Electrical information flows across the sporocarps of two ectomycorrhizal fungi in the field - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Sci Rep . 2026 Mar 6;16:12397. doi: 10.1038/s41598-026-42673-y Search in PMC Search in PubMed View in NLM Catalog Add to search Electrical information flows across the sporocarps of two ectomycorrhizal fungi in the field Yu Fukasawa Yu Fukasawa 1 Graduate School of Agricultural Science, Tohoku University, Yomogida, Naruko, 232-3, 989-6711 Osaki, Miyagi Japan Find articles by Yu Fukasawa 1, ✉ , Daisuke Akai Daisuke Akai 2 School of Engineering, Tohoku University, 6-6 Aramaki Aza Aoba, Aoba-ku, Sendai, 980-8579 Miyagi Japan Find articles by Daisuke Akai 2 , Takayuki Takehi Takayuki Takehi 3 National Institute of Technology, Nagaoka College, 888 Nishi- Katakaimachi, Nagaoka, 940-0817 Niigata Japan Find articles by Takayuki Takehi 3 , Daiki Takahashi Daiki Takahashi 4 Faculity of Agriculture, Kyushu University, 744 Motooka, Fukuoka, 819- 0395 Japan Find articles by Daiki Takahashi 4 , Yutaka Osada Yutaka Osada 5 Advanced Institute for Marine Ecosystem Change, Tohoku University, 6-6 Aramaki Aza Aoba, Aoba-ku, Sendai, 980-8578 Miyagi Japan Find articles by Yutaka Osada 5 Author information Article notes Copyright and License information 1 Graduate School of Agricultural Science, Tohoku University, Yomogida, Naruko, 232-3, 989-6711 Osaki, Miyagi Japan 2 School of Engineering, Tohoku University, 6-6 Aramaki Aza Aoba, Aoba-ku, Sendai, 980-8579 Miyagi Japan 3 National Institute of Technology, Nagaoka College, 888 Nishi- Katakaimachi, Nagaoka, 940-0817 Niigata Japan 4 Faculity of Agriculture, Kyushu University, 744 Motooka, Fukuoka, 819- 0395 Japan 5 Advanced Institute for Marine Ecosystem Change, Tohoku University, 6-6 Aramaki Aza Aoba, Aoba-ku, Sendai, 980-8578 Miyagi Japan ✉ Corresponding author. Received 2025 Sep 4; Accepted 2026 Feb 26; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13083946 PMID: 41792215 Abstract Electrical signal transfer across fungal mycelia in forest floor is a fascinating idea in ecology but not fully explored yet. We measured the extracellular bioelectrical activities of two ectomycorrhizal basidiomycete species belonging to the genus Hebeloma in a field to examine the potential information flow across the sporocarps and its reponse to artificial stimulation. The 37 sporocarps (29 H. danicum and 8 H. cylindrosporum ) occurring within a 5 m × 5 m quadrat on the forest floor of an oak-dominated stand were measured for their electrical potential every second for 3.5 days using subdermal stainless electrodes. Causality analysis of electrical potential during a period without artificial stimulation indicated that the magnitude of potential information flow across the sporocarps was not restricted to the sporocarps belonging to the same genet (clone) nor within a species. However, this was still negatively associated with genetic as well as spatial distances. Local addition of tap water to the base of a certain sporocarp significantly increased the average magnitude of information flow across the sporocarps, whereas the global addition of tap water to all the sporocarps greatly reduced the information flow. The addition of urine had minimal effect on the magnitude of information flow. These results indicate the presence of underground electrical information flows across fungal sporocarps which respond vigorously to the environmental conditions. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-026-42673-y. Keywords: Ammonia fungi, Causality analysis, Ectomycorrhizal fungi, Electrical potential, Information flow, Mushrooms Subject terms: Ecology, Ecology, Zoology Introduction Fungi develop vast mycelial networks in the litter-soil interface of the forest floor and have an important role in nutrient and carbon dynamics by decomposing dead plant materials 1 and transporting water, nutrients, and carbon across their network 2 . Recent studies suggest that mycorrhizal fungi networks connect multiple plants and transport materials as well as signals between mycelia and plants under laboratory conditions 3 . However, such signal transfer through fungal mycelia has rarely been recorded in the field 4 . Bioelectricity is used for signal transfer in various organisms, from brained animals to single-celled bacteria 5 . In fungi, electricity has been measured in single hyphae 6 , mycelium 7 , and sporocarps 8 . Adamatzky 8 reported that electricity patterns caused by artificial stimulation, such as flame and salt, could be transported across sporocarps of cultivated oyster mushrooms. Under field conditions, Fukasawa et al. 9 recorded patterns of electrical potentials of six sporocarps of the ectomycorrhizal fungus Laccaria bicolor on the forest floor using a subdermal electrode and indicated that electricity patterns might be transported across sporocarps with directionality. However, this preliminary data was only measured on a limited number of sporocarps within a short distance. Further research is required on a larger spatial scale to investigate the general features of electrical signal transfer across the fungal mycelial network in the field. Fungi are microorganisms, and their individual hyphae are approximately 10 μm thick and invisible to the unaided eye, and thus it is challenging to record their electrical potential in the field. In this regard, sporocarps emerging on the ground surface are large and strong enough to be settled with electrodes for a while and are thus a good option to measure the electrical potential 8 . However, the occurrence of sporocarps is highly dependent on environmental conditions, such as temperature and precipitation, and therefore, the opportunities to measure the electrical potential of multiple sporocarps are limited by chance. To overcome this limitation, we focused on ammonia fungi. Ammonia fungi are an ecological group of fungi that prefer and fruit in the environmental conditions created after the addition of high concentrations of ammonia 10 . By adding urea to the forest floor in spring as an ammonia precursor, we can expect mass fruiting of ectomycorrhizal ammonia fungi in the urea-fertilized area in the fall. In the present study, we conducted field experiments to stimulate genetically identified ammonia fungi (genetically identical clones, possibly connected by hyphae underground) and investigate the effects of genetic and spatial distance on electrical activities and information flow between the sporocarps. To our knowledge, this is the first experiment where urea-induced mass fruiting technique was introduced to measure the electricity of sporocarps in the field. We hypothesized that the spatial distance between sporocarps has a significant effect on the magnitude of electrical information flow between the sporocarps because a previous study showed that spatially closer sporocarps exhibited greater electrical information flow 9 . Moreover, we hypothesized that the genetic distance between the sporocarps has a significant effect on the magnitude of electrical information flow between the sporocarps, because mycelial connectivity might be important for the transduction of electrical activity. We predicted stronger information flow within the identical genet compared to that of genetically distant sporocarps of the same species or different species that may not be connected by the mycelial network. Our third hypothesis is that different stimulations induce different electrical information flow of the sporocarps because different stimulants induce different electrical responses in fungi 8 . We investigated the addition of tap water and urine as pulse stimulants. We predict that the electrical response of the sporocarps is greater when urine is added compared to that of tap water, because the focal sporocarps are ammonia fungi, and urine is an ammonia precursor in soil. Materials and methods Study site This study was performed in a secondary mixed forest dominated by Quercus serrata in Kami town, Miyagi, Japan (38°37′23.29″ N, 140°48′57.53″ E, 130 m a.s.l.). In 2022, the mean annual temperature at the nearest meteorological station at Kawatabi (38°45′19.28″ N, 140°45′14.38″ E, 321 m a.s.l.) was 11.1 °C (range: − 1.2 °C in January to 23.6 °C in July), and the annual precipitation was 1675.5 mm. Snow covers the ground from December to March (Japan Meteorological Agency, https://www.jma.go.jp/jma/indexe.html ). On April 18, 2022, a 5 m × 5 m study plot was established in the study site, and urea (640 g m –2 ) was added to three spatially separated areas in the plot to stimulate ammonia fungi fruiting (Fig. 1 A). Fig. 1. Open in a new tab ( A ) Map of the study plot on October 23, 2022, showing locations of sporocarps#1–#37. Urea was added to shaded areas on April 18 to stimulate sporocarp fruiting of ammonia fungi. Rectangular shapes are fallen branches of Quercus serrata . A triangle-like shape at the middle is the base of a Q. serrata snag. Blue and red dots represent sporocarps of Groups I and II, respectively. Dots surrounded by blue and red circles indicate clusters of genetically identical sporocarps (genets) of the Groups I and II, respectively. ( B ) Phylogenetic relationships among sporocarps#1–#37. The maximum likelihood tree was constructed from 12,073 SNPs obtained via MIG-seq analysis. Sporocarp#8 and #8 − 2, #33 and #33 − 2, and #37 and #37 − 2 represent samples and their duplicates, respectively. ( C ) Scatter plot of the sporocarp’s pairwise spatial and genetic distances, with histograms showing the distribution of genetic distance in Groups I and II. A regression line with a 95% confidence interval was shown in Group I because the correlation was statistically significant (Pearson’s r = 0.226, p < 0.001) but was not significant in Group II. Recording the electrical activities of fruit bodies On October 23, 2022, we found multiple fruiting bodies in the plot (Figs. 1 A and 2 ). Some occurred within a cluster, and others formed discrete groups. We inserted electrodes into the cap and stipe for 37 sporocarps (sporocarp#1–#37), with the recording electrode on the cap and the reference electrode on the stipe, to record the electrical potential difference between the cap and stipe (Fig. 2 ). We used subdermal needle electrodes (SPES MEDICAL SRL Via Buccari 21 16153 Genova, Italy). The distance between the recording and reference electrodes in a fruit body was approximately 4 cm. Recording started at 11:45 on October 23 (Day 1) and stopped at 8:00 on October 27 (Day 5). Electrical activity of the sporocarp was recorded using a GRAPHTEC GL840-M midi logger (GRAPHTEC, Kanagawa, Japan) with a voltage range of − 100 to 100 mV. Electrical activity was recorded every second. Fig. 2. Open in a new tab Sporocarps with electrodes. The recording electrode was inserted on the top of the cap, and the reference electrode at the bottom of the stipe. During recording, stimulation experiments were performed by adding 200 mL of tap water or urine to the bottom of sporocarp#1 at 20:31 on Day 1 (Stim0, water), 5:52 on Day 2 (Stim1, urine), 5:55 on Day3 (Stim2, water), and 6:47 (Stim4, urine) and 17:13 on Day 4 (Stim5, urine). The urine was collected from one of the authors (YF, 44-year-old male without any chronic disease). In addition, tap water (10 L m –2 ) was added to all the area treated with urea (shaded area in Fig. 1 A) to maintain the sporocarp’s electrical activities at 5:55 on October 25 (Stim3). The water and urine were gently added to the ground without spraying directly onto the sporocarps and electrodes. The daily mean temperature at Kawatabi, the nearest meteorological station, was 12.3 °C on Day 1, while it decreased to 7.6 °C on Day 2 and continued decreasing to 6.6 °C on Day 5 (Japan Meteorological Agency, https://www.jma.go.jp/jma/indexe.html ). The study site experienced little rain during recording except for a short shower on Day 1, which was not sufficient to be recorded by the meteorological system. Clonal and species identification of sporocarps At the end of recording on October 27, the 37 sporocarps were collected, brought to the laboratory, and stored at − 30 °C until DNA extraction using the CTAB method 11 . Clonal analysis was performed by using genome-wide SNPs obtained from multiplexed inter-simple sequence repeats genotyping by sequencing (MIG-seq) analysis 12 , 13 . To distinguish true genetic differences among different genets from potential PCR and sequence errors, we included sample duplicates (sporocarps#8, #31, and #37). Library preparation for the MIG-seq analysis was performed as described by Suyama et al. 13 . Sequencing was performed on the Illumina MiSeq platform (Illumina, San Diego, CA, USA) by using the MiSeq Reagent Kit v3 (150-cycle) (Illumina). The data were deposited in the National Center for Biotechnology Information (NCBI; BioProject ID: PRJNA1165506) database. Forward and reverse data of each sample were merged and analyzed as single-end data following Suyama and Matsuki 12 . Raw reads were filtered and trimmed using Trimmomatic v.0.32 software 14 with the following options: HEADCRAP:6, CROP:77, SLIDINGWINDOW:10:30, and MINLEN:51. We assembled datasets using the Stacks v.2.41 pipeline 15 , and default values were employed for other parameters. We performed phylogenetic analysis using all samples and divided the datasets based on their genetic relationships to include as many SNPs as possible in clonal analysis. Phylogenetic analysis was performed using a maximum likelihood search with the GTR + G model using raxMLGUI v.2.0 16 . We extracted SNPs with a minimum genotyping rate of 10% (-R 0.1) using populations software of Stacks pipeline, and 12,073 SNPs were used for phylogenetic analysis. According to the result (Fig. 1 B), the samples were divided into two groups (Group I: 31 samples including two duplicate samples, and Group II: 9 samples, including one duplicated sample). We extracted SNPs with a minimum genotyping rate of 90% (-R 0.9), and clonal analysis was performed by using 360 and 87 SNPs for Groups I and II, respectively. For each dataset, we calculated pairwise genetic distance (number of SNPs) between samples, and samples with a distance below the threshold value were assigned to the same genet. The threshold value for each dataset was set by the number of SNPs between the duplicated samples (four in Group I, and two in Group II). Clonal analysis was performed using GenoDive version 3.05 17 . DNA samples of the three sporocarps were randomly selected from each of the two groups (sporocarp#9, #21, and #34 for Group I; #6, #8, and #12 for Group II) and identified by clonal analysis, and their internal transcribed spacer (ITS) region was analyzed further for species identification. Fungal rDNA was PCR-amplified using the ITS1F and ITS4 primer pair 18 . The PCR products were purified using Multiplex PCR Assay Kit Ver. 2 (Takara Bio, Kusatsu, Japan). Sequencing was performed by FASMAC (Atugi, Japan) using the same primer pair. An ITS region with a 97% shared identity was used as the criterion for the molecular identification of species 19 . BLAST searches were performed against publicly available sequences in the NCBI database (accession#: LC844115 for Group I; LC844116 for Group II) to identify the sporocarp species. Causality analysis of electrical potentials Using the time-series data of electrical activity from the 37 sporocarps, we estimated transfer entropy ( TE 20 , to identify potential causal relationships among the sporocarps. Transfer entropy, a generalization of Granger causality, quantifies the directional flow of information between two entities in a nonparametric manner 21 . More precisely, it defines the flow of information from a causal variable ( ) to an effect variable ( ) with a causal time delay , as follows: where and represent the embedding dimension of the effect variable and time interval required for the embedding time delay, respectively, and is the number of time points used to calculate TE . The unified information-theoretic causality (UIC) method was used to estimate TE and assess its significance 22 . To reduce bias effects because of the small sample size, we applied effective transfer entropy ( ETE ), which adjusts for TE bias using surrogate data 23 . Before performing UIC analysis, the time-series data of the electrical activity were differenced to create a stationary time-series. Moreover, we separated the data into several time windows for analysis (Fig. 3 ). The first and second windows were each 30 min of data without artificial stimulation (resting periods 1 and 2). A 45-min break was inserted between resting periods 1 and 2, accounting for the brief rain shower. The successive windows are each 9 h for the six stimulation events described above (Stim0–5, addition of water or urine). For each window, the stimulation event occurred in the middle of the 9 h data window, consisting of 4.5 h before and after each event. Each 9-h data set was divided into 18 segments (30 min each). The embedding dimension ( ) was selected to enhance the predictive accuracy of the simplex projection method 24 . The time interval ( ) was set to the recording interval (i.e., 1 s) because the electrical activity of the sporocarps is expected to change within a shorter time scale than the recorded interval. In the UIC analysis, we calculated the ETE of aggregated data across the entire record of each time segment, which enabled us to examine the average trends of the information flow in each segment. The analysis was conducted using a causal time delay ( t ) of − 30 to 0 s with a 1 s interval to assess the duration of causal influence. We calculated the maximum ETE and associated tp (–30 to 0 s) in each time segment for subsequent analyses. Fig. 3. Open in a new tab Full data of the sporocarp’s electrical potentials and position of time windows used for causality analysis (red bars). A 30-min window for resting period 1 before rain (R1), a 30-min window for resting period 2 after rain (R2), and a 9 h window for each of Stim#0–5, with 4.5 h for before and after the stimulation timing, which are shown as red arrows in the figure. Statistical analysis All analyses were performed using R version 4.4.2 25 . Pearson’s correlation coefficient was calculated between spatial and genetic distances of the sporocarps for each of the Group I and II datasets. The ETE matrix of the sporocarps during resting period 1 was used to visualize the network of information flow across all 37 sporocarps. The ETE and tp matrices during resting period 1 were used to analyze the relationships between effect ETE and causal ETE , as well as between effect tp and causal tp of the 37 sporocarps, which were visualized in a two-dimensional space and as a cluster dendrogram using the Ward method. The shift of the relationship between effect ETE and causal ETE , and between effect tp and causal tp of the 37 sporocarps during resting periods 1 and 2, was also shown in two-dimensional space. The Mantel test was used to analyze relationships between causality indices and the sporocarps’ spatial and genetic distances using the mantel command in the vegan package 26 . Mantel tests were performed for each Group I and Group II dataset and the combined dataset. For the combined dataset, species identity was used as the genetic distance, whereas the number of SNPs was used for each group dataset. The ETE and tp asymmetric matrices were converted to be symmetric by taking the averages of the opposite matrices, i.e., ( ETE ij + ETE ji )/2, where i and j are sporocarp individuals. Furthermore, to make the correlation between causality indices and spatial and genetic distances positive, the reciprocal of causality indices was used for Mantel correlation analysis. The effects of artificial stimulation events on information flow across the sporocarps were evaluated using the Regression Discontinuity Design ( rdrobust command in the rdrobust package). The ETE across the 37 sporocarps at each of the 18 time segments was compared before and after the stimulation event by setting the threshold between time segments 8 and 9 (the timing of the stimulation event). Results Genetic relationship of the sporocarps Amplicon sequencing of the rDNA ITS region indicated that three sporocarps from Group I (#9, #21, and #34) and three from Group II (#6, #8, and #12) exhibited 100% similarity within each group, respectively. The BLAST result of the ITS sequence of Group I showed 99.85% similarity with Hebeloma danicum (accession# MT117079.1 ), and that of Group II revealed 98.38% similarity with H. cylindrosporum (accession# FJ769363.1 ). These ITS sequences are provided in supplementary FASTA file. MIG-seq showed that sequences of the sporocarp#33 and #37 in Group I had four SNPs (genetic distance) between their duplicate samples (Supplementary Table S1 ) and two SNPs in the duplicate samples of sporocarp#8 in Group II (Supplementary Table S2 ). There were < 4 SNPs between sporocarp#26 and #27 in Group I and thus regarded as an identical genet (Table S1 ; Fig. 1 A). Similarly, there were < 2 SNPs between sporocarp#6 and #7 in Group II and were also regarded as another identical genet (Table S2 ; Fig. 1 A). In Fig. 1 C, a histogram of full-factorial pairwise SNPs across the sporocarps revealed bimodal distributions in both groups, with 20–30 SNPs as the gap between the peaks. In Group I, sporocarp#28 showed < 20 SNPs with all other sporocarps (Supplementary Table S1 ). Similarly, sporocarp#13, #14, and #15, and sporocarp#23 and #24 showed < 20 SNPs with each other, respectively. There was a significant correlation between spatial distance and SNPs among the sporocarps in Group I ( r = 0.226, p < 0.001), whereas no correlation was observed in Group II (Fig. 1 C). Electrical potential The electrical potential of the sporocarps fluctuated from the start of recording, ranging from − 110 to 80 (Fig. 3 , Supplementary Fig. S1 for data of each sporocarp). Most of the sporocarps responded to the stimulation event Stim3, which was water added to the areas that received urea. Other stimulation events, except for Stim2, also altered some electrical potential of sporocarp#1, but their effects on other sporocarps were not clear. ETE of the electricity data during resting period 1 before rain revealed the network of information flow across 37 sporocarps, where sporocarp#1 exhibit the strongest information flow to the distant sporocarp#23 of a different species (Fig. 4 ). Similarly, sporocarp#1 tended to have strong information flow toward sporocarps #2, #3, #5, and #15. Relationships between causal and effect ETE for the 37 sporocarps are shown in Fig. 5 A, which were categorized into two clusters (Fig. 5 C). One consists of the sporocarps showing larger effect ETE than causal ETE (dots on the left above the y = x line), and the other showing larger causal ETE than effect ETE (dots on the right below the y = x line). The species identity of sporocarps was not associated with this clustering. Moreover, the same relationship was shown between effect and causal tp value (Fig. 5 B, D). Sporocarp#1 showed relatively large causal ETE but relatively small effect ETE , as well as relatively large causal tp and small effect tp . However, the clustering of ETE and tp was not always the same. For instance, sporocarp#1 and #31 were in the same ETE category (Fig. 5 C) but in different tp categories (Fig. 5 D). A weak but significant correlation was detected between ETE and tp (Supplementary Fig. S2 ) ( r = 0.0538, p = 0.04963). After the rain between resting periods 1 and 2, the relationships between causal and effect ETE and between causal and effect tp changed substantially for most of the sporocarps (Fig. 6 ). Fig. 4. Open in a new tab Network of information flow across sporocarps in the study plot during resting period 1. The thickness of blue arrows represents the magnitude of information flow ( ETE ). Only significant ( p < 0.05) flows were shown. Dots show the location of investigated sporocarps: blue, Group I; red, Group II. Blue and red circles enclose genetically identical sporocarp pairs in Groups I and II, respectively. The area enclosed by gray squares are the area where urea was added in the spring. Fig. 5. Open in a new tab Relationship between causal and effect ETE ( A ) and tp ( B ) in the 37 sporocarps during resting period 1. Dots and error bars represent the mean and standard errors of 36 causal relationships with other sporocarps. Slopes in the figures indicate the y = x lines. Cluster dendrogram of the 37 sporocarps drawn using the Ward method by the distance calculated from causal and effect ETE ( C ) and tp ( D ). Fig. 6. Open in a new tab Relationship between causal and effect ETE ( A ) and tp ( B ) in the 37 sporocarps during resting period 1 (before rain) and resting period 2 (after rain), connected by a line. The identity of the resting period (1 or 2) was not identified in the figure. The black slopes in the figures indicate the y = x lines. The data for resting period 1 are the same as Fig. 5 , and error bars are not shown for the sake of simplicity. The Mantel test for the combined dataset showed that the spatial distance (Mantel’s r = 0.1141, p = 0.014) and species identity (Mantel’s r = 0.1386, p = 0.028) were positively associated with the ETE reciprocal. For the group datasets, the reciprocal of Group I ETE was significantly (Mantel’s r = 0.1097, p = 0.033) associated with the sporocarp’s spatial distance but not with their genetic distance. Neither the spatial nor the genetic distances of Group II sporocarps were significantly associated with ETE . The reciprocals of tp had no associations with spatial distance and species difference (and genetic distance). Figure 7 shows the changes in the mean ETE of the information flow across the 37 sporocarps during time segments, including the six artificial stimulations. The changes were dependent on the type of disturbance. Stim1, Stim4, and Stim5, where 200 mL of urine was added to the ground near the sporocarp#1, did not change the mean ETE of the 37 sporocarps, except for a small but significant change after Stim1. However, addition of 200 mL tap water to the ground near the sporocarp#1 (Stim0 and Stim2) significantly increased the mean ETE , and the addition of tap water onto the whole area treated with urea (Stim3) greatly reduced the mean ETE of the 37 sporocarps. Fig. 7. Open in a new tab Mean ETE at each time segment for 4.5 h before and after the five stimulation events (totally 9 h for each window). Stim0 and Stim2: 200 mL of water added to sporocarp#1; Stim1, Stim4, and Stim5: 200 mL of urine to sporocarp#1; Stim3: tap water (10 L m –2 ) added to the whole area treated with urea. Error bars show the standard error. The effect of stimulation events between time segments 8 and 9 (shown as a dotted line) was evaluated using the Regression Discontinuity Design ( p -values were shown in the figure). Discussion In contrast to a previous report 9 where little electrical potential was detected at the beginning, the sporocarps’ electrical potential was detected from the start of the recording in the present study. Precipitation timing may be a reason for this difference, in addition to the possibility of intrinsic difference of fungal species ( L. bicolor in the previous paper). In the previous study on L. bicolor , the sporocarps experienced little precipitation for two weeks before recording. Whereas, in this study, the sporocarps experienced rain (4 mm) a day before the start of recording (Japan Meteorological Agency), which could activate the sporocarps’ electrical potential 9 . After the start of recording, little rain was experienced except for a small rain shower between resting periods 1 and 2. Therefore, the fluctuations in electrical potentials recorded during resting period 1 might not be due to the direct effects of raindrops (inherent electrical charges, nutrient ions, and physical impacts 27 – 29 . Several abiotic and biotic factors could affect the electrical potential of the sporocarps. For example, flickering sunlight filtered through trees may affect the electrical potential of fungal sporocarps, as light can alter the electricity of fungi 30 , 31 . Various organisms, such as slugs and insects, eat fungal sporocarps by grazing 32 , 33 , and such physical disturbance could also alter the electricity of fungi by increasing Ca 2+ concentration in hyphal cells and generating membrane potential 34 . ETE was calculated using time-series data of sporocarps’ electrical potentials and indicates the potential information flow across the sporocarps 22 . In the present study, information flow across the sporocarps was analyzed on the largest scale to date (in terms of spatial and sporocarp number) using causality analysis of time-series data of electrical potentials. The ETE of the 37 sporocarps during resting period 1 without artificial stimulation before rain indicated that substantial information flows was detected between sporocarps of the same and different species. The mechanism of this interspecific information flow was not clear. A potential mechanism is chemical signaling across species. Adjacent fungal colonies interact with each other by secreting various chemicals 35 – 37 , which may affect the physiology of neighboring colonies of different fungal species 38 and alter their electrical potential. Given that the sporocarps are made of interwoven hyphae, the electrode inserted into the caps and stipes measured the extracellular electrical potential difference between the cap and stipe. Such potentials could be influenced by not only membrane potential of fungal cells but also pH conditions altered by the secretion of organic acids, which can change the extracellular ionic balance 39 – 41 . This is expected to affect the electrophysiology of neighboring fungal colonies regardless of species. The Mantel test revealed that spatial distance has a significant effect on information flow across sporocarps. The stronger information flow in the closer location is consistent with a previous study 9 . In addition, the present study showed that genetic distance within a species, as well as species difference, has a significant effect on the magnitude of information flow across sporocarps, which is a novel finding. However, genetically identical sporocarps did not show particularly stronger information flow with each other compared to that of sporocarps of the same species but genetically distant and of different species. Two explanations are possible for this phenomenon. First, the information flow across the sporocarps might not be through hyphae, and thus, the direct hyphal connection between clones is not necessary. In contrast, the second explanation is based on the speculation that most of the sporocarps (particularly in Group I) were connected by underground hyphae instead of their genetic distances. Previous studies reported that the heterothallic nature of fungi induces genetic variation across cells even within a single mycelium 42 – 44 . The histogram of SNPs in sporocarps of both groups was bimodal (Fig. 1 C), suggesting that sporocarps included in the fewer SNPs peak were genetically closer than those in the other peak. In the case of sporocarps in Group I (identified as H. danicum ), the largest number of SNPs in the fewer SNPs peak was 18, which was larger than 4, defined as the threshold of clones based on the duplicate samples in the present study. However, despite the relatively large number of SNPs, these genetically distant sporocarps could be included in the same mycelial network as discussed above. If so, sporocarp#28 might be connected to the other 28 sporocarps in Group I, and thus all 29 sporocarps in Group I were connected by a mycelial network. The Mantel test using data of Group I sporocarps indicated that only spatial distance was significantly associated with ETE , and genetic distance had no effect. This result supports the speculation that all sporocarps in Group I were connected to the same mycelial network. Although few studies have shown the size of underground mycelial network of Hebeloma spp., that can be comparable to those of other ectomycorrhizal species, such as Suillus bovinus , S. pictus , Rhizopogon vesiculosus (≥ 20 m) and Tricholoma matsutake (≥ 10 m) 45 – 48 . The histgram of genetic distance among sporocarps in Group II (identified as H. cylindrosporum ) was also bimodal (Fig. 1 C). However, only a couple of sporocarps were included in the fewer SNPs peak, suggesting that these sporocarps were clones connected by underground mycelium, and the other sporocarps might belong to genetically distant multiple small mycelia. Dahlberg and Stenlid 45 reported that the majority of S. bovinus mycelia in a pine-birch stand were small and produced only a single sporocarp, although some of the mycelia grew large (≥ 20 m) enough to produce dozens of sporocarps. Nevertheless, the number of sporocarps in Group II was too small to analyze its genetic structure and ETE -distance (genetic and spatial) associations, and compare those with Group I. The directionality of information flow across sporocarps 9 was also observed in the present study. During the resting period 1, sporocarps were categorized into two types based on the information flow pattern regardless of the species. One consists of sporocarps with strong (large ETE ) and quick (large tp ) information flow when they are “causal” (sender). The other is sporocarps with strong and quick information flow when they are “result” (receiver). In addition, a positive correlation between ETE and tp suggested that strong information flows are transmitted quickly. This categorization was not stable and changed temporarily. However, it was not clear what factors influenced the flow strength and direction and how these fluctuated temporarily. If the electrical potential was transmitted through an underground mycelial network, the spatial location of sporocarps within the mycelial network topology could affect the information flow magnitude and direction, which may fluctuate due to environmental conditions and the physiology of mycelium. Visualized material flow across the mycelial network revealed that the flow direction was outward in the case of growing mycelium, whereas reciprocal transmission was observed in mature mycelium 49 – 51 . Interestingly, the effect of artificial stimulation on information flow across sporocarps differed depending on the stimulation type. The addition of tap water to the base of sporocarp#1 significantly increased the electrical potential of sporocarp#1 and the average ETE among sporocarps within 30 min in both trials. As previously reported, the electrical potential of wild sporocarps increases after rain 9 . Similarly, the addition of tap water to sporocarps and mycelium increased their electrical potential 52 . Water is important to retain physiological processes within cells and produce turgor pressure, which is necessary for hyphal tip growth 2 . Therefore, spatially heterogeneous distribution of water may be important information for fungi, and thus the addition of water to a particular site could induce information flow across the mycelial network. Fukasawa et al. 53 reported that the addition of sterilized wood bait to a certain position of a growing mycelium of the wood decay fungus Pholiota brunnescens promoted the electrical information flow from the bait position to other parts of the mycelium. Given that absorbed water would be transmitted through hyphae and improve the cells’ activity at the destination 54 , 55 , this water flow and subsequent activation of forwarded sporocarps could be detected as information flow in this system. In contrast, watering the entire area substantially reduced information flow across the sporocarps. Although we require replicates of this treatment to confirm its reproducibility, the reduction of information flow was significant and worthy of discussion. As opposed to adding water to just one sporocarp, water application to all the sporocarps may have activated all the sporocarps independently, making the patterns of electrical activity less related to each other. Among the emerged sporocarps in this study, H. danicum is known as an ammonia fungus 56 and is expected to be activated by the presence of ammonia. However, H. cylindrosporum is known to exhibit reduced growth with ammonia compared with that of other nitrogen sources such as nitrate 57 . The addition of urine as an ammonia precursor had little effect on information flow among the sporocarps. This might be due to the slow process of urine hydrolysis to ammonia. Approximately five days are required at < 20 °C to alter the soil microbial community and process the urea in urine to ammonia 58 . Considering the lower temperature of the recording period (late October, mean 6.6–12.3 °C), it may require longer. Therefore, the time window to detect information flow (30 s) and the whole recording period of electrical potential (3 days) in the present study might be too short to detect the physiological change of fungi related to urine hydrolysis. As shown by Fukasawa et al. 53 , recording for a longer time (≥ 100 days) may be required to detect a slow process. However, recording the electricity of wild sporocarps using electrodes for a long time is challenging because of the following reasons. First, the short longevity of soft sporocarps of Hebeloma spp. Putting electrodes onto underground hyphae may be a solution, but it is challenging because of the small size of hyphae. Second, fluctuation of multiple factors over a longer time in the field makes it challenging to detect the effect of ammonia. The simultaneous recording of environmental factors, such as air temperature, precipitation, light, soil temperature, and soil moisture, and including these in the analysis, might be a solution to an extent. To summarize, the findings of this study imply that fungal sporocarps in the field may exchange information intra- and inter-specifically in response to environmental conditions. To verify whether the detected information flow constitutes functional signaling, physiological or functional changes in the recipient sporocarps must be examined. As sporocarps are reproductive organs for spore dispersal, they may exhibit lower electrical activity than the vegetative mycelium responsible for growth and nutrient uptake 59 . Future efforts should develop methods for measuring the electrical potential of the subterranean mycelium to elucidate the patterns and mechanisms of fungal electrical communication. Supplementary Information Below is the link to the electronic supplementary material. Supplementary Material 1 (480KB, pdf) Supplementary Material 2 (14.2KB, xlsx) Supplementary Material 3 (13.8KB, docx) Acknowledgements This study was financially supported by the Japan Society for the Promotion of Science’s Grant-in-Aid for Transformative Research Areas (A) (JP22H05669, JP24H02110) to YF. Author contributions Y.F. conceptuarize the study and designed the experiment; D.A. and T.T. measured the electricity; D.T. conducted molecular analysis; D.A. and Y.O. conducted causality of electricity data; Y.F. and D.T. wrote the manuscript with imput from all authors; Y.F., D.T., and Y.O. reviewed and edited the manuscript. All authors approved the final version. Funding This study was financially supported by the Japan Society for the Promotion of Science’s Grant-in-Aid for Transformative Research Areas (A) (JP22H05669, JP24H02110) to YF. Data availability The sequences related to fungal sporocarps have been uploaded to the NCBI database (accession#: LC844115 for Group I; LC844116 for Group II). The dataset generated and analyzed during the current study are available in the Zenodo repository (DOI: 10.5281/zenodo.17027394). Declarations Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Boddy, L. Saprotrophic cord-forming fungi: Warefare strategies and other ecological aspects. Mycol. Res. 97 , 641–655 (1993). [ Google Scholar ] 2. Fricker, M. D. et al. The mycelium as a network. Microbiol. Spectr. 5 , FUNK–0033 (2017). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. Johnson, D. & Gilbert, L. Interplant signalling through hyphal networks. New Phytol 205 , 1448–1453 (2015). [ DOI ] [ PubMed ] [ Google Scholar ] 4. Karst, J., Jones, M. D. & Hoeksema, J. D. Positive citation bias and overinterpreted results lead to misinformation on comon mycorrhizal networks in forests. Nat. Ecol. Evol. 7 , 501–511 (2023). [ DOI ] [ PubMed ] [ Google Scholar ] 5. Hanson, A. Spontaneous electrical low-frequency oscillations: A possible role in Hydra and all living systems. Philos. Trans. R. Soc. B Biol. Sci. 376 , 20190763 (2021). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Slayman, C. L., Long, W. S. & Gradmann, D. “Action potentials” in Neurospora crassa , a mycelial fungus. Biochim. Biophys. Acta 426 , 732–744 (1976). [ DOI ] [ PubMed ] [ Google Scholar ] 7. Olsson, S. & Hansson, B. S. Action potential-like activity found in fungal mycelia is sensitive to stimulation. Naturwissenschaften 82 , 30–31 (1995). [ Google Scholar ] 8. Adamatzky, A. Towards fungal computer. Interface Focus . 8 , 20180029 (2018a). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Fukasawa, Y. et al. Electrical potentials in the ectomycorrhizal fungus Laccaria bicolor after a rainfall event. Fungal Ecol. 63 , 101229 (2023). [ Google Scholar ] 10. Sagara, N. Ammonia fungi – A chemoecological grouping of terrestrial fungi. Contr. Biol. Lab. Kyoto Univ. 24 , 205–290 (1975). [ Google Scholar ] 11. Doyle, J. & Doyle, J. L. Genomic plant DNA preparation from fresh tissue-CTAB method. Phytochem. Bull. 19 , 11–15 (1987). [ Google Scholar ] 12. Suyama, Y. & Matsuki, Y. MIG-seq: An effective PCR-based method for genome-wide single-nucleotide polymorphism genotyping using the next-generation sequencing platform. Sci. Rep. 5 , 1–12. 10.1038/srep16963 (2015). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. Suyama, Y. et al. Complementary combination of multiplex high-throughput DNA sequencing for molecular phylogeny. Ecol. Res. 37 , 171–181. 10.1111/1440-1703.12270 (2022). [ Google Scholar ] 14. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30 , 2114–2120. 10.1093/BIOINFORMATICS/BTU170 (2014). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Rochette, N. C., Rivera-Colon, A. G. & Catchen, J. M. Stacks 2: Analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol. Ecol. 28 , 4737–4754. 10.1111/mec.15253 (2019). [ DOI ] [ PubMed ] [ Google Scholar ] 16. Edler, D. et al. RAxMLGUI 2.0: A graphical interface and toolkit for phylogenetic analyses using RAxML. Methods Ecol. Evol. 12 , 373–377. 10.1111/2041-210X.13512 (2021). [ Google Scholar ] 17. Meirmans, P. G. Genodive version 3.0: Easy-to‐use software for the analysis of genetic data of diploids and polyploids. Mol. Ecol. Resour. 20 , 1126–1131 (2020). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Gardes, M. & Bruns, T. D. ITS primers with enhanced specificity for basidiomycetes – Application to the identification of mycorrhizae and rusts. Mol. Ecol. 2 , 113–118 (1993). [ DOI ] [ PubMed ] [ Google Scholar ] 19. Osono, T. Metagenomic approach yields insights into fungal diversity and functioning. In: (eds Sota, T., Kagata, H., Ando, Y., Ustumi, S. & Osono, T.) Species diversity and community structure. Berlin: Springer, 1–23. (2014). 20. Schreiber, T. Measuring information transfer. Phys. Rev. Lett. 85 , 461–464 (2000). [ DOI ] [ PubMed ] [ Google Scholar ] 21. Barnett, L., Barrett, A. B. & Seth, A. K. Granger causality and transfer entropy are equivalent for Gaussian variables. Phys. Rev. Lett. 103 , 238701 (2009). [ DOI ] [ PubMed ] [ Google Scholar ] 22. Osada, Y., Ushio, M. & Kondoh, M. Unified understanding of nonparametric causality detection in time series. bioRxiv 2023.04.20.537743. 10.1101/2023.04.20.537743 23. Marschinski, R. & Kantz, H. Analysing the information flow between financial time series. Eur. Phys. J. B . 30 , 275–281 (2002). [ Google Scholar ] 24. Sugihara, G. & May, R. M. Applications of fractals in ecology. Tre Ecol. Evol. 5 , 79–86 (1990). [ DOI ] [ PubMed ] [ Google Scholar ] 25. R core team. R: a language and environment for statistical computing. The R Foundation for statistical computing (Austria, 2024). 26. Oksanen, J. et al. Vegan: Community Ecology Package. R package version 2.3.4. (2016). https://cran.r-project.org/package=vegan 27. Takahashi, T. Measurement of electric charge of cloud droplets, drizzle, and raindrops. Rev. Geophys. Space Phys. 11 , 903–924 (1973). [ Google Scholar ] 28. Selvam, A. M. et al. Characteristics of raindrops charge and associated electric field in different types of rain. J. Atmos. Sci. 34 , 1791–1796 (1977). [ Google Scholar ] 29. Chiwa, M., Kim, D. H. & Sakugawa, H. Rainfall, stemflow, and throughfall chemistry at urban- and mountain-facing sites at Mt. Gokurakuji, Hiroshima, Western Japan. Water Air Soil Pollut. 146 , 93–109 (2003). [ Google Scholar ] 30. Adamatzky, A., Gandia, A. & Chiolerio, A. Fungal sensing skin. Fung. Biol. Biotechnol. 8 , 3 (2021). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [ Retracted ] 31. Oguntoyinbo, B. et al. Development of the SMA (speaking mushroom approach) environmental control system: Automated cultivation control system characterization. Environ. Control Biol. 53 , 55–62 (2015). [ Google Scholar ] 32. Kitabayashi, K., Kitamura, S. & Tuno, N. Fungal spore transport by omnivorous mycophagous slug in temperate forest. Ecol. Evol. 12 , e8565 (2022). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 33. Tuno, N., Nitta, M. & Kobayashi, M. Diversity and host associations of dipteran insects exploiting fungal fruit bodies in Hokuriku central Japan. Entomol. Sci. 22 , 161–166 (2019). [ Google Scholar ] 34. Itani, A. et al. Local calcium signal transmission in mycelial network exhibits decentralized stress responses. PNAS Nexus 2 , 1–10 (2023). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 35. Hiscox, J. et al. Changes in oxidative enzyme activity during interspecific mycelial interactions involving the white-rot fungus Trametes versicolor . Fungal Genet. Biol. 47 , 562–571 (2010). [ DOI ] [ PubMed ] [ Google Scholar ] 36. O’Leary, J. et al. The whiff of decay: Linking volatile production and extracellular enzymes to outcomes of fungal interactions at different temperatures. Fung Ecol. 39 , 336–348 (2019). [ Google Scholar ] 37. O’Leary, J. et al. Space and patchiness affects diversity-function relationships in fungal decay communities. ISME J. 15 , 720–731 (2021). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Fukasawa, Y. et al. Inoculum volume effects on competitive outcome and wood decay rate of brown- and white-rot basidiomycetes. Fung Ecol. 45 , 100938 (2020). [ Google Scholar ] 39. Takao, S. Organic acid production by basidiomycetes I. Screening of acid-producing strains. Appl. Microbiol. 13 , 732–737 (1965). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 40. Espejo, E. & Agosin, E. Production and degradation of oxalic acid by brown rot fungi. Appl. Environ. Microbiol. 57 , 1980–1986 (1991). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 41. Whiteford, R. et al. The white-rot fungus, Phanerochaete chrysosporium , under combinatorial stress produces variable oil profiles following analysis of secondary metabolites. J. Appl. Microbiol. 131 , 1305–1317 (2021). [ DOI ] [ PubMed ] [ Google Scholar ] 42. Vydryakova, G. A. et al. Observations on morphologic and genetic diversity in populations of Filoboletus manipularis (Fungi: Mycenaceae) in southern Viet Nam. Mycology 5 , 81–97 (2014). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 43. Grum-Grzhimaylo, A. A. et al. Somatic deficiency causes reproductve parasitism in a fungus. Nat. Comm. 12 , 783 (2021). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 44. Jalalzadeh, B. et al. Experimental outcrossing in Agaricus bisporus revealed a major and unexpected involvement of airborne mycelial fragments. J. Fungi . 8 , 1278 (2022). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 45. Dahlberg, A. & Stenlid, J. Population structure and dynamics in Suillus bovinus as indicated by spatial distribution of fungal clones. New Phytol. 115 , 487–493 (1990). [ DOI ] [ PubMed ] [ Google Scholar ] 46. Hirose, D. et al. Genet distribution of sporocarps and ectomycorrhizas of Suillus pictus in a Japanese white pine plantation. New. Phytol . 164 , 527–541 (2004). [ Google Scholar ] 47. Lian, C. et al. Tricholoma matsutake in a natural Pinus densiflora forest: correspondence between above- and below-ground genets, association with multiple host trees and alteration of existing ectomycorrhizal communities. New. Phytol . 171 , 825–836 (2006). [ DOI ] [ PubMed ] [ Google Scholar ] 48. Beiler, K. J., Durall, D. M., Simard, S. W., Maxwell, S. A. & Kretzer, A. M. Architecture of the wood-wide web: Rhizopogon spp. genets link multiple Douglas-fir cohorts. New. Phytol . 185 , 543–553 (2010). [ DOI ] [ PubMed ] [ Google Scholar ] 49. Tlalka, M. et al. Emergence of self-organised oscillatory domains in fungal mycelia. Fun Gen. Biol. 44 , 1085–1095 (2007). [ DOI ] [ PubMed ] [ Google Scholar ] 50. Hammer, E. C. et al. Hyphal exploration strategies and habitat modification of an arbuscular mycorrhizal fungus in microengineered soil chips. Fung Ecol. 67 , 101302 (2024). [ Google Scholar ] 51. Galvez, L. O. et al. A travelling-wave strategy for plant-fungal trade. Nature 639 , 172–180 (2025). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 52. Adamatzky, A. On spiking behaviour of oyster fungi Pleurotus djamor . Sci. Rep. 8 , 7873 (2018b). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 53. Fukasawa, Y. et al. Electrical integrity and week-long oscillation in fungal mycelia. Sci. Rep. 14 , 15601 (2024a). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 54. Guhr, A. et al. Redistribution of soil water by a saprotrophic fungus enhances carbon mineralization. Proc. Natl. Acad. Sci. U. S. A. 112 , 1 (2015). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 55. Fukasawa, Y. et al. Hyphal connection increases net resource utilization of saprotrophic fungal mycelia by improving local performance: A case study of the cord-forming basidiomycete, Phanerochaete velutina . Fungal Ecol. 71 , 101362 (2024b). [ Google Scholar ] 56. Sagara, N. et al. Mushrooms arising from mole latrines reveal the life of moles: Proposals of “myco-talpology” and “habitat-cleaning symbiosis”. Eur. Zool. J. 92 , 817–834 (2025). [ Google Scholar ] 57. Scheromm, P., Plassard, C. & Salsac, L. Effect of nitrate and ammonium nutrition on the metabolism of the ectomycorrhizal basidiomycete, Hebeloma cylindrosporum Romagn. New Phytol. 114 , 227–234 (1990). [ DOI ] [ PubMed ] [ Google Scholar ] 58. Singh, U. et al. Nitrogen transformation, ammonia volatilization loss, and nitrogen leaching in organically enhanced nitrogen fertilizers relative to urea. Soil. Sci. Soc. Am. J. 76 , 1842–1854 (2012). [ Google Scholar ] 59. Phillips, N. et al. Electrical signal transfer characteristics of mycelium-bound composites and fungal fruiting bodies. Fungal Ecol. 71 , 101358 (2024). [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplementary Material 1 (480KB, pdf) Supplementary Material 2 (14.2KB, xlsx) Supplementary Material 3 (13.8KB, docx) Data Availability Statement The sequences related to fungal sporocarps have been uploaded to the NCBI database (accession#: LC844115 for Group I; LC844116 for Group II). The dataset generated and analyzed during the current study are available in the Zenodo repository (DOI: 10.5281/zenodo.17027394). Articles from Scientific Reports are provided here courtesy of Nature Publishing Group ACTIONS View on publisher site PDF (3.0 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top