Vertical profiles of leaf photosynthesis and leaf traits, and soil 1 nutrients in two tropical rainforests in French Guiana before and 2 after a three-year nitrogen and phosphorus addition experiment

. Terrestrial biosphere models typically use the biochemical model of Farquhar, von Caemmerer and Berry (1980) to 34 simulate photosynthesis, which requires accurate values of photosynthetic capacity of different biomes. However, data on 35 tropical forests are sparse and highly variable due to the high species diversity, and it is still highly uncertain how these tropical 36 forests respond to nutrient limitation in terms of C uptake. Tropical forests often grow on phosphorus (P)-poor soils and are, 37 in general, assumed to be P- rather than nitrogen (N)-limited. However, the relevance of P as a control of photosynthetic 38 capacity is still debated. Here, we provide a comprehensive dataset of vertical profiles of photosynthetic capacity and important 39 leaf traits, including leaf N and P concentrations, from two three-year, large-scale nutrient addition experiments conducted in 40 two tropical rainforests in French Guiana. These data present a unique source of information to further improve model 41 representations of the roles of N, P, and other leaf nutrients, in photosynthesis in tropical forests. To further facilitate the use 42 of our data in syntheses and model studies, we provide an elaborate list of ancillary data, including important soil properties 43 and nutrients, along with the leaf data. As environmental drivers are key to improve our understanding of carbon (C)-nutrient 44 cycle interactions, this comprehensive dataset will aid to further enhance our understanding of how nutrient availability 45 interacts with C uptake in tropical forests. The data are available at DOI 10.5281/zenodo.5638236 10.5281/zenodo.4719242 46 (Verryckt, 2021). 47


Introduction 48
Tropical forests play a significant role in the global carbon (C) cycle, contributing more than one third of global terrestrial 49 gross primary productivity (GPP) (Beer et al., 2010;Malhi, 2010). To obtain accurate estimations of the global C budgets, a 50 thorough understanding of the functioning of these tropical forests is thus important. It is still highly uncertain how these 51 tropical forests, and in particular lowland tropical forests, respond to nutrient limitation and to global change in terms of C 52 uptake (Fleischer et al., 2019;Wieder et al., 2015). 53 Leaf photosynthetic capacity is the primary driver of C uptake and its accurate representation in terrestrial biosphere models 54 (TBMs) is essential for robust projections of C stocks and fluxes under global change scenarios. Photosynthesis in C3 species 55 is typically represented in nearly every major large-scale TBM by the Farquhar, von Caemmerer and Berry (FvCB) model of 56 photosynthesis (Farquhar et al., 1980;von Caemmerer and Farquhar, 1981). 57 The FvCB model determines photosynthesis (A) by the most limiting of two processes, Rubisco activity and electron transport. 58 Empirical studies to determine the key parameters of these two processes (i.e. the maximum rate of carboxylation Vcmax, the 59 maximum electron transport rate, Jmax) and to test empirically their limitations on the leaf-and canopy-scale are necessary for 60 obtaining the data required for parameterizing the FvCB model (Medlyn et al., 2015). Plant trait databases, now widely 61 available, offer an excellent opportunity for parameterizing models. However, data on tropical forests are sparse and highly 62 variable due to the huge species diversity (Rogers, 2014). 63 Moreover, empirical studies on leaf photosynthesis and leaf traits in tropical forests have mainly focused on upper canopy 64 leaves (i.e. Bahar et al. (2016), Berry and Goldsmith (2020), Rowland et al. (2015)) as a trade-off to cover a broader set of tree 3 species in these highly diverse tropical forests. Subsequently, light and leaf nitrogen (N) profiles are used to upscale leaf-to 66 canopy-level photosynthesis, as is also common practice in temperate and boreal forests (Bonan, 2015). However, the leaf N 67 gradient is shallower than the light gradient (Bonan, 2015) and accumulating evidence suggests a regulating role of phosphorus 68 (P) for photosynthesis in tropical trees growing on low-P soils (Walker et  Environmental drivers are key to improve our understanding of C-nutrient cycle interactions and our ability to model them. 72 Climate data are often directly available at high spatial resolution and at the global scale from databases such as Worldclim 73 (Ruiz-Benito et al., 2020), while observations of soil properties and soil nutrient availability are often missing (Vicca et al., 74 2018). Soil variables have been shown to be strong predictors of leaf traits in higher plants (Maire et al., 2015). Comprehensive 75 soil data, including soil properties such as texture and pH as well as important nutrients, are needed to further enhance our 76 understanding of how and why nutrient availability interacts with C uptake in tropical ecosystems and their responses to global 77 environmental change (Vicca et al., 2018). 78 As most tropical forests are growing on highly weathered soils and contain N-fixing plants and free-living organisms, the 79 widely accepted ecological paradigm states that they tend to be limited by P rather than by N (Wright et al., 2018;Walker and 80 Syers, 1976). Nutrient addition experiments are a great asset to offset possible nutrient limitations and to see how the system 81 reacts (Vitousek and Howarth, 1991). Long-term nutrient addition experiments are important to study the role of leaf nutrients 82 in key role processes such as photosynthesis. However, in tropical forests only a few large-scale nutrient addition experiments 83 have been carried out and the results are ambiguous (Wright et al., 2018;Wright, 2019). 84 Here, we provide photosynthesis data and a set of leaf traits collected at multiple canopy levels at two forest sites in French 85 Guiana, as well as data on responses to two three-year, large-scale N and P nutrient addition experiments. Given the importance 86 of ancillary data such as environmental data and soil properties for model and synthesis studies (Vicca et al., 2018), we also 87 provide an extensive dataset of environmental data, including pre-treatment soil properties and nutrients. 88 2 Sampling sites 89

Study site description 90
The data were collected in French Guiana, South America at two old-growth, lowland tropical rainforest sites, Paracou and 91 Nouragues ( Figure 1A, B). The climate in French Guyana is tropical wet, characterized by a wet and a dry season due to the 92 north-south movement of the Inter-Tropical Convergence Zone (ITCZ) (Bonal et al., 2008). From December to July, the ITCZ 93 brings heavy rains, which peak in May when monthly rainfall typically exceeds 600 mm. The dry season, with < 100 mm 94 rainfall each month, lasts from August to November, with an additional short, dry period in March. Mean annual air 95 temperature is near 26°C for both sites (Bongers et al., 2001;Gourlet-Fleury et al., 2004). Nouragues, respectively (Courtois et al., 2018). 124 In October 2016, a field nutrient addition experiment at both sites was initiated and is ongoing to this day. In each block, one 125 plot served as control plot and the remaining three plots received one of three nutrient addition treatments (+N, +P or +NP). 126 Fertilizer was applied twice per year by hand-broadcasting commercial urea ((NH2)2CO) and/or triple superphosphate 127 (Ca(H2PO4)2) at a rate of 125 kg N ha -1 y -1 (+N treatment) or 50 kg P ha -1 y -1 (+P treatment), or both amounts together (+NP

Fertilizer composition 137
Within the nutrient addition experiment, N was added as commercial urea ((NH2)2CO) and P as triple superphosphate 138 (Ca(H2PO4)2). The chemical composition of the applied fertilizers was analyzed to know the exact composition. Samples of 139 both fertilizers were dried at 70°C for 48 h, after which they were ground. Total N of the fertilizers was determined by dry 140 combustion using a Skalar Primacs (Skalar Holding, The Netherlands). P2O5 and MgO in mineral acid where determined by 141 an iCAP 7400 radial optical emission spectrometer (Thermo Fisher Scientific, Germany). The ground samples were analyzed 142 with an iCAP 7400 radial optical emission spectrometer (Thermo Fisher Scientific, Germany) to determine the potassium (K), 143 calcium (Ca) and magnesium (Mg) concentrations, as well as the heavy metal concentrations (arsenic (As), cadmium (Cd), 144 chromium (Cr), copper (Cu), iron (Fe), nickel (Ni), lead (Pb), zinc (Zn), molybdenum (Mo)). 145 3 Data and Methods: soil sampling 146

Sampling design 147
In 2015, we sampled soil to a depth of 30 cm, according to a five-on-dice sampling pattern within the 20 x 20 m plots ( Figure  148 2). At each sampling point, we sampled bulk density at a depth of 0-15 cm and 15-30 cm using an auger with a 15-cm long 149 cylindrical head (8-cm diameter). Additionally, we took three soil cores with a gouge auger (30-cm length, 5-cm diameter). 150 These three cores were split into two depths (0-15 cm and 15-30 cm), pooled together per depth and used for gravimetric soil 151 water content determination, soil particle size distribution analysis and chemical analysis after sieving (< 2 mm). We the soil sampling was repeated. However, these data have not been processed yet and will be made available through 156 publication as soon as possible. 6

Bulk density 159
We sampled soil bulk density in the wet season of 2015. In each plot, we took five cores at two depths and these samples were 160 sieved through a 2-mm sieve. We collected the soil fraction, the roots and the stones, which were dried and weighed separately 161 at 105°C for 24 h. In our database, we report two measures of bulk density: inclusive bulk density is the weight of the dried 162 soil core divided by its volume (i.e. the volume of the auger, 754 cm³), whereas exclusive bulk density is calculated by dividing 163 the total weight of the soil fraction (excluding roots and stones) by the volume of the entire core (i.e. 754 cm³). 164

Soil particle size distribution 165
The particle size distribution at plot level was analysed only in the wet season of 2015, assuming it would not change with 166 seasonality. Therefore, we mixed by hand the five samples per plot that were sieved (< 2 mm) after extraction using a gouge 167 auger, and analyzed these mixed samples as one composite sample per depth and per plot. We determined the soil particle size 168 distribution using sedimentation with the hydrometer method (Gee and Bauder, 1986) after SOM oxidation with H2O2, as 169 described in protocol 1.3.5 Soil texture in the Supporting information S1 Site characteristics and data management in Halbritter 170 et al. (2020). Soil particles were dispersed with sodium hexametaphosphate and the quantity of sand, silt and clay were 171 determined using a hydrometer. 172

Soil moisture 173
The gravimetric soil water content (%) was determined in both the wet and the dry season of 2015. We weighed roughly 10 g 174 of fresh soil, which was then dried at 70°C to constant mass and weighed to obtain the dry mass. The gravimetric water content 175 is calculated as the mass of water (i.e. the difference in mass weight of fresh and dried soil) per mass of dry soil. 176

Chemical analyses: concentrations and availability 178
Freshly sieved soil was used for the measurement of pH and the extraction of inorganic N (Ni) and inorganic P (Pi). We 179 measured the soil pH using a pH meter (HI 2210-01, Hanna Instruments, USA) after adding 1M KCl to the soil in a 1:2.5 w:v 180 ratio and shaking it for 1 h. The same solution was passed through a 42-µm filter and the filtrate's concentration of NH4 + and 181 NO3was determined colourimetrically (SAN++ continuous flow analyzer, Skalar Inc., The Netherlands). The Pi was extracted 182 with the Olsen-P bicarbonate extraction (Olsen et al., 1954) and measured on an iCAP 6300 Duo ICP optical emission 183 spectrometer (Thermo Fisher Scientific, Germany). 184 Sieved soil samples were dried at 60°C to constant mass and was then ground in a ZM 200 ball mill (Retsch GmbH, Haan, 185 Germany). We extracted Pi on previously dried soil with the Bray P acid fluoride extraction (Bray and Kurtz, 1945) Table 2). 221

Leaf gas exchange 223
We measured leaf gas exchange measurements using a set of infrared gas analyzers (IRGAs) incorporated into a portable

Photosynthetic CO2-response curves 240
Photosynthetic CO2-response curves (Figure 4) were established by measuring net photosynthetic rates (An) at different CO2 241 concentrations by controlling the reference CO2 concentrations, while maintaining a constant temperature and photosynthetic 242 photon flux density (PPFD). The An-Ci (Ci, the CO2 concentration of the leaf intercellular spaces) measurements began at the 243 ambient CO2 concentration of 400 ppm. Once a steady state of photosynthesis was reached, the CO2 concentrations was 244 reduced stepwise to 50 ppm, then returned to 400 ppm, and thereafter increased to 2000 ppm, to obtain a total of 10-14 245 measurements per leaf. We measured An-Ci curves for one to three leaves per canopy level, resulting in a total of 1708 curves 246 measured ( Table 3)

Light-saturated photosynthesis (Asat) of saplings 258
Light-saturated photosynthesis (Asat) and the stomatal conductance (gs) were measured, separately from the An-Ci curves, for 259 the saplings at the Paracou plots situated at the bottom valleys and hilltops. We used the methods described in protocol 2. For saplings, total phenolic concentration of the leaves was measured using an improved Folin-Ciolcalteu assay (Singleton 283 and Rossi, 1965;Marigo, 1973) and total leaf tannin concentration of the leaves was determined with the butanol/HCl method 284 (Porter et al., 1985) modified as in Makkar and Goodchild (1996). The extracts for both phenol and tannin concentrations were 285 determined using a Helios Alpha spectrophotometer (Thermo Spectronic, Cambridge, UK) at 760 and 550 nm, respectively. 286 Both methods are described in detail in Peñuelas et al. (2010). 287

Ancillary data 288
The vertical structure of a tropical rainforest is complex and multi-layered, resulting in great variation in light availability 289 within the canopy (Yoda, 1974). We assessed the light environment of each studied tree by visually estimating the canopy 290 light exposure or Dawkins' crown illumination index (Dawkins, 1958). This index describes a tree's light environment based 291 on a five-point scale ranging from (1) no direct light for suppressed trees to (5) crown fully illuminated for emergent trees 292 ( Figure 5). 293 Sampling height of the mature trees was measured with a Forestry Pro rangefinder (Nikon, Tokyo, Japan) by tree climbers 294 situated at sampling height pointing towards the soil. For saplings, we measured the total height of the tree using a measuring 295 tape, and additionally we measured the diameter at 10 cm and at 50 cm above surface level. On top of each sapling, we 296 measured leaf area index (LAI) with the LAI-2000 (LI-COR, Lincoln, NE, USA) during periods of overcast sky. 297 Herbivory rates, i.e. foliar damage by herbivores, of the saplings was estimated as punctual herbivory (%) according to Pirk 298 and Farji-Brener (2012). We visually assessed the missing area of the leaf and assigned each leaf to the following categories: 299 0, 0.1-5, 5.1-25, 25.1-50, 50.1-100 % area consumed. We calculated the percentage of foliar damage per sapling by multiplying 300 the number of leaves of each category by the mid-point foliar damage of each category (i.e. 0, 2.5, 15, 37.5, 75 % respectively) 301 and dividing this result by the total number of leaves per sapling. To test the accuracy of this method, we photographed 106 302 leaves we visually assessed and compared visual estimations of herbivory in the field using ImageJ. In 90% of the cases 303 categories were well assigned. 304

Vcmax of sunlit, upper canopy leaves 305
TBMs use plant functional types (PFT) to represent broad groupings of plant species that share similar characteristics (e.g. 306 growth form) and roles (e.g. photosynthetic pathway) in ecosystem function (Rogers et al., 2017). Although all TBMs share 307 this approach, they differ from each other in how narrow or broad the PFTs are defined. Depending on the TBM, tropical 308 rainforests belong to "broadleaf evergreen tropical tree", "tropical tree", "rainforest", "evergreen broadleaf tree" or "broadleaf 309 11 tree" (Figure 6A). Although our mean values of photosynthetic capacity of sunlit, upper canopy leaves are in line with those 310 of other tropical rainforest sites ( Figure 6B), many TBMs use estimates for Vcmax that are much higher than the estimates from 311 leaf-level measurements ( Figure 6A). Only three TBMs (Orchidee, O-CN, and Bethy), which have adopted detailed PFTs, are 312 within the range of our measurements. Some TBMs might obtain more accurate estimates of the global C budget by dividing 313 the adopted PFTs into more detailed PFTs. Hybrid, for example, uses the classification "rainforest", which includes both 314 temperate and tropical rainforest, leading to much higher Vcmax values. This is also true for the PFT "evergreen broadleaf tree" 315 used in CTEM and BIOME-BGC, and the PFT "broadleaf tree" used in "JULES". The combination of high temperature and humidity poses an additional hurdle as this generally makes physical exertion harder 325 than in temperate climates and, most importantly, decreases the longevity of most if not all electronic devices. Indeed, we 326 suffered from several Li-6400XT malfunctions, as well as defects of laptops, freezers and drying ovens. However, these defects 327 did not reduce the reliability of our data, but it required extra precautions and increased expenses. We tested, for example, the 328 Li-6400XT devices each morning and the devices were regularly cross-calibrated. Malfunctions of these devices led to 329 troubleshooting and extra testing before new measurements were carried out, which can be very time consuming and did have 330 an impact on the amount of data that could be gathered. Laptops and other electronic devices were best stored overnight in a 331 waterproof bag/barrel, whereas regularly moving them in and out of air-conditioned rooms increased malfunctions. 332 Additionally, access to power was limited and a portable generator was often required to carry out all photosynthesis 333 measurements. 334 The tropical soil is hard, making it very labour intensive to take soil cores and posing several other problems. Soil corers 335 deformed as they are not developed for tropical soil types and the installation of PRS probes without breaking them was very 336 challenging. Another challenge is reaching upper canopy leaves up to > 50 m height above ground level, which required 337 technical tree climbing skills and equipment from experienced tree climbers. 338 High species diversity and stand structural complexity of tropical forests are a major challenge to understand the ecosystem 339 functioning of tropical forests and force researchers to study either some abundant species following them in time or to take 340 into account the high diversity limiting the number of replicates per species. 341 values making these values immediately available to the modelling community. We provide leaf-level photosynthesis data at 356 several heights within the canopy from mature trees and saplings allowing to study differences in sunlit and shaded leaves. 357 Ancillary data such as herbivory and leaf phenol concentration can be of great value as additional data to other studies on these 358 topics. A large set of soil properties and nutrient availabilities in the soils underlying the studied trees were made available as 359 these data are highly relevant to understand how and why nutrient availability interacts with C uptake in tropical forests.

Competing interests 365
The authors declare that they have no conflict of interest. 366 Tables 539 Table 1