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https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. Phosphorus regulates fungal biomass production in a Norway spruce forest Juan Pablo Almeidaa, Lorenzo Menichettib, Alf Ekbladc, Nicholas P. Rosenstockd, & HåkanWallandera a Lund University, Microbial Ecology, Dept of Biology, SE-223 62 Lund, Sweden b Sveriges Lantbruksuniversitet (SLU), Department of Ecology, Ulls Väg 17, Uppsala, Sweden c School of Science and Technology, Örebro University, SE- 701 82, Örebro, Sweden d Center for Environmental and Climate Research, Lund University, SE-22362 Lund, Sweden Corresponding author: Juan Pablo Almeida, jpalmeidava@gmail.com Abstract 1 2 Ectomycorrhizal fungi (EMF) are important components of the soil microbial 3 communities and EMF biomass can potentially increase carbon (C) stocks by 4 accumulating in the soils as necromass and producing recalcitrant structures. EMF 5 growth depends on the C allocated belowground by the host trees and the nutrient 6 limitation on tree growth is expected to influence this allocation. Therefore, studying 7 EMF production and understanding the factors that regulates it in natural soils is 8 important to understand C cycling in forests. 9 10 Ingrowth meshbags are commonly used to estimate EMF production, but these 11 measurements might not reflect the total EMF production since turnover rates of the 12 hyphae are not considered. Here we estimated fungal production and turnover in 13 response to P fertilization in a Norway spruce forest where nitrogen (N) deposition 14 has resulted in phosphorus (P) limitation of plant production by using a combination 15 of meshbags with different incubation periods and with Bayesian inferences. To test 16 how localized patches of N and P influence EMF production and turnover we 17 amended some bags with a nitrogen source (methylene urea) or P source (apatite). 1 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 18 Additionally, the Bayesian model tested the effect of seasonality (time of meshbag 19 harvesting) on fungal production and turnover. 20 21 We found that turnover of EMF and was not affected by P fertilization or meshbag 22 amendment. P fertilization had a negative effect on EMF production in all the 23 meshbag amendments suggesting a reduced belowground C allocation to the 24 extramatrical mycelium under high P status. Apatite amendment significantly 25 increased EMF biomass production in comparison with the pure quartz bags in the 26 control plots but not in the P-fertilized plots. This indicates that P-rich patches 27 enhance EMF production in P limited forests, but not when P is not limiting. Urea 28 amendment had a general positive effect on EMF production, but this was 29 significantly reduced by P fertilization, suggesting that a decrease in EMF production 30 under high P status also will affect N foraging. Seasonality had a significant effect on 31 fungal production and the differences registered between the treatments were higher 32 during the warmer months and disappeared at the end of the growing season. 33 34 Many studies highlight the importance of N for regulating belowground C allocation 35 to EMF in northern coniferous forests, but here we show that the P status of the forest 36 can be equally important for belowground carbon allocation to EMF production in 37 areas with high N deposition. 38 39 40 Key words: Ectomycorrhizal fungi, fungal growth, fungal turnover, nitrogen deposition, phosphorus limitation, apatite, methylene urea, Bayesian inference. 41 42 43 2 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 44 1 Introduction: 45 In terrestrial ecosystems forest soils are important reservoirs for carbon (Falkowski et 46 al., 2000). Boreal forests contribute approximately 50% of the total forest carbon 47 stock from which around 85% is stored in the soil (Malhi et al., 1999). At least half of 48 the carbon stock in boreal soils originates from belowground carbon allocation 49 through roots (Clemmensen et al., 2013) and a large portion of boreal forest primary 50 production is allocated belowground by the trees (Gill & Finzi 2016). The carbon 51 dynamics in forest soils are highly dependent on the soil microbial communities that 52 either enhance C losses by degrading organic matter or increase C stocks by 53 immobilizing C (Clemmensen et al., 2013). Filamentous fungi forming mycorrhizal 54 associations for example, play an important role for C fluxes since some species have 55 the capability to degrade a great variety of organic compounds while others can 56 contribute to soil organic matter formation by releasing exudates that promote soil 57 aggregation (Rillig, 2005) or produce slowly decomposing and highly melanized 58 hydrophobic tissues (Almeida et al., 2022). The effect of EMF on soil microbial 59 communities might not be trivial since up to 20% of the net primary production is 60 allocated belowground to support the symbiosis (Hobbie, 2006). Therefore, 61 ectomycorrhizal mycelium is expected to be a significant part of the soil fungal 62 biomass and its production and turnover play an important role in forest carbon 63 cycling and organic matter formation (Ekblad et al., 2013). For that reason, the 64 development of methods that allows us to quantify EMF growth in forests natural 65 soils is of paramount importance (Fernandez, 2021) 66 67 Therefore, understanding the factors that regulate the growth rates of filamentous 68 fungi like EMF is important to understand carbon dynamics in soils. Growth rates of 3 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 69 free-living fungi from natural soils has been studied in laboratory by measuring 70 labeled acetate incorporated in the fungal membrane component ergosterol (Sheng et 71 al., 2022; Rousk and Bååth, 2007) or labeled water incorporated into DNA (Schwartz 72 et al., 2016). Quantifying growth (production) of EMF natural communities on the 73 other hand is more complicated since EMF are dependent on plant roots (Smith and 74 Read, 2008) and such measurements must be performed when the fungi is living in 75 symbiosis. Many studies have attempted to quantify EMF production in situ in forests 76 soils by using ingrowth meshbags and fungal biomarkers like ergosterol or PLFAs 77 (Wallander et al., 2013). In those studies, EMF production has been estimated based 78 on the standing fungal biomass measured in meshbags after a specific time of 79 incubation in the soil (Ekblad et al., 2013; Wallander et al., 2013; Wallander et al., 80 2001). However, the standing biomass does not necessary reflect growth since the 81 standing biomass is the result of the interaction between fungal growth and the 82 residence time of the fungal mycelium in the meshbag (Ekblad et al., 2016). In order 83 to overcome these shortcomings, some studies have estimated EMF production and 84 mycelium turnover by repeated harvests of mycelial meshbags, applying ergosterol as 85 a marker of mycelial biomass and mathematical models to estimate the production 86 and turnover of EMM biomass (Hagenbo et al., 2021; Hagenbo et al., 2017) or, 87 combined with analyses of chitin, to enable estimates of production and turnovers of 88 both bio- and necromass (Ekblad et al., 2016). In these studies, the standing biomass 89 and necromass were analyzed in bags incubated over periods varying in length, 90 combining several shorter periods, one after the other, with overlapping longer 91 periods. Common assumptions in these studies were that EMF growth occurs at a 92 constant rate and that biomass and necromass were lost at constant exponential rates 93 (Ekblad et al., 2016). 4 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 94 95 By using this approach, Ekblad et al. (2016) tested the effect of nitrogen (N) 96 fertilization on EMF turnover and growth in a Pinus taeda forest. They reported that 97 fertilization significantly decreased both fungal standing biomass and growth but 98 turnover rates of biomass and necromass were not affected. It was suggested that the 99 decrease in fungal growth was regulated by changes in carbon allocation as a result of 100 an increase in soil fertility. These results are in line with evidence indicating that the 101 relative amount of carbon allocated to EMF is sensitive to plant nutrient status and 102 soil fertility (Gill & Finzi 2016). Thus, in boreal forests where N is the nutrient that 103 limits tree growth (Högberg et al., 2017), high amounts of carbon are invested below 104 ground to support ectomycorrhizal symbiosis to facilitate N uptake (Gill & Finzi 105 2016). 106 107 The role of N as limiting nutrient in high latitude forested ecosystems and its effect on 108 EMF is well known and has been described in several studies (Binkley & Högberg, 109 2016; Hedwall et al., 2013 ; Gill & Finzi, 2016) . However, it has been suggested that 110 anthropogenic N deposition can potentially change the forests nutrient requirements 111 and push the system toward phosphorus (P) limitation (Tarvainen et al., 2016; Du & 112 Fang, 2014; Akselsson et al., 2010; Vitousek et al., 2010). In fact, in a region with 113 high N deposition in southwest Sweden, Almeida et al. (2019) reported that P 114 fertilization had a stronger effect on tree growth than N fertilization, subverting the 115 expectation that N is the main nutrient regulating plant growth in northern forests. The 116 effect of the transition from N to P limitation on the below ground C allocation and 117 EMF growth has not been studied in natural soils, but P deficiency is expected to 118 increase EFM biomass to improve P foraging and uptake (Rosenstock et al., 2016; 5 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 119 Ekblad et al. 1995; Wallander & Nylund 1992). In a field study, Rosenstock et al., 120 (2016) reported an increase in root- and ECM standing biomass in a Norway spruce 121 (Picea alba) forest limited by P compared to forests with sufficient P. In the field 122 study performed by Almeida et al. (2019) however, no effect on EMF standing 123 biomass was found in meshbags incubated for 133 days. Yet, since only the standing 124 biomass was measured and the turnover rates and production were not estimated, we 125 cannot exclude the possibility that P fertilization had an effect on EMF production, an 126 effect that cannot be detected by studying the standing biomass alone. 127 128 In this study, we aimed to improve our understanding of EMF production and 129 turnover in natural soils and to test how EMF production is affected when P is 130 limiting tree growth. In the forest described by Almeida et al. (2019) we estimated 131 EMF production and turnover using the mathematical model of Ekblad et al. (2016) 132 with Bayesian inferences. Since EMF production is likely to follow root growth 133 which varies with season (Coutts & Nicoll, 1990 ; Walker et al., 1986), we performed 134 a more extensive incubation scheme and more frequent harvests of bags than in 135 Ekblad et al., (2016). This allowed us to test the model considering the treatments 136 effects (P fertilization and meshbags amendments) and also considering their 137 interactions with seasonality (time of the growing season). Because EMF growth is 138 subsidized by the host, in exchange for N and P, EMF production should be affected 139 by the nutrients found at the hyphal front. Indeed, EMF biomass in P-poor forests is 140 stimulated around localized patches of the P-rich mineral apatite (Rosenstock et al., 141 2016; Berner et al., 2012; Hagerberg et al., 2003). Therefore, besides purely sand- 142 filled meshbags, we incubated meshbags amended with apatite or methylene urea 6 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 143 (referred as urea throughout the manuscript) in order to simulate soil N and P nutrient 144 patches respectively. 145 146 Our hypotheses were: 147 148 • P fertilization will decrease the biomass production of EMF mycelia. 149 • Apatite amendment will increase EMF biomass production in the control plots 150 151 152 but not in P fertilized plots. • Urea amendment will increase EMF biomass production in the P fertilized but not in the control plots. 153 154 2 Materials and Methods: 155 156 2.1 Field site and fertilization treatments 157 This study was performed at Tönnersjöheden forestry research station (56° 41’ N, 13° 158 6’ E, 80 m a.s.l.) with a mean annual temperature of 6.4 °C and a mean annual 159 precipitation of 1064 mm (Högberg et al., 2013). Soils are podzols developed in a 160 glaciofluvial parent material with a pH (in H2O) of 4.05 and a C/N of 25.1 in the mor 161 layer (Hansson, 2011; Högberg et al., 2013). The forests consist of managed Norway 162 spruce (Picea abies) planted on former pastureland in 1979. The site is in southwest 163 Sweden with an N deposition of 14.5 kg N-1 ha-1 yr-1 (Rosenqvist et al., 2007), which 164 is high in comparison with most other forests in the country (Akselsson, 2010; 165 Högberg et al., 2013). The experiment consisted of 6 plots (30-40 m x 25 m); 3 166 control and 3 fertilized with 200 kg P ha-1 of superphosphate (100 kg ha-1 applied 167 twice in September 2011 and July 2012). 7 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 168 2.2 Experimental design 169 To estimate EMF mycelial production, ingrowth meshbags (Wallander et al., 2001) 170 were incubated in the plots. The meshbags were cylindrical, 2 cm wide and 10 cm 171 long. They were made of 50 µm nylon mesh and filled with approximately 40 g of 172 quartz sand. Three different amendments in the meshbags were used: pure-quartz, 173 apatite-amended (quartz and 2% (w/w) crushed apatite mineral with a grain size of 50 174 to < 250 nm) and urea-amended (quartz and 0.5% (w/w) granulated methylene urea). 175 The mesh-bags were vertically installed into holes made with a soil corer (2 cm 176 diameter) with the upper end of the bag at level with the soil surface. 177 178 To calculate turnover rates and biomass production as done by Ekblad et al. (2016), 179 sequential meshbag incubations were performed. For a five-month period starting in 180 July 2015 and ending in November 2015, the meshbags were incubated for variable 181 periods of time (30, 60, 90, 120 or 150 days; Fig 1). 182 183 There were five different 30-day incubation periods. Four 60-day incubation periods 184 each overlapping with two 30-day incubation periods. Three 90-day incubation 185 periods each overlapping with three 30-day incubation periods. Two 120-day 186 incubation periods each overlapping with four 30-day incubation periods. One 150- 187 day incubation period overlapping with all 30-day incubation periods. 188 The bags incubated over 30 days were incubated sequentially and when one set of 189 bags was collected, a new set of bags was directly installed using the same holes as 190 the ones just emptied (Fig 1). 191 In each plot, a pure-quartz meshbag for each of the incubation periods described 192 above was placed along a 15 m long transect. The distance between each meshbag 8 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 193 was approximately 1.5 m. The apatite-amended and urea-amended bags were placed 194 10 cm (perpendicular to the long transect) at each side of the quartz meshbags. Three 195 15 m long transects were done to have three sub-replicates (for each set of bags) that 196 were pooled before further analysis to give one sample from each incubation period 197 and amendment (quartz, apatite and urea) per plot. 198 199 Each incubation period consisted of 54 meshbags (2 treatments C/P, 3 replicated 200 plots, three sub-replicates, three amendments (2 x 3 x 3 x 3 =54). In total, 810 201 meshbags were installed and collected according to their incubation period. 202 203 July August September October November 204 205 206 207 208 209 210 211 212 213 214 215 216 217 Figure 1: Overview of the incubation design. Different color bars represent the incubation time periods: Yellow corresponds to 30 days, Light green to 60 days, Dark green to 90 days, Purple to 120 days and Blue to 150 days of incubation. The arrows represent the points in time when the same holes from the previous incubation were used to incubate the next set of meshbags. 218 219 Upon harvest, the meshbags were kept in an icebox until arrival to the laboratory 220 where they were stored at -20oC. 9 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 221 The fungal cell membrane compound ergosterol, a proxy for fungal biomass, was 222 extracted and measured from 5 g of the pooled samples as per Bahr et al. (2013) 223 using high-pressure liquid chromatography (auto sampler L2130 with UV detector 224 L2400 by Hitachi, Japan). The fungal biomass was then expressed as µg of ergosterol 225 per gram of sand in the meshbag. 226 227 2.3 Mathematical models 228 The turnover rates and fungal biomass production were estimated applying the 229 mathematical model used in Ekblad et al. (2016). In this paper however the 230 mathematical model was tested under two assumptions: 231 Fungal production was dependent on the treatments alone (Model 1), or fungal 232 production was depended on treatments and sampling season (Model 2), allowing to 233 test for the interactions between treatment and seasonal effects. 234 235 Model 1: 236 237 This model works under the assumption that EMF production occurs at a constant rate 238 and that biomass is lost at a constant exponential rate (see Hagenbo et al., 2017 & 239 Ekblad et al., 2016). Briefly, the sum of the biomass during two sequential short 240 incubation periods is expected to exceed the biomass in an overlapping longer 241 incubation period due to an on average older mycelium and hence larger turnover in 242 bags with a longer incubation period. 10 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 243 244 The model in its differential form is defined as: 245 𝑑𝐵 =𝑃− 𝜇∙𝐵 𝑑𝑡 246 247 248 Equation 1 249 Where 𝑃 is the production of new mycelium (in mass units), 𝐵 is the mycelium 250 biomass (also in mass units) and 𝜇 represent the mortality, the fraction dying over a 251 specified time-period (adimensional). This equation is solved over time as: 252 253 Equation 2 𝐵(𝑡) = 254 𝑃! ∙ (1 − 𝑒 "! # ) 𝜇! 255 In our case we assumed that both 𝑃! and 𝜇! are influenced by the fertilization 256 treatments, denoted here by 𝑘, and we therefore assigned a specific (unknown) P and 257 𝜇 to each treatment in the Bayesian model. 258 259 Model 2: 260 261 Equation 2 has been utilized in other publications (Hagenbo et al. 2021; Hagenbo et 262 al. 2017; Ekblad et al., 2016) and one of the main assumptions of this model is that 263 fungal production occurs at a constant rate. However, fungal production can vary 264 depending on the time of the year (Coutts & Nicoll, 1990 ; Walker et al., 1986) so we 11 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 265 tested a modification of the model by introducing an additional degree of freedom 266 into the model represented by the term 𝛽!,% , dependent on sampling seasons ( 𝑗) and 267 their interactions with treatments (𝑘) so that the calibration can apply to each 268 treatment a correction for seasonality (independent from the other treatments). When 269 the term 𝛽!,% = 1 then the model is equivalent to what described in eq. 1 and 2. We 270 utilized this model to decompose 𝑃 in two components, defining a new term 𝑃′: 271 272 273 Equation 3 𝑃′!,% = 𝑃0! ∙ 𝛽!,% 274 275 𝑃′!,% corresponds to 𝑃! (if the distributions were perfectly symmetric the average for P 276 and P´ should converge to the same value) but the predicted biomass production now 277 is the results from the interactions between sampling season and treatments. 278 279 Eq. 3 is then substituted into Eq. 2 by substituting 𝑃 with 𝑃′. The resulting model is 280 equivalent to the one described by Eq. 2 for certain parameter combinations and 281 describes the same curve. The only difference is that now two components are used to 282 decompose the variance explained by the calibrated model in two separate terms: 𝑃0! 283 which expresses the production variable with treatments only (𝑘); and 𝛽!,% which 284 expresses the effects of seasonality and their interactions with treatments. 𝑃0! is now 285 equivalent to the production normalized by the seasonality effect 286 and 𝛽!,% vary independently (therefore describing each point as a combination of k 287 and j) we avoid to make any strong assumption on the effect of seasonality (since we 288 are not imposing a parametric function of time to describe it but we let it free to vary &'!,# (!,# . By letting 𝑃0! 12 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 289 for each time point) or on its interactions with treatments (which are still free to vary 290 depending on the treatment), while on the other end we maximize the information we 291 can extract from the data by representing the interactions between the terms in one 292 single model calibration. If we instead relied on fully independent calibrations within 293 each subset of seasons × treatments we would have had to divide the data in 𝑗 × 𝑘 294 subsets where we would calibrate each model parameter independently, limiting each 295 calibration to a smaller number of samples. 296 2.4 The calibration: 297 The model was calibrated within a formal Bayesian framework, developed with the 298 Stan toolbox (Stan Development Team, 2021). This approach is based on a numerical 299 implementation of Bayesian statistics, which allows for a continuous update of the 300 knowledge while new data are developed, based on stochastic principles (through a 301 modification of the Metropolis-Hastings sampler). While we refer to relative 302 publications for technical details, the main assets of the method are that: a) we can 303 integrate and utilize previous information in the calibration, defining it as prior 304 probability distributions of model parameters (from now on, “priors), b) such 305 information is combined with the statistical information contained in the data to 306 determine the posterior distributions of model parameters and consequently 307 predictions, and such distribution is non-parametric (so not assuming any specific 308 shape but determined only by the available information). The methodology is 309 therefore extremely useful to combine multiple sources of information and very 310 valuable when information is scarce, and at the same time quite robust given that it 311 estimates detailed posterior probability distributions (which can be examined closely). 312 13 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 313 In our case the methodology allows us to draw information from publications. This 314 information is considered probabilistically. It does add information to our final results 315 (our posterior distributions), but such information is combined with the information 316 contained in our data. The chosen statistical approach updates the old information 317 with new data, and old and new information can be therefore compared. 318 319 We calibrated both a model with only Eq. 2 (so considering only treatment effects; 320 Model 1) and one considering Eq. 2 and Eq. 3 (considering treatments × seasonality 321 effects; Model 2). 322 Priors for 𝑃! and 𝜇! were derived from the literature, both expressed as normal 323 distributions with deviation prudentially estimated as 25% of the mean (please note 324 that this does not mean that the prior was limited within this range, due to the tails of 325 the normal distributions). 326 𝑃! was expressed as 𝑃! ∼ 𝑁(0.099, 0.099 ∙ 0.25) 327 328 329 While 𝜇! as 𝜇! ∼ 𝑁(0.009, 0.009 ∙ 0.25) 330 331 332 Both priors were based on the mean fungal biomass production and turnover for forest 333 of similar age as the forest in the current study estimated by Hagenbo et al. (2017) 334 after unit conversion. The Bayesian system was run considering one independent 𝑃! 335 and 𝜇! for each treatment. 336 14 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 337 When we also considered Eq. 3, priors for 𝑃0! were defined as the priors for 𝑃! while 338 priors for 𝛽% were set as uniform between 0 and 5. 339 𝛽 ∼ 𝑈(0,5) 340 Please note that 𝛽% = 1 means no seasonality effect, 𝛽% = 5 means a five-fold increase 341 of production due to seasonality, while 𝛽% = 0 means a complete halt of production 342 due to seasonal effect. 343 344 2.5 Statistical analysis and probability distribution comparisons 345 The standing biomass, data was tested for homogeneity of variances and normal 346 distribution using Levene’s and Shapiro Wilk tests, respectively. Analysis of the 347 variances (ANOVA), Tukey’s Post-hoc test and Dunn analyses were performed on the 348 data to check for statistical differences between the fertilization treatments and 349 meshbag amendments. The Levene’s and Shapiro Wilk tests, as well as ANOVA and 350 Dunn analyses were done by using R (R Core Team, 2014). 351 352 The stochastic approach of the Bayesian method produces Markov chains Monte 353 Carlo (MCMC) that represents a probability distribution with as many discrete 354 parameter values as iterations in the chains (in our case 10 independent chains of 355 10000 iterations, so a total of 100000 iterations), with a histogram that approximates a 356 continuous distribution (probability distribution). Thus, the predicted fungal 357 production and turnover for each treatment (fertilization regime and meshbag 358 amendment) is represented by a probability distribution. 359 360 The means of the probability distributions were calculated and the highest density 361 intervals of the estimated parameters were interpreted as confidence intervals at 95% 15 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 362 and 90% (Kruschke and Liddel, 2018). To test the significance of the treatments 363 (fertilization regime, meshbag amendment and season), the confidence intervals of the 364 probability distributions were compared. 365 3 Results: 366 367 3.1 Mycelial standing biomass 368 The standing biomass of mycelia in the meshbags was significantly affected by 369 incubation period (time of the year) (Kruskal-Wallis, p < 0.0001, X2 = 116.4). 370 Biomass in one-month incubation mesh bags from July, August and September was 371 significantly higher than the biomass collected in October and November for both 372 control plots and P fertilized plots (Dunn´s test, p < 0.001, X2 = 26.1) (Fig 2). 373 Biomass in two-months incubation mesh bags from July-August and August- 374 September was significantly higher than the biomass collected in September-October 375 and October-November for both control plots and P fertilized plots (Dunn´s test, p < 376 0.001, X2 = 27.7; Fig 2). Fertilization significantly affected the standing biomass in 377 the quartz, apatite and urea-amended meshbags (Kruskal-Wallis, p < 0.05, X2 = 6.5; p 378 < 0.0001, X2 = 18; p < 0.0001, X2 = 15.5; respectively). Phosphorus fertilization 379 reduced the standing biomass in all the incubation times (numbers of incubation days) 380 for apatite urea and amended meshbags (Fig 3). Apatite amendment significantly 381 increased the standing biomass in comparison with the pure-quartz bags in the control 382 plots after 60 and 150 days of incubation (Dunn´s test, p < 0.05, X2 = 18; p < 0.05, X2 383 = 11.2, respectively), and the effect of apatite was stronger after 150 days of 384 incubation where on average the biomass in the apatite bags was three-fold higher 385 than the biomass in the pure-quartz bags. Apatite amendment did not increase 386 biomass in the P-fertilized plots in any incubation time while urea amendment 16 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 387 increased biomass in most of the incubation times and for both C and P fertilized plots 388 (Dunn´s test, p < 0.05) (Fig 3). Control Control 0.5 0.5 a 0.4 0.4 ab 0.3 0.2 0.1 0.1 0.0 0.5 0.0 0.5 a ab a 0.2 b 0.2 0.1 0.0 0.5 0.0 0.5 a 0.4 ab b ab 0.2 0.1 a ab ab b ab a ab b b a 0.3 0.2 0.1 0.0 0.0 Phosphorus Phosphorus 0.5 0.5 0.4 0.4 b 0.1 a a a a ab 0.2 b b 0.1 ab ab b b quartz a 0.3 0.3 0.2 a 0.1 0.0 0.5 0.0 0.5 a 0.4 b 0.3 0.2 urea ab urea ab 0.3 0.1 a a a a a 0.1 r ov em N ob ct O em pt be er r be t us 2 FW RE HU Se Uí EH HP SW 6H Au g be í1 ov e m FW R 2 HP SW 6H Wí XV AX J r U EH U EH us t JX O\ íA ug Ju ly 0.0 0.0 390 391 392 393 394 395 a 0.4 0.4 0.2 389 0.2 0.0 0.5 0.0 0.5 0.4 0.3 0.1 quartz Standing biomass (ergosterol ug per g) b apatite ab apatite a 0.3 0.2 urea b urea 0.3 b 0.3 0.1 0.4 ab quartz 0.3 ab 0.4 0.4 quartz Standing biomass (ergosterol ug per g) b 0.2 a apatite ab apatite 0.3 Figure 2: Boxplot of the standing fungal biomass in the meshbags incubated in the soil for 2 and 1 months. The boxes represent the interquartile range of the data (The central represents the median). Higher and lower whiskers represent minimum and maximum range of the data (1.5 times the length of the interquartile range). Lowercase letters represents statistically significant (P<0.05) differences between the incubation periods according to Dunn´s test. 396 397 17 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 398 399 400 Figure 3: Standing fungal biomass in the three meshbags amendments (quartz-only, apatite and urea) and 401 in the control plots (red symbols) and P-fertilized plots (blue symbols) and control plots during different 402 incubation times (30, 60, 90, 120 and 150 days). The error bars represent the standard error of the mean. 403 404 3.2 Fungal production and turnover rates (Model 1) 405 The predicted fungal biomass production varied between the P-fertilized plots and the 406 control plots and between the meshbag amendments (Fig 4a). P fertilization 407 significantly decreased fungal production in all the meshbag amendments (urea and 408 apatite and quartz) (Table 1). In the P-fertilized plots the fungal production was 409 reduced to a third in the apatite and pure quartz bags in comparison with the prior 410 used to set the model (0.099 g m2 day-1). P fertilization caused a reduction on average 18 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 411 of 43% in the quartz bags, 60% in the apatite bags and 39% in the urea bags in 412 comparison with the control plots. 413 414 The meshbags amended with urea had the highest predicted biomass production in 415 both control and P-fertilized plots (Fig 4). Relative to the quartz bags, the urea 416 amendment doubled the production in both fertilizer treatments. The apatite 417 amendment, in contrast, gave no significant change in production relative to the 418 quartz bags in the P-fertilized plots while a 35% increase was found relative to the 419 quartz bags in the Control plots (Table 1). 420 421 According to the mathematical modeling, the biomass turnover rates were not affected 422 by P fertilization or meshbag amendment (Fig 4 b). 19 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 423 424 425 426 427 428 Figure 4: a) Probability distribution of the predicted fungal biomass production (Pk) (g m2 day-1) for the different fertilizer treatments (Control and P fertilization) and meshbag amendments (quartz-only, apatite and urea). b) Probability distribution of the turnover rates (day-1) for the different fertilizer treatments (Control and P fertilization) and meshbag amendments (quartz-only, apatite or urea). 429 430 431 20 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 432 Table 1. Mean of the fungal production in different treatments (Pk) estimated by Model 1. The Highest 433 Density Intervals (HDI, Kurshke and Liddel, 2018) represent the boundaries of each estimate at 434 different degrees of confidence. Fertilization and Mean fungal HDI low HDI high HDI low HDI high amendment production (g m2 day-1) (95%) (95%) (90%) (90%) control/apatite 0.094 0.072 0.117 0.075 0.113 control/urea 0.129 0.103 0.156 0.107 0.152 control/quartz 0.061 0.045 0.079 0.047 0.076 phosphorous/apatite 0.038 0.028 0.05 0.029 0.048 phosphorous/urea 0.079 0.059 0.1 0.062 0.096 phosphorous/quartz 0.035 0.026 0.045 0.027 0.043 435 436 437 3.3 Seasonal effect (Model 2) 438 The effect of seasonality as described by β had a positive effect on the predicted 439 fungal production and this effect was highest in July and decreased over time. 440 Moreover, the effect of β on fungal production differed depending on the fertilization 441 and on the meshbag amendment (Fig 5). 442 443 For example, in July the model suggests a seasonal effect increasing the predicted 444 fungal production by up to 5 times in the quartz meshbags from the P-fertilized plots 445 and up to 2.5 times in the urea meshbags in the control plots in comparison with the 446 apatite bags from the P-fertilized plots where season had no effect on fungal 447 production. The positive effect of sampling season on the fungal production, as 448 identified by the model, decreased in general with time and at the end of the growing 449 season (October and November) 𝛽 had the same effect on all the samples 450 independently from the treatment (fertilization and meshbag amendment). 21 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 451 Even though the 𝛽 probability distributions of the different treatments were not 452 significantly different, the effect of the season on biomass production was important 453 and when we decompose fungal production by seasonality (P´k), the differences in 454 fungal production between P fertilized and control plots and between the meshbag 455 amendments are present only early in the season (July, August) and disappear in 456 September October and November (Fig 6). 457 458 459 Figure 5: Seasonality effect on biomass production expressed by the 𝛽 parameter for the different months of the growing season. 22 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 460 461 Figure 6: Probability distribution of P´k (g m2 day-1) for the different months of the growing season. 462 463 464 465 466 467 468 469 470 23 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 471 4 Discussion: 472 473 4.1 Effect of P fertilization on fungal biomass production and turnover 474 In support of our first hypothesis, fungal biomass production declined in response to P 475 fertilization in all meshbag amendments (Fig 4a). These results contrast with those of 476 Almeida et al. (2018) who tested the effect of P fertilization on the fungal standing 477 biomass in the same plots as in the present study. This contrast is not depending on 478 variation in turnover rates between control and P fertilized plots since mortality was 479 not significantly affected by fertilization as shown in the current results. In the present 480 study, P had a negative effect on the fungal standing biomass in most of the 481 incubation periods (Fig 3). The fact that more incubation periods and a larger number 482 of bags were used makes the present study more reliable. Thus, the standing biomass 483 of one given incubation time might not truly reflect the effect of fertilization on fungal 484 growth. The use of the sequential incubation method and the mathematical model 485 allowed us to have a more robust estimate of the effect of P fertilization on the 486 extramatrical mycelium in this forest. P as a nutrient regulating fungal growth in 487 boreal forest was not reported before. 488 489 Fertilization experiments have been largely used to evaluate the effect of soil fertility 490 and nutrient status of the trees on carbon allocation and EMF production (Bahr et al., 491 2015; Ekblad et al., 2013). However, studies on the effect of nutrient additions on 492 EMF in boreal forests have predominantly focused on N fertilization (Leppälammi- 493 Kujansu et al., 2013) probably because N is the most common limiting nutrient in 494 boreal forests (Högberg et al., 2017). Therefore, the effects of P additions alone on 495 boreal forests have not been widely tested. Due to the steep increase in anthropogenic 24 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 496 C and N inputs relative to P inputs, plant nutrient stoichiometry can be altered and 497 lead to unbalanced nutrition and lead to P limitation (Jonard et al., 2015; Peñuelas et 498 al., 2013). Indeed, P fertilization enhanced tree growth in the forest where this study 499 was performed as reported by Almeida et al. (2019). 500 Belowground carbon allocation is expected to be reduced by P fertilization when the 501 system is P limited (Gower & Vitousek 1989; Keith et al. 1997) leading to a decrease 502 in EMM production (Treseder, 2004). We propose that the decreased fungal 503 production in the P-fertilized plots in our study is a result of a decrease in 504 belowground C allocation due to alleviated P limitation that reduced tree dependency 505 on EMF for P foraging and acquisition. 506 507 This reduction in fungal production was not trivial and P fertilization decreased the 508 predicted fungal production to a third in comparison with the fungal production of a 509 forest of similar age estimated by Hagenbo et al. (2017) (0.099 g m2 day-1). More 510 studies on the effect of P fertilization alone in northern forested ecosystems receiving 511 high levels of N deposition should be performed to test if P-limitation is widespread 512 in these ecosystems as reported in this single forest. 513 514 A decrease in EMF production caused by fertilization might reflect a change in the 515 fungal communities. When there is a decrease in belowground C allocation, some 516 EMF species that require less C for growth and produce lower biomass relative to 517 other members of the community might be selected. In the previous study in the same 518 research forest (Almeida et al., 2019), EMF fungal communities from soil and 519 meshbag samples significantly changed after P fertilization and P + N fertilization 520 respectively. In particular, the most abundant EMF species Tylospora asterophora 25 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 521 increased when the plots were fertilized with P or P + N. Tylospora asterophora, a 522 short exploration type (Agerer & Raidl, 2004), is expected to produce less biomass 523 than species with long exploration mycelia. Therefore, it is possible than an increase 524 of this species relative abundance in the meshbags of the present study might be 525 related to the lower growth detected in the P fertilized plots. It is also expected that 526 turnover rates vary depending on the species traits of the EMF community (Ekblad et 527 al., 2016). For example, certain traits like rhizomorphs are expected to have longer 528 life span in comparison with smooth and short exploration type mycelium (Pritchard 529 et al., 2008; Ekblad et al., 2016). The significant increase of T. asterophora after P 530 fertilization could increase the overall mycelial turnover rate in these. However, there 531 was not a detectable effect on the turnover rates between control and P fertilized plots. 532 In a tree age chronosequence study in a boreal forest in central Sweden, Hagenbo et 533 al. (2018) reported no clear pattern in exploration types despite a significant shift in 534 fungal community composition and turnover with forest age. This suggests that 535 factors other than exploration types are also important to explain turnover rates. 536 Species-specific traits like mycelial life span, the degree of internal autolysis and the 537 amount of melanin in cell walls could potentially affect biomass turnover in EMF 538 communities (Hagenbo et al., 2018; Fernandez et al., 2013). 539 540 4.2 Effect of nutrient amendment on biomass production and turnover 541 Both nutrient amendments (urea and apatite) increased EMF production in 542 comparison with the quartz-only meshbags in the control plots. This is consistent with 543 mesocosm experiments that have shown that when organic (Wallander & Pallon, 544 2005; Leake et al., 2001; Bending & Read 1995 ) and mineral nutrient patches (Smits 545 et al., 2012 & Leake et al., 2008) are colonized by EMF, mycelial branching and 26 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 546 proliferation increase to explore the nutrient patch. In support of our hypothesis, 547 apatite amendment increased EMF production in comparison with the pure quartz 548 bags but only in the control plots. Our results are consistent with the view that trees in 549 the control plots are P limited, and that they allocate more resources to the EMF when 550 exploring a P source like apatite. When P limitation is alleviated by fertilization 551 however, there is probably a decrease in C allocation to the root symbionts which 552 could cause the reduced EMF colonization in the apatite bags. This is supported by 553 other studies reporting that apatite amendment increases EMF standing biomass in 554 meshbags under P-poor conditions (Rosenstock et al., 2016; Berner et al., 2012; Hedh 555 et al., 2008; Hagerberg et al., 2003). In a fertilization study in nearby plots in the same 556 forest, Bahr et al., (2015) showed that apatite addition stimulated EMF standing 557 biomass in mesh bags, in control and in N-fertilized plots, but when N was added in 558 combination with P, on the other hand, no significant differences were found between 559 apatite amended and pure-quartz bags. All together these results provide evidence that 560 EMF growth is responsive to P nutrient patches, but this response is depended on the 561 P demand of the host. 562 563 From the two nutrient amendments, urea had the highest effect on fungal growth and 564 both in the control and P-fertilized plots. From a phytocentric point of view it could 565 be expected that EMF growing on a P rich source like apatite are rewarded with more 566 C from the P limited trees than EMF colonizing N bags. The stronger response of 567 EMF growth to the N nutrient patches than to P nutrient patches in the P-limited 568 control plots suggests that even though the forest is limited by P, N still has an 569 important effect on the growth of EMM. 570 27 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 571 It is possible that P limitation results in a general increase in C allocation to the root 572 symbionts and the C invested by the tree is delivered indiscriminately among its 573 fungal symbionts, independently of the nutrient patch they are colonizing. 574 Probably this is not surprising since N is needed by fungus and plant alike and in 575 order to produce biomass to forage for P and enzymes to mineralize it, EMF requires 576 N. Thus, N uptake can improve the P nutrition of the mycorrhizal system and positive 577 feedback between plant and fungus might happen. 578 579 Despite the strong effect of N patches on fungal growth, P fertilization decreased 580 growth in all meshbags independent of the amendment. EMF communities in forests 581 are diverse and composed of species with different abilities to mineralize the different 582 nutrients present in the soils (Lilleskov et al., 2011). By amending the meshbags with 583 different nutrient types, fungal communities are selected depending on the nutrient 584 added (Almeida et al., 2019; Rosenstock et al., 2016). The consistent effect of P 585 fertilization on both nutrient patches and even in the barren quartz-only bags suggests 586 that P fertilization affects growth of different EMF communities alike and reduces 587 nutrient foraging for both N and P. This is consistent with the idea that alleviated P 588 limitation results in a general decrease of C delivered to the roots and the mycorrhizal 589 symbionts. 590 591 Previous studies on EMF growth have focused on fungal biomass collected from 592 meshbags filled with acid washed sand (see Hagenbo et al. 2021; Hagenbo et al. 2017; 593 Ekblad et al 2016). However, since the pure quartz mesh bags are devoid of nutrients 594 (except probably for dissolved organic material entering the bags during incubation), 595 they might underestimate EMF production in soils. Moreover, in soils most of N and 28 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 596 P are heterogeneously distributed in nutrient patches (Hodge, 2006). For this reason, 597 amending the meshbags made possible to imitate the soil nutrient conditions that 598 influence EMF growth in forests and to understand how the nutrient regimes (both as 599 inorganic nutrient fertilization and as nutrient patches) affect EMF production. In fact, 600 the EMF growth in this study was influenced both by the nutrient at the hyphal front 601 (N and P amendment) and by the C provided by the roots (as shown by the effect of P 602 fertilization). 603 604 There were not differences in mycelium turnover between the different meshbag 605 amendments. This contrast with previous studies showing that the nature of a nutrient 606 patch could also affect hyphal turnover (Ekblad et al., 2013; Jansa et al., 2011). 607 Mineral substrates like feldspar have been shown to maintain fungal growth for up to 608 15 weeks (Rosling et al., 2004), while organic nutrient patches have been shown to 609 sustain fungal growth for around 5 weeks (Bending & Read 1995). Therefore, organic 610 substrates like urea are expected to be quickly depleted in soils. As a result, the EMF 611 hyphae is expected to autolyse and transfer the nutrients to other locations of the 612 exploring mycelium faster than during the slow weathering of mineral substrates like 613 apatite (Ekblad et al., 2013 ; Jansa et al., 2011). Therefore, it should be expected that 614 the apatite bags show lower turnover rates than the urea bags. In the present study 615 however, we could not detect differences between the two nutrient patches. The 616 material used to amend the urea meshbags in this study is methyleneurea which is a 617 slow N release molecule. Thus, methylene urea is hydrolyzed to ammonium at a 618 slower rate than the urea molecules (Högberg et al., 2020). Therefore, even if there is 619 evidence that some EMF species can directly consume urea (Morel et al., 2008; 29 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 620 Yamanaka, 1999), these slow releasing nutrient sources might require a more 621 persistent mycelium than other organic sources. 622 623 Additionally, previous mesocosm experiments have shown that when EMF mycelium 624 grows on sand, longevity is enhanced in comparison with EMF growing on nutrient 625 patches (Wallander & Pallon 2005). Nutrient patches enhance growth and metabolic 626 activity of EMF, which may enhance turnover rates. For example, Bidartondo et al. 627 (2001) tested ectomycorrhizal growth response to apatite and ammonium in growth 628 chambers with EMF colonized Pinus muricata seedlings. It was found that apatite 629 and ammonium addition increased the respiration rates of EMF, which could be taken 630 as an indication of higher metabolic activity and probably higher mortality. Thus, it 631 can be expected that EMF growing on the quartz bags have lower turnover than the 632 mycelium colonizing the nutrient amendments, but this was not the case in this study. 633 These discrepancies relating EMF turnover rates between the current and previous 634 studies might be caused by shortcomings on the sequential incubation method used 635 for the model in this paper. This method relies on the premise that the sum of the 636 biomass from meshbags incubated for short continuous periods should exceed the 637 biomass from meshbags incubated from a long incubation time. However, in a 638 number of cases the mycelial biomass from a long incubation period was greater than 639 the sum of the consecutive shorter intervals. This could be caused by a delay or a lag 640 phase in fungal colonization inside the bags. It is possible that when a meshbag was 641 collected and the same hole was used to replace a new bag (Fig 2) there was a lag 642 phase before the hyphae could colonize the newly placed meshbag (Wallander et al., 643 2013). Thus, those data points could have created noise in the data making the 644 turnover estimates less robust. In any case, if turnover in the EMF communities 30 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 645 colonizing the nutrient amended bags is higher (as suggested by previous studies), and 646 was underestimated in the current study, then the high standing biomass measured in 647 the urea and apatite bags can only be explained by even higher EMF production than 648 the predicted in these results. 649 650 4.3 Seasonal effects on fungal growth 651 The general assumption of Model 1 is that fungal growth occurs at a constant rate. 652 However, this approximation has some limitations, since seasonality usually affects 653 the amount of C allocated to the roots (Coutts & Nicoll, 1990) and consequently EMF 654 root colonization (Walker et al., 1986). Indeed, the standing fungal biomass in the 655 mesh bags peaked in July and decreased over autumn (Fig 2). In this paper Model 2 656 allowed the predicted fungal growth to vary both with seasonality and with the 657 treatments (P fertilization and meshbag amendment). The introduction of these 658 different dependencies in the model allowed us to test for the interactions between 659 treatment and seasonal effects. It must be noted that the predicted fungal growth 660 resulting from Model 1 is not incorrect and truly reflects the fungal growth 661 differences between the treatments. However, by including seasonality in Model 2, we 662 could detect that those differences predicted earlier were highly dependent on the 663 season. Indeed, fungal growth not only increased early in the season, but the 664 magnitude of this increase depended on the treatments (Fig 5). Therefore, the 665 differences in biomass production between the fertilization regime and meshbag 666 amendments were significant only early in the season (Fig 6). 667 668 The fungal biomass seasonal peak reported in the current paper contrasts with 669 previous studies that have reported that the standing biomass in meshbags collected 31 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 670 from a Pinus sylvestris (Hagenbo et al., 2021; Wallander et al., 2001), Pinus pinaster 671 (Hagenbo et al., 2021) and Picea albies (Wallander et al., 2001) forests was higher 672 during the autumn season. However, in a study performed in the same experimental 673 area as the present study, Wallander et al. (2013) found that the standing biomass in 674 September-October incubations was lower than the standing biomass in July-August 675 incubations. It has been reported that different EMF species have different seasonal 676 peaks (Castaño et al., 2017; Iotti et al., 2014; De la Varga et al., 2013) which could 677 explain the differences in fungal growth between previous studies and the current 678 experiment. Our results are also consistent with those from Coutts & Nicoll (1990) 679 who found that the mycelium extension of Laccaria proxima and Telephora terrestris 680 inoculated in Picea sitchensis peaked during July and decreased in autumn. The 681 mycelial extension was associated with soil temperature, which peaked early in the 682 growing season. 683 684 It could be also possible that non-mycorrhizal fungi had an important contribution to 685 the fungal growth detected in the current study. The meshbag system favors the 686 growth of EMF over non-mycorrhizal fungi as it has been shown in some studies 687 (Almeida et al., 2018; Rosenstock et al., 2016; Berner et al., 2012) which might 688 suggest that fungal growth in this study is influenced mostly by EMF. However, it has 689 been shown that the shorter the time period a meshbag remains underground the 690 higher the proportion of non-mycorrhizal fungi inside the bags (as measured by the 691 proportion of non-mycorrhizal DNA in Hagenbo et al., 2018). Non-mycorrhizal fungi 692 growth has been reported to respond positively to temperature (Pietikäinen et al., 693 2005) which might imply that during the warmer months of July and August 694 filamentous non-mycorrhizal fungi growth was promoted and there was a higher 32 https://doi.org/10.5194/bg-2022-165 Preprint. Discussion started: 20 September 2022 c Author(s) 2022. CC BY 4.0 License. 695 colonization of this fungal guild inside the meshbags. Even so, the effects of the P 696 fertilization and meshbag amendment on fungal growth were higher early in the 697 season which might imply that the seasonal effect seen in the current study is 698 explained mostly by EMF as it was discussed previously. 699 700 In conclusion, EMF production was strongly reduced when P was added to the 701 forests, suggesting a decline in belowground C allocated by the trees when the P 702 limitation was alleviated. This decline affected not only the foraging for P (apatite) 703 but also foraging for N (urea). The strong negative effect of P fertilization on EMF 704 production suggests a central role of P in regulating EMF biomass production in N 705 rich forests. 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