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This is also consistent with results from large-scale tree-girdling experiments in nurseries that experimentally terminated photosynthate supply to roots and associated ECM fungi, showing that ECM fruiting body biomass increased with host photosynthesis rates and seasonal photosynthate allocation to roots  , . Owing to the pulsed and seasonal photosynthate flux from host plants, seasonal changes in available carbohydrates are predictable for ECM fungi.
However, for SAP fungi, the decomposing substrate supply e. Here, we report our results from a field survey of mushroom-forming fungi during monthly intervals from to in an evergreen oak Castanopsis -dominant forest in Japan. In particular, we compared the phenology and mushroom productivity between three different functional groups of fungi: ECM fungi, leaf decomposing fungi, and wood-decomposing fungi. We hypothesized that the fruiting phenology of ECM fungi is more seasonal than that of SAP fungi owing to the pulsed and intermittent photosynthate flux from host plants to ECM fungi.
The aim of this study is to show what climatic factors are correlated with mushroom productivity of each functional group, thereby testing the above mentioned hypothesis. The study site was located in the Higashiyama hills of the eastern part of Kyoto city in Japan The forested study site covers an area of ca.
The forest was dominated by C. The climate in this region is humid with a mean annual rainfall of mm and an average annual temperature of Temperatures range from 4. Monthly surveys were carried out at the study site from May to March The mycologists walked in the daytime along a linear census route ca. The present survey used the number of fruiting species as a surrogate for fruiting body phenology and production. We believe that the survey results represent seasonal changes in fungal biomass because a greater encounter probability of productive species results in part from higher diversity .
Specimen identification was based on morphological characteristics using several key references; unfortunately, fungal materials in our surveys were not preserved, and thus we may have overlooked cryptic species that could have been distinguished by DNA sequencing. Nevertheless, sampling and identification methods were consistent across seasons. We state that no specific permits were required for the described field studies, the location was not privately-owned or protected in any way and the field studies did not involve endangered or protected species.
We analyzed data from a total of censuses with several missing data. Hypogenous species were excluded from analysis because the participants' sampling expertise could have caused sampling bias. All statistical analyses except for Bayesian modeling were performed using R statistical software version 2.
We modeled the total number of fruiting species of each trophic group detected in each survey as a function of external factors such as climatic factors and time elapsed since the first observation. To further detect changes in phenological responses, we also fitted the model to the seven ECM genera, the seven litter-decomposing genera, and the seven wood-decomposing genera separately genera selection was the same for concordance analysis.
The observed data were fitted to a zero-inflated binomial ZIB model  ,  to account for a large proportion of zero values that usually violate the distributional assumptions i. The first part, p i,j , models the probability that a fruiting body of an arbitrary species in fungal group j ECM fungi, litter-decomposing fungi, wood-decomposing fungi, and each fungal genus is truly present in the i th survey.
This probability is assumed to be equal for all species in the same fungal group, but the presence of fruiting bodies in individual species is independent of that of other species. The second part, d i,j , models the probability that a fruiting body of an arbitrary fungal species in fungal group j is detected in the i th survey, if present. Failure to detect a fruiting body in a given survey occurs either when the species is actually absent or when it is present but not found in the survey. Therefore, the ZIB model is described as follows: where N j is the total number of fungal species in trophic group j that were observed throughout the survey years, X i,j is the number of species that are actually present in i th survey and trophic group j , D i,j is the number of species detected in i th survey and trophic group j , and p i,j and d i,j are defined as above.
The model can be extended to allow explanatory variables to influence p i,j. Climatic data were sourced from weather station data supplied by the Japan Meteorological Agency www. Daily climate data were obtained from an automated station at Kyoto city We used a Bayesian framework particularly useful for fitting hierarchical statistical models that include random effects and determining the uncertainty in parameter estimation .
Choosing a very large value for the variance resulted in a vague or uninformative prior distribution. In addition, the detection rate of fruiting bodies on each survey date was predicted using an uninformative prior. The first , interactions were discarded as a burn-in period to guarantee convergence to the target posterior distribution. We used three chains with different initial values and thereafter evaluated the Markov-chain Monte Carlo convergence on the posterior distribution using Gelman-Rubin statistic diagnostics .
Within 11, total records, epigeous fungal species 9, cumulative number of records were identified to the species level in the study site Table S2 , including ECM species 3, records , litter-decomposing fungal species 1, records , wood-decomposing fungal species 3, records and 24 species records that were not assigned into these functional groups e. ECM fungi continuously showed clear intra-annual fruiting changes throughout the survey years, but showed an unclear inter-annual fruiting change Fig.
Litter-decomposing fungi showed a moderate intra-annual change and showed an obscure inter-annual fruiting change Fig. The number of fruiting species in wood-decomposing fungi showed indistinct intra-annual changes and a gradual time-dependent increase Fig.
We found a striking difference in the fruiting phenology among fungal functional groups. First, ECM fungi showed a clear phenology in the number of fruiting species throughout the survey with the highest number in July and the lowest in March Fig. The number of litter-decomposing fungi showed a moderate seasonal change with the highest in early autumn and the lowest in winter Fig.
Wood-decomposing fungi showed a much less clear seasonal peak in the observed number of species than litter-decomposing fungi Fig. This trend held when fungal species that form long-lived fruiting bodies i. Relative richness represents the standardized monthly mean number of fruiting species in each fungal genus.
Notably, key fruiting determinants were very similar among the examined ECM fungal genera Fig. Fruiting ECM species increased more frequently with higher temperatures and increased monthly accumulated rainfall Fig. Weekly accumulated rainfall before the surveys slightly decreased the number of fruiting species, although the number of elapsed years somewhat increased the number of fruiting ECM fungi.
The importance of fungi and of mycology for a global development of the bioeconomy
Key factors increasing the number of fruiting species were somewhat similar within litter-decomposing fungal genera, but were much more divergent than that of ECM fungi Fig. The number of Lepiota and Marasmius fruiting species were affected by temperature, although those in Entoloma and Hygrocybe were primarily influenced by monthly rainfall. In wood-decomposing fungi, each wood-decomposing fungal genus was influenced by a different set of factors Fig. Monthly temperature had a positive influence on fruiting Crepidotus , Pluteus , and Pholiota , but had a negative influence on Mycena and Tremella.
Monthly accumulated rainfall had a positive influence on the number of Polyporus fruiting bodies and the number of elapsed years had a positive effect on Polyporus and Psathyrella. A positive regression coefficient value indicates positive effects on the number of fruiting species, whereas a negative value indicates the reverse.
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Details concerning posterior distribution of regression coefficients are shown in Figure S3. We found that seasonal fungal fruiting patterns were remarkably different depending on the fungal trophic group Fig. ECM fungal taxa virtually had clear unimodal fruiting patterns from middle summer to early autumn, although the seasonal patterns among wood-decomposing fungal taxa varied greatly. Moreover, we showed that unclear seasonal peak fruiting of wood-decomposing fungi may not be attributed to the perennial nature of aphyllophoraceous fungi.
Although there is, to a greater or lesser extent, an inter-annual fruiting phenology difference at the same calendar dates in each trophic fungal group Fig. Nevertheless, we found a time-dependent fruiting increase in wood-decomposing fungi, possibly due to the progressive effects of forest aging that can increase the abundance and variety of coarse woody debris .
These results provide strong support for our hypothesis that phenological ECM fungal patterns are more seasonally predictable than those of SAP fungi, owing to the predictable seasonal patterns in the preceding assimilate fluxes from host plants to ECM fungi. The different timing of the fruiting peak may be attributed to regional timing difference in which host plants of ECM fungi show the maximum rates of net photosynthesis. A clearer seasonality of litter-decomposing fungi compared to wood-decomposing fungi may reflect greater predictability of seasonal availability of decomposing substrates.
The peak leaf fall of deciduous trees occurs in late autumn in a temperate forest, whereas that of evergreen trees is usually in late spring  , .