased runoff, but decreased soil loss, possibly due to protection against splash erosion.
4. These results can be used to predict the effects of plant traits on the soil water balance and soil integrity protection through ecohydrological interception by the litter layer. The next research steps will be to extend our model to multiple-species litter layers and to validate and calibrate our model in different field situations in different ecosystems.

Species selection and litter collection

We selected 16 woody species belonging to different families and comprising a broad range of leaf litter size, shape and hydrological traits (Table 1). To standardize across species, and because the fresh litter layer properties driven by undecomposed litter were expected to affect hydraulic properties most strongly, we hand-collected intact, virtually undecomposed leaves from the top of the litter layer at the National Park of Floresta da Tijuca, in Rio de Janeiro, Brazil (22° 57 'S and 43° 17' W). Litter leaves were taken to the laboratory in large plastic bags with care to not compress and pack the leaves. On the same day, leaves were laid out to air-dry on a bench at room temperature. 

Leaf litter traits 

We identified five key leaf traits to litter hydraulic properties according to previous evidence (Sato et al., 2004; Guevara-Escobar et al., 2007; Li et al., 2013; Kim et al., 2014; Zhao et al., 2019; Li et al., 2021): leaf area (LA), leaf curliness (CUR), leaf water-holding capacity (WHC), leaf water repellency (L_rep) and leaf water-droplet retention (L_ret). LA and CUR (representing the degree of three-dimensional space occupied by leaves in the litter layer) are descriptors of the Size and Shape Spectrum (SSS, Dias et al., 2017), which can affect litter layer porous structure and compaction (Cornwell et al., 2015; Cornelissen et al., 2017; Fujii et al., 2020; Burton et al., 2020) with potential effects on water storage (Walsh & Voigt, 1977; Sato et al., 2004; Li et al., 2017) and drainage direction (Walsh & Voigt, 1977; Sato et al., 2004; Sidle et al., 2007; Li et al., 2013). Additionally, hydraulic traits indicate how leaf tissue and its surface interact with water. WHC reflects the capacity of the leaf tissue to absorb water (Makkonen et al., 2013), leading to a potential increase in litter water storage capacity. Lrep and Lret reflect the capacity of the leaf surface to repel and retain water, respectively (Rosado & Holder, 2013; Matos & Rosado, 2016); these traits were shown to impact canopy storage and the heterogeneity of canopy drainage (Holder, 2013).

We measured the above-mentioned traits in leaf litter using modifications of standard protocols whenever necessary. CUR of dry leaf litter was measured as the maximum height of a leaf placed on a flat surface (after Engber & Varner, 2012). Curliness represents the propensity to occupy the three-dimensional space, with low values indicating flat leaves and large values indicating more curled leaves. For each species, ten air-dried leaves were randomly selected. Each leaf was placed on a bench and turned in several positions to find all its equilibrium points. Height was measured in all equilibrium points of the leaf and the average value was considered the curliness of the leaf. For measuring LA, ten leaves per species were wetted in plastic bags with saturated paper towels overnight, allowing them to flatten without breaking. Leaves were scanned and LA was measured using the ImageJ software (following Pérez-Harguindeguy et al., 2013). WHC was measured after one hour of submersion, reflecting the capacity and speed of leaf tissue to absorb water (Makkonen et al., 2013; Zhou et al., 2018). Ten leaves per species were submerged in ziplock bags filled with tap water and no air for one hour. After this, leaves were carefully surface-dried with a paper towel, weighed and placed on a bench for air-drying. WHC was calculated as the difference between dry and wet weight, expressed as a percentage of dry weight. For measuring L_rep and L_ret, leaf litter was wetted in plastic bags with a soaked paper towel to allow them to be cut and flattened without breaking.  From each leaf, a 3x3 cm strip of the central part of the leaf was cut and horizontally fixed with pins onto a styrofoam block. A 5μL drop of distilled water was tapped on the leaf strip surface with the aid of an automatic micropipette. A digital photo from the side of the drop on the leaf surface was taken and the angle formed between the drop and the leaf surface was measured using ImageJ software. The same procedure was repeated for the abaxial and adaxial surfaces and the Lrep was calculated as the average value of both leaf surfaces (Matos & Rosado, 2016). For measuring Lret, using an automatic micropipette, a 50μL drop of distilled water was tapped on the leaf strip surface on the styrofoam block initially in a horizontal position. The styrofoam block was inclined progressively from 0 to 90°. The angle of inclination at the moment the drop begins to move represents the measure of water retention. Lret was calculated as the average of the values of the abaxial and adaxial surfaces (Matos & Rosado, 2016).

Rainfall simulation

Rainfall simulations were performed at the experimental unit at Fiocruz Mata Atlântica, in Rio de Janeiro (22° 56' S and 43° 24' W). We used a rainfall simulator consisting of a 50 thousand L water tank connected to a water pump that directed the flow of water with constant pressure through a 12,7mm (PVC) pipe to a FULLJET GG-30W spray nozzle (Sprayng Systems Co.). This nozzle sprayed drops of water of approximately 3 mm diameter, producing a full cone covering an area of ​​about 15 m². This rainfall simulator is cheap and simple to operate (Gemlack Ngasoh et al., 2020). The input pressure was set at approximately 2.0 kPa, with the nozzle at 1.70 m in height from the flume. This setting generated uniform precipitation with an intensity between 90 and 110 mm.h^-1. For each experimental round, the rainfall simulator was turned on for 15 minutes, totaling precipitation of about 25 mm.  

We recognize that throughfall can show significant heterogeneity in drop size, volume, and kinetic energy, and that such throughfall properties change with the species present in the canopy (Levia et al. 2017). However, we decided to use a rainfall simulator that produces a homogeneous precipitation because our main objective was to quantify species’ leaf trait effects on litter interception. Standardized and homogeneous experimental conditions are better to control other sources of variation, enabling species effects to be properly quantified. A recent review on throughfall drop size distributions showed that many tree species generate throughfall median drop size ranging from 1.5 to 5.7 mm, indicating that the drop size produced by our rainfall simulator (3 mm) is within the range of natural throughfall observed beneath many tree species (Levia et al. 2017). Nevertheless, maximum values for drop size can be as high as 8 mm; such large drops should have much more erosive kinetic energy. Because of this, future studies should validate our results under these conditions, evaluating how traits effects shown here are modulated by throughfall characteristics.

We used high precipitation intensity because of its relevance to erosion processes. Therefore, under heavy precipitation plant species effects on soil hydrological fluxes and soil erosive processes should be more relevant. Additionally, short-intensity precipitation bursts are highly relevant to erosion and are more common than those usually reported because most precipitation data is aggregated to the hourly level (Dunkerley 2019). These short and intense rainfall simulation events were able to generate runoff without exceeding the infiltration capacity of the soil, preventing other types of runoffs due to overflow, and allowing us to have replicates for our 17 experimental treatments. In this way, short and intense precipitation events allow for a better quantification of litter interception and is commonly used in simulation experiments (Walsh & Voight 1977, Sato et al. 2004, Guevara-Escobar et al. 2007, Seitz et al., 2015). The influence of different precipitation intensities on the effects of leaf litter traits on hydrological and erosive processes should be investigated in future studies.

Flume

We used a flume consisting of a wooden box (60x40x10 cm, length x width x height). To prevent overflow due to soil saturation in cases of high infiltration, holes of 1.5cm in diameter were drilled in the bottom and lower part of the walls allowing the drainage of infiltrated water. The downstream wall of the flume was 5 cm lower than the other walls, where an aluminum gutter was placed to direct the runoff water to a plastic container. At the entrance of the plastic container, we used a filter consisting of a fine nylon cloth (0.5mm mesh size) for retaining the coarse soil particles transported by runoff water. During rain simulations, a sediment box was placed at an inclination of 20°. This inclination was chosen because it was reported to promote downslope litter water flow without reducing litter storage capacity (Du et al., 2019).

The soil, composed mostly of clay with about 20% sand, was collected near the experimental unit at Fiocruz Mata Atlântica (22° 56' S and 43° 24' W). Soil macro-aggregates were broken up with a hammer and sieved (in 0.5mm mesh size) for removing stones and coarse organic matter. For each rain simulation run, the sediment box was filled with new soil with a moisture content ranging from 11 to 14 % dry weight. The soil layer was standardized to a depth of 6 cm, that is 1 cm above the level of the gutter. Before placing the air-dried leaf litter, the soil was compressed using a wooden board, with standard gentle force, covering the whole area of the flume. On top of the soil, a leaf litter layer of 4 cm was placed, filling the remaining volume of the box. The litter layer with 4 cm depth was chosen because for some large-leaf species, the litter layer formed with only one layer of leaves, or with very little overlap, down to almost 4 cm depth. Therefore, to standardize our experimental units by the litter-layer volume, we chose to build litter layers with 4 cm depth for all species. For this, leaves were randomly dropped from 40 cm above the flume, mimicking the natural litter packing. We used litter of only one species each time we ran the rain simulation. We performed five replicates for each species, totaling 85 experimental rounds (16 species plus control without litter cover). A PVC plate was placed on top of the gutter, preventing water from directly entering the gutter by experimental precipitation. Three rain gauges were placed next to the flume to record the rainfall input (see Supplementary Information, Fig. S1).

Each rainfall simulation lasted 15 minutes and we recorded the rainfall inlet in the rain gauges. After stopping the rain, we waited for all the water to drain and drip through the gutter. This took no longer than one minute. The drained volume was then recorded using the plastic container. The sediment filter was dried at 40oC and the difference between initial and final mass was used to quantify sediment yield. The sediment yield is a measure of potential erosive processes since we did not use structured soil in our experiment.  

Litter layer properties, hydrology, and soil erosion

As response variables, we measured (i) litter layer density, (ii) litter water storage capacity and (iii) time to start water runoff (drainage proxy), (iv) runoff and (v) sediment yield (potential for soil erosion). For measuring litter water storage capacity, after a rain simulation event the litter layer was carefully transferred to a plastic bag to avoid the water dripping out. Litter was immediately weighed and dried at 50°C until constant weight. Litter storage capacity was calculated as the difference between wet and dry weight and expressed in mm and % of precipitation. We recorded the time to start water runoff as a drainage proxy, indicating the direction of water flow within the litter. For measuring the time to start runoff, we recorded the time until the first drop appeared from the gutter after starting the simulated rain. The shorter the time, the more laterally litter flow drains the incoming rainwater. More lateral water litter flow promotes a downslope flow faster as compared to more vertical water litter flow (Sidle et al., 2007; Kim et al., 2014; Bai et al., 2021). We also measured litter layer density as the total dry litter mass divided by the litter layer volume. Water runoff was measured as the final volume drained by the gutter and was expressed in mm and % of precipitation. Sediment yield was measured as the dry weight, in grams, of soil particles trapped in the nylon cloth filter in the plastic container. 

Data analyses and litter Ecohydrological Model (SEM)

All data management and statistical analyses were performed using the R language and environment with RStudio (RStudio Team, 2016).

First, we tested if water runoff and sediment yield differed in the presence and absence of litter using a Kruskal-Wallis test (alpha = 0.05), since these variables did not follow a normal distribution. We also tested whether there was a difference in water runoff, sediment yield, water storage and time to start runoff between litter from different species. For this, we used a Kruskal-Wallis test, followed by a post hoc Dunn test. For Kruskal-Wallis tests, we used the PMCMR, an R package, and for Dunn tests, we used the FSA package.

We used Structural Equation Models (SEM) to evaluate the direct and indirect effects of leaf traits on litter hydraulic and structural properties, and their consequences on water runoff and sediment yield. By using SEM, we could assess the relative importance of traits related to the Size and Shape Spectrum and hydrological traits in determining litter hydraulic properties and runoff and sediment yield.

Our SEM tested our main hypothesis that leaf traits, especially leaf area, directly affect litter layer structural and hydrological properties, which, in turn, affect water runoff and soil loss. More specifically, our model tested if (i) a larger litter leaf area favors the lateral flow of water through litter and reduces water storage in hillslopes; (ii) leaf water repellency, in contrast to leaf water-droplet retention and leaf water-holding capacity, increases lateral litter flow and decrease litter storage which, in turn, (iii) should increase runoff with consequent enhancement in soil loss. We fitted SEM using Shipley´s d-sep test due to our low number of replicates. For that, we used the piecewiseSEM R package (Lefcheck et al., 2016).