Spatial and temporal patterns in Carbon and Nitrogen inputs by net precipitation in Atlantic Forest, Brazil

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Thus, studying precipitation-forest interactions is essential to understand the consequences of land-use and climate change for the hydrological and nutrient cycles. This study aimed to quantify the contribution of the net precipitation on Atlantic Forest’s total carbon (C) and total nitrogen (N), identifying potential differences between these chemistry inputs regarding temporal (seasonal and monthly) and spatial scales. Results The rainfall was enriched after crossing the forest canopy. For gross rainfall and net precipitation, respectively, statistical differences were found between annual inputs of carbon (104.13 kg ha − 1 and 193.18 kg ha − 1 ) and nitrogen (16.81 kg ha − 1 and 36.95 kg ha − 1 ). Moreover, there was a seasonal variability in the inputs of C and N since 75% occurred in the wet season. November and December concentrated the largest nutrient contribution throughout the year. The spatial variability of C and N was higher in the wet season. Overall, the spatial patterns revealed that the same locations had the highest inputs regardless of the analyzed period. Conclusion Our findings reinforce that forests promote rainfall enrichment with C and N. The forest-rainfall interactions provide constant input of these nutrients, especially in the wet season, being fundamental for maintenance of ecological processes. Despite the nutrient inputs presented some variability, this study provides useful information on the changes of C and N inputs after forest-rainfall interactions. Thus, it can support the estimation of atmospheric deposition and advance the knowledge of the contribution of the leaching and absorption processes by canopies. Ecological Modeling Forestry Rainfall Forest hydrology Semideciduous forest Throughfall Stemflow Nutrient inputs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Forests maintain the entire ecosystem balanced by controlling the biogeochemical cycles and decreasing the transport of nutrients to watercourses (Tundisi and Matsumura-Tundisi 2013 ). In this regard, forests are important because they are the first rain contact with terrestrial ecosystems, conducting singular biogeochemical processes (Van Stan and Stubbins 2018 ). When rainwater interacts with the forest canopy, a portion is stored in it, returning to the atmosphere through evaporation (wet canopy evaporation). The water routed to the soil surface by dripping off the canopy, along with rainfall that crosses the forest without hitting the vegetation, composes the “throughfall” (Van Stan and Friesen 2020 ). Finally, the “stemflow” is the rainfall portion that drains through the tree trunks (Muzylo et al. 2009 ). Therefore, “net precipitation” (NP) is the portion of rainfall that reaches the forest floor (throughfall plus stemflow) (Sadeghi et al. 2020 ). The nutrient input occurs preferentially by rainfall, highlighting NP as the major pathway of nutrients into forests (Sadeghi et al. 2020 ; Haag 1985 ). Although stemflow is a small portion of NP, it is a highly concentrated flux, responsible for water and nutrients inputs near the trees. Thus, it can directly change the roots’ physical, chemical and biological properties and accelerate the redistribution of nutrients in the forest ecosystem (Germer et al. 2012 ; Terra et al. 2018a ; Su et al. 2019 ). Every process of rainfall partitioning and nutrient input rely on both forest characteristics and canopy-rain interactions (Terra et al. 2018a ; Sadeghi et al. 2020 ). Therefore, NP flux and chemistry are generally affected by vegetation type, trees morphological traits, atmospheric deposition and materials derived from the canopies and trunks (Hofhansl et al. 2012 ; Marques et al. 2019 ; Su et al. 2019 ). The nitrogen and carbon cycles are essential for living organisms and for sustaining a number of processes that occur on the planet. The importance of the forest as carbon sinks and its role on climate change mitigation have been widely discussed recently (Silveira et al. 2019 ; Sullivan et al. 2020 ). However, forests are susceptible to different types of extreme events that could affect the carbon balance in the atmosphere such as droughts and fires, that might reduce productivity and increase tree mortality (Phillips et al. 2010 ; Reichstein et al. 2013 ). The natural presence of nitrogen in tropical forests is less limited than in temperate forests (Hietz et al. 2011 ). However, the nitrogen cycle has been changed in the last centuries due to the contribution of anthropogenic sources (Jaffe and Weiss-Penzias 2003 ). The increase of nitrogen might improve ecosystem productivity by fertilization or decrease by acidification, eutrophication, or unbalance (Galloway et al. 2004 ). However, there are knowledge gaps regarding the process of storing and distributing nutrients in forest stands, that must be fulfilled. Despite the considerable increase in studies, only a few addresses the spatial and temporal variability of nutrient concentration in throughfall and stemflow in tropical areas (Levia and Frost 2006 ; Zimmermann et al. 2007 ; Ponette-González et al. 2020 ). This kind of research and data is scarce due to difficulties imposed by logistic and costs as continuous data collection and lab analysis are necessary (Levia and Frost 2006 ). The few initiatives usually take into account only punctual rain events or selected events in a time period (Schroth et al. 2001 ; Tobón et al. 2004 ; Germer et al. 2007 ; Hofhansl et al. 2011 ; Hofhansl et al. 2012 ), specific periods data like wet seasons (Ciglasch et al. 2004 ; Möller et al. 2005 ; Oziegbe et al. 2011 ), and weekly or each two weeks samplings (Lilienfein and Wilcke 2004 ; Schwendenmann and Veldkamp 2005 ; Goller et al. 2006 ; Dezzeo and Chacón 2006 ; Fujii et al. 2009 ; Souza et al. 2015 ; Neu et al. 2016 ). Moreover, this source of study is particularly scarce for total carbon and total nitrogen concerning Neotropical forests. The Brazilian Atlantic Forest is a world biodiversity hotspot (Myers et al. 2000 ) and the second-largest South American rainforest, encompassing tropical and subtropical regions with different altitudes and rainfall amounts (Ribeiro et al. 2009 ; Oliveira-Filho and Fontes 2000 ). The Atlantic Forest is composed of two types of forest – rainforests and semideciduous forests –, that have idiosyncrasies related to seasonality. Rainfall, temperature, and altitude are the main drivers of the distribution of these vegetation types (Oliveira-Filho and Fontes 2000 ; Terra et al. 2018b ). Atlantic rainforests are restricted to Brazilian coasts and mountain range regions, while the semideciduous forests extend across the southeastern Brazil. These latter forests are composed by tree species that have strategies to cope with the up-to-six-months dry season (Morellato and Haddad 2000 ), notably the leaf loss of up to 50% of the trees during the dry season (Veloso et al. 1991 ; IBGE 2012). Despite the well-kwon ecosystem services provided by these environments, the Atlantic forest has been threatened by anthropic activities (Ribeiro et al. 2009 ). For improving the protection programs and management strategies, studies for understanding the Atlantic forest intrinsic dynamics is urgent. Thus, this research aimed to assess the contribution of net precipitation on the chemistry (C and N) of an Atlantic Forest remnant relying on data collected throughout one hydrological year. In this context, we sought to: i) compare the changes in concentration and inputs of C and N as the rainfall is partitioned by forest canopy; ii) identify the impacts of seasonality on C and N inputs considering the dry and wet periods and also monthly; and iii) to assess the spatial behavior of C and N inputs across the forest stand. Methods Site description The study site is a 6.30 ha Atlantic Forest remnant (AFR) located in Southeastern Brazil (21º13’40’’S and 44º57’50’’W) with an average elevation of 925 m. This forest remnant is classified as “montane seasonal semideciduous forest” (Oliveira-Filho et al. 1996 ). This forest type is widespread throughout the more inland portion of the Atlantic Forest biome in Southeastern Brazil, where the seasonal rainfall makes up to 50% of trees in these forests to lose their leaves in the dry season (Scolforo and Carvalho 2006 ). The relief is gently undulated and the soil classified as Dystrophic Red Latosol (Rhodic Hapludox) (Junqueira Junior et al. 2017 ). The Köppen-type climate of the studied region is Cwa with well-defined seasons, characterized by rainfall concentration in the summer (December to March) (Junqueira Junior et al. 2019 ). Long-term average annual precipitation (1981–2010) is 1461.8 mm in which 85% of it falls during the wet period (October to March) (INMET 2018). The mean annual temperature is 20.3ºC ranging from 16.9ºC (June and July) to 22.5 ºC in February (INMET 2018). Gross rainfall (GR), Throughfall (Tf), and Stemflow (Sf) measurements The gross rainfall (rainfall not affected by trees canopy - GR), throughfall (Tf), and stemflow (Sf) were monitored from May 2018 to April 2019, totalizing 86 daily rainfall events. The monitoring was performed the day after each rainfall event (at approximately 9:00 AM local time) or at least 4 hours after the rainfall event had ceased to avoid events overlapping. For measuring SF, 10 trees were selected from the three most abundant species to better represent the characteristics of the AFR. According to Oliveira-Filho et al. ( 1996 ), the most abundant species in the AFR are Copaifera langsdorfii Desf. (Fabaceae), Xylopia brasiliensis Sprengel (Annonaceae) and Miconia pepericarpa DC. (Melastomataceae) (Table 1 ). The selected trees were well-distributed in the site (Fig. 1) and can be classified according to their DBH (diameter at 1.3 m above ground; see Table 1 ). For Sf measurements, collectors built with a hose slit open toward the length was nailed in a spiral around the tree trunk and connected to a collection bin (Fig. 2 a). Table 1 Identification of trees species in the Atlantic Forest remnant. Tree code Scientific name DBH (cm) 01 Xylopia brasiliensis 27.37 02 Copaifera langsdorfii 32.15 03 Copaifera langsdorfii 14.96 04 Copaifera langsdorfii 31.83 05 Miconia pepericarpa 21.65 06 Miconia pepericarpa 12.10 07 Xylopia brasiliensis 50.29 08 Xylopia brasiliensis 11.46 09 Miconia pepericarpa 24.19 10 Xylopia brasiliensis 8.91 Figure 1. The geographical location of the AFR in Brazil and the positions of the measuring points. GR was measured through 3 fixed Ville de Paris-type rain gauges placed around the forest remnant (Fig. 1 and Fig. 2 b). Thereafter, the GR over the AFR was assessed by means of the Thiessen Polygon approach. In the case of gaps in the dataset, the climatological station from the “Brazilian National Meteorological Institute” (INMET 2019) was used to fill them. In addition, for Tf, 10 fixed Ville de Paris-type rain gauges were installed near the selected trees 1.5 m above the forest floor to avoid splash-in (Fig. 2 a). GR and Tf were converted to depth by dividing the collected volume (L) by the rain-gauge catchment area (m²). On the other hand, for Sf, the volume stored in the bin (L) was divided by the total projected crown area (m²). This projected area was determined according to the methodology described by Shinzato et al. ( 2011 ) in which 8 vertical projections far-between 45° were set in the ground. Then, the area for each canopy was calculated as follow: A (m²) = Σ \(\frac{\text{(a }\text{*}\text{ b }\text{*}\text{ }\text{sen}\text{ 45º)}}{\text{2}}\) (1) where A is the projected crown area; “a” and “b” are the vertical projections with far-between 45°. This measurement was performed twice, by considering the projected crown areas in the dry (July 2018) and wet (March 2019) periods. Chemical analysis GR, Tf, and Sf water samples were collected for events with at least 5 mm of rainfall due to the minimum necessary volume for lab analysis, summing up 60 events. GR were composed by the samples of the three external rain gauges (Fig. 1). The evaluated physical and chemical parameters included pH, Electric Conductivity (EC), Total Carbon (C), Nitrate (NO 3 − ), Nitrite (NO 2 − ), and Total Kjeldahl Nitrogen (which refers to Ammonia and Organic Nitrogen; TKN). Regarding pH, EC, and carbon, the samples were analyzed after every rainfall event (i.e. single samples). On the other hand, for analyzing NO 3 − , NO 2 − , and TKN, the samples were firstly accumulated on a monthly scale (i.e. composite sample) before the analysis could be carried out. All procedures concerning the samples (taking, preserving, and analyzing) followed the Standard Methods (APHA 2014) criteria (Table 2 ). These ensured that the nitrogen was not lost during storage. Table 2 Physical and chemical water variables evaluated, preservation procedures, the lab method used, and respective reference. Variable Preservation procedures Lab Method References pH Refrigerated at 4 ºC Eletrometric method (Method 4500 H +) APHA (2014) EC Conductimetric method (Method 2510 B) APHA (2014) C Filtered and refrigerated at 4 ºC Shimadzu total carbon analyzer (TOC-V CPH) (Method TC-IC) Shimadzu ( 2003 ) NO 3 − Filtered and frozen Yang et al. ( 1998 ) Method Yang et al. ( 1998 ) NO 2 − Calorimetric Method (Method 4500- NO 2 − B) APHA (2014) TKN Acidification and refrigerated at 4 ºC NBR 13.796:1997 Method ABNT (1997) Data analysis For some events, it was not possible to measure Tf and Sf because the rain gauge remained open and/or the collectors were knocked over by animals. Thus, with GR data, linear regressions were fitted to overcome these issues and to fill the unavoidable gaps in the measuring points. The regressions have been performed for each point separately, resulting in R 2 greater than 0.78 and 0.54 for Tf and Sf, respectively. For pH, EC, and C, which were analyzed in all the rainfall events, the mean monthly values of the concentrations in GR, Tf, and Sf were estimated by the volume-weighted mean (VWM), as follows: \(VWM= \frac{\sum _{n=1}^{i}{C}_{i,e} .{V}_{i,e}}{\sum _{n=1}^{i}{V}_{i,e}}\) (2) where C represents the concentration of the parameters (pH, EC, and C) at collector i for event e , and V represents the total volume at collector i for event e . As abovementioned, the chemical analyses for TKN, NO 3 − , and NO 2 − were carried out in a month scale. Then, with the total concentration of nitrogen (N = TKN + NO 3 − + NO 2 − ) and carbon (C) together with the total volume of rainfall collected in the analyzed period, it was possible to estimate the monthly inputs of N (kg.ha − 1 ) and C (kg.ha − 1 ) by means of the following equation: \(I=\frac{C . D}{100}\) (3) where I represent the input of Nitrogen or Carbon (kg.ha − 1 ); C represents the monthly concentration average (mg.L − 1 ) of N or C; and D represents the monthly GR, Tf and Sf (mm). Temporal analysis The temporal analysis was assessed through the monthly inputs of GR and NP. For this, NP was firstly averaged across the area from the 10 sample points (Tf + Sf) inputs. Then, statistical differences in C and N inputs (kg.ha − 1 ), between GR and NP (objective i) as well as the NP differences between the dry and wet season, and monthly were assessed by means of the analysis of variance (one-way ANOVA). Further, Tukey’s test was applied to analyze whether NP differentiates throughout the months (objective ii). All the statistical analyses were performed on R environment (version 3.6.2) (R Core Team 2018). Spatial analysis Spatial analysis was performed to identify the spatial patterns of C and N inputs (kg.ha − 1 ) in the AFR. Thus, the samples were accumulated to account for three periods of analysis: annual; the dry season; and the wet season (objective iii). Spatial variability was assessed by means of the coefficient of variation (CV). Moreover, the Inverse Distance Weighting (IDW, specifically inverse of the square of the distance) interpolation method was applied for mapping the spatial distribution of N and C to highlight areas of greater contribution on the nutrient cycle. This method considers only the distance between the points to predict a value for any unmeasured location. The interpolations were performed in the Quantun GIS software (QGIS Version 3.14.0). Methods Site description The study site is a 6.30 ha Atlantic Forest remnant (AFR) located in Southeastern Brazil (21º13’40’’S and 44º57’50’’W) with an average elevation of 925 m. This forest remnant is classified as “montane seasonal semideciduous forest” (Oliveira-Filho et al. 1996). This forest type is widespread throughout the more inland portion of the Atlantic Forest biome in Southeastern Brazil, where the seasonal rainfall makes up to 50% of trees in these forests to lose their leaves in the dry season (Scolforo and Carvalho 2006). The relief is gently undulated and the soil classified as Dystrophic Red Latosol (Rhodic Hapludox) (Junqueira Junior et al. 2017). The Köppen-type climate of the studied region is Cwa with well-defined seasons, characterized by rainfall concentration in the summer (December to March) (Junqueira Junior et al. 2019). Long-term average annual precipitation (1981-2010) is 1461.8 mm in which 85% of it falls during the wet period (October to March) (INMET 2018). The mean annual temperature is 20.3ºC ranging from 16.9ºC (June and July) to 22.5 ºC in February (INMET 2018). Gross rainfall (GR), Throughfall (Tf), and Stemflow (Sf) measurements The gross rainfall (rainfall not affected by trees canopy - GR), throughfall (Tf), and stemflow (Sf) were monitored from May 2018 to April 2019, totalizing 86 daily rainfall events. The monitoring was performed the day after each rainfall event (at approximately 9:00 AM local time) or at least 4 hours after the rainfall event had ceased to avoid events overlapping. For measuring SF, 10 trees were selected from the three most abundant species to better represent the characteristics of the AFR. According to Oliveira-Filho et al. (1996), the most abundant species in the AFR are Copaifera langsdorfii Desf. (Fabaceae), Xylopia brasiliensis Sprengel (Annonaceae) and Miconia pepericarpa DC. (Melastomataceae) (Table 1). The selected trees were well-distributed in the site (Fig. 1) and can be classified according to their DBH (diameter at 1.3 m above ground; see Table 1). For Sf measurements, collectors built with a hose slit open toward the length was nailed in a spiral around the tree trunk and connected to a collection bin (Fig.2a). Table 1. Identification of trees species in the Atlantic Forest remnant. Tree code Scientific name DBH (cm) 01 Xylopia brasiliensis 27.37 02 Copaifera langsdorfii 32.15 03 Copaifera langsdorfii 14.96 04 Copaifera langsdorfii 31.83 05 Miconia pepericarpa 21.65 06 Miconia pepericarpa 12.10 07 Xylopia brasiliensis 50.29 08 Xylopia brasiliensis 11.46 09 Miconia pepericarpa 24.19 10 Xylopia brasiliensis 8.91 GR was measured through 3 fixed Ville de Paris-type rain gauges placed around the forest remnant (Fig. 1 and Fig. 2b). Thereafter, the GR over the AFR was assessed by means of the Thiessen Polygon approach. In the case of gaps in the dataset, the climatological station from the “Brazilian National Meteorological Institute” (INMET 2019) was used to fill them. In addition, for Tf, 10 fixed Ville de Paris-type rain gauges were installed near the selected trees 1.5 m above the forest floor to avoid splash-in (Fig. 2a). GR and Tf were converted to depth by dividing the collected volume (L) by the rain-gauge catchment area (m²). On the other hand, for Sf, the volume stored in the bin (L) was divided by the total projected crown area (m²). This projected area was determined according to the methodology described by Shinzato et al. (2011) in which 8 vertical projections far-between 45° were set in the ground. Then, the area for each canopy was calculated as follow: A (m²) = Σ (1) where A is the projected crown area; “a” and “b” are the vertical projections with far-between 45°. This measurement was performed twice, by considering the projected crown areas in the dry (July 2018) and wet (March 2019) periods. Chemical analysis GR, Tf, and Sf water samples were collected for events with at least 5 mm of rainfall due to the minimum necessary volume for lab analysis, summing up 60 events. GR were composed by the samples of the three external rain gauges (Fig. 1). The evaluated physical and chemical parameters included pH, Electric Conductivity (EC), Total Carbon (C), Nitrate (NO 3 - ), Nitrite (NO 2 - ), and Total Kjeldahl Nitrogen (which refers to Ammonia and Organic Nitrogen; TKN). Regarding pH, EC, and carbon, the samples were analyzed after every rainfall event (i.e. single samples). On the other hand, for analyzing NO 3 - , NO 2 - , and TKN, the samples were firstly accumulated on a monthly scale (i.e. composite sample) before the analysis could be carried out. All procedures concerning the samples (taking, preserving, and analyzing) followed the Standard Methods (APHA 2014) criteria (Table 2). These ensured that the nitrogen was not lost during storage. Table 2. Physical and chemical water variables evaluated, preservation procedures, the lab method used, and respective reference. Variable Preservation procedures Lab Method References pH Refrigerated at 4 ºC Eletrometric method (Method 4500 H +) APHA (2014) EC Conductimetric method (Method 2510 B) APHA (2014) C Filtered and refrigerated at 4 ºC Shimadzu total carbon analyzer (TOC-V CPH) (Method TC-IC) Shimadzu (2003) NO 3 - Filtered and frozen Yang et al. (1998) Method Yang et al. (1998) NO 2 - Calorimetric Method (Method 4500- NO 2 - B) APHA (2014) TKN Acidification and refrigerated at 4 ºC NBR 13.796:1997 Method ABNT (1997) Data analysis For some events, it was not possible to measure Tf and Sf because the rain gauge remained open and/or the collectors were knocked over by animals. Thus, with GR data, linear regressions were fitted to overcome these issues and to fill the unavoidable gaps in the measuring points. The regressions have been performed for each point separately, resulting in R 2 greater than 0.78 and 0.54 for Tf and Sf, respectively. For pH, EC, and C, which were analyzed in all the rainfall events, the mean monthly values of the concentrations in GR, Tf, and Sf were estimated by the volume-weighted mean (VWM), as follows: where C represents the concentration of the parameters (pH, EC, and C) at collector i for event e , and V represents the total volume at collector i for event e . As abovementioned, the chemical analyses for TKN, NO 3 - , and NO 2 - were carried out in a month scale. Then, with the total concentration of nitrogen (N = TKN + NO 3 - + NO 2 - ) and carbon (C) together with the total volume of rainfall collected in the analyzed period, it was possible to estimate the monthly inputs of N (kg.ha -1 ) and C (kg.ha -1 ) by means of the following equation: where I represent the input of Nitrogen or Carbon (kg.ha -1 ); C represents the monthly concentration average (mg.L -1 ) of N or C; and D represents the monthly GR, Tf and Sf (mm). Temporal analysis The temporal analysis was assessed through the monthly inputs of GR and NP. For this, NP was firstly averaged across the area from the 10 sample points (Tf +Sf) inputs. Then, statistical differences in C and N inputs (kg.ha -1 ), between GR and NP (objective i) as well as the NP differences between the dry and wet season, and monthly were assessed by means of the analysis of variance (one-way ANOVA). Further, Tukey’s test was applied to analyze whether NP differentiates throughout the months (objective ii). All the statistical analyses were performed on R environment (version 3.6.2) (R Core Team 2018). Spatial analysis Spatial analysis was performed to identify the spatial patterns of C and N inputs (kg.ha -1 ) in the AFR. Thus, the samples were accumulated to account for three periods of analysis: annual; the dry season; and the wet season (objective iii). Spatial variability was assessed by means of the coefficient of variation (CV). Moreover, the Inverse Distance Weighting (IDW, specifically inverse of the square of the distance) interpolation method was applied for mapping the spatial distribution of N and C to highlight areas of greater contribution on the nutrient cycle. This method considers only the distance between the points to predict a value for any unmeasured location. The interpolations were performed in the Quantun GIS software (QGIS Version 3.14.0). Results Hydrological monitoring GR was monitored from May 2018 to April 2019, summing up 86 events, which corresponded to 1601.6 mm. Total precipitation for the same period in a meteorological station (MS) located at 1 km from the forest edge was 1535.3 mm (INMET 2019). Considering that the long-term annual average rainfall (1981-2010) is 1461.8 mm, the sampling year represented a typical year in terms of rainfall, with almost 10% above the average (Table 3). Approximately 85% of gross rainfall in the AFR occurred in the wet season (October to March) which is in accordance with the expected pattern of the region (Table 3). Table 3. Comparison of the monitored dry and wet seasons (INMET and AFR) against the long-term average (1981-2010). INMET meteorological station (mm) Atlantic Forest remnant (AFR) (mm) Long-term average rainfall (1981-2010) - (mm) Dry season 226.4 241.0 218.0 Wet season 1308.9 1360.5 1243.8 Total 1535.3 1601.6 1461.8 The maximum and minimum GR were monitored in December (328.4 mm) and July (0.0 mm), respectively (Fig.3). Tf was the main portion of GR across the monitoring period, totalizing 1278.6 mm, which represents 79.8% of it, ranging from 70.78% (June) to 85.72% (December). Interception loss totalized 319.7 mm (20.0% of GR), with a maximum percentage in June (29.17%) and a minimum in December (14.05%). Sf totalized 3.2 mm, representing 0.20% of GR, with a maximum contribution in August (0.42 %) and a minimum in June (0.04%). Chemical analysis For chemical analysis, rainfall samples were collected from 60 events (only those with more than 5 mm) totalizing 1529.6 mm of GR, 1232.1 mm of Tf, and 3.1 mm of Sf, which represents approximately 96% of the entire monitoring period (Table 4). Table 4. Gross rainfall (GR), average throughfall (Tf) and average stemflow (Sf) with their respective standard deviations and coefficients of variation (CV). Months GR (mm) Tf (mm) Sf (mm) May 2018 10.61 8.32 ± 1.84 (22%) 0.005 ± 0.006 (120%) Jun 2018 14.08 11.13 ± 2.62 (24%) 0.007 ± 0.007 (100%) Jul 2018 0 0.00 ± 0.00 (0%) 0.000 ± 0.000 (0%) Aug 2018 60.2 46.39 ± 9.34 (20%) 0.287 ± 0.209 (73%) Sep 2018 46.37 38.25 ± 5.18 (14%) 0.079 ± 0.060 (76%) Oct 2018 205.95 164.11 ± 21.26 (13%) 0.364 ± 0.188 (52%) Nov 2018 240.64 182.03 ± 32.19 (18%) 0.638 ± 0.576 (90%) Dec 2018 319.86 276.54 ± 35.08 (13%) 0.699 ± 0.389 (56%) Jan 2019 147.76 117.89 ± 22.64 (19%) 0.342 ± 0.215 (63%) Feb 2019 201.5 159.57 ± 32.35 (20%) 0.277 ± 0.173 (62%) Mar 2019 199.21 160.88 ± 23.49 (15%) 0.292 ± 0.246 (84%) Apr 2019 83.39 67.00 ± 11.49 (17%) 0.119 ± 0.097 (82%) Entire period (mm) 1529.57 1232.11 3.109 The annual average pH of GR was 7.16. For Tf and Sf, this value was 6.96 and 5.23, respectively. The average EC of GR was 16.23 µS/cm, while the EC of Tf and SF was 46.86 µS.cm -1 and 53.46 µS.cm -1 , respectively. These values are 2.9 and 3.3 times greater than GR, considering Tf and Sf, respectively (Table 5). Table 5. pH and electric conductivity (EC) of gross rainfall (GR), average throughfall (Tf) and average stemflow (Sf) with their respective standard deviations and coefficients of variation (CV). GR Tf Sf Months pH EC (µS.cm -1 ) pH EC (µS.cm -1 ) pH EC (µS.cm -1 ) May 2018 7.44 32 7.18 ± 0.37 (5.2%) 182.10 ± 47.36 (26%) 7.47 ± 0.44 (6%) 301.20 ± 278.12 (92%) Jun 2018 7.29 58 7.30 ± 0.31 (4.2%) 206.00 ± 271.08 (132%) 7.15 ± 0.51 (7%) 117.44 ± 54.60 (46%) Jul 2018 0.00 0.00 0.00 ± 0.00 (0%) 0.00 ± 0.00 (0%) 0.00 ± 0.00 (0%) 0.00 ± 0.00 (0%) Aug 2018 5.56 15.57 5.40 ± 1.31 (24.3%) 81.24 ± 30.46 (37%) 5.05 ± 0.48 (10%) 138.25 ± 48.44 (35%) Sep 2018 7.35 34.3 7.17 ± 0.05 (0.7%) 78.92 ± 15.59 (20%) 5.51 ± 0.34 (6%) 153.88 ± 48.88 (32%) Oct 2018 7.26 19.22 7.04 ± 0.08 (1.1%) 56.24 ± 16.93 (30%) 5.37 ± 0.47 (9%) 69.73 ± 31.36 (45%) Nov 2018 7.13 17.22 6.93 ± 0.07 (1.0%) 40.83 ± 14.24 (35%) 5.09 ± 0.66 (13%) 43.88 ± 19.85 (45%) Dec 2018 6.86 15.23 6.71 ± 0.09 (1.3%) 33.88 ± 7.71 (23%) 5.11 ± 0.44 (9%) 29.50 ± 17.40 (59%) Jan 2019 7.61 21.48 7.40 ± 0.15 (2.0%) 43.56 ± 10.03 (23%) 5.56 ± 0.31 (6%) 31.52 ± 17.18 (55%) Feb 2019 7.48 10.26 7.17 ± 0.08 (1.1%) 42.45 ± 6.58 (16%) 5.24 ± 0.62 (12%) 45.86 ± 28.17 (61%) Mar 2019 7.24 8.88 7.06 ± 0.05 (0.7%) 37.75 ± 8.66 (23%) 5.19 ± 0.85 (16%) 35.55 ± 18.56 (52%) Apr 2019 7.36 13.84 7.20 ± 0.05 (0.7%) 46.70 ± 10.07 (22%) 5.38 ± 0.34 (6%) 35.15 ± 12.50 (36%) Mean annual 7.16 16.23 6.96 46.86 5.23 53.46 The C and N concentrations were higher in Sf, followed by Tf and GR, respectively (Table 6). For all months, excepted May, Sf concentration of N was higher than the concentration in Tf (Figs. 4a and 4b). Considering annual average concentration (mg.L -1 ) in GR, C was almost three times higher in Tf, and more than five times higher in Sf, whereas N concentration was more than twice in Tf and more than four times in Sf. Table 6. Carbon and Nitrogen concentration from gross rainfall (GR), average throughfall (Tf), and average stemflow (Sf) with their respective standard deviations and coefficients of variation (CV). GR (mg.L -1 ) Tf (mg.L -1 ) Sf ( mg.L -1 ) C N C N C N May 2018 18.0 9.2 111.6±3.02 (29%) 13.8±6.9 (50%) 126.8±76.5 (60%) 11.5±16.6 (144%) Jun 2018 22.9 2.3 50.6±13.2 (26%) 5.4±3.9 (72%) 70.0±28.4 (41%) 12.1±14.2 (118%) Jul 2018 0.0 0.0 0±0 (0%) 0±0 (0%) 0±0 (0%) 0± 0 (0%) Aug 2018 14.0 1.7 32.7±17.1 (52%) 8.9±4.4 (49%) 83.4±35.9 (43%) 14.0±6.5 (47%) Sep 2018 12.8 1.9 30.5±9.8 (32%) 3.8±1.7 (46%) 96.7±52.0 (54%) 11.8±7.7 (66%) Oct 2018 6.0 0.8 18.4±6.4 (35%) 2.9±1.5 (52%) 45.9±22.2 (48%) 8.9±5.9 (67%) Nov 2018 7.8 0.8 14.2±4.8 (34%) 4.0±2.2 (54%) 27.3±13.4 (49%) 10.9±5.8 (53%) Dec 2018 6.5 0.9 12.2±3.5 (29%) 2.5±1.5 (59%) 24.9±13.9 (56%) 6.8±5.9 (87%) Jan 2019 6.2 1.1 12.4±3.7 (29%) 3.0±1.3 (42%) 22.7±11.3 (50%) 4.9±2.8 (56%) Feb 2019 5.4 1.1 12.7±3.3 (26%) 1.5±0.3 (19%) 33.0±18.5 (56%) 4.1±3.2 (77%) Mar 2019 4.3 1.2 10.8±3.6 (34%) 1.7±0.6 (33%) 27.7±17.1 (62%) 3.4±1.9 (55%) Apr 2019 4.7 1.1 11.6±2.7 (23%) 2.4±1.1 (48%) 23.0±11.4 (49%) 3.2±2.2 (70%) Annual average 9.06 1.84 26.47 4.17 48.46 7.64 The total annual C and N in GR was 104.13 kg.ha -1 and 16.81 kg.ha -1 , respectively; in Tf, they were, respectively, 191.97 kg.ha -1 and 36.69 kg.ha -1 ; and in Sf, 1.21 kg.ha -1 and 0.27 kg.ha -1 , respectively (Table 7). The total annual flux of C and N in NP (Tf + Sf) increased by 86% and 120%, respectively, regarding GR. Analyzing the seasonality of nutrient inputs, we observed that 74.3% (143.52 kg.ha -1 ) of C and 75.1% (27.76 kg.ha -1 ) of N reached the forest floor during the wet season. Table 7. Monthly inputs of Carbon and Nitrogen from gross rainfall (GR), throughfall (Tf), and stemflow (Sf) in the AFR. GR (kg.ha -1 ) Tf (kg.ha -1 ) Sf (kg.ha -1 ) Months C N C N C N May 2018 1.91 0.97 8.94 1.10 0.00 0.00 Jun 2018 3.23 0.32 5.47 0.55 0.00 0.00 Jul 2018* 0.00 0.00 0.00 0.00 0.00 0.00 Aug 2018 8.43 1.01 15.53 4.46 0.25 0.04 Sep 2018 5.95 0.87 11.48 1.42 0.09 0.01 Oct 2018 12.43 1.74 29.65 4.63 0.19 0.04 Nov 2018 18.84 1.96 26.04 7.18 0.20 0.08 Dec 2018 20.89 2.88 34.56 7.18 0.19 0.05 Jan 2019 9.21 1.59 14.09 3.40 0.07 0.02 Feb 2019 10.80 2.11 20.84 2.49 0.11 0.01 Mar 2019 8.55 2.41 17.50 2.68 0.09 0.01 Apr 2019 3.89 0.94 7.88 1.60 0.02 0.00 Annual total 104.13 16.81 191.97 36.69 1.21 0.27 *Means no observed rain in the month. Temporal analyses ANOVA detected significant differences for C ( p = 0.027) and N ( p = 0.022) inputs between GR and NP. For NP, significant differences between dry and wet seasons for C ( p = 2.72E-06) and N ( p = 2.81E-06) inputs, as well as monthly (C: p = 1.19E-18; N: p = 2.38E-15), were found. Based on Tukey’s test, it was possible to group months without differences for C and N inputs in NP (Fig. 5 and Fig. 6). Considering NP, the months with the highest C inputs were December, October, and November respectively (a), whereas the months with the lowest contributions were September, May, and June (f). C inputs followed the climatic seasonality of the region, meaning that months with higher precipitation have higher inputs. The highest N inputs by NP were observed in November and December (a), and these months showed no statistical differences. August and October were statistically similar (ab), which the same observed in January, February, and March (bc). Apart from August, N inputs were greater throughout the wet season (October to May). Thus, the months with the lowest inputs were April, May, June, and September, which no statistical differences among them (c). Spatial analyses The spatial variability of N and C inputs (kg.ha -1 ) was assessed through the coefficient of variation (CV) regarding the monthly, the seasonal (dry and wet seasons), and the annual scales (Table 8). In general, the annual and the wet season demonstrated the same variability for both, C and N inputs. However, in the dry season, the spatial variability of N (26%) was higher than that of C (18%). Considering the seasonality, for both C and N, the variability was higher in the wet season than in the dry season. Throughout the year, the CV of C ranged from 19% (January) to 47% (August), while N varied from 17% (February) to 60% (September). Table 8. Coefficient of variation (CV) of C and N inputs (kg.ha -1 ) in the AFR. Coefficient of variation (%) in C and N inputs from net precipitation Months Carbon Nitrogen May 2018 22 43 Jun 2018 22 52 Jul 2018* 0 0 Aug 2018 47 44 Sep 2018 29 60 Oct 2018 29 44 Nov 2018 38 50 Dec 2018 39 58 Jan 2019 19 31 Feb 2019 29 17 Mar 2019 41 20 Apr 2019 24 27 Dry season 18 26 Wet season 30 30 Annual total 26 27 *July 2018 there was no rainfall events IDW method was carried out to assess the spatial distribution of C and N inputs (kg.ha -1 ) regarding the dry and wet seasons and the entire period (total annual) (Fig. 7). Overall, there is a consensus between the spatial patterns of N and C in the dry season. However, in the wet season, the spatial patterns of C and N presented remarkable differences, especially in the southwest and the northeast regions of the AFR. Regardless the analyzed period, the greatest inputs for both C and N occurred in the same places, in the central, southwestern and eastern areas (near the points 3, 4 and 8). Discussion This study aimed to (i) compare the changes in C and N inputs before and after the rainfall passes through the forest canopy; (ii) identify differences in C and N inputs in net precipitation considering the dry and wet seasons and also the monthly scale; and (iii) identify spatial differences in C and N inputs within the Atlantic forest remnant. Our main findings are (i) statistical differences in C and N inputs between gross rainfall and net precipitation, with an increase in the inputs after rainfall passes through the canopy; (ii) statistical differences were also observed in the seasonal and monthly scales, as a higher input occurred during the wet season, accounting for almost 75% of the total contribution; and (iii) the spatial variability was higher in the wet season which the largest inputs for both C and N occurring in the same places (near the points 3, 4 and 8). Gross Rainfall x Net Precipitation The concentration of NO 3− , NO 2− , TKN, total nitrogen and total carbon in gross rainfall, throughfall and stemflow were similar to what have been found by other studies in Tropical and Temperate forests worldwide (Hölscher et al. 2003 ; Markewitz et al. 2004 ; Lilienfein and Wilcke 2004 ; Oziegbe et al. 2011 ; Tu et al. 2013 ; Ukonmaanaho et al. 2014 ; Neu et al. 2016 ; Izquieta-Rojano et al. 2016 ; Limpert and Siegert 2019 ; You et al. 2020 ). Our results show that the rain is enriched with N and C when crossing the forest canopy because the average annual concentration of these nutrients is higher in throughfall and stemflow than in gross rainfall. These results are in accordance with other studies in both temperate and tropical forests that indicate leaching of N and C from the forest (Schroth et al. 2001 ; Hölscher et al. 2003 ; Goller et al. 2006 ; Hofhansl et al. 2011 ; Liu and Sheu 2003 ; Heartsill-Scalley et al. 2007 ; Izquieta-Rojano et al. 2016 ; Van Stan et al. 2017 ; Limpert and Siegert 2019 ). However, specifically for nitrogen forms, the results found do not corroborate with some other studies, in which N concentrations decreased in throughfall and stemflow, indicating retention of N by the forest (Parron et al. 2011 ; Tu et al. 2013 ; Ukonmaanaho et al. 2014 ; Su et al. 2019 ). We observed that concentrations decrease only for NO 2− in stemflow (0.05 mg. L − 1 ) comparing with throughfall (0.11 mg. L − 1 ). Results regarding NO 2− concentrations in rainfall partitioning are extremely rare in the literature, which hampers further comparisons. As the results of nitrogen are variable in the literature, future studies are necessary to deepen in the relationship between the nitrogen and the hydrological cycles in tropical forests. The increase of C and N concentrations in throughfall and stemflow results from the washing process of atmospheric dry deposition accumulated in forest canopy between rainfall events and from the leaching process of the trees’ materials (Parker 1983 ; Schroth et al. 2001 ; Liu and Sheu 2003 ; Corti et al. 2019 , You et al. 2020 ). The main sources of C in net precipitation are derived from the forest system (Parker 1983 ; You et al. 2020 ). Although the forest system also has a contribution to increase N concentration in net precipitation, it is remarkable the influence of the atmospheric gaseous and aerosol deposition (Parker 1983 ; Schroth et al. 2001 ). In this regard, rainfall that crosses the canopy is an important source of nutrients for forests, and has a great significance in the nutrient cycle, because tree canopy works like a funnel capturing rain and transferring dry deposition from the canopy to the soil surface. This process is controlled by biotic and meteorological factors (Parron et al. 2011 ; Van Stan and Stubbins 2018 ). The process of nutrients enrichment in net precipitation can be reinforced by pH results. We observed that pH decreases when the rainwater passes through the forest canopy, resulting in relevant decrease in the average annual pH. This pH reduction is similar to what happens in other types of forests: Brazilian Cerrado, Amazonia Rainforest, and Temperate Oak Forest (Lilienfein and Wilcke 2004 ; Tobón et al. 2004 ; Corti et al. 2019 ). Organic matter presence is the main cause of the decrease in pH, since organic acids are leached from the leaves of canopies, branches, and trunks (Liu and Sheu 2003 ; Tobón et al. 2004 ). Furthermore, the annual average of EC for throughfall and stemflow are, respectively, 2.9 and 3.3 times greater than that of gross rainfall, which shows that the rain was enriched with solid particles, similar to that observed by Su et al. ( 2019 ). Considering total annual C and N inputs to the canopy (GR) and forest floor (Tf, Sf), we observed that, in general, the amount found in our study is in agreement with that found for other tropical and temperate forests (Table 9 ). For total C, the annual input in gross rainfall (104.13 kg. ha − 1 . year − 1 ) was similar to that found by Neu et al. ( 2016 ) in an evergreen tropical forest (121 kg. ha − 1 . year − 1 ). Results of the total carbon are scarce in the literature; however, Neu et al. ( 2016 ) observed that 32% of total C found in gross rainfall is from inorganic sources, and the organic carbon fraction is influenced by agricultural activities and fires. The climate seasonality of the study region, summed to the drier vegetation of the surrounding areas, may contribute to the incidence of natural or non-natural fires. In this regard, the carbon present in rainfall may be related to biomass burn (condensation nuclei) as we found results slightly lower than those of regions strongly influence by fires, like Cerrado and Amazonian Rainforest (Markewitz et al. 2004 ; Germer et al. 2007 ; Neu et al. 2016 ). Table 9 Summary of the key N and C inputs (kg.ha − 1 .year − 1 ) in rainfall parts (Gross rainfall, Throughfall and, Stemflow) in forests around the world. Rainfall Gross rainfall (kg.ha − 1 .year − 1 ) Reference Forest type Long-term (mm) Mean annual (mm) NH 4 + NO 3 − NO 2 − DON TKN N DOC C This study Atlantic Forest 1461.8 1601.6 - 2.82 0.81 - 13.08 16.81 - 104.13 Schroth et al. ( 2001 ) Amazonia Rainforest 2622 2672 1.8 1.4 - 2.3 4.1* 5.5 - - Hölscher et al. ( 2003 ) Rainforest 2812 2900 1.4–2.2 1.7-2.0 - - - - - - Markewitz et al. ( 2004 ) Tropical Moist Forest 1803 - 1.5 0.2 - - - 4 123.4 - Lilienfein and Wilcke ( 2004 ) Cerrado 1550 1815 2.7–3.1 2.1–2.4 - - - 5.7–6.4 47–55 - Schwendenmann and Veldkamp ( 2005 ) Tropical Wet Forest 4200 4073 - - - 1–6 - 5–14 22–36 - Schrumpf et al. ( 2006 ) Rainforest 1840 1960 to 2600 - - - 3.36–5.97 - - 59.4-143.9 - Germer et al. ( 2007 ) Tropical Rainforest 2300 2286 4.46 0.8 - - - - 106.45 - Souza and Marques ( 2010 ) Atlantic Rainforest 2240.1 2406.96 - 2.3 - - - - - - Hofhansl et al. ( 2011 ) Wet Tropical Rainforest 5810 5720 - - - - - 7.7 30.9 Oziegbe et al. ( 2011 ) Rainforest 1413 1079 - 10.43 - - - - - - Parron et al. ( 2011 ) Cerrado - 1400 - - - - - 12.6 - - Souza et al. ( 2015 ) Atlantic Forest 2800 2649 6 5 - 4.1 10.1* 15.1 - - Zhou et al. ( 2016 ) Tropical Forest 1557 - - - - - - - 41.9 - Neu et al. ( 2016 ) Evergreen Tropical Forest 1905 1829 - - - - - - 82.3 121.2 Liu and Sheu ( 2003 ) Subtropical Forest 2300 to 2700 - - - - - - - 142.8 - Tu et al. ( 2013 ) Subtropical Forest 1490 1984.2 61.9 24.9 - 26.9 88.8* 113.8 - - Izquieta-Rojano et al. ( 2016 ) Evergreen Holm Oak Forests 364–840 - 0.68–6.55 1.08–3.54 - 1.08–12.27 1.76–18.82* - - - You et al. ( 2020 ) Coniferous, Deciduous and Evergreen forest 1416.4 - - - - - - 30.4 22.6 - Rainfall Throughfall (kg.ha − 1 . year − 1 ) Reference Forest type Long-term (mm) Mean annual (mm) NH 4 + NO 3 − NO 2 − DON TKN N DOC C This study Atlantic Forest 1461.8 1601.6 8.89 1.54 - 26.25 36.68 - 191.97 Schroth et al. ( 2001 ) Amazonia Rainforest 2622 2672 2.2 1.9 - 7.2 9.4* 11.3 - - Hölscher et al. ( 2003 ) Rainforest 2812 2900 2.4–5.7 0.6-1 - - - - - - Markewitz et al. ( 2004 ) Tropical Moist Forest 1803 - 2.9 1.7 - - - 9.5 83.1 - Tobón et al. ( 2004 ) Tropical Rainforest 3100 3400 9.72–12.98 17.07–31.98 - - - - 148.43 -190.42 - Lilienfein and Wilcke ( 2004 ) Cerrado 1550 1815 2.3–3.4 3-3.9 - - - 9.9–11 66–70 - Schwendenmann and Veldkamp ( 2005 ) Tropical Wet Forest 4200 4073 - - - 9 - 17 232 - Schrumpf et al ( 2006 ) Montane Rainforest 1840 1960 to 2600 - - - 6.24–10.31 - - 102.8-218.5 - Germer et al. ( 2007 ) Tropical Rainforest 2300 2286 5.71 2.11 - - - - 301.59 - Fujii et al. ( 2009 ) Tropical Forest - 2187–2427 - - - - - - 97–182 - Souza and Marques ( 2010 ) Atlantic Rainforest 2240.1 2406.96 - 2.51–5.93 - - - - - - Schmidt et al. (2010) Subtropical Montane Forest 2000 to 5000 4169 1.9 2.8 - 3.4 5.3* - 106 - Hofhansl et al. ( 2011 ) Wet Tropical Rainforest 5810 5720 - - - - 10.5–13.8 74.7–94.9 - Oziegbe et al. ( 2011 ) Rainforest 1413 1079 - 39.27 - - - - - - Parron et al. ( 2011 ) Cerrado - 1400 - - - - - 5.9–8.3 - - Diniz et al. ( 2013 ) Atlantic Forest 1300 1533.3 - - - - - - - 23.1–30.5 Souza et al. ( 2015 ) Atlantic Forest 2800 2649 9.1 5.3 0.2 19.7 28.8* 34.3 - - Zhou et al. ( 2016 ) Tropical Forest 1557 - - - - - - 113.5 - Neu et al. ( 2016 ) Evergreen Tropical Forest 1905 1829 - - - - - - 150.8 167.4 Liu and Sheu ( 2003 ) Subtropical Forest 2300 to 2700 - - - - - - - 188.8-231.3 - Tu et al. ( 2013 ) Subtropical Forest 1490 1984.2 80.1 13.7 - 20.1 - 113.8 - - Izquieta-Rojano et al. ( 2016 ) Evergreen Holm Oak Forests 364–840 - 0.44–3.7 1.66–8.81 - 5.3-11.91 - - - - Van Stan et al. ( 2017 ) Oak - Cedar Forest 750 to 1200 - - - - - - 9–29 230–480 - You et al. ( 2020 ) Coniferous, Deciduous and Evergreen Forest 1416.4 - - - - - 22.92–27.38 52-75.94 - Rainfall Stemflow (kg.ha − 1 . year − 1 ) Reference Forest type Long-term (mm) Mean annual (mm) NH 4 + NO 3 − NO 2 − DON TKN N DOC C This study Atlantic Forest 1461.8 1601.6 - 0.05 0.001 - 0.21 0.261 - 1.21 Hölscher et al. ( 2003 ) Rainforest 2812 2900 0.1–0.4 0.1 - - - - Tobón et al. ( 2004 ) Tropical Rainforest 3100 3400 0.18–0.26 0.32–0.62 - - 2.82–6.14 - Hofhansl et al. ( 2012 ) Tropical Rainforest 5810 - - - - 0.16 2.75 - Diniz et al. ( 2013 ) Atlantic Forest 1300 1533.3 - - - - - 0.93–1.55 Neu et al. ( 2016 ) Evergreen Tropical Forest 1905 1829 - - - - 1.5 2.1 Liu and Sheu ( 2003 ) Subtropical Forest 2300 to 2700 - - - - - 6.7–15.3 - Van Stan et al. ( 2017 ) Oak - Cedar Forest 750 to 1200 - - - - 0.15–2.4 7–75 - You et al. ( 2020 ) Coniferous, Deciduous and Evergreen forest 1416.4 - - - - 0.14–0.91 0.43–4.43 - Legend: C: carbon; N: nitrogen; TKN: total kjeldahl nitrogen; DON: dissolved organic nitrogen; DOC: dissolved organic carbon; NH 4 + : ammonia; NO 3 − : nitrate; NO 2 − : nitrite. *TKN = DON + NH 4 + The total N inputs in gross rainfall (16.81 kg. ha − 1 . year − 1 ) was in good agreement with that found in the Brazilian Cerrado biome (12.6 kg. ha − 1 . year − 1 ) by Parron et al. ( 2011 ) and for an Atlantic forest (15.1 kg. ha − 1 . year − 1 ) by Souza et al. ( 2015 ). However, our results were lower than those found in China in Subtropical forests by Tu et al. ( 2013 ) (114 kg. ha − 1 . year − 1 ) and by You et al. ( 2020 ) (30 kg. ha − 1 . year − 1 ). The major proximity of our results with Parron et al. ( 2011 ) and Souza et al. ( 2015 ) could be related with the activities that condition atmospheric emissions of reactive nitrogen, from global to local scale. For instance, on a global scale, the increase in emission of anthropogenic nitrogen was observed especially in North America, Europe, and Asia (Van Aardenne et al. 2001 ) in the last decades, but the natural presence of nitrogen in tropical regions is higher than in temperate ones (Galloway et al. 2004 ; Van Aardenne et al. 2001 ). On the regional and local scale, such as our study, the mains anthropogenic nitrogen sources are related to the economic characteristics of the Southern Minas Gerais state, with urban influence (automobilist exhaust), industries (fossil fuel combustion), fires, and agricultural activities such as fertilizer use and cattle breeding. Similarity with other studies was also observed when we consider nitrogen forms separately. The NO 3− annual flux in gross rainfall (2.82 kg. ha − 1 . year − 1 ) was very close to that observed in the Brazilian Cerrado biome (2.1 to 2.4 kg. ha − 1 . year − 1 ; Lilienfein and Wilcke 2004 ) and in an Atlantic Rainforest (2.3 kg. ha − 1 . year − 1 ; Souza and Marques 2010 ). Considering TKN (DON + NH 4 +) the annual input in gross rainfall (13.08 kg. ha − 1 . year − 1 ) was similar to that of an Atlantic forest (10.1 kg. ha − 1 . year − 1 ; Souza et al. 2015 ). The similarity between our results with other studies in Brazil may be related to the influence of regional and local anthropogenic sources and singular conditions of the tropical regions. When studies of gross rainfall consider N forms separately, it is possible to improve the understanding of the local sources and the processes that controls N depositions. The anthropogenic activities are considered the most important sources of anthropogenic N since food production and fossil fuels demands are increasing constantly (Galloway et al. 2004 ). This reinforces the importance and urgency of these studies. We found statistical differences between gross rainfall and net precipitation annual inputs (kg.ha − 1 ) for both C ( p = 0.027) and N ( p = 0.022). The vegetation structure, the leaching of dry deposition in the forest, and the exchange with trees surface can explain these differences. (Parker 1983 ). Forest-rainfall interaction can be responsible for an increase of 89 kg. ha − 1 . year − 1 of C and 20 kg. ha − 1 . year − 1 of N to the forest floor. This enrichment of C and N inputs in net precipitation was approximately to 86% and 120%, respectively. Regarding N inputs, almost the same was identified in an Atlantic Forest (127% by Souza et al. 2015 ). Further analysis demonstrated that the increment of N was superior than that of C which may be explained by the fact that dry deposition of N is enhanced in the region (Parker 1983 ; You et al. 2020 ; Schroth et al. 2001 ). The total C input in net precipitation (193.18 kg. ha − 1 . year − 1 ) was 1.85 times greater than in gross rainfall (104.13 kg. ha − 1 . year − 1 ). According to Neu et al. ( 2016 ), organic C is more representative in net precipitation, attaining up to 90% of total C, than in gross rainfall with only 68%. The presence of organic C in forests is the result of several sources, as forest metabolism, decomposition processes, and animal excrement (Parker 1983 ; Neu et al. 2016 ). Moreover, the leaching process of the organic matter contributes to an increase in C inputs in net precipitation (Schrumpf et al. 2006 ; Mellec et al. 2010 ). The inputs of total N (36.95 kg. ha − 1 . year − 1 ), TKN (26.46 kg. ha − 1 . year − 1 ), NO 3 − (8.94 kg. ha − 1 . year − 1 ), and NO 2 − (1.54 kg. ha − 1 . year − 1 ) in net precipitation were 2.2, 2, 3.2 and 1.9 times greater than in gross rainfall, respectively, demonstrating significant dry deposition and leaching processes in the Atlantic Forest remnant. Besides dry deposition of reactive nitrogen forms (NO 3 − and NH 4 + ) derived from the anthropogenic sources, the increase in N in net precipitation could be attributed to the leaching of the organic N provided by biological processes (Mellec et al. 2010 ). Despite NO 2 − represents a small portion of total N (4%), the enrichment in net precipitation could be attributed to fog and dew formations which withdraw the dry depositions from the leaves’ surface (Acker et al. 2008 ). Considering the total area of the Atlantic Forest remnant (6.3 ha), the annual inputs of C and N in gross rainfall can reach up to 656 kg.yr − 1 and 106 kg.yr − 1 , respectively, whereas for net precipitation an amount of 1217 kg.yr − 1 and 233 kg.yr − 1 , respectively. The Atlantic Forest remnant is responsible for the considerable increase of local C and N, directly influencing the nutrient cycle, the availability of N and the stocking of C in the soil. Thus, Atlantic forest environments outstand as an important sinking for C and N. Temporal variations of the net precipitation in AFR There was a seasonal variability of total C and N in gross rainfall (Fig. 4 ). These concentrations were higher in the dry season (April to September) and decreased throughout the wet season (October to March). Such pattern was also observed for throughfall and stemflow according with other forest stands in seasonal climate regions (Neu et al. 2016 ; Germer et al. 2007 ; Lilienfein and Wilcke 2004 ). This seasonal variability in net precipitation is more common in semideciduous and deciduous forests than in evergreen forests according to Van Stan and Stubbins ( 2018 ). This is the result of some factors explained as follows. The accumulation of particles in the atmosphere and in the canopies surfaces in the dry season ensures the high concentrations in the first rainfalls after long dry periods. These events are called “first flush events” and are responsible for “washing” the atmosphere and the forests canopy (Neu et al. 2016 ; You et al. 2020 ). This accumulation starts decreasing in the wet season as the time between rainfall events decreases (rainfall events are more frequent), explaining the behavior in the wet season (You et al. 2020 ). In addition, intense rainfall events are more common in the wet season which causes dilution of the compounds and further reductions in concentration (Michalzik and Matzner 1999 ; Zhang et al. 2016 ). Furthermore, there were statistical differences (C p = 2.72E-06 and N p = 2.81E-06) in net precipitation regarding the dry and wet season. Such differences could be explained by the semi-deciduousness, as up to 50% of the trees lose up their leaves in the dry season. This behavior influences the dry deposition, the amount of rainfall that passes through the canopy and, hence, the interactions between rainfall and forest. In the dry season, trees with few leaves drain more water by the trunk (Terra et al 2018a ), however, the presence of more leaves increases interactions between rainfall and forests, affecting the nutrient inputs in the wet season. These conditions drive the dynamics of nutrient transport from the atmosphere to the forest floor in the dry and wet seasons as they are strictly associated with the characteristics of the vegetation and rainfall seasonality. The seasonality of rainfall and the semi-deciduousness are conditioning factors for C and N inputs being more significant in the wet season, concentrating 74.3% (143.52 kg.ha − 1 ) of total annual C and 75.1% (27.76 kg.ha − 1 ) of total annual N. Although the concentration is considerably higher in the dry season, because of the effects from long periods between rainfall and atmospheric deposition, the considerably higher total of rainfall in the wet season is responsible for the most part of C and N that reaches the forest floor annually. In this sense, frequent precipitation is responsible for leaching C and N from the atmosphere and forest surfaces (Tu et al. 2013 ; Van Stan et al. 2017 ). For the monthly step, it was also observed statistical differences (C p = 1.19E-18 and N p = 2.38E-15). Using the Tukey’s test, it was possible to group statistically equal months (Fig. 5 and Fig. 6). Considering net precipitation, the months with the highest C inputs were December, October, and November, respectively, with no statistical differences (a). These months represent the onset of the wet season and, thus, the greater inputs are because of the dry deposition accumulated throughout the dry season that was washed by the significant amount of rainfall events. The months with the lowest C inputs were statistically equal (f) and correspond to the beginning of the dry season (April, May, and June). The inputs of C followed the seasonality of the region, in which the higher the rainfall amount the greater the input of C. For N in net precipitation, November and December showed no statistical differences (a). These months represented the highest inputs of N throughout the year and are the months with the highest amounts of rainfall. October and August were also statistically equal (a), both had above-average rainfall and were preceded by months with below-average rainfall. This evidenced the influence of the first rainfall events and the importance of washing-off the atmosphere and the particles deposited in the crowns and branches of the trees after a long dry period. October and August were also statistically equal (b) to January, February and March, which was also expected as these months represent the last ones of the wet season. These months (January, February and March) were also statistically equal to April, May, June, and September, the months with the lowest N inputs coinciding with the lowest rainfalls in the year. Although April had a higher rainfall amount than August, the long antecedent dry period provided a greater input of N as a consequence of the greater number of particles stored in the atmosphere and canopies. This confirms that the amount and seasonality of rainfall drive N inputs in the Atlantic Forest remnant. Spatial variation of the net precipitation in AFR The spatial variability of the nutrients inputs is expressive between different types of forest (Levia and Frost 2006 ). Despite some studies tried to describe the spatial variability of solute depositions in forests by applying the coefficient of variation (Raat et al. 2002 ; Staelens et al. 2006 ; Zimmermann et al. 2007 ; Zimmermann et al. 2008 ), the sampling designs, collection periods, and element analysis were very different, which impair comparisons, even though our results were close to other studies carried out in temperate and tropical ecosystems. Throughout the year, the coefficient of variation ranged from 19–47% and from 17–60% for C and N inputs, respectively. These amounts are within the range (12–78%) found in other studies, where tropical forests showed the largest coefficients of variation, mainly in the wet season (Raat et al. 2002 ; Staelens et al. 2006 ; Zimmermann et al. 2007 ; Zimmermann et al. 2008 ). Overall, the wet season presented higher spatial variability than the dry season. However, N inputs presented the lowest coefficient of variation in both the end of the wet season and the beginning of the dry season. For C inputs, the lowest coefficients of variation were during the dry season. Excepting for August, the dry months with higher CV are likely associated with rainfall above average, whereas the low CV of January is related to rainfall below the average. The potential accumulation of organic matter, the greater rainfall amounts and the heterogeneity of the canopy are some of the factors responsible for the increased spatial variability in tropical forest (Zimmermann et al. 2008 ). The Atlantic Forest remnant has a heterogeneous canopy because of the high variability of species, size, and age of the trees, which is expected in tropical forests. The high incidence of lianas and the canopy gaps caused by the fall of trees further increase the intrinsic heterogeneity of this type of forest. In the same forest remnant, Rodrigues et al. ( 2018 ) related the spatial variability of net precipitation to the semideciduous characteristics. However, Terra et al. ( 2018a ) did not found a spatial pattern in stemflow, associating it to the Atlantic Forest remnant heterogeneity of species. The edge effect also contributes to this variability as it directly impacts the characteristic of the vegetation, altering forest structure (density and size of trees) and the abundance of species (Benítez-Malvido and Martínez-Ramos 2003 ). In addition to the heterogeneous structure of the canopy, the spatial pattern of nutrient inputs is also influenced by dry deposition, leaching process, and meteorological conditions (Forti and Neal 1992 ; Levia and Frost 2006 ). Marques et al. ( 2019 ) highlighted the influence of the animals in the surrounding areas of the AFR in N forms (ammonia and ammonium), which may have increased the N inputs in the southwest and central portion of the forest. The southwest area with the higher C and N inputs may also be influenced by vehicles, as this part of the forest is closer to a busy road. The eastern portion, that has high N inputs throughout the year and high C inputs in the dry season, is located close to a “candeia” ( Eremanthus erythropappus (DC.)) forest stand and a Eucalyptus stand, which can influence the high depositions in this location. Overall, these biotic and abiotic factors interaction between rainfall and forests may be responsible for high spatial variability in inputs of nutrients to the forest floor (Levia and Frost 2006 ; Zimmermann et al. 2008 ; Zhang et al. 2019 ). Despite the high amounts of rainfall being responsible for the dilution, it is not possible to select only one factor that controls spatial patterns (Robson et al. 1994 ). Thus, the factors that influence spatial variability are uncertain, and studies of canopy structure and meteorological conditions could help to understand spatial variability of chemicals in tropical forests (Levia and Frost 2006 ; Zhang et al. 2019 ). Conclusions Both concentration (mg. L − 1 ) and inputs (kg.ha − 1 ) of N and C were higher in net precipitation than in gross rainfall. The rainfall leaches the atmosphere and forest’s structures and can be considered an important transfer pathway of C and N toward the forest floor. This forest-rainfall interaction is responsible to increase the total amount of C and N that reaches the forest floor annually. The seasonal variability of C and N was remarkable. The inputs were higher in the wet season and represented, on average, 75% of the total annual contribution. These results confirm the hypothesis that the behavior of these nutrients is conditioned by the seasonal variability of precipitation. Analyzing inputs during the year, the impacts of extensive dry periods are more important for increasing N instead of C. This fact may be a direct result of atmospheric depositions from anthropogenic sources. The spatial variability of C and N was higher in the wet season. The spatial patterns revealed that, in general, the same locations had the highest inputs for both C and N throughout the year. Furthermore, the canopy heterogeneity and the proximity to potential sources seem responsible for breaking any continuity in the C and N inputs in the Atlantic forest remnant. The AFR increases the contribution of C and N that reaches the forest floor. The constant input of these nutrients, especially in the wet season, is extremely important in the nutrient cycle since it aids in the sustainability of ecological processes. Despite the nutrient inputs presented some variability, this study provides useful information on the changes of C and N inputs after forest-rainfall interactions. Thus, it can support the estimation of atmospheric deposition and advance the knowledge of the contribution of the leaching and absorption processes by canopies. Abbreviations C: Carbon; N:Nitrogen; ANOVA:analysis of variance; CV:coefficient of variation; IDW:inverse distance weight; NP:net precipitation; AFR:Atlantic Forest remnant; GR:gross rainfall; Tf:throughfall; Sf:stemflow; DBH:diameter at 1.3 m above ground; EC:electric conductivity; NO 3 − :nitrate; NO 2 − :nitrite; TKN:total kjeldahl nitrogen; MS:meteorological station; DON:dissolved organic nitrogen; DOC:dissolved organic carbon; NH 4 + :ammonia. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was financially by Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq (grant numbers 301556/2017-6 and 401760/2016-2). Authors’ contributions Conceived and designed the study: CRdM, VAM and MdCNST. Led the research project: CRdM. Performed the experiments, collected data and samples in the field: VAM, AFR and VAdO. Processed samples in the lab: VAM. Wrote the paper: VAM and MdCNST. Critical Revision: CRdM and AFR. Statistical Support: MdCNST and LORP. All authors read and approved the final manuscript. Acknowledgements The authors thank to Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq (grant numbers 301556/2017-6 and 401760/2016-2), and to Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES (for the Ph.D research grant for the first author). Special thanks go to “Laboratório de Gestão de Resíduos Químicos da Universidade Federal de Lavras (LGRQ-UFLA)”, and to “Laboratório de Análise de Água da Universidade Federal de Lavras (LAADEG-UFLA)” for the facilities and equipments used in this study. References ABNT (Associação Brasileira de Normas Técnicas) (1997). Água. ABNT NBR 13796:1997- Determinação de nitrogênio orgânico, Kjeldahl e total - Métodos macro e semimicro Kjeldahl. Rio de Janeiro Acker K, Beysens D, Möller D (2008) Nitrite in dew, fog, cloud and rain water: An indicator for heterogeneous processes on surfaces. Atmos Res 87:200–212. https://doi.org/10.1016/j.atmosres.2007.11.002 APHA (American Public Health Association).(2014). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-57462","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":1364521,"identity":"5e51a01a-5425-4958-97f3-b52aa1fb2241","order_by":0,"name":"Vanessa Alves Mantovani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYJCCAwimgU0CmE4oIEILD0RLWgIDG0iLARFWQbQwHIZoYcCjxeD42YMHf+bY5dmz9xh+Lig4n8cv35344YEBgzy/2AHsWs7kJRzm3ZZczMNzxlh6hsHtYsk23s0SQIcZzpydgFWLZEOOwWHGbcyJPRJpCdI8BrcTNxzj3QDSkmBwG4eW/jcGB39uqwdpSf7NY3AOpGXzD3xa+CVyDA7wbjsM1JJ8DGjLAZCWbXht4Zd4YwD0y/HEnjOHj1nzGCQnzmzL3WaRYCCB0y9s/DnGH39uq05sb29svs3zxy6xn/ns5ps/Kmzk+aWxa8EJJEhTPgpGwSgYBaMABQAAeO1ebawZDZ8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-3493-5261","institution":"Universidade Federal de Lavras","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Vanessa","middleName":"Alves","lastName":"Mantovani","suffix":""},{"id":1364522,"identity":"c9d2a2a8-970e-4ba9-a15b-6dfe3a376404","order_by":1,"name":"Marcela de Castro Nunes Santos Terra","email":"","orcid":"","institution":"Universidade Federal de Lavras Departamento de Ciencias Florestais","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marcela","middleName":"de Castro Nunes Santos","lastName":"Terra","suffix":""},{"id":1364523,"identity":"6375f884-1785-4584-a6ef-ac5a58a3837c","order_by":2,"name":"Carlos Rogério de Mello","email":"","orcid":"","institution":"Universidade Federal de Lavras Departamento de Engenharia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"Rogério","lastName":"de Mello","suffix":""},{"id":1364524,"identity":"f6512547-d842-4aff-89b5-b66462065400","order_by":3,"name":"André Ferreira Rodrigues","email":"","orcid":"","institution":"Universidade Federal de Lavras Departamento de Engenharia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"André","middleName":"Ferreira","lastName":"Rodrigues","suffix":""},{"id":1364525,"identity":"87bd11b1-a7d8-4357-b092-4cf49e46dacf","order_by":4,"name":"Vinicius Augusto de Oliveira","email":"","orcid":"","institution":"Universidade Federal de Lavras Departamento de Engenharia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Vinicius","middleName":"Augusto","lastName":"de Oliveira","suffix":""},{"id":1364526,"identity":"20029df6-cd89-4d6e-a9bf-8991c48d0f78","order_by":5,"name":"Luiz Otávio Rodrigues Pinto","email":"","orcid":"","institution":"Universidade Federal de Lavras Departamento de Ciencias Florestais","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Luiz","middleName":"Otávio Rodrigues","lastName":"Pinto","suffix":""}],"badges":[],"createdAt":"2020-08-11 11:42:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-57462/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-57462/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1093/forsci/fxab056","type":"published","date":"2021-12-21T14:12:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":1904280,"identity":"e2fd674f-9b37-4549-af08-a83c2c3cfd80","added_by":"auto","created_at":"2020-08-12 21:52:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92891,"visible":true,"origin":"","legend":"The geographical location of the AFR in Brazil and the positions of the measuring points. ","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-57462/v1/Figure1.png"},{"id":1904281,"identity":"79acfa80-664d-4630-ba12-832a1359d67e","added_by":"auto","created_at":"2020-08-12 21:52:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3646648,"visible":true,"origin":"","legend":"Rain gauge and stemflow apparatus for, respectively, throughfall and stemflow measurements (a) and external rain gauge for gross rainfall measurements (b).","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-57462/v1/Figure2.png"},{"id":1904282,"identity":"84447912-c9d6-43cd-98ea-4bb3fcae6672","added_by":"auto","created_at":"2020-08-12 21:52:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":264245,"visible":true,"origin":"","legend":"Monthly inputs of water in the Atlantic Forest remnant (AFR) through throughfall (Tf), stemflow (Sf), and the interception loss, and the long-term average rainfall of the meteorological station (MS).","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-57462/v1/Figure3.png"},{"id":1904283,"identity":"5534f4e6-2b8a-4e46-ae58-87411cc88e00","added_by":"auto","created_at":"2020-08-12 21:52:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":292856,"visible":true,"origin":"","legend":" Carbon (a) and nitrogen concentrations (b) in gross rainfall (GR), throughfall (Tf), and stemflow (Sf), with their respective standard deviations.","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-57462/v1/Figure4.png"},{"id":1904284,"identity":"165c8094-2c47-4c61-b35a-c26b9c986430","added_by":"auto","created_at":"2020-08-12 21:52:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":201789,"visible":true,"origin":"","legend":"Monthly average carbon inputs in net precipitation (NP) (kg.ha-1) and monthly gross rainfall (GR) (mm). Different letters above bars mean statistical differences (p = 1.19E-18) between months.","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-57462/v1/Figure5.png"},{"id":1904285,"identity":"19fe5365-d58a-4a19-b000-84640730ca64","added_by":"auto","created_at":"2020-08-12 21:52:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":224310,"visible":true,"origin":"","legend":" Monthly average nitrogen inputs in net precipitation (NP) (kg.ha-1) and monthly gross rainfall (GR) (mm). Different letters above bars mean statistical differences (p = 2.38E-15) between months.","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-57462/v1/Figure6.png"},{"id":1904286,"identity":"86246326-ab57-45d7-909b-25c6a51bb528","added_by":"auto","created_at":"2020-08-12 21:52:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":186071,"visible":true,"origin":"","legend":"Spatial pattern of C and N inputs (kg.ha-1) regarding the dry and wet seasons, and the entire period (total annual).","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-57462/v1/Figure7.png"},{"id":16652624,"identity":"31478dc7-1cb4-47fd-a44b-323363e755ff","added_by":"auto","created_at":"2021-12-21 14:12:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3897403,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-57462/v1/36adafd4-4fa0-4596-ae14-9c4e70fc223f.pdf"}],"financialInterests":"","formattedTitle":"Spatial and temporal patterns in Carbon and Nitrogen inputs by net precipitation in Atlantic Forest, Brazil","fulltext":[{"header":" Background","content":" \u003cp\u003eForests maintain the entire ecosystem balanced by controlling the biogeochemical cycles and decreasing the transport of nutrients to watercourses (Tundisi and Matsumura-Tundisi \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In this regard, forests are important because they are the first rain contact with terrestrial ecosystems, conducting singular biogeochemical processes (Van Stan and Stubbins \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen rainwater interacts with the forest canopy, a portion is stored in it, returning to the atmosphere through evaporation (wet canopy evaporation). The water routed to the soil surface by dripping off the canopy, along with rainfall that crosses the forest without hitting the vegetation, composes the \u0026ldquo;throughfall\u0026rdquo; (Van Stan and Friesen \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Finally, the \u0026ldquo;stemflow\u0026rdquo; is the rainfall portion that drains through the tree trunks (Muzylo et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Therefore, \u0026ldquo;net precipitation\u0026rdquo; (NP) is the portion of rainfall that reaches the forest floor (throughfall plus stemflow) (Sadeghi et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe nutrient input occurs preferentially by rainfall, highlighting NP as the major pathway of nutrients into forests (Sadeghi et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Haag \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Although stemflow is a small portion of NP, it is a highly concentrated flux, responsible for water and nutrients inputs near the trees. Thus, it can directly change the roots\u0026rsquo; physical, chemical and biological properties and accelerate the redistribution of nutrients in the forest ecosystem (Germer et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Terra et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e; Su et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Every process of rainfall partitioning and nutrient input rely on both forest characteristics and canopy-rain interactions (Terra et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e; Sadeghi et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, NP flux and chemistry are generally affected by vegetation type, trees morphological traits, atmospheric deposition and materials derived from the canopies and trunks (Hofhansl et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Marques et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Su et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe nitrogen and carbon cycles are essential for living organisms and for sustaining a number of processes that occur on the planet. The importance of the forest as carbon sinks and its role on climate change mitigation have been widely discussed recently (Silveira et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sullivan et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, forests are susceptible to different types of extreme events that could affect the carbon balance in the atmosphere such as droughts and fires, that might reduce productivity and increase tree mortality (Phillips et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Reichstein et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The natural presence of nitrogen in tropical forests is less limited than in temperate forests (Hietz et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, the nitrogen cycle has been changed in the last centuries due to the contribution of anthropogenic sources (Jaffe and Weiss-Penzias \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The increase of nitrogen might improve ecosystem productivity by fertilization or decrease by acidification, eutrophication, or unbalance (Galloway et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, there are knowledge gaps regarding the process of storing and distributing nutrients in forest stands, that must be fulfilled. Despite the considerable increase in studies, only a few addresses the spatial and temporal variability of nutrient concentration in throughfall and stemflow in tropical areas (Levia and Frost \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zimmermann et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ponette-Gonz\u0026aacute;lez et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This kind of research and data is scarce due to difficulties imposed by logistic and costs as continuous data collection and lab analysis are necessary (Levia and Frost \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The few initiatives usually take into account only punctual rain events or selected events in a time period (Schroth et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Tob\u0026oacute;n et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Germer et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hofhansl et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hofhansl et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), specific periods data like wet seasons (Ciglasch et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; M\u0026ouml;ller et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Oziegbe et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and weekly or each two weeks samplings (Lilienfein and Wilcke \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Schwendenmann and Veldkamp \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Goller et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Dezzeo and Chac\u0026oacute;n \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Fujii et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Souza et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Neu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, this source of study is particularly scarce for total carbon and total nitrogen concerning Neotropical forests.\u003c/p\u003e \u003cp\u003eThe Brazilian Atlantic Forest is a world biodiversity hotspot (Myers et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and the second-largest South American rainforest, encompassing tropical and subtropical regions with different altitudes and rainfall amounts (Ribeiro et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Oliveira-Filho and Fontes \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The Atlantic Forest is composed of two types of forest \u0026ndash; rainforests and semideciduous forests \u0026ndash;, that have idiosyncrasies related to seasonality. Rainfall, temperature, and altitude are the main drivers of the distribution of these vegetation types (Oliveira-Filho and Fontes \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Terra et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e). Atlantic rainforests are restricted to Brazilian coasts and mountain range regions, while the semideciduous forests extend across the southeastern Brazil. These latter forests are composed by tree species that have strategies to cope with the up-to-six-months dry season (Morellato and Haddad \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), notably the leaf loss of up to 50% of the trees during the dry season (Veloso et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; IBGE 2012). Despite the well-kwon ecosystem services provided by these environments, the Atlantic forest has been threatened by anthropic activities (Ribeiro et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For improving the protection programs and management strategies, studies for understanding the Atlantic forest intrinsic dynamics is urgent.\u003c/p\u003e \u003cp\u003eThus, this research aimed to assess the contribution of net precipitation on the chemistry (C and N) of an Atlantic Forest remnant relying on data collected throughout one hydrological year. In this context, we sought to: i) compare the changes in concentration and inputs of C and N as the rainfall is partitioned by forest canopy; ii) identify the impacts of seasonality on C and N inputs considering the dry and wet periods and also monthly; and iii) to assess the spatial behavior of C and N inputs across the forest stand.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e Methods\u003c/h2\u003e \u003cdiv id=\"Sec3\" class=\"Section3\"\u003e \u003ch2\u003e Site description\u003c/h2\u003e \u003cp\u003eThe study site is a 6.30\u0026nbsp;ha Atlantic Forest remnant (AFR) located in Southeastern Brazil (21\u0026ordm;13\u0026rsquo;40\u0026rsquo;\u0026rsquo;S and 44\u0026ordm;57\u0026rsquo;50\u0026rsquo;\u0026rsquo;W) with an average elevation of 925\u0026nbsp;m. This forest remnant is classified as \u0026ldquo;montane seasonal semideciduous forest\u0026rdquo; (Oliveira-Filho et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). This forest type is widespread throughout the more inland portion of the Atlantic Forest biome in Southeastern Brazil, where the seasonal rainfall makes up to 50% of trees in these forests to lose their leaves in the dry season (Scolforo and Carvalho \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The relief is gently undulated and the soil classified as Dystrophic Red Latosol (Rhodic Hapludox) (Junqueira Junior et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The K\u0026ouml;ppen-type climate of the studied region is Cwa with well-defined seasons, characterized by rainfall concentration in the summer (December to March) (Junqueira Junior et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Long-term average annual precipitation (1981\u0026ndash;2010) is 1461.8\u0026nbsp;mm in which 85% of it falls during the wet period (October to March) (INMET 2018). The mean annual temperature is 20.3\u0026ordm;C ranging from 16.9\u0026ordm;C (June and July) to 22.5 \u0026ordm;C in February (INMET 2018).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e Gross rainfall (GR), Throughfall (Tf), and Stemflow (Sf) measurements\u003c/h2\u003e \u003cp\u003eThe gross rainfall (rainfall not affected by trees canopy - GR), throughfall (Tf), and stemflow (Sf) were monitored from May 2018 to April 2019, totalizing 86 daily rainfall events. The monitoring was performed the day after each rainfall event (at approximately 9:00 AM local time) or at least 4 hours after the rainfall event had ceased to avoid events overlapping.\u003c/p\u003e \u003cp\u003eFor measuring SF, 10 trees were selected from the three most abundant species to better represent the characteristics of the AFR. According to Oliveira-Filho et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), the most abundant species in the AFR are \u003cem\u003eCopaifera langsdorfii\u003c/em\u003e Desf. (Fabaceae), \u003cem\u003eXylopia brasiliensis\u003c/em\u003e Sprengel (Annonaceae) and \u003cem\u003eMiconia pepericarpa\u003c/em\u003e DC. (Melastomataceae) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The selected trees were well-distributed in the site (Fig.\u0026nbsp;1) and can be classified according to their DBH (diameter at 1.3\u0026nbsp;m above ground; see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For Sf measurements, collectors built with a hose slit open toward the length was nailed in a spiral around the tree trunk and connected to a collection bin (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIdentification of trees species in the Atlantic Forest remnant.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTree code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScientific name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDBH (cm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eXylopia brasiliensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCopaifera langsdorfii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCopaifera langsdorfii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCopaifera langsdorfii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMiconia pepericarpa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMiconia pepericarpa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eXylopia brasiliensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eXylopia brasiliensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMiconia pepericarpa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eXylopia brasiliensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eFigure\u0026nbsp;1. The geographical location of the AFR in Brazil and the positions of the measuring points.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eGR was measured through 3 fixed Ville de Paris-type rain gauges placed around the forest remnant (Fig.\u0026nbsp;1 and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Thereafter, the GR over the AFR was assessed by means of the Thiessen Polygon approach. In the case of gaps in the dataset, the climatological station from the \u0026ldquo;Brazilian National Meteorological Institute\u0026rdquo; (INMET 2019) was used to fill them. In addition, for Tf, 10 fixed Ville de Paris-type rain gauges were installed near the selected trees 1.5\u0026nbsp;m above the forest floor to avoid splash-in (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGR and Tf were converted to depth by dividing the collected volume (L) by the rain-gauge catchment area (m\u0026sup2;). On the other hand, for Sf, the volume stored in the bin (L) was divided by the total projected crown area (m\u0026sup2;). This projected area was determined according to the methodology described by Shinzato et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) in which 8 vertical projections far-between 45\u0026deg; were set in the ground. Then, the area for each canopy was calculated as follow:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA (m\u0026sup2;) = Σ\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\text{(a }\\text{*}\\text{ b }\\text{*}\\text{ }\\text{sen}\\text{ 45º)}}{\\text{2}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ewhere A is the projected crown area; \u0026ldquo;a\u0026rdquo; and \u0026ldquo;b\u0026rdquo; are the vertical projections with far-between 45\u0026deg;. This measurement was performed twice, by considering the projected crown areas in the dry (July 2018) and wet (March 2019) periods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e Chemical analysis\u003c/h2\u003e \u003cp\u003eGR, Tf, and Sf water samples were collected for events with at least 5\u0026nbsp;mm of rainfall due to the minimum necessary volume for lab analysis, summing up 60 events. GR were composed by the samples of the three external rain gauges (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eThe evaluated physical and chemical parameters included pH, Electric Conductivity (EC), Total Carbon (C), Nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), Nitrite (NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), and Total Kjeldahl Nitrogen (which refers to Ammonia and Organic Nitrogen; TKN). Regarding pH, EC, and carbon, the samples were analyzed after every rainfall event (i.e. single samples). On the other hand, for analyzing NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, and TKN, the samples were firstly accumulated on a monthly scale (i.e. composite sample) before the analysis could be carried out. All procedures concerning the samples (taking, preserving, and analyzing) followed the \u003cem\u003eStandard Methods\u003c/em\u003e (APHA 2014) criteria (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These ensured that the nitrogen was not lost during storage.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysical and chemical water variables evaluated, preservation procedures, the lab method used, and respective reference.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreservation procedures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLab Method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRefrigerated at 4 \u0026ordm;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEletrometric method (Method 4500 H\u003csup\u003e+)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAPHA (2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConductimetric method (Method 2510 B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAPHA (2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFiltered and refrigerated at 4 \u0026ordm;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShimadzu total carbon analyzer (TOC-V CPH) (Method TC-IC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShimadzu (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFiltered and frozen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYang et al. (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) Method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYang et al. (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1998\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCalorimetric Method (Method 4500- NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003eB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAPHA (2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTKN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcidification and refrigerated at 4 \u0026ordm;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNBR 13.796:1997 Method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eABNT (1997)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e Data analysis\u003c/h2\u003e \u003cp\u003eFor some events, it was not possible to measure Tf and Sf because the rain gauge remained open and/or the collectors were knocked over by animals. Thus, with GR data, linear regressions were fitted to overcome these issues and to fill the unavoidable gaps in the measuring points. The regressions have been performed for each point separately, resulting in R\u003csup\u003e2\u003c/sup\u003e greater than 0.78 and 0.54 for Tf and Sf, respectively.\u003c/p\u003e \u003cp\u003eFor pH, EC, and C, which were analyzed in all the rainfall events, the mean monthly values of the concentrations in GR, Tf, and Sf were estimated by the volume-weighted mean (VWM), as follows:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(VWM= \\frac{\\sum _{n=1}^{i}{C}_{i,e} .{V}_{i,e}}{\\sum _{n=1}^{i}{V}_{i,e}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003ewhere C represents the concentration of the parameters (pH, EC, and C) at collector \u003cem\u003ei\u003c/em\u003e for event \u003cem\u003ee\u003c/em\u003e, and V represents the total volume at collector \u003cem\u003ei\u003c/em\u003e for event \u003cem\u003ee\u003c/em\u003e. As abovementioned, the chemical analyses for TKN, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e were carried out in a month scale. Then, with the total concentration of nitrogen (N\u0026thinsp;=\u0026thinsp;TKN\u0026thinsp;+\u0026thinsp;NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e + NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) and carbon (C) together with the total volume of rainfall collected in the analyzed period, it was possible to estimate the monthly inputs of N (kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and C (kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) by means of the following equation:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(I=\\frac{C . D}{100}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eI\u003c/em\u003e represent the input of Nitrogen or Carbon (kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); \u003cem\u003eC\u003c/em\u003e represents the monthly concentration average (mg.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of N or C; and \u003cem\u003eD\u003c/em\u003e represents the monthly GR, Tf and Sf (mm).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e Temporal analysis\u003c/h2\u003e \u003cp\u003eThe temporal analysis was assessed through the monthly inputs of GR and NP. For this, NP was firstly averaged across the area from the 10 sample points (Tf\u0026thinsp;+\u0026thinsp;Sf) inputs. Then, statistical differences in C and N inputs (kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), between GR and NP (objective i) as well as the NP differences between the dry and wet season, and monthly were assessed by means of the analysis of variance (one-way ANOVA). Further, Tukey\u0026rsquo;s test was applied to analyze whether NP differentiates throughout the months (objective ii). All the statistical analyses were performed on R environment (version 3.6.2) (R Core Team 2018).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e Spatial analysis\u003c/h2\u003e \u003cp\u003eSpatial analysis was performed to identify the spatial patterns of C and N inputs (kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in the AFR. Thus, the samples were accumulated to account for three periods of analysis: annual; the dry season; and the wet season (objective iii). Spatial variability was assessed by means of the coefficient of variation (CV). Moreover, the Inverse Distance Weighting (IDW, specifically inverse of the square of the distance) interpolation method was applied for mapping the spatial distribution of N and C to highlight areas of greater contribution on the nutrient cycle. This method considers only the distance between the points to predict a value for any unmeasured location. The interpolations were performed in the Quantun GIS software (QGIS Version 3.14.0).\u003c/p\u003e \u003c/div\u003e "},{"header":"Methods","content":"\u003ch2\u003eSite description\u003c/h2\u003e\n\u003cp\u003eThe study site is a 6.30 ha Atlantic Forest remnant (AFR) located in Southeastern Brazil (21\u0026ordm;13\u0026rsquo;40\u0026rsquo;\u0026rsquo;S and 44\u0026ordm;57\u0026rsquo;50\u0026rsquo;\u0026rsquo;W) with an average elevation of 925 m. This forest remnant is classified as \u0026ldquo;montane seasonal semideciduous forest\u0026rdquo; (Oliveira-Filho et al. 1996). This forest type is widespread throughout the more inland portion of the Atlantic Forest biome in Southeastern Brazil, where the seasonal rainfall makes up to 50% of trees in these forests to lose their leaves in the dry season (Scolforo and Carvalho 2006). The relief is gently undulated and the soil classified as Dystrophic Red Latosol (Rhodic Hapludox) (Junqueira Junior et al. 2017). The K\u0026ouml;ppen-type climate of the studied region is Cwa with well-defined seasons, characterized by rainfall concentration in the summer (December to March) (Junqueira Junior et al. 2019). Long-term average annual precipitation (1981-2010) is 1461.8 mm in which 85% of it falls during the wet period (October to March) (INMET 2018). The mean annual temperature is 20.3\u0026ordm;C ranging from 16.9\u0026ordm;C (June and July) to 22.5 \u0026ordm;C in February (INMET 2018).\u003c/p\u003e\n\u003ch2\u003e\u003ca name=\"_Toc23327213\"\u003e\u003c/a\u003eGross rainfall (GR), Throughfall (Tf), and Stemflow (Sf) measurements\u003c/h2\u003e\n\u003cp\u003eThe gross rainfall (rainfall not affected by trees canopy - GR), throughfall (Tf), and stemflow (Sf) were monitored from May 2018 to April 2019, totalizing 86 daily rainfall events. The monitoring was performed the day after each rainfall event (at approximately 9:00 AM local time) or at least 4 hours after the rainfall event had ceased to avoid events overlapping.\u003c/p\u003e\n\u003cp\u003eFor measuring SF, 10 trees were selected from the three most abundant species to better represent the characteristics of the AFR. According to Oliveira-Filho et al. (1996), the most abundant species in the AFR are \u003cem\u003eCopaifera langsdorfii \u003c/em\u003eDesf. (Fabaceae), \u003cem\u003eXylopia brasiliensis \u003c/em\u003eSprengel (Annonaceae) and \u003cem\u003eMiconia pepericarpa \u003c/em\u003eDC. (Melastomataceae) (Table 1). The selected trees were well-distributed in the site (Fig. 1) and can be classified according to their DBH (diameter at 1.3 m above ground; see Table 1). For Sf measurements, collectors built with a hose slit open toward the length was nailed in a spiral around the tree trunk and connected to a collection bin (Fig.2a).\u003c/p\u003e\n\u003cp\u003eTable 1. Identification of trees species in the Atlantic Forest remnant.\u003c/p\u003e\n\u003ctable width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eTree code\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49%\"\u003e\n\u003cp\u003eScientific name\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eDBH (cm)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49%\"\u003e\n\u003cp\u003e\u003cem\u003eXylopia brasiliensis\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e27.37\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49%\"\u003e\n\u003cp\u003e\u003cem\u003eCopaifera langsdorfii\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e32.15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49%\"\u003e\n\u003cp\u003e\u003cem\u003eCopaifera langsdorfii\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e14.96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49%\"\u003e\n\u003cp\u003e\u003cem\u003eCopaifera langsdorfii\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e31.83\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49%\"\u003e\n\u003cp\u003e\u003cem\u003eMiconia pepericarpa\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e21.65\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49%\"\u003e\n\u003cp\u003e\u003cem\u003eMiconia pepericarpa\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e12.10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49%\"\u003e\n\u003cp\u003e\u003cem\u003eXylopia brasiliensis\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e50.29\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49%\"\u003e\n\u003cp\u003e\u003cem\u003eXylopia brasiliensis\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e11.46\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49%\"\u003e\n\u003cp\u003e\u003cem\u003eMiconia pepericarpa\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e24.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49%\"\u003e\n\u003cp\u003e\u003cem\u003eXylopia brasiliensis\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e8.91\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003cp\u003eGR was measured through 3 fixed Ville de Paris-type rain gauges placed around the forest remnant (Fig. 1 and Fig. 2b). Thereafter, the GR over the AFR was assessed by means of the Thiessen Polygon approach. In the case of gaps in the dataset, the climatological station from the \u0026ldquo;Brazilian National Meteorological Institute\u0026rdquo; (INMET 2019) was used to fill them. In addition, for Tf, 10 fixed Ville de Paris-type rain gauges were installed near the selected trees 1.5 m above the forest floor to avoid splash-in (Fig. 2a).\u003c/p\u003e\n\u003cp\u003eGR and Tf were converted to depth by dividing the collected volume (L) by the rain-gauge catchment area (m\u0026sup2;). On the other hand, for Sf, the volume stored in the bin (L) was divided by the total projected crown area (m\u0026sup2;). This projected area was determined according to the methodology described by Shinzato et al. (2011) in which 8 vertical projections far-between 45\u0026deg; were set in the ground. Then, the area for each canopy was calculated as follow:\u003c/p\u003e\n\u003ctable\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"534\"\u003e\n\u003cp\u003eA (m\u0026sup2;) = \u0026Sigma;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ewhere A is the projected crown area; \u0026ldquo;a\u0026rdquo; and \u0026ldquo;b\u0026rdquo; are the vertical projections with far-between 45\u0026deg;. This measurement was performed twice, by considering the projected crown areas in the dry (July 2018) and wet (March 2019) periods.\u003c/p\u003e\n\u003ch2\u003eChemical analysis\u003c/h2\u003e\n\u003cp\u003eGR, Tf, and Sf water samples were collected for events with at least 5 mm of rainfall due to the minimum necessary volume for lab analysis, summing up 60 events. GR were composed by the samples of the three external rain gauges (Fig. 1).\u003c/p\u003e\n\u003cp\u003eThe evaluated physical and chemical parameters included pH, Electric Conductivity (EC), Total Carbon (C), Nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e), Nitrite (NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e), and Total Kjeldahl Nitrogen (which refers to Ammonia and Organic Nitrogen; TKN). Regarding pH, EC, and carbon, the samples were analyzed after every rainfall event (i.e. single samples). On the other hand, for analyzing NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, and TKN, the samples were firstly accumulated on a monthly scale (i.e. composite sample) before the analysis could be carried out. All procedures concerning the samples (taking, preserving, and analyzing) followed the \u003cem\u003eStandard Methods\u003c/em\u003e (APHA 2014) criteria (Table 2). These ensured that the nitrogen was not lost during storage.\u003c/p\u003e\n\u003cp\u003eTable 2. Physical and chemical water variables evaluated, preservation procedures, the lab method used, and respective reference.\u003c/p\u003e\n\u003ctable\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"148\"\u003e\n\u003cp\u003ePreservation procedures\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"276\"\u003e\n\u003cp\u003eLab Method\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003eReferences\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003epH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"148\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRefrigerated at 4 \u0026ordm;C\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"276\"\u003e\n\u003cp\u003eEletrometric method (Method 4500 H\u003csup\u003e+)\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003eAPHA (2014)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003eEC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"276\"\u003e\n\u003cp\u003eConductimetric method (Method 2510 B)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003eAPHA (2014)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"148\"\u003e\n\u003cp\u003eFiltered and refrigerated at 4 \u0026ordm;C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"276\"\u003e\n\u003cp\u003eShimadzu total carbon analyzer (TOC-V CPH) (Method TC-IC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003eShimadzu (2003)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"148\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFiltered and frozen\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"276\"\u003e\n\u003cp\u003eYang et al. (1998) Method\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003eYang et al. (1998)\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"276\"\u003e\n\u003cp\u003eCalorimetric Method (Method 4500- NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003eB)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003eAPHA (2014)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003eTKN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"148\"\u003e\n\u003cp\u003eAcidification and refrigerated at 4 \u0026ordm;C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"276\"\u003e\n\u003cp\u003eNBR 13.796:1997 Method\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"118\"\u003e\n\u003cp\u003eABNT (1997)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eData analysis\u003c/h2\u003e\n\u003cp\u003eFor some events, it was not possible to measure Tf and Sf because the rain gauge remained open and/or the collectors were knocked over by animals. Thus, with GR data, linear regressions were fitted to overcome these issues and to fill the unavoidable gaps in the measuring points. The regressions have been performed for each point separately, resulting in R\u003csup\u003e2\u003c/sup\u003e greater than 0.78 and 0.54 for Tf and Sf, respectively.\u003c/p\u003e\n\u003cp\u003eFor pH, EC, and C, which were analyzed in all the rainfall events, the mean monthly values of the concentrations in GR, Tf, and Sf were estimated by the volume-weighted mean (VWM), as follows:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr /\u003ewhere C represents the concentration of the parameters (pH, EC, and C) at collector \u003cem\u003ei\u003c/em\u003e for event \u003cem\u003ee\u003c/em\u003e, and V represents the total volume at collector \u003cem\u003ei\u003c/em\u003e for event \u003cem\u003ee\u003c/em\u003e. As abovementioned, the chemical analyses for TKN, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e were carried out in a month scale. Then, with the total concentration of nitrogen (N = TKN + NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e + NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e) and carbon (C) together with the total volume of rainfall collected in the analyzed period, it was possible to estimate the monthly inputs of N (kg.ha\u003csup\u003e-1\u003c/sup\u003e) and C (kg.ha\u003csup\u003e-1\u003c/sup\u003e) by means of the following equation:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca name=\"_Toc23327216\"\u003e\u003c/a\u003ewhere\u003cem\u003e I\u003c/em\u003e represent the input of Nitrogen or Carbon (kg.ha\u003csup\u003e-1\u003c/sup\u003e);\u003cem\u003e C\u003c/em\u003e represents the monthly concentration average (mg.L\u003csup\u003e-1\u003c/sup\u003e) of N or C; and \u003cem\u003eD\u003c/em\u003e represents the monthly GR, Tf and Sf (mm).\u003c/p\u003e\n\u003ch2\u003eTemporal analysis\u003c/h2\u003e\n\u003cp\u003eThe temporal analysis was assessed through the monthly inputs of GR and NP. For this, NP was firstly averaged across the area from the 10 sample points (Tf +Sf) inputs. Then, statistical differences in C and N inputs (kg.ha\u003csup\u003e-1\u003c/sup\u003e), between GR and NP (objective i) as well as the NP differences between the dry and wet season, and monthly were assessed by means of the analysis of variance (one-way ANOVA). Further, Tukey\u0026rsquo;s test was applied to analyze whether NP differentiates throughout the months (objective ii). All the statistical analyses were performed on R environment (version 3.6.2) (R Core Team 2018).\u003c/p\u003e\n\u003ch2\u003eSpatial analysis\u003c/h2\u003e\n\u003cp\u003eSpatial analysis was performed to identify the spatial patterns of C and N inputs (kg.ha\u003csup\u003e-1\u003c/sup\u003e) in the AFR. Thus, the samples were accumulated to account for three periods of analysis: annual; the dry season; and the wet season (objective iii). Spatial variability was assessed by means of the coefficient of variation (CV). Moreover, the Inverse Distance Weighting (IDW, specifically inverse of the square of the distance) interpolation method was applied for mapping the spatial distribution of N and C to highlight areas of greater contribution on the nutrient cycle. This method considers only the distance between the points to predict a value for any unmeasured location. The interpolations were performed in the Quantun GIS software (QGIS Version 3.14.0).\u003c/p\u003e"},{"header":" Results","content":"\u003ch2\u003e\u003ca name=\"_Toc23327218\"\u003e\u003c/a\u003eHydrological monitoring\u003c/h2\u003e\n\u003cp\u003eGR was monitored from May 2018 to April 2019, summing up 86 events, which corresponded to 1601.6 mm. Total precipitation for the same period in a meteorological station (MS) located at 1 km from the forest edge was 1535.3 mm (INMET 2019). Considering that the long-term annual average rainfall (1981-2010) is 1461.8 mm, the sampling year represented a typical year in terms of rainfall, with almost 10% above the average (Table 3). Approximately 85% of gross rainfall in the AFR occurred in the wet season (October to March) which is in accordance with the expected pattern of the region (Table 3).\u003c/p\u003e\n\u003cp\u003eTable 3. Comparison of the monitored dry and wet seasons (INMET and AFR) against the long-term average (1981-2010).\u003c/p\u003e\n\u003ctable border=\"1\" width=\"96%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"12%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eINMET meteorological station (mm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003eAtlantic Forest remnant (AFR) (mm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003eLong-term average rainfall (1981-2010) - (mm)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"12%\"\u003e\n\u003cp\u003eDry season\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e226.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e241.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003e218.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"12%\"\u003e\n\u003cp\u003eWet season\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e1308.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e1360.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003e1243.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"12%\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e1535.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e1601.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003e1461.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe maximum and minimum GR were monitored in December (328.4 mm) and July (0.0 mm), respectively (Fig.3). Tf was the main portion of GR across the monitoring period, totalizing 1278.6 mm, which represents 79.8% of it, ranging from 70.78% (June) to 85.72% (December).\u003c/p\u003e\n\u003cp\u003eInterception loss totalized 319.7 mm (20.0% of GR), with a maximum percentage in June (29.17%) and a minimum in December (14.05%). Sf totalized 3.2 mm, representing 0.20% of GR, with a maximum contribution in August (0.42 %) and a minimum in June (0.04%).\u003c/p\u003e\n\u003ch2\u003eChemical analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eFor chemical analysis, rainfall samples were collected from 60 events (only those with more than 5 mm) totalizing 1529.6 mm of GR, 1232.1 mm of Tf, and 3.1 mm of Sf, which represents approximately 96% of the entire monitoring period (Table 4).\u003c/p\u003e\n\u003cp\u003eTable 4. Gross rainfall (GR), average throughfall (Tf) and average stemflow (Sf) with their respective standard deviations and coefficients of variation (CV).\u003c/p\u003e\n\u003ctable border=\"1\" width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eMonths\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eGR (mm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eTf (mm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003eSf (mm)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eMay 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e10.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e8.32 \u0026plusmn; 1.84 (22%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003e0.005 \u0026plusmn; 0.006 (120%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eJun 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e14.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e11.13 \u0026plusmn; 2.62 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003e0.007 \u0026plusmn; 0.007 (100%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eJul 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e0.00 \u0026plusmn; 0.00 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003e0.000 \u0026plusmn; 0.000 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eAug 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e60.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e46.39 \u0026plusmn; 9.34 (20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003e0.287 \u0026plusmn; 0.209 (73%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eSep 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e46.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e38.25 \u0026plusmn; 5.18 (14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003e0.079 \u0026plusmn; 0.060 (76%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eOct 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e205.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e164.11 \u0026plusmn; 21.26 (13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003e0.364 \u0026plusmn; 0.188 (52%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eNov 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e240.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e182.03 \u0026plusmn; 32.19 (18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003e0.638 \u0026plusmn; 0.576 (90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eDec 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e319.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e276.54 \u0026plusmn; 35.08 (13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003e0.699 \u0026plusmn; 0.389 (56%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eJan 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e147.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e117.89 \u0026plusmn; 22.64 (19%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003e0.342 \u0026plusmn; 0.215 (63%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eFeb 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e201.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e159.57 \u0026plusmn; 32.35 (20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003e0.277 \u0026plusmn; 0.173 (62%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eMar 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e199.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e160.88 \u0026plusmn; 23.49 (15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003e0.292 \u0026plusmn; 0.246 (84%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eApr 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e83.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e67.00 \u0026plusmn; 11.49 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003e0.119 \u0026plusmn; 0.097 (82%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eEntire period (mm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e1529.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e1232.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"30%\"\u003e\n\u003cp\u003e3.109\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe annual average pH of GR was 7.16. For Tf and Sf, this value was 6.96 and 5.23, respectively. The average EC of GR was 16.23 \u0026micro;S/cm, while the EC of Tf and SF was 46.86 \u0026micro;S.cm\u003csup\u003e-1\u003c/sup\u003e and 53.46 \u0026micro;S.cm\u003csup\u003e-1\u003c/sup\u003e, respectively. These values are 2.9 and 3.3 times greater than GR, considering Tf and Sf, respectively (Table 5).\u003c/p\u003e\n\u003cp\u003eTable 5. pH and electric conductivity (EC) of gross rainfall (GR), average throughfall (Tf) and average stemflow (Sf) with their respective standard deviations and coefficients of variation (CV).\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"90\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"114\"\u003e\n\u003cp\u003eGR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"191\"\u003e\n\u003cp\u003eTf\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"209\"\u003e\n\u003cp\u003eSf\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eMonths\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003epH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eEC (\u0026micro;S.cm\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003epH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003eEC (\u0026micro;S.cm\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003epH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003eEC (\u0026micro;S.cm\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eMay 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e7.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e7.18 \u0026plusmn; 0.37 (5.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e182.10 \u0026plusmn; 47.36 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e7.47 \u0026plusmn; 0.44 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e301.20\u0026nbsp; \u0026plusmn; 278.12 (92%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eJun 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e7.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e7.30 \u0026plusmn; 0.31 (4.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e206.00 \u0026plusmn; 271.08 (132%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e7.15 \u0026plusmn; 0.51 (7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e117.44\u0026nbsp; \u0026plusmn; 54.60 (46%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eJul 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e0.00 \u0026plusmn; 0.00 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e0.00 \u0026plusmn; 0.00 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e0.00 \u0026plusmn; 0.00 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e0.00 \u0026plusmn; 0.00 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eAug 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e5.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e15.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e5.40 \u0026plusmn; 1.31 (24.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e81.24 \u0026plusmn; 30.46 (37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e5.05 \u0026plusmn; 0.48 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e138.25\u0026nbsp; \u0026plusmn; 48.44 (35%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eSep 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e7.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e34.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e7.17 \u0026plusmn; 0.05 (0.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e78.92 \u0026plusmn; 15.59 (20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e5.51 \u0026plusmn; 0.34 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e153.88\u0026nbsp; \u0026plusmn; 48.88 (32%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eOct 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e7.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e19.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e7.04 \u0026plusmn; 0.08 (1.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e56.24 \u0026plusmn; 16.93 (30%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e5.37 \u0026plusmn; 0.47 (9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e69.73\u0026nbsp; \u0026plusmn; 31.36 (45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eNov 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e7.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e17.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e6.93 \u0026plusmn; 0.07 (1.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e40.83 \u0026plusmn; 14.24 (35%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e5.09 \u0026plusmn; 0.66 (13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e43.88\u0026nbsp; \u0026plusmn; 19.85 (45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eDec 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e6.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e15.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e6.71 \u0026plusmn; 0.09 (1.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e33.88 \u0026plusmn; 7.71 (23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e5.11 \u0026plusmn; 0.44 (9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e29.50\u0026nbsp; \u0026plusmn; 17.40 (59%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eJan 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e7.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e21.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e7.40 \u0026plusmn; 0.15 (2.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e43.56 \u0026plusmn; 10.03 (23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e5.56 \u0026plusmn; 0.31 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e31.52\u0026nbsp; \u0026plusmn; 17.18 (55%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eFeb 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e7.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e10.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e7.17 \u0026plusmn; 0.08 (1.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e42.45 \u0026plusmn; 6.58 (16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e5.24 \u0026plusmn; 0.62 (12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e45.86\u0026nbsp; \u0026plusmn; 28.17 (61%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eMar 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e7.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e8.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e7.06 \u0026plusmn; 0.05 (0.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e37.75 \u0026plusmn; 8.66 (23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e5.19 \u0026plusmn; 0.85 (16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e35.55\u0026nbsp; \u0026plusmn; 18.56 (52%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eApr 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e7.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e13.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e7.20 \u0026plusmn; 0.05 (0.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e46.70 \u0026plusmn; 10.07 (22%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e5.38 \u0026plusmn; 0.34 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e35.15\u0026nbsp; \u0026plusmn; 12.50 (36%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eMean annual\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e7.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e16.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e6.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e46.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e5.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e53.46\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe C and N concentrations were higher in Sf, followed by Tf and GR, respectively (Table 6). For all months, excepted May, Sf concentration of N was higher than the concentration in Tf (Figs. 4a and 4b). Considering annual average concentration (mg.L\u003csup\u003e-1\u003c/sup\u003e) in GR, C was almost three times higher in Tf, and more than five times higher in Sf, whereas N concentration was more than twice in Tf and more than four times in Sf.\u003c/p\u003e\n\u003cp\u003eTable 6. Carbon and Nitrogen concentration from gross rainfall (GR), average throughfall (Tf), and average stemflow (Sf) with their respective standard deviations and coefficients of variation (CV).\u003c/p\u003e\n\u003ctable border=\"1\" width=\"612\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"82\"\u003e\n\u003cp\u003eGR (mg.L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"218\"\u003e\n\u003cp\u003eTf (mg.L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"240\"\u003e\n\u003cp\u003eSf ( mg.L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"116\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eMay 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e18.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e9.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"116\"\u003e\n\u003cp\u003e111.6\u0026plusmn;3.02 (29%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e13.8\u0026plusmn;6.9 (50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e126.8\u0026plusmn;76.5 (60%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e11.5\u0026plusmn;16.6 (144%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eJun 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e22.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"116\"\u003e\n\u003cp\u003e50.6\u0026plusmn;13.2 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e5.4\u0026plusmn;3.9 (72%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e70.0\u0026plusmn;28.4 (41%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e12.1\u0026plusmn;14.2 (118%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eJul 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"116\"\u003e\n\u003cp\u003e0\u0026plusmn;0 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e0\u0026plusmn;0 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e0\u0026plusmn;0 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e0\u0026plusmn; 0 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eAug 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e14.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"116\"\u003e\n\u003cp\u003e32.7\u0026plusmn;17.1 (52%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e8.9\u0026plusmn;4.4 (49%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e83.4\u0026plusmn;35.9 (43%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e14.0\u0026plusmn;6.5 (47%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eSep 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e12.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"116\"\u003e\n\u003cp\u003e30.5\u0026plusmn;9.8 (32%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e3.8\u0026plusmn;1.7 (46%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e96.7\u0026plusmn;52.0 (54%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e11.8\u0026plusmn;7.7 (66%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eOct 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e6.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"116\"\u003e\n\u003cp\u003e18.4\u0026plusmn;6.4 (35%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e2.9\u0026plusmn;1.5 (52%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e45.9\u0026plusmn;22.2 (48%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e8.9\u0026plusmn;5.9 (67%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eNov 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e7.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"116\"\u003e\n\u003cp\u003e14.2\u0026plusmn;4.8 (34%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e4.0\u0026plusmn;2.2 (54%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e27.3\u0026plusmn;13.4 (49%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e10.9\u0026plusmn;5.8 (53%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eDec 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e6.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"116\"\u003e\n\u003cp\u003e12.2\u0026plusmn;3.5 (29%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e2.5\u0026plusmn;1.5 (59%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e24.9\u0026plusmn;13.9 (56%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e6.8\u0026plusmn;5.9 (87%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eJan 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e6.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e1.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"116\"\u003e\n\u003cp\u003e12.4\u0026plusmn;3.7 (29%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e3.0\u0026plusmn;1.3 (42%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e22.7\u0026plusmn;11.3 (50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e4.9\u0026plusmn;2.8 (56%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eFeb 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e5.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e1.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"116\"\u003e\n\u003cp\u003e12.7\u0026plusmn;3.3 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e1.5\u0026plusmn;0.3 (19%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e33.0\u0026plusmn;18.5 (56%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e4.1\u0026plusmn;3.2 (77%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eMar 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e4.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e1.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"116\"\u003e\n\u003cp\u003e10.8\u0026plusmn;3.6 (34%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e1.7\u0026plusmn;0.6 (33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e27.7\u0026plusmn;17.1 (62%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e3.4\u0026plusmn;1.9 (55%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eApr 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e4.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e1.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"116\"\u003e\n\u003cp\u003e11.6\u0026plusmn;2.7 (23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e2.4\u0026plusmn;1.1 (48%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e23.0\u0026plusmn;11.4 (49%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e3.2\u0026plusmn;2.2 (70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eAnnual average\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e9.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e1.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"116\"\u003e\n\u003cp\u003e26.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e4.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e48.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003e7.64\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe total annual C and N in GR was 104.13 kg.ha\u003csup\u003e-1\u003c/sup\u003e and 16.81 kg.ha\u003csup\u003e-1\u003c/sup\u003e, respectively; in Tf, they were, respectively, 191.97 kg.ha\u003csup\u003e-1\u003c/sup\u003e and 36.69 kg.ha\u003csup\u003e-1\u003c/sup\u003e; and in Sf, 1.21 kg.ha\u003csup\u003e-1\u003c/sup\u003e and 0.27 kg.ha\u003csup\u003e-1\u003c/sup\u003e, respectively (Table 7). The total annual flux of C and N in NP (Tf + Sf) increased by 86% and 120%, respectively, regarding GR. Analyzing the seasonality of nutrient inputs, we observed that 74.3% (143.52 kg.ha\u003csup\u003e-1\u003c/sup\u003e) of C and 75.1% (27.76 kg.ha\u003csup\u003e-1\u003c/sup\u003e) of N reached the forest floor during the wet season.\u003c/p\u003e\n\u003cp\u003eTable 7. Monthly inputs of Carbon and Nitrogen from gross rainfall (GR), throughfall (Tf), and stemflow (Sf) in the AFR.\u003c/p\u003e\n\u003ctable border=\"1\" width=\"362\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"95\"\u003e\n\u003cp\u003eGR (kg.ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"99\"\u003e\n\u003cp\u003eTf (kg.ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"83\"\u003e\n\u003cp\u003eSf\u0026nbsp; (kg.ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eMonths\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"46\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eMay 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e1.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e8.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e1.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"46\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eJun 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e3.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e5.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e0.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"46\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eJul 2018*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"46\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eAug 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e8.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e15.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e4.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"46\"\u003e\n\u003cp\u003e0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eSep 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e5.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e11.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e1.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"46\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eOct 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e12.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e1.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e29.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e4.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"46\"\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eNov 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e18.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e1.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e26.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e7.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"46\"\u003e\n\u003cp\u003e0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eDec 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e20.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e2.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e34.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e7.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"46\"\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eJan 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e9.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e1.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e14.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e3.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"46\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eFeb 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e10.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e2.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e20.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e2.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"46\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eMar 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e8.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e2.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e17.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e2.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"46\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eApr 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e3.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e7.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e1.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"46\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eAnnual total\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e104.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e16.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e191.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e36.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"46\"\u003e\n\u003cp\u003e1.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Means no observed rain in the month.\u003c/p\u003e\n\u003ch2\u003eTemporal analyses\u003c/h2\u003e\n\u003cp\u003eANOVA detected significant differences for C (\u003cem\u003ep\u003c/em\u003e = 0.027) and N (\u003cem\u003ep\u003c/em\u003e = 0.022) inputs between GR and NP. For NP, significant differences between dry and wet seasons for C (\u003cem\u003ep\u003c/em\u003e = 2.72E-06) and N (\u003cem\u003ep\u003c/em\u003e = 2.81E-06) inputs, as well as monthly (C: \u003cem\u003ep\u003c/em\u003e = 1.19E-18; N:\u003cem\u003e p\u003c/em\u003e = 2.38E-15), were found. Based on Tukey\u0026rsquo;s test, it was possible to group months without differences for C and N inputs in NP (Fig. 5 and Fig. 6).\u003c/p\u003e\n\u003cp\u003eConsidering NP, the months with the highest C inputs were December, October, and November respectively (a), whereas the months with the lowest contributions were September, May, and June (f). C inputs followed the climatic seasonality of the region, meaning that months with higher precipitation have higher inputs.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; The highest N inputs by NP were observed in November and December (a), and these months showed no statistical differences. August and October were statistically similar (ab), which the same observed in January, February, and March (bc). Apart from August, N inputs were greater throughout the wet season (October to May). Thus, the months with the lowest inputs were April, May, June, and September, which no statistical differences among them (c).\u003c/p\u003e\n\u003ch2\u003eSpatial analyses\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; The spatial variability of N and C inputs (kg.ha\u003csup\u003e-1\u003c/sup\u003e) was assessed through the coefficient of variation (CV) regarding the monthly, the seasonal (dry and wet seasons), and the annual scales (Table 8). In general, the annual and the wet season demonstrated the same variability for both, C and N inputs. However, in the dry season, the spatial variability of N (26%) was higher than that of C (18%). Considering the seasonality, for both C and N, the variability was higher in the wet season than in the dry season. Throughout the year, the CV of C ranged from 19% (January) to 47% (August), while N varied from 17% (February) to 60% (September).\u003c/p\u003e\n\u003cp\u003eTable 8. Coefficient of variation (CV) of C and N inputs (kg.ha\u003csup\u003e-1\u003c/sup\u003e) in the AFR.\u003c/p\u003e\n\u003ctable border=\"1\" width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"37%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"62%\"\u003e\n\u003cp\u003eCoefficient of variation (%) in C and N inputs from net precipitation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"37%\"\u003e\n\u003cp\u003eMonths\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\"\u003e\n\u003cp\u003eCarbon\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003eNitrogen\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"37%\"\u003e\n\u003cp\u003eMay 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"37%\"\u003e\n\u003cp\u003eJun 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"37%\"\u003e\n\u003cp\u003eJul 2018*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"37%\"\u003e\n\u003cp\u003eAug 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"37%\"\u003e\n\u003cp\u003eSep 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"37%\"\u003e\n\u003cp\u003eOct 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"37%\"\u003e\n\u003cp\u003eNov 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"37%\"\u003e\n\u003cp\u003eDec 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\"\u003e\n\u003cp\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e58\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"37%\"\u003e\n\u003cp\u003eJan 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"37%\"\u003e\n\u003cp\u003eFeb 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"37%\"\u003e\n\u003cp\u003eMar 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\"\u003e\n\u003cp\u003e41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"37%\"\u003e\n\u003cp\u003eApr 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"37%\"\u003e\n\u003cp\u003eDry season\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"37%\"\u003e\n\u003cp\u003eWet season\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"37%\"\u003e\n\u003cp\u003eAnnual total\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*July 2018 there was no rainfall events\u003c/p\u003e\n\u003cp\u003eIDW method was carried out to assess the spatial distribution of C and N inputs (kg.ha\u003csup\u003e-1\u003c/sup\u003e) regarding the dry and wet seasons and the entire period (total annual) (Fig. 7). Overall, there is a consensus between the spatial patterns of N and C in the dry season. However, in the wet season, the spatial patterns of C and N presented remarkable differences, especially in the southwest and the northeast regions of the AFR. Regardless the analyzed period, the greatest inputs for both C and N occurred in the same places, in the central, southwestern and eastern areas (near the points 3, 4 and 8).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":" Discussion","content":" \u003cp\u003eThis study aimed to (i) compare the changes in C and N inputs before and after the rainfall passes through the forest canopy; (ii) identify differences in C and N inputs in net precipitation considering the dry and wet seasons and also the monthly scale; and (iii) identify spatial differences in C and N inputs within the Atlantic forest remnant. Our main findings are (i) statistical differences in C and N inputs between gross rainfall and net precipitation, with an increase in the inputs after rainfall passes through the canopy; (ii) statistical differences were also observed in the seasonal and monthly scales, as a higher input occurred during the wet season, accounting for almost 75% of the total contribution; and (iii) the spatial variability was higher in the wet season which the largest inputs for both C and N occurring in the same places (near the points 3, 4 and 8).\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e Gross Rainfall x Net Precipitation\u003c/h2\u003e \u003cp\u003eThe concentration of NO\u003csup\u003e3\u0026minus;\u003c/sup\u003e, NO\u003csup\u003e2\u0026minus;\u003c/sup\u003e, TKN, total nitrogen and total carbon in gross rainfall, throughfall and stemflow were similar to what have been found by other studies in Tropical and Temperate forests worldwide (H\u0026ouml;lscher et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Markewitz et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Lilienfein and Wilcke \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Oziegbe et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tu et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ukonmaanaho et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Neu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Izquieta-Rojano et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Limpert and Siegert \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; You et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our results show that the rain is enriched with N and C when crossing the forest canopy because the average annual concentration of these nutrients is higher in throughfall and stemflow than in gross rainfall. These results are in accordance with other studies in both temperate and tropical forests that indicate leaching of N and C from the forest (Schroth et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; H\u0026ouml;lscher et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Goller et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Hofhansl et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Liu and Sheu \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Heartsill-Scalley et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Izquieta-Rojano et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Van Stan et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Limpert and Siegert \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, specifically for nitrogen forms, the results found do not corroborate with some other studies, in which N concentrations decreased in throughfall and stemflow, indicating retention of N by the forest (Parron et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tu et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ukonmaanaho et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Su et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We observed that concentrations decrease only for NO\u003csup\u003e2\u0026minus;\u003c/sup\u003e in stemflow (0.05\u0026nbsp;mg. L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) comparing with throughfall (0.11\u0026nbsp;mg. L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Results regarding NO\u003csup\u003e2\u0026minus;\u003c/sup\u003e concentrations in rainfall partitioning are extremely rare in the literature, which hampers further comparisons. As the results of nitrogen are variable in the literature, future studies are necessary to deepen in the relationship between the nitrogen and the hydrological cycles in tropical forests.\u003c/p\u003e \u003cp\u003eThe increase of C and N concentrations in throughfall and stemflow results from the washing process of atmospheric dry deposition accumulated in forest canopy between rainfall events and from the leaching process of the trees\u0026rsquo; materials (Parker \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Schroth et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Liu and Sheu \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Corti et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, You et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The main sources of C in net precipitation are derived from the forest system (Parker \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; You et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although the forest system also has a contribution to increase N concentration in net precipitation, it is remarkable the influence of the atmospheric gaseous and aerosol deposition (Parker \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Schroth et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In this regard, rainfall that crosses the canopy is an important source of nutrients for forests, and has a great significance in the nutrient cycle, because tree canopy works like a funnel capturing rain and transferring dry deposition from the canopy to the soil surface. This process is controlled by biotic and meteorological factors (Parron et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Van Stan and Stubbins \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe process of nutrients enrichment in net precipitation can be reinforced by pH results. We observed that pH decreases when the rainwater passes through the forest canopy, resulting in relevant decrease in the average annual pH. This pH reduction is similar to what happens in other types of forests: Brazilian Cerrado, Amazonia Rainforest, and Temperate Oak Forest (Lilienfein and Wilcke \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Tob\u0026oacute;n et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Corti et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Organic matter presence is the main cause of the decrease in pH, since organic acids are leached from the leaves of canopies, branches, and trunks (Liu and Sheu \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Tob\u0026oacute;n et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Furthermore, the annual average of EC for throughfall and stemflow are, respectively, 2.9 and 3.3 times greater than that of gross rainfall, which shows that the rain was enriched with solid particles, similar to that observed by Su et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsidering total annual C and N inputs to the canopy (GR) and forest floor (Tf, Sf), we observed that, in general, the amount found in our study is in agreement with that found for other tropical and temperate forests (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). For total C, the annual input in gross rainfall (104.13\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was similar to that found by Neu et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) in an evergreen tropical forest (121\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Results of the total carbon are scarce in the literature; however, Neu et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) observed that 32% of total C found in gross rainfall is from inorganic sources, and the organic carbon fraction is influenced by agricultural activities and fires. The climate seasonality of the study region, summed to the drier vegetation of the surrounding areas, may contribute to the incidence of natural or non-natural fires. In this regard, the carbon present in rainfall may be related to biomass burn (condensation nuclei) as we found results slightly lower than those of regions strongly influence by fires, like Cerrado and Amazonian Rainforest (Markewitz et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Germer et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Neu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the key N and C inputs (kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in rainfall parts (Gross rainfall, Throughfall and, Stemflow) in forests around the world.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c12\" namest=\"c5\"\u003e \u003cp\u003eGross rainfall (kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eForest type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLong-term (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMean annual (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eNH\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eDON\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eTKN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eDOC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtlantic Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1461.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1601.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e104.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchroth et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2001\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmazonia Rainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u0026ouml;lscher et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4\u0026ndash;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.7-2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarkewitz et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTropical Moist Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e123.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLilienfein and Wilcke (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCerrado\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7\u0026ndash;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1\u0026ndash;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.7\u0026ndash;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e47\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchwendenmann and Veldkamp (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTropical Wet Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22\u0026ndash;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchrumpf et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1960 to 2600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.36\u0026ndash;5.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e59.4-143.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermer et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTropical Rainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e106.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouza and Marques (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtlantic Rainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2240.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2406.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHofhansl et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWet Tropical Rainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e30.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOziegbe et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParron et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCerrado\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouza et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtlantic Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhou et al. (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTropical Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e41.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeu et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvergreen Tropical Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e82.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e121.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu and Sheu (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtropical Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2300 to 2700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e142.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTu et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtropical Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1984.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e88.8*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e113.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIzquieta-Rojano et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvergreen Holm Oak Forests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e364\u0026ndash;840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68\u0026ndash;6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08\u0026ndash;3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.08\u0026ndash;12.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.76\u0026ndash;18.82*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYou et al. (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConiferous, Deciduous and Evergreen forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1416.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRainfall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c12\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eThroughfall (kg.ha\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e.\u003cb\u003eyear\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eForest type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLong-term (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMean annual (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eNH\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eDON\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eTKN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eDOC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtlantic Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1461.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1601.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e36.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e191.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchroth et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2001\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmazonia Rainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.4*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u0026ouml;lscher et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4\u0026ndash;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarkewitz et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTropical Moist Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e83.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTob\u0026oacute;n et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTropical Rainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.72\u0026ndash;12.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.07\u0026ndash;31.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e148.43 -190.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLilienfein and Wilcke (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCerrado\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3\u0026ndash;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3-3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.9\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e66\u0026ndash;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchwendenmann and Veldkamp (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTropical Wet Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchrumpf et al (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMontane Rainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1960 to 2600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.24\u0026ndash;10.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e102.8-218.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermer et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTropical Rainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e301.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFujii et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTropical Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2187\u0026ndash;2427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e97\u0026ndash;182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouza and Marques (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtlantic Rainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2240.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2406.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.51\u0026ndash;5.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchmidt et al. (2010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtropical Montane Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000 to 5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.3*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHofhansl et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWet Tropical Rainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.5\u0026ndash;13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e74.7\u0026ndash;94.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOziegbe et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParron et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCerrado\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.9\u0026ndash;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiniz et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtlantic Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1533.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e23.1\u0026ndash;30.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouza et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtlantic Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28.8*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhou et al. (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTropical Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e113.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeu et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvergreen Tropical Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e150.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e167.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu and Sheu (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtropical Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2300 to 2700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e188.8-231.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTu et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtropical Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1984.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e113.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIzquieta-Rojano et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvergreen Holm Oak Forests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e364\u0026ndash;840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.44\u0026ndash;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.66\u0026ndash;8.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.3-11.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVan Stan et al. (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOak - Cedar Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e750 to 1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e230\u0026ndash;480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYou et al. (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConiferous, Deciduous and Evergreen Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1416.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.92\u0026ndash;27.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e52-75.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRainfall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c12\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eStemflow (kg.ha\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e.\u003cb\u003eyear\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eForest type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLong-term (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMean annual (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eNH\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eDON\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eTKN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eDOC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtlantic Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1461.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1601.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u0026ouml;lscher et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u0026ndash;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTob\u0026oacute;n et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTropical Rainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u0026ndash;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u0026ndash;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.82\u0026ndash;6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHofhansl et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTropical Rainforest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiniz et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtlantic Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1533.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.93\u0026ndash;1.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeu et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvergreen Tropical Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu and Sheu (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtropical Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2300 to 2700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.7\u0026ndash;15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVan Stan et al. (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOak - Cedar Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e750 to 1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.15\u0026ndash;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u0026ndash;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYou et al. (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConiferous, Deciduous and Evergreen forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1416.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.14\u0026ndash;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.43\u0026ndash;4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eLegend: C: carbon; N: nitrogen; TKN: total kjeldahl nitrogen; DON: dissolved organic nitrogen; DOC: dissolved organic carbon; NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e: ammonia; NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e: nitrate; NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e: nitrite.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e*TKN\u0026thinsp;=\u0026thinsp;DON\u0026thinsp;+\u0026thinsp;NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe total N inputs in gross rainfall (16.81\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was in good agreement with that found in the Brazilian Cerrado biome (12.6\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) by Parron et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and for an Atlantic forest (15.1\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) by Souza et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, our results were lower than those found in China in Subtropical forests by Tu et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) (114\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and by You et al. (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (30\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The major proximity of our results with Parron et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Souza et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) could be related with the activities that condition atmospheric emissions of reactive nitrogen, from global to local scale. For instance, on a global scale, the increase in emission of anthropogenic nitrogen was observed especially in North America, Europe, and Asia (Van Aardenne et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) in the last decades, but the natural presence of nitrogen in tropical regions is higher than in temperate ones (Galloway et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Van Aardenne et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). On the regional and local scale, such as our study, the mains anthropogenic nitrogen sources are related to the economic characteristics of the Southern Minas Gerais state, with urban influence (automobilist exhaust), industries (fossil fuel combustion), fires, and agricultural activities such as fertilizer use and cattle breeding.\u003c/p\u003e \u003cp\u003eSimilarity with other studies was also observed when we consider nitrogen forms separately. The NO\u003csup\u003e3\u0026minus;\u003c/sup\u003e annual flux in gross rainfall (2.82\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was very close to that observed in the Brazilian Cerrado biome (2.1 to 2.4\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; Lilienfein and Wilcke \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and in an Atlantic Rainforest (2.3\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; Souza and Marques \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Considering TKN (DON\u0026thinsp;+\u0026thinsp;NH\u003csub\u003e4\u003c/sub\u003e+) the annual input in gross rainfall (13.08\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was similar to that of an Atlantic forest (10.1\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; Souza et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The similarity between our results with other studies in Brazil may be related to the influence of regional and local anthropogenic sources and singular conditions of the tropical regions. When studies of gross rainfall consider N forms separately, it is possible to improve the understanding of the local sources and the processes that controls N depositions. The anthropogenic activities are considered the most important sources of anthropogenic N since food production and fossil fuels demands are increasing constantly (Galloway et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This reinforces the importance and urgency of these studies.\u003c/p\u003e \u003cp\u003eWe found statistical differences between gross rainfall and net precipitation annual inputs (kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for both C (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) and N (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022). The vegetation structure, the leaching of dry deposition in the forest, and the exchange with trees surface can explain these differences. (Parker \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). Forest-rainfall interaction can be responsible for an increase of 89\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of C and 20\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of N to the forest floor. This enrichment of C and N inputs in net precipitation was approximately to 86% and 120%, respectively. Regarding N inputs, almost the same was identified in an Atlantic Forest (127% by Souza et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Further analysis demonstrated that the increment of N was superior than that of C which may be explained by the fact that dry deposition of N is enhanced in the region (Parker \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; You et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schroth et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe total C input in net precipitation (193.18\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was 1.85 times greater than in gross rainfall (104.13\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). According to Neu et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), organic C is more representative in net precipitation, attaining up to 90% of total C, than in gross rainfall with only 68%. The presence of organic C in forests is the result of several sources, as forest metabolism, decomposition processes, and animal excrement (Parker \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Neu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, the leaching process of the organic matter contributes to an increase in C inputs in net precipitation (Schrumpf et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Mellec et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe inputs of total N (36.95\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), TKN (26.46\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (8.94\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (1.54\u0026nbsp;kg. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in net precipitation were 2.2, 2, 3.2 and 1.9 times greater than in gross rainfall, respectively, demonstrating significant dry deposition and leaching processes in the Atlantic Forest remnant. Besides dry deposition of reactive nitrogen forms (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e) derived from the anthropogenic sources, the increase in N in net precipitation could be attributed to the leaching of the organic N provided by biological processes (Mellec et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Despite NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e represents a small portion of total N (4%), the enrichment in net precipitation could be attributed to fog and dew formations which withdraw the dry depositions from the leaves\u0026rsquo; surface (Acker et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsidering the total area of the Atlantic Forest remnant (6.3\u0026nbsp;ha), the annual inputs of C and N in gross rainfall can reach up to 656\u0026nbsp;kg.yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 106\u0026nbsp;kg.yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, whereas for net precipitation an amount of 1217\u0026nbsp;kg.yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 233\u0026nbsp;kg.yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. The Atlantic Forest remnant is responsible for the considerable increase of local C and N, directly influencing the nutrient cycle, the availability of N and the stocking of C in the soil. Thus, Atlantic forest environments outstand as an important sinking for C and N.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e Temporal variations of the net precipitation in AFR\u003c/h2\u003e \u003cp\u003eThere was a seasonal variability of total C and N in gross rainfall (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These concentrations were higher in the dry season (April to September) and decreased throughout the wet season (October to March). Such pattern was also observed for throughfall and stemflow according with other forest stands in seasonal climate regions (Neu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Germer et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lilienfein and Wilcke \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis seasonal variability in net precipitation is more common in semideciduous and deciduous forests than in evergreen forests according to Van Stan and Stubbins (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This is the result of some factors explained as follows. The accumulation of particles in the atmosphere and in the canopies surfaces in the dry season ensures the high concentrations in the first rainfalls after long dry periods. These events are called \u0026ldquo;first flush events\u0026rdquo; and are responsible for \u0026ldquo;washing\u0026rdquo; the atmosphere and the forests canopy (Neu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; You et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This accumulation starts decreasing in the wet season as the time between rainfall events decreases (rainfall events are more frequent), explaining the behavior in the wet season (You et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, intense rainfall events are more common in the wet season which causes dilution of the compounds and further reductions in concentration (Michalzik and Matzner \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, there were statistical differences (C \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.72E-06 and N \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.81E-06) in net precipitation regarding the dry and wet season. Such differences could be explained by the semi-deciduousness, as up to 50% of the trees lose up their leaves in the dry season. This behavior influences the dry deposition, the amount of rainfall that passes through the canopy and, hence, the interactions between rainfall and forest. In the dry season, trees with few leaves drain more water by the trunk (Terra et al \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e), however, the presence of more leaves increases interactions between rainfall and forests, affecting the nutrient inputs in the wet season. These conditions drive the dynamics of nutrient transport from the atmosphere to the forest floor in the dry and wet seasons as they are strictly associated with the characteristics of the vegetation and rainfall seasonality.\u003c/p\u003e \u003cp\u003eThe seasonality of rainfall and the semi-deciduousness are conditioning factors for C and N inputs being more significant in the wet season, concentrating 74.3% (143.52\u0026nbsp;kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of total annual C and 75.1% (27.76\u0026nbsp;kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of total annual N. Although the concentration is considerably higher in the dry season, because of the effects from long periods between rainfall and atmospheric deposition, the considerably higher total of rainfall in the wet season is responsible for the most part of C and N that reaches the forest floor annually. In this sense, frequent precipitation is responsible for leaching C and N from the atmosphere and forest surfaces (Tu et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Van Stan et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor the monthly step, it was also observed statistical differences (C \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.19E-18 and N \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.38E-15). Using the Tukey\u0026rsquo;s test, it was possible to group statistically equal months (Fig.\u0026nbsp;5 and Fig.\u0026nbsp;6). Considering net precipitation, the months with the highest C inputs were December, October, and November, respectively, with no statistical differences (a). These months represent the onset of the wet season and, thus, the greater inputs are because of the dry deposition accumulated throughout the dry season that was washed by the significant amount of rainfall events. The months with the lowest C inputs were statistically equal (f) and correspond to the beginning of the dry season (April, May, and June). The inputs of C followed the seasonality of the region, in which the higher the rainfall amount the greater the input of C.\u003c/p\u003e \u003cp\u003eFor N in net precipitation, November and December showed no statistical differences (a). These months represented the highest inputs of N throughout the year and are the months with the highest amounts of rainfall. October and August were also statistically equal (a), both had above-average rainfall and were preceded by months with below-average rainfall. This evidenced the influence of the first rainfall events and the importance of washing-off the atmosphere and the particles deposited in the crowns and branches of the trees after a long dry period. October and August were also statistically equal (b) to January, February and March, which was also expected as these months represent the last ones of the wet season. These months (January, February and March) were also statistically equal to April, May, June, and September, the months with the lowest N inputs coinciding with the lowest rainfalls in the year. Although April had a higher rainfall amount than August, the long antecedent dry period provided a greater input of N as a consequence of the greater number of particles stored in the atmosphere and canopies. This confirms that the amount and seasonality of rainfall drive N inputs in the Atlantic Forest remnant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e Spatial variation of the net precipitation in AFR\u003c/h2\u003e \u003cp\u003eThe spatial variability of the nutrients inputs is expressive between different types of forest (Levia and Frost \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Despite some studies tried to describe the spatial variability of solute depositions in forests by applying the coefficient of variation (Raat et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Staelens et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zimmermann et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zimmermann et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), the sampling designs, collection periods, and element analysis were very different, which impair comparisons, even though our results were close to other studies carried out in temperate and tropical ecosystems. Throughout the year, the coefficient of variation ranged from 19\u0026ndash;47% and from 17\u0026ndash;60% for C and N inputs, respectively. These amounts are within the range (12\u0026ndash;78%) found in other studies, where tropical forests showed the largest coefficients of variation, mainly in the wet season (Raat et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Staelens et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zimmermann et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zimmermann et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the wet season presented higher spatial variability than the dry season. However, N inputs presented the lowest coefficient of variation in both the end of the wet season and the beginning of the dry season. For C inputs, the lowest coefficients of variation were during the dry season. Excepting for August, the dry months with higher CV are likely associated with rainfall above average, whereas the low CV of January is related to rainfall below the average. The potential accumulation of organic matter, the greater rainfall amounts and the heterogeneity of the canopy are some of the factors responsible for the increased spatial variability in tropical forest (Zimmermann et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Atlantic Forest remnant has a heterogeneous canopy because of the high variability of species, size, and age of the trees, which is expected in tropical forests. The high incidence of lianas and the canopy gaps caused by the fall of trees further increase the intrinsic heterogeneity of this type of forest. In the same forest remnant, Rodrigues et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) related the spatial variability of net precipitation to the semideciduous characteristics. However, Terra et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e) did not found a spatial pattern in stemflow, associating it to the Atlantic Forest remnant heterogeneity of species. The edge effect also contributes to this variability as it directly impacts the characteristic of the vegetation, altering forest structure (density and size of trees) and the abundance of species (Ben\u0026iacute;tez-Malvido and Mart\u0026iacute;nez-Ramos \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In addition to the heterogeneous structure of the canopy, the spatial pattern of nutrient inputs is also influenced by dry deposition, leaching process, and meteorological conditions (Forti and Neal \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Levia and Frost \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMarques et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) highlighted the influence of the animals in the surrounding areas of the AFR in N forms (ammonia and ammonium), which may have increased the N inputs in the southwest and central portion of the forest. The southwest area with the higher C and N inputs may also be influenced by vehicles, as this part of the forest is closer to a busy road. The eastern portion, that has high N inputs throughout the year and high C inputs in the dry season, is located close to a \u0026ldquo;candeia\u0026rdquo; (\u003cem\u003eEremanthus erythropappus\u003c/em\u003e (DC.)) forest stand and a \u003cem\u003eEucalyptus\u003c/em\u003e stand, which can influence the high depositions in this location.\u003c/p\u003e \u003cp\u003eOverall, these biotic and abiotic factors interaction between rainfall and forests may be responsible for high spatial variability in inputs of nutrients to the forest floor (Levia and Frost \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zimmermann et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite the high amounts of rainfall being responsible for the dilution, it is not possible to select only one factor that controls spatial patterns (Robson et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Thus, the factors that influence spatial variability are uncertain, and studies of canopy structure and meteorological conditions could help to understand spatial variability of chemicals in tropical forests (Levia and Frost \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e "},{"header":" Conclusions","content":" \u003cp\u003eBoth concentration (mg. L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and inputs (kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of N and C were higher in net precipitation than in gross rainfall. The rainfall leaches the atmosphere and forest\u0026rsquo;s structures and can be considered an important transfer pathway of C and N toward the forest floor. This forest-rainfall interaction is responsible to increase the total amount of C and N that reaches the forest floor annually.\u003c/p\u003e \u003cp\u003eThe seasonal variability of C and N was remarkable. The inputs were higher in the wet season and represented, on average, 75% of the total annual contribution. These results confirm the hypothesis that the behavior of these nutrients is conditioned by the seasonal variability of precipitation. Analyzing inputs during the year, the impacts of extensive dry periods are more important for increasing N instead of C. This fact may be a direct result of atmospheric depositions from anthropogenic sources.\u003c/p\u003e \u003cp\u003eThe spatial variability of C and N was higher in the wet season. The spatial patterns revealed that, in general, the same locations had the highest inputs for both C and N throughout the year. Furthermore, the canopy heterogeneity and the proximity to potential sources seem responsible for breaking any continuity in the C and N inputs in the Atlantic forest remnant.\u003c/p\u003e \u003cp\u003eThe AFR increases the contribution of C and N that reaches the forest floor. The constant input of these nutrients, especially in the wet season, is extremely important in the nutrient cycle since it aids in the sustainability of ecological processes. Despite the nutrient inputs presented some variability, this study provides useful information on the changes of C and N inputs after forest-rainfall interactions. Thus, it can support the estimation of atmospheric deposition and advance the knowledge of the contribution of the leaching and absorption processes by canopies.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eC: Carbon; N:Nitrogen; ANOVA:analysis of variance; CV:coefficient of variation; IDW:inverse distance weight; NP:net precipitation; AFR:Atlantic Forest remnant; GR:gross rainfall; Tf:throughfall; Sf:stemflow; DBH:diameter at 1.3\u0026nbsp;m above ground; EC:electric conductivity; NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e:nitrate; NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e:nitrite; TKN:total kjeldahl nitrogen; MS:meteorological station; DON:dissolved organic nitrogen; DOC:dissolved organic carbon; NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e:ammonia.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003ch1\u003eEthics approval and consent to participate\u003c/h1\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch1\u003eConsent for publication\u003c/h1\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch1\u003eAvailability of data and materials\u003c/h1\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch1\u003eCompeting interests\u003c/h1\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch1\u003eFunding\u003c/h1\u003e\n\u003cp\u003eThis work was financially by Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico \u0026ndash; CNPq (grant numbers 301556/2017-6 and 401760/2016-2).\u003c/p\u003e\n\u003ch1\u003eAuthors\u0026rsquo; contributions\u003c/h1\u003e\n\u003cp\u003eConceived and designed the study: CRdM, VAM and MdCNST. Led the research project: CRdM. Performed the experiments, collected data and samples in the field: VAM, AFR and VAdO. Processed samples in the lab: VAM. Wrote the paper: VAM and MdCNST. Critical Revision: CRdM and AFR. Statistical Support: MdCNST and LORP. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch1\u003eAcknowledgements\u003c/h1\u003e\n\u003cp\u003eThe authors thank to Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico - CNPq (grant numbers 301556/2017-6 and 401760/2016-2), and to Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior - CAPES (for the Ph.D research grant for the first author). Special thanks go to \u0026ldquo;Laborat\u0026oacute;rio de Gest\u0026atilde;o de Res\u0026iacute;duos Qu\u0026iacute;micos da Universidade Federal de Lavras (LGRQ-UFLA)\u0026rdquo;, and to \u0026ldquo;Laborat\u0026oacute;rio de An\u0026aacute;lise de \u0026Aacute;gua da Universidade Federal de Lavras (LAADEG-UFLA)\u0026rdquo; for the facilities and equipments used in this study.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eABNT (Associa\u0026ccedil;\u0026atilde;o Brasileira de Normas T\u0026eacute;cnicas) (1997). \u0026Aacute;gua. ABNT NBR 13796:1997- Determina\u0026ccedil;\u0026atilde;o de nitrog\u0026ecirc;nio org\u0026acirc;nico, Kjeldahl e total - M\u0026eacute;todos macro e semimicro Kjeldahl. Rio de Janeiro\u003c/p\u003e\n\u003cp\u003eAcker K, Beysens D, M\u0026ouml;ller D (2008) Nitrite in dew, fog, cloud and rain water: An indicator for heterogeneous processes on surfaces. 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J Hydrol 343:80\u0026ndash;96. https://doi.org/10.1016/j.jhydrol.2007.06.012\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rainfall, Forest hydrology, Semideciduous forest, Throughfall, Stemflow, Nutrient inputs","lastPublishedDoi":"10.21203/rs.3.rs-57462/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-57462/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eForests are important for governing local and regional water and nutrient dynamics. Thus, studying precipitation-forest interactions is essential to understand the consequences of land-use and climate change for the hydrological and nutrient cycles. This study aimed to quantify the contribution of the net precipitation on Atlantic Forest’s total carbon (C) and total nitrogen (N), identifying potential differences between these chemistry inputs regarding temporal (seasonal and monthly) and spatial scales.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eThe rainfall was enriched after crossing the forest canopy. For gross rainfall and net precipitation, respectively, statistical differences were found between annual inputs of carbon (104.13\u0026nbsp;kg ha\u003csup\u003e− 1\u003c/sup\u003e and 193.18\u0026nbsp;kg ha\u003csup\u003e− 1\u003c/sup\u003e) and nitrogen (16.81\u0026nbsp;kg ha\u003csup\u003e− 1\u003c/sup\u003e and 36.95\u0026nbsp;kg ha\u003csup\u003e− 1\u003c/sup\u003e). Moreover, there was a seasonal variability in the inputs of C and N since 75% occurred in the wet season. November and December concentrated the largest nutrient contribution throughout the year. The spatial variability of C and N was higher in the wet season. Overall, the spatial patterns revealed that the same locations had the highest inputs regardless of the analyzed period.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eOur findings reinforce that forests promote rainfall enrichment with C and N. The forest-rainfall interactions provide constant input of these nutrients, especially in the wet season, being fundamental for maintenance of ecological processes. Despite the nutrient inputs presented some variability, this study provides useful information on the changes of C and N inputs after forest-rainfall interactions. Thus, it can support the estimation of atmospheric deposition and advance the knowledge of the contribution of the leaching and absorption processes by canopies.\u003c/p\u003e","manuscriptTitle":"Spatial and temporal patterns in Carbon and Nitrogen inputs by net precipitation in Atlantic Forest, Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-08-12 21:51:55","doi":"10.21203/rs.3.rs-57462/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aa40891f-8121-40c0-b43a-0c3fe5bb3f67","owner":[],"postedDate":"August 12th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":285300,"name":"Ecological Modeling"},{"id":285301,"name":"Forestry"}],"tags":[],"updatedAt":"2021-12-21T14:12:15+00:00","versionOfRecord":{"articleIdentity":"rs-57462","link":"https://doi.org/10.1093/forsci/fxab056","journal":{"identity":"forest-science","isVorOnly":true,"title":"Forest Science"},"publishedOn":"2021-12-21 14:12:15","publishedOnDateReadable":"December 21st, 2021"},"versionCreatedAt":"2020-08-12 21:51:55","video":"","vorDoi":"10.1093/forsci/fxab056","vorDoiUrl":"https://doi.org/10.1093/forsci/fxab056","workflowStages":[]},"version":"v1","identity":"rs-57462","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-57462","identity":"rs-57462","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}