DOI: 10.5061/dryad.x0k6djhr5
title: Data for: The effects of leaf traits on litter rainfall interception with consequences for runoff and soil conservation

description: 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. 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.
 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. 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.
description: 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<sub>rep</sub>) and leaf water-droplet retention
 (L<sub>ret</sub>). 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<sub>rep</sub> and
 L<sub>ret</sub>, 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 m<sup>2</sup>. 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<sup>-1</sup>. 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 <em>PMCMR</em>, an R package, and for Dunn tests, we used
 the <em>FSA</em> 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
 <em>piecewiseSEM</em> R package (Lefcheck et al., 2016). 
description: # Data for: The effects of leaf traits on litter rainfall interception
 with consequences for runoff and soil conservation ########## 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. Our results revealed the direct and indirect effects of
 species leaf size and hydraulic traits on litter rainfall interception,
 runoff and soil loss. We propose a new litter-soil ecohydrological model,
 by using structural equation models (SEM), which can be used as a tool to
 predict ecosystem functioning, and guide management and restoration
 actions with water and soil conservation targets. Keywords: litter
 interception, rainfall interception, soil erosion, leaf-litter size and
 shape, leaf-litter hydrological traits, water runoff, soil erosion.
 ########## Our data is available in .CSV format with the following columns
 and variables. Missing values are denoted by NA. * ID: Code/abbreviation
 for each species. * ID\_REPLICA: Code/abbreviation for each species with
 the number of experimental replicates. * WHCv: The Water Holding Capacity
 (WHC) represents the ability of leaf tissue to absorb water, potentially
 increasing the capacity to store litter water. WHC was assessed after one
 hour of submersion, which reflects the leaf tissue's ability to
 absorb water in terms of both capacity and speed. For each species, ten
 leaves were submerged in ziplock bags filled with tap water and devoid of
 air for one hour. Following this, the leaves were gently surface-dried
 with a paper towel, weighed, and then placed on a bench for air-drying.
 WHC was calculated as the species' mean difference between dry and
 wet weights, expressed as a percentage of the dry weight. * desvWHCv:
 variance of WHCv for ID\_REPLICA. * WHCmax: The Water Holding Capacity
 (WHC) represents the maximum ability of leaf tissue to absorb water,
 potentially increasing the capacity to store litter water. WHC was
 assessed after 24 hours of submersion, which reflects the leaf
 tissue's ability to absorb water in terms of both capacity and speed.
 For each species, ten leaves were submerged in ziplock bags filled with
 tap water and devoid of air for one hour. Following this, the leaves were
 gently surface-dried with a paper towel, weighed, and then placed on a
 bench for air-drying. WHC was calculated as the species' mean
 difference between dry and wet weights, expressed as a percentage of the
 dry weight. * desvWHCmax: variance of WHCmax for ID\_REPLICA. * LRET:
 Water leaf retention measurement; leaf Hydrophobicity. 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. Finally, we calculated the
 mean of species. * desvLRET: variance of LRET for ID\_REPLICA. * LREP:
 Water leaf repellency measurement; leaf Hydrophobicity. For measuring
 LREP, 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 * desvLREP: variance of LREP for ID\_REPLICA. * CUR: leaf
 Curliness, measured in centimeters (cm); that represents the degree of
 three-dimensional space occupied by leaves in the litter layer,
 descriptors of the Size and Shape Spectrum, which can affect litter layer
 porous structure and compaction with potential effects on water storage
 and drainage direction. 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. * desvCUR: variance of CUR for ID\_REPLICA. *
 LA\_LA: leaf Area, measured in square centimeters (cm²); that represents
 the degree of bi-dimensional space occupied by leaves in the litter layer,
 descriptors of the Size and Shape Spectrum, which can affect litter layer
 porous structure and compaction with potential effects on water storage
 and drainage direction. 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. * desvLA\_LA: variance of LA\_LA for
 ID\_REPLICA. * SLA: is a measure that describes a plant's leaf
 surface area relative to its dry mass. It is typically expressed in square
 centimeters per gram (cm²/g). We collected 10 leaves from each species in
 the litter layer, cleaned them, and allowed them to dry. We weighed them
 (in grams - g) and then scanned the leaves, measuring their leaf area
 using ImageJ. Finally, we calculated the mean leaf area for each replicate
 divided by its weight for each species. * desvSLA: variance of SLA for
 ID\_REPLICA. * PESO: mean for Fresh weight of the leaves (in grams - g)
 per species. We measured the thickness of species' leaves in the
 litter layer using a precision digital balance with a precision of 0.1g *
 desvPESO: variance of PESO for ID\_REPLICA. * ESPESSURA: "Mean Leaf
 Thickness (in centimeters - cm) per species. We measured the thickness of
 species' leaves in the litter layer using a precision digital caliper
 with a precision of 0.01cm. In grams. * desvESPESSURA: variance of
 desvESPESSURA for ID\_REPLICA. * DUREZA: A stapler that measures the force
 applied to break materials like paper, cardboard, and... leaves. The
 higher the HARDNESS, the harder it is to break the leaf tissue." *
 desvDUREZA: variance of DUREZA for ID\_REPLICA. * ESPECIE: scientific name
 of plant species selected. * AREA\_CAIXA: in centimeters, of the surface
 runoff plots, constructed for our experimental approach. *
 AREA\_COLETOR\_ESC: pluviometers area (cm²). * UMIDADE\_SOLO\_perc: We
 measured soil moisture as a way to control for potential
 noise/significance in its role. We collected a soil sample immediately
 after simulating rainfall. We then dried this soil in an oven (at 110°C)
 and calculated the percentage difference between wet soil minus dry soil
 divided by the dry soil. * PRESSAO\_kgcm2: Operating pressure of the rain
 simulator (kg.cm-²). This was the fundamental parameter for determining
 the simulated rainfall intensity. * TEMPO\_min: Rainfall simulation
 duration. Time for each round/experimental replicate. * PREC\_mm INT\_mmh:
 Simulated rainfall intensity, measured in millimeters for each 15-minute
 experimental replicate and then converted to millimeters per hour
 (mm.h⁻¹). * PREC\_L: Simulated rainfall volume, measured in millimeters
 for each 15-minute experimental and then converted to liters (L). *
 ESC\_mm\_mensurado: Volume of surface runoff collected at the end of each
 experimental flume replicate, measured in grams (g). * ESC\_L: Volume of
 surface runoff collected at the end of each experimental flume replicate,
 measured in Liters (L). * ESC\_mm: Volume of surface runoff collected at
 the end of each experimental flume replicate, measured in millimeters
 (mm). Equal liter per square meter (L.m-²). * COEF\_ESC: Percentage of
 runoff (ESC\_mm) in relation to the simulated rainfall (PREC\_mm). *
 INICIO\_ESC\_seg: Time, in seconds (s), taken for each experimental
 replicate to yield the first drop of surface runoff into the collector at
 the end of the flume. We used this as a proxy for drainage, where a
 shorter time indicates more lateral drainage, meaning that drainage
 through the litter layer occurs more parallel to the ground. *
 PESO\_SECO\_FOL\_kg: Weight, in grams (g), of the litter layer for each
 species in each experimental replicate after drying for at least 72 hours
 (3 days) in a shaded and well-ventilated area. * PESO\_UMID\_FOL\_kg:
 Weight, in grams (g), of the litter layer for each species in each
 experimental replicate after rainfall simulation. * ESTOQUE\_mm:
 Difference between the wet weight and dry weight of the litter layer for
 each species in each experimental replicate, measured in grams (g) and
 converted to millimeters (mm). * COEF\_ESTOQUE: Percentage of litter
 storage (ESTOQUE\_mm) in relation to the simulated rainfall (PREC\_mm). *
 SEDIM\_g: All sediment (soil) that was carried by surface runoff to the
 collector at the end of the flume was captured by a filter placed at the
 entrance of the collector. At the end of each experimental replicate, we
 weighed this material in grams (g). ########################