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Many species of fungi produce ephemeral autumnal fruiting bodies to spread and multiply. Despite their attraction for mushroom pickers and their economic importance, little is known about the phenology of fruiting bodies. Using ≈34,500 dated herbarium records we analyzed changes in the autumnal fruiting date of mushrooms in Norway over the period 1940–2006. We show that the time of fruiting has changed considerably over this time period, with an average delay in fruiting since 1980 of 12.9 days. The changes differ strongly between species and groups of species. Early-fruiting species have experienced a stronger delay than late fruiters, resulting in a more compressed fruiting season. There is also a geographic trend of earlier fruiting in the northern and more continental parts of Norway than in more southern and oceanic parts. Incorporating monthly precipitation and temperature variables into the analyses provides indications that increasing temperatures during autumn and winter months bring about significant delay of fruiting both in the same year and in the subsequent year. The recent changes in autumnal mushroom phenology coincide with the extension of the growing season caused by global climate change and are likely to continue under the current climate change scenario. • phenology • global warming • herbarium data • fungi • agarics
Mushroom fruiting and climate change
Håvard Kauserud*, Leif Christian Stige
, Jon Olav Vik
, Rune H. Økland
, Klaus Høiland*, and Nils Chr. Stenseth
*Microbial Evolution Research Group and
Centre for Ecological and Evolutionary Synthesis, Department of Biology, University of Oslo,
P.O. Box 1066 Blindern, NO-0316 Oslo, Norway; and
Department of Botany, Natural History Museum, University of Oslo, P.O. Box 1172 Blindern,
NO-0318 Oslo, Norway
Edited by Hans R. Herren, Millennium Institute, Arlington, VA, and approved January 22, 2008 (received for review September 23, 2007)
Many species of fungi produce ephemeral autumnal fruiting bodies
to spread and multiply. Despite their attraction for mushroom
pickers and their economic importance, little is known about the
phenology of fruiting bodies. Using 34,500 dated herbarium
records we analyzed changes in the autumnal fruiting date of
mushrooms in Norway over the period 1940 –2006. We show that
the time of fruiting has changed considerably over this time period,
with an average delay in fruiting since 1980 of 12.9 days. The
changes differ strongly between species and groups of species.
Early-fruiting species have experienced a stronger delay than late
fruiters, resulting in a more compressed fruiting season. There is
also a geographic trend of earlier fruiting in the northern and more
continental parts of Norway than in more southern and oceanic
parts. Incorporating monthly precipitation and temperature vari-
ables into the analyses provides indications that increasing tem-
peratures during autumn and winter months bring about signifi-
cant delay of fruiting both in the same year and in the subsequent
year. The recent changes in autumnal mushroom phenology coin-
cide with the extension of the growing season caused by global
climate change and are likely to continue under the current climate
change scenario.
phenology global warming herbarium data fungi agarics
henological changes are among the most sensitive ec ological
responses to changing climate (1–3). The observed extension
of the average annual growing season in Europe by nearly 11
days since the early 1960s (4) has been followed by rapid and
recent changes in plant flowering time (5–8) and earlier spring
migration in several bird species (9). In a recent study from the
Un ited Kingdom, it was reported for a set of mushroom species
that fruiting on average started earlier and ended later in the
season in recent years than 20 years ago, i.e., that the fruiting
period has been greatly extended (10). These changes were
linked to increased temperature and rainfall in August and
October, respectively (10).
Most fungi produce ephemeral fruiting bodies that can be
observed only for a few days each year, which makes phenolog-
ical dat a difficult and time-consuming to obtain. However,
because of the short endurance of the fr uiting bodies, collection
time is a good estimate of fruiting time. A potential source of
phenological information for this group of organisms is therefore
herbarium c ollections, which, although sampled in a nonsystem-
atic manner, share properties with random sampling processes.
Herbarium data can enable us to understand and predict
climate-induced ec ological changes in the future by understand-
ing how climate has affected ec ological processes in the past.
Several studies have already documented that herbarium col-
lections may represent a valuable source of long-term and
reliable phenological information (e.g., refs. 7 and 8).
Our study of temporal trends in fruiting phenology is based on
34,500 herbarium records collected in Norway during the
period 1940–2006 and representing 83 agaricoid (mushroom)
species [supporting infor mation (SI) Table 1]. By thorough
analyses of these data we aim to est ablish quantitative relation-
ships between climate (and climate change) and fungal autumnal
f ruiting time in Norway.
Results and Discussion
In an analysis of variance, the observed variation in fr uiting dates
can be partitioned into bet ween-species differences (15.8%),
variabilit y within species between years (25.9%), and variability
within species within years (58.3%). Using generalized additive
models (GAMs) (11) (see below and SI Table 2 for model
descriptions), we find that geographic differences in fruiting time
(across all species) explain 3.5% of the tot al variation, that
temporal trends (across groups of species) explain 3.9% of the
variation, and that 7.2% of the variation can be attributed to
shared responses of species to interannual variability in temper-
ature and precipitation (no temporal trend term in the model)
(P 0.001 in all cases; bootstrap tests).
Mushroom fr uiting date changed considerably during the
period 1940–2006, with earlier fruiting in the early years (1940
1950) and later fruiting in the last 15 years (Fig. 1A). On average
across the period (and across all species) there has been a delay
in fr uiting of 13.3 1.2 days [linear rate of change per 60 years
bootstrap standard error (P 0.001); GAM, correcting for
location and species effects]. Most of the shift took place
bet ween the 1980s and the 2000s [12.9 1.2 days per 20 years
(P 0.001); analysis of data 1980–2006]. The displacement of
f ruiting date parallels the delay of other autumn events, such as
leaf coloring being delayed by 4.8 days in Europe during the
period 1959–1993 in response to climate change (4). The delay
of mushroom fruiting does, however, contrast with the general
climate-induced advance in plant fruiting and ripening (1),
suggesting that constraints on fr uiting differ between fungi and
Fr uiting was more strongly delayed for the early autumnal
f ruiters, as revealed by a continuous interaction term fitted
bet ween initial fruiting day (1940–1959) and year (Fig. 1B).
Whereas fruiting of early f ruiters was delayed by 30 days over
the entire period, late fruiters had no fr uiting-time delay. This
ac cords with a highly sign ificant linear relationship between the
in itial (1940–1959) mean day of fruiting for each species and the
displacement of fruiting time from 1940 to 2006 (Fig. 1C).
Studies of spring phenology i n plants have also shown differences
bet ween ‘‘early’’ and ‘‘late’’ species in their response to climate
change (5, 12). The stronger delay for early fruiters compared
with late fruiters implies that the start of the fruiting season has
been delayed while the end has remained more or less un-
changed. Thus, the mushroom fruiting season in Norway has
bec ome progressively more compressed into late autumn in
Nor way. A decrease in residual variation (most at the within-
species within-year level; restricted maximum-likelihood analy-
sis) with time also indicated that the length of the overall fruiting
Author contributions: H.K. and L.C.S. contributed equally to this work; H.K., L.C.S., K.H., and
N.C.S. designed research; H.K., L.C.S., J.O.V., R.H.Ø., and N.C.S. performed research; H.K.
and L.C.S. analyzed data; and H.K., L.C.S., J.O.V., R.H.Ø., K.H., and N.C.S. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
To whom correspondence should be addressed. E-mail:
This article contains supporting information online at
© 2008 by The National Academy of Sciences of the USA
www.pnas.orgcgidoi10.1073pnas.0709037105 PNAS
March 11, 2008
vol. 105
no. 10
season has been c ompressed (Fig. 1D and SI Text). Our results
c ontrast with Gange et al.’s recent finding that the mushroom
f ruiting season has ex panded in both directions in the United
K ingdom (10). We see no obvious reason why first and last
f ruiting dates in the United Kingdom should show trends
dif ferent from the early and late quantiles of Norwegian mush-
room fruiting dates (Fig. 1D), nor are we aware of differences in
the mechanisms controlling mushroom f ruiting between the
Un ited Kingdom and Norway. Studies of the cues and con-
straints that govern fungal fruiting might clarify this issue. Cues
might relate to autumnal events that occur later than before,
whereas constraints on resource acquisition and achieving ‘‘fruit-
ing potential’’ might be fulfilled earlier when the climate is
milder. In many mushrooms, f ruiting can be induced experi-
ment ally after veget ative growth by reducing the temperature by
at least 5°C (13), and this might be an important environmental
cue that has been delayed because of global warming.
Geographic location was also found to be highly import ant for
f ruiting date. Fruiting bodies typically appear considerably ear-
lier (in the range of 10–20 days) in northern, continental, and
alpine regions of Norway compared with more southern and
ocean ic regions (Fig. 2). This latitudinal pattern follows general
trends well known f rom plant phenology (14). However, we did
not find significant dependencies of temporal changes in fr uiting
date on location (SI Table 2).
We found no relation between trends in fruiting time and
fungal feeding mode, i.e., whether fungi live in symbiosis with
plants (ectomycorrhizal mode) or feed on dead organ ic matter
(saprotrophic mode) (SI Table 2). Gange et al. (10) found
delayed fruiting only for myc orrhizal fungi living in association
with deciduous trees but not for fungi associated with conifers
(which lack a concentrated period of leaf shedding). Unfortu-
nately, our herbarium data do not allow a similar comparison.
Temperature and moisture are exogenous key factors known
to influence the production of autumnal fruit bodies (15). We
analyzed the effects on f ruiting time of monthly regional anom-
alies in temperature and precipitation from June the preceding
year to November the immediate year. Our results suggest that
high temperature during November the preceding year, high
precipit ation during July the preceding year, and high temper-
atures during February, August, and October (the current year)
are associated with delayed fruiting (Fig. 3). Furthermore, high
temperatures during May and June, high precipitation during
June and October, and intermediate amounts of precipitation
during November (all factors referring to the current year) are
associated with earlier fruiting (Fig. 3). Worth noticing is that the
overall year effect (Fig. 1 A) was no longer statistically significant
when added to the best model with climatic predictors (SI Table
2). Hence, the documented overall year effect is explainable as
a direct effect of significant increases of winter and autumn
temperatures in Norway during the last decades (SI Tables 3
and 4).
It might be claimed that our results have been derived f rom
biases in the data. However, we find it most unlikely that
sampling bias, for which herbarium data may be criticized on
theoretical grounds, can acc ount for the observed delay in
f ruiting. For our data to be biased with respect to fruiting time,
a temporal shift in collecting ef fort toward later in the autumn
1940 1960 1980 2000
Normalized fruiting date
1940 1960 1980 2000
Normalized fruiting date
220 240 260 280
Initial mean fruiting day
Estimated change
1940 1960 1980 2000
Fig. 1. Temporal variation in fruiting during the period 1940–2006. (A)
Diagram showing temporal changes in seasonal fruiting time during the
period 1940–2006. Lines indicate fitted year effect with 95% bootstrap con-
fidence limits from a GAM in which species and location effects were ac-
counted for. The trend was modeled as a smooth effect of time and was
significantly positive (P 0.001, bootstrap test). Points indicate partial resid-
uals averaged for each species and year combination. (B) Changes in fruiting
during the period 1940 –2006, partitioned on early (red; 28 species), middle,
(black; 27 species) and late (blue; 28 species) fruiters. Lines indicate fitted year
effects with 95% bootstrap confidence limits (for the median of the initial
mean fruiting dates within each group) from a GAM in which species and
location effects were accounted for. The trend was modeled as a tensor-
product smooth function of time and initial fruiting day. This model provided
significantly better fit to the data than the model with a common trend for all
species (P 0.001, bootstrap test). Points indicate partial residuals averaged
for each species and year combination. (C) Displacement in fruiting time
during the period 1940–2006 for the 83 fungal taxa related to initial (1940
1959) mean day of fruiting of each species. Displacement in fruiting (per 60
years) was calculated for each species separately by using GAMs with linear
time effects and the geographic effects accounted for by smooth functions of
longitude and latitude (thin-plate regression spline with maximally 11° of
freedom). Filled points indicate statistically significant effects (P 0.05,
bootstrap tests for each species). Lines indicate linear regression line 1.96
standard error (across species). (D) Residuals from the model shown in B. Lines
indicate quantiles (5%, 10%, 25%, 50%, 75%, 90%, and 95%) as estimated by
quantile regression (23, 24). The standard deviations of the residuals within
years were negatively correlated with year (Pearson’s correlation coefficient,
r ⫽⫺0.46, 95% bootstrap c.i. ⫽⫺0.64, 0.26), as were the standard deviations
of the raw observations (r ⫽⫺0.47, c.i. ⫽⫺0.63, 0.28).
5 1015202 0
Longitude (°E)
Latitude (°N)
Fig. 2. Spatial patterns in mean day of fruiting of 83 fungal species in
Norway. The isoclines (lines of different colors) represent iso-lines with 95%
bootstrap confidence limits. The effect of geographic location was estimated
as smooth functions of longitude and latitude (thin-plate regression spline
with maximally 11° of freedom) by using GAM also accounting for effects of
species and temporal trends (Fig. 1B).
www.pnas.orgcgidoi10.1073pnas.0709037105 Kauserud et al.
would have to have taken place during the period 1940–2006,
most dramatically during the last 20 years when the most
dramatic changes in f ruiting time have taken place. It seems
dif ficult to imagine any rational reason for such a change of
behavior among fungus collectors. Furthermore, we are unable
to see how sampling bias possibly c ould lead to significant
associations between monthly climate variables and fruiting
time, especially with time lags. Consequently, we think that the
delay in fruiting is most parsimoniously explained through the
documented effects of climate.
Through our analysis we have demonstrated changes in the
temporal pattern of fungal fruiting in Norway during the period
1940–2006 that most likely are responses to climate change.
Worth noticing is that the accelerated delay of fruiting in the last
20 years has coincided with dramatic global warming (16). We
predict that the projected rise of global temperatures by up to
4°C by 2100 (16) will have drastic ef fects on fungal fruiting
phenology. Because fruit bodies function as habitat and diet for
many organ isms, these changes may have profound side ef fects.
Materials and Methods
Data. Data for agaricoid species (mushrooms) with at least 250 entries in the
Norwegian Mycology Database (Natural History Museum, University of Oslo)
and with mainly autumnal fruit bodies were included in the study (n 83
species) (SI Table 1). Only herbarium records with a proper dating (day) and
geographic localization (municipality) were used for analyses. The approxi-
mate geographic positions of the records were obtained by allocating geo-
graphic coordinates for the municipalities to the records. Categorical infor-
mation for the species feeding mode (saprotrophic or ectomycorrhiza) was
also included. The number of records analyzed for each species ranged from
226 to 945; the total number of records was 34,528. Climate data for monthly
temperature and precipitation anomalies in Norway’s five main regions, for
the period 1939–2006, were obtained from the Norwegian Meteorological
Statistical Analyses. We used the GAM implementation of the ‘‘mgcv’’ library of
R (11). To compare competing models we computed genuine cross-validation
(CV) errors, models with lower CV having higher out-of-sample predictive power.
Because of within-year correlations in the response variable, CV was calculated by
leaving data for 1 year out at a time. Outlier observations (n 60), identified by
using Grubb’s test (17) on the residuals from a GAM accounting for species and
location effects, were removed before final analyses. All outlier observations
were records made before day 155 (June 4). A total of 34,468 records (after outlier
exclusion) for all of the 83 species were analyzed in one model with collection
time (reflecting the time of fruiting) as response. Thus, we did not use first/last
observations as, e.g., Gange et al. (10) did, but instead included all records
throughout the entire fruiting season, revealing trends both in mean fruiting
date and in the variability around these trends. Differences between species in
mean fruiting time were accounted for in all models, as were location effects.
Location effects (as shown in Fig. 2) were assumed to be similar across species and
were modeled by a thin-plate regression spline of longitude and latitude (max-
imally 12 knots, i.e., 11° of freedom). Temporal trends were modeled as either
linear or smooth effects (natural cubic splines with maximally 4 knots) of year
(1940–2006). Possible differences in temporal trends between (i) different re-
gions (central, east, north, south, and west Norway), (ii) species groups defined by
feeding mode (saprotrophic and ectomycorrhiza), or (iii) species groups charac-
terized by the initial (1940 –1959) mean day of fruiting (the 28 earliest-fruiting
species, the 27 intermediate-fruiting species, and the 28 latest-fruiting species)
were accounted for in models with group-specific year terms (i.e., with 5, 3, or 2
smooth year terms instead of 1). A continuous interaction between initial fruiting
day and year was modeled by a tensor-product smooth function constructed
from linear combinations of terms that were cubic regression spline basis func-
tions of the two variables (each with maximally 4 knots) (11). CV showed that only
the interaction terms including initial fruiting day and year improved the model
(SI Table 2). Accordingly, results from the continuous interaction model, which
had the lowest CV prediction error, are shown in Fig. 1B. Data points are colored
by intervals of initial fruiting date as detailed in the figure legend.
A separate model was fitted to identify climate variables accounting for
interannual variation in fruiting. Linear effects of 36 different climate vari-
ables were considered (monthly regional anomalies in temperature and pre-
cipitation from June the preceding year to November the current year).
Starting with the full model, terms were removed until the model with the
lowest CV prediction error was found. CV prediction error was reduced further
by substituting two of the selected linear climatic effects with smooth terms
and subsequently removing two more linear terms. We also explored the
possible direct or lagged effects of the North Atlantic Oscillation (NAO) using
the PC-based NAO index described in refs. 18 and 19 (
jhurrell/, but we found this to have lower ex-
planatory power than the precipitation and temperature indices (results not
shown). Finally we tested for linear interaction effects between the selected
climatic variables and feeding mode or initial fruiting time and for the
combined effects of climatic variables and year. We found no significant
interactions between the climatic variables and feeding mode. We found
evidence for a negative interaction between initial fruiting day and temper-
ature in February and October (SI Table 2), suggesting a stronger response of
early fruiters to these climate variables. However, these interaction effects did
not fully explain the stronger year effect of early fruiters, because adding an
interaction term between year and initial fruiting day improved the predictive
power of the model (SI Table 2). In contrast, the model with only climatic
variables was not improved by adding a smooth year effect (SI Table 2),
suggesting that the overall temporal trends are indeed explainable by the
measured climatic variables.
The statistical significance of terms and confidence intervals were com-
puted by using a modified wild bootstrap approach (20, 21), as described in
ref. 22, which accounted for both heteroscedasticity and within-year correla-
tion of residuals. When calculating the significance of terms, bootstrap data
sets were constructed from residuals and fitted values from models without
the given terms, and the increase in variance explained (R
) by including the
given terms was used as test criterion. When calculating the significance of the
interaction between initial fruiting day and year, initial fruiting day was
recalculated for each bootstrap sample, thus accounting for bias resulting
from one of the predictor variables being derived from the response variable.
A quantile regression analysis, using the Frisch–Newton interior point
−4 −2 0 2 4
Temp. Nov. t 1
(°C anomaly)
fruiting date
−8 −4 0 4 8
Temp. Feb. t
(°C anomaly)
−2 0 2
Temp. May t
(°C anomaly)
−2 0 2 4
Temp. June t
(°C anomaly)
−2 0 2 4
Temp. Aug. t
(°C anomaly)
−6 −2 0 2 4
Temp. Oct. t
(°C anomaly)
50 150 250
Precip. July t 1
(% of normal)
fruiting date
50 100 200
Precip. June t
(% of normal)
50 100 200
Precip. Oct. t
(% of normal)
50 150 250
Precip. Nov. t
(% of normal)
Fig. 3. Climatic effects on interannual changes in fruiting of 83 fungal species. Temperature and precipitation variables are referring to either the preceding
year (t 1) or the same year (t) as fungal fruiting. The climatic effects shown were estimated as linear or smooth terms in one GAM also accounting for location
and species effects (SI Table 2). Whole and broken lines indicate fitted partial effects with 95% bootstrap confidence limits. The tick marks on the x axis show
the location of the covariates (see also SI Fig. 4, showing partial residuals).
Kauserud et al. PNAS
March 11, 2008
vol. 105
no. 10
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residual variance structure was analyzed by restricted maximum-likelihood
methods (25) using the nlme library of R (26), selecting the most parsimonious
model based on Akaike’s information criterion.
ACKNOWLEDGMENTS. We acknowledge all who have contributed with spec-
imens to the Norwegian mycological herbaria, Einar Timdal for making the
data available through the web interface, the Climate Division at the Norwe-
gian Meteorological Institute for providing climate data, and two anonymous
reviewers for valuable comments on earlier versions of the manuscript.
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... Climate change has a major impact on wild mushroom production [1]. Many studies deal with the impacts of climate change, specifically the amount of precipitation, on mushrooms in various European locations such as Finland [7], Norway [8], and Spain [9,10]. Only a limited amount of research is focused on the area of Central Europe [10,11]. ...
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... These are climates that can produce an increase in the potential area of the holm oak groves [61] and that could cause significant changes in the composition of the fungal community without effects on the overall richness of the mycorrhizal community [26], with a possible increase in Climate models predict an increase in average temperature, with a potential decrease in annual rainfall and dramatic changes in its distribution, with rainfall concentration, in the Mediterranean basin during the 21st century [32]. These are climates that can produce an increase in the potential area of the holm oak groves [61] and that could cause significant changes in the composition of the fungal community without effects on the overall richness of the mycorrhizal community [26], with a possible increase in growth rates and alteration in the distribution of mycorrhizal species with an advantage for generalist mycorrhizae [62], delays in autumn fruiting, early spring or shorter duration of fruiting [7][8][9]. The projections of the meteorological variables studied seem to corroborate that the new conditions will modify the fruiting waves. ...
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The fungal biodiversity associated with a stable plant community appears to vary from year to year. To analyse the annual behaviour in the formation of fruiting bodies, a statistical study of the data obtained for 4 years (2009–2012) in an apparently undisturbed Mediterranean ecosystem dominated by the holm oak (Quercus ilex L. subsp. ballota (Desf.) Samp.), located in the Midwest of the Iberian Peninsula, was carried out. These data were related to the main meteorological variables. The 150 species collected showed a significant annual, monthly, and weekly difference in their fruiting during the collection period. All this implies a variation in the annual fungal fruiting which can modify the moment of when maximum peaks of fruiting appear, their duration, and the number of species that compose them. In addition, the results make it possible to establish an annual behaviour pattern, with sporocarp formation throughout the year and four fruiting groups (two of them in the dry season). They also allow for inferring a possible response to climate change, with a delay in the fruiting of the autumn-winter group and earlier fruit bearing in the winter-spring group.
... Variability in fruiting duration across grids in these biomes indicates localityspecific fruiting duration constraints in a given year. Previous studies from boreal and temperate Europe showed that fruiting duration change is correlated with climate variables (Boddy et al., 2014;Büntgen et al., 2012;Kauserud et al., 2008). This implies that the observed spatial variability in boreal and temperate biomes is caused by specific climate conditions in a year, favouring or limiting fruiting duration (e.g., temperature and moisture conditions). ...
... This indicates that the effect of increasing warming is biome-dependent. Previous studies showed that in European boreal and temperate biomes, fruiting duration has, on average, extended (Boddy et al., 2014;Gange et al., 2007;Kauserud et al., 2008). Based on our dataset, we found an increase and then saturation in duration within the northern temperate biome ( Figure 6). ...
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The Earth's ecosystems are affected by a complex interplay of biotic and abiotic factors. While global temperatures increase, associated changes in the fruiting behaviour of fungi remain unknown. Here, we analyse 6.1 million fungal fruit body (mushroom) records and show that the major terrestrial biomes exhibit similarities and differences in fruiting events. We observed one main fruiting peak in most years across all biomes. However, in boreal and temperate biomes, there was a substantial number of years with a second peak, indicating spring and autumn fruiting. Distinct fruiting peaks are spatially synchronized in boreal and temperate biomes, but less defined and longer in the humid tropics. The timing and duration of fungal fruiting were significantly related to temperature mean and variability. Temperature‐dependent aboveground fungal fruiting behaviour, which is arguably also representative of belowground processes, suggests that the observed biome‐specific differences in fungal phenology will change in space and time when global temperatures continue to increase.
... These climate-related factors can affect the physiology and distribution of living organisms, such as plants and fungi. In this context, there is evidence that climate change has an impact on pollen and spore production by plants and fungi and on various phenological events [144,145]. This is reflected in the production, distribution, dispersion, and content of aeroallergens in the air, which may result in changes in the incidence of allergic diseases and/or the severity of their symptoms [146]. ...
... Climate change has also been found to induce morphological changes in fungal spores. Kauserud et al. found that spores produced at the beginning of autumn exhibited higher water accumulation, which increased their size, while spores produced at the end of autumn were smaller [145]. In terms of allergy, changes in the spore size are important because a smaller size makes spores more accessible and inhalable; hence, they are more likely to be deposited deeper in the human respiratory system. ...
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The incidence of allergic diseases worldwide is rapidly increasing, making allergies a modern pandemic. This article intends to review published reports addressing the role of fungi as causative agents in the development of various overreactivity-related diseases, mainly affecting the respiratory tract. After presenting the basic information on the mechanisms of allergic reactions, we describe the impact of fungal allergens on the development of the allergic diseases. Human activity and climate change have an impact on the spread of fungi and their plant hosts. Particular attention should be paid to microfungi, i.e., plant parasites that may be an underestimated source of new allergens.
... The composition of wood-inhabiting fungal community is affected by many factors, including environmental elements (such as precipitation, latitude, light, temperature and humidity) [45][46][47], tree species and the characters of host wood (chemical composition, physical structure, diameter, volume and decay degree) [14,19]. ...
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Background Deadwood is an important structural component of forest ecosystem and its decaying process is crucial for nutrient cycles. Wood-inhabiting fungi play the vital function in the decomposition of deadwood. The composition of wood-inhabiting fungal communities change over the course of the deadwood decaying process. As the typical forest, the temperate broad-leaved Korean pine mixed forest of Changbaishan Nature Reserve (CBS) has been the studying focus area. Since the wood-inhabiting fungal communities of deadwood would still be litter known, we need to reveal wood properties, differences between wood-inhabiting fungal communities of different tree species during process of the wood decomposition and the main influencing factors. To achieve this goal, we exposed deadwood logs of 7 dominant tree species in CBS, covering gymnosperm and angiosperm with three decaying levels. Results We found the distinct varieties of wood properties, including total C, total N and total P etc. between different tree species and decaying levels. These factors caused the different wood-inhabiting fungal community composition of deadwood between whether tree species or decaying levels. The 50 dominant fungal species showed the clear nutrient preference. In general, most Basidiomycota tend to use woody substrate with high N content while most Ascomycota prefer high P content. Some of them may like high C content more. Conclusions The composition of wood-inhabiting fungal communities changed both over the course of the deadwood decaying process and between the different host tree species. The results of NMDS analysis of wood-inhabiting fungal community of seven tree species logs with three decay levels showed that the nutrients of deadwood, etc total C, total N and total P content, were the main driving factor. The preference of dominant fungal species represent the community it is located in a certain.
... Moreover, it is a labor-intensive process, and the changing outdoor conditions result in volatile harvests. Furthermore, climate change further limits outdoor mushroom growing and harvesting opportunities [1,2]. Indoor mushroom farms with controlled growth environments allow for an all-year-round growing and harvesting of mushrooms in sensor-controlled grow rooms and grow tents. ...
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Gourmet mushrooms are foraged from the wild or grown indoors in controlled environments. Indoor mushroom farms with controlled growth environments allow for all-year-round growing. However, it remains a labor-intensive process. We propose MushR as a modular and scalable gourmet mushroom growing and harvesting system that goes beyond the state of the art, which merely monitors and controls the growing environment, by introducing an image recognition system that determines when and which mushrooms are ready to be harvested in conjunction with a proof of concept of an automated mushroom harvesting mechanism for harvesting the mushrooms without human interaction. The image recognition setup monitors the growing status of the mushrooms and guides the harvesting process. We present a Mask R-CNN model for the detection of oyster mushroom maturity with a 91.7% training accuracy and a semiautomated harvesting system, integrating a Raspberry Pi for control, an electrical switch, an air compressor, and a pneumatic cylinder with a cutting knife to facilitate timely mushroom harvesting. The modularity and scalability of the system allow for industry-level usage and can be scaled according to the required mushroom-growing systems within the facility. The AI model, its underlying dataset, a digital twin for mushroom production, the setup of our growth and control chambers, and additional information are all made available under an open-source license.
... While such phenological changes in fruiting are more or less predictable, they are strongly modulated by interannual and spatial variation in precipitation and temperature (Straatsma and Krisai-Greilhuber 2003, Polevoi et al. 2006, Krebs et al. 2008, Sato et al. 2012, Andrew et al. 2018. Importantly, fungal species and taxa have different phenologies (Pinna et al. 2010, Sato et al. 2012, respond differently to variation in temperature and precipitation (Kauserud et al. 2008, Heegaard et al. 2017, and exhibit different preferences with regard to symbiont trees and forest types (Newton and Haigh 1998, Bruns 2002, Lang et al. 2011). These interspecific differences -and the extent to which they are phylogenetically structured -can create highly variable fungal assemblages across space and time. ...
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Through their ephemeral reproductive structures (fruiting bodies), ectomycorrhizal forest soil fungi provide a resource for a plethora of organisms. Thus, resolving what biotic and abiotic factors determine the occurrence and abundance of fruiting bodies is fundamental for understanding the dynamics of forest trophic networks. While the influence of abiotic factors such as moisture and temperature on fungal fruiting are relatively well established, little is known about how these processes interact with the evolutionary history of fungal species to determine when, where, and in which abundance fungal fruiting bodies will emerge. A specific knowledge gap relates to whether species' responses to their environment are phylogenetically structured. Here, we ask whether related fungal taxa respond similarly to climatic factors and forest habitat characteristics, and whether such correlated responses will affect the assembly of fungal fruiting communities. To resolve these questions, we fitted joint species distribution models combining data on the species composition and abundance of fungal fruiting bodies, environmental variation, and phylogenetic relationships among fungal taxa. Our results show that both site‐level forest characteristics (dominant tree species and forest age) and climatic factors related to phenology (effective heat sum) greatly influence the occurrence and abundance of fruiting bodies. More importantly, while different fungal species responded unequally to their shared environment, there was a strong phylogenetic signal in their responses, so that related fungal species tended to fruit under similar environmental conditions. Thus, not only are fruiting bodies short‐lived and patchily distributed, but the availability of similar resources will be further aggregated in time and space. These strong constraints on resource availability for fungus‐associated taxa highlight the potential of fungus‐based networks as a model system for studies on the ecology and evolution of resource–consumer relations in ephemeral systems of high spatiotemporal patchiness.
... Past studies have reported that climate change is already impacting macrofungal reproduction, distribution and physiology (Willis, 2018). For example, the timing of macrofungi fruiting has been found to be sensitive to climate and has changed over the last century (Diez et al., 2013;Gange et al., 2007;Kauserud et al., 2008Kauserud et al., , 2012; the fruiting patterns of many fungal species in the Alps exhibited altitudinal upwards shifts between 1960 and 2010 (Diez et al., 2020). But few studies predicted change in macrofungal diversity and distribution in future climate change scenarios. ...
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Aim Climate change is affecting biodiversity at an accelerating rate. Despite the importance of fungi in ecosystems in general, and in the global carbon and nitrogen cycle in particular, there is little research on the response of fungi to climate change compared with plants and animals. Earlier studies show that climatic factors and tree species are key determinants of macrofungal diversity and distribution at large spatial scales. However, our knowledge of how climate change will affect macrofungal diversity and distribution in the future remains poorly understood. Location Europe. Methods Using openly available occurrence data of 1845 macrofungal species from eight European countries (i.e. Norway, Sweden, Finland, Denmark, Netherlands, Germany, France and Spain), we built ensemble species distribution models to predict macrofungal response to climate change alone and combined climate and tree distribution change under the IPCC special report on 2080 emissions scenarios (SRES A2 and B2). Results Considering climate change alone, we predict that about 77% (74.1%–80.7%) of the modelled species will expand their distribution range, and around 57% (56.1%–58.4%) of the modelled area will have an increase in macrofungal species richness. However, when considering the combined climate and tree species distribution change, only 50% (50%–50.9%) of the species are predicted to expand their distribution range and 49% (47.4%–51.1%) of the modelled area will experience an increase in macrofungal species richness. Main Conclusions Overall, our models projected that large areas would exhibit increased macrofungal species richness under future climate change. However, tree species distribution might play a restrictive role in the future distributional shifts of macrofungi. In addition, macrofungal responses appear heterogeneous, varying among species and regions. Our findings highlight the importance of including tree species in the projection of climate change impacts on the macrofungal diversity and distribution on a continental scale.
... For example, in southern England the average fruiting period of 315 fungal species has more than doubled from 33.2 ± 1.6 d to 74.8 ± 7.6 d in the timespan of 1950-2005(Gange et al., 2007. Kauserud et al. (2008) likewise demonstrated a similar pattern in Norway, when investigating the phenology of 83 agaricoid species. By utilizing large fungarium datasets of records from 1940 to 2006, they detected a delay in fungal fruiting by 13.3 ± 1.2 d across all species. ...
Wood decomposing fungi differ in their substrate affinities, but to what extent factors like wood properties influence host specialization, compared to climate, is largely unknown. In this study, we analysed British field observations of 61 common wood decay species associated with 41 tree and shrub genera. While white rot fungi ranged from low-to high-substrate affinity, brown rot fungi were exclusively mid-to high-affinity. White rot fungi associated with dead fallen wood demonstrated the least substrate affinity. The composition of wood decomposer fungi was mostly structured by substrate properties, sorted between angiosperms and conifers. Any relationships with temporal and regional climate variability were of far less significance, but did predict community-based and substrate-usage host shifts, especially for fungi on fallen deadwood. Our results demonstrate that substrate shifts by wood-decay fungi will depend primarily upon their degree of affinity to, and the distribution of, related woody genera, followed less at regional levels by climate impacts.
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The semi natural grassland described in this paper, Hovaneset in Stord municipality, South- Western Norway, has been examined for grass- land fungi for 20 years, from 2003 until today (2022). This has resulted in the discovery of 92 different species after a total of 180 visits to the locality. Change in the frequency of survey to approximately a weekly visit during the seasons from 2010 resulted in a large increase in species diversity, also of red-listed species. During these last 13 years of survey, the total number of species more than doubled, and the number of red-listed species more than tripled, which clearly shows the huge effect in changing the frequency of visits to a visit once a week during the season. The survey also clearly shows that the number of finds during a visit varies greatly from year to year and from week to week. In an optimal year for grassland fungi, around 70% of the species that grow there can be found in a weekly survey throughout the season, while a less good year gives a maximum of 40% finds. The survey also shows that the time of visit in a particular year is decisive for how many species are found in one visit. In an optimal year for grassland fungi, at the best possible match with time of survey, you can find up to 50% of the species growing there in one visit, but more likely it will be around 35%. It therefore shows very clearly that visits during the whole season is necessary to get hold on all fungi growing here. The survey also shows that many grass- land fungi have very irregular fructification from one year to the other, which particularly applies to species within the genera Clavaria and Entoloma. Only ten species were found each year during the survey, most of which were waxcaps (Hygrocybe s.l.), while 14 species were only found in one of the years of survey. The time of fruiting also seems to have changed during the 20 years the survey has been ongoing. The week with the highest number of species recorded in 2021/22 is approximately 3-4 weeks later than it was in 2013/14. This is particularly clear for the genus Entoloma, which normally has an early and limited fruiting period.
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Models of climate change predict that global temperatures and precipitation will increase within the next century, with the most pronounced changes occurring in northern latitudes and during winter. A large-scale atmospheric phenomenon, the North Atlantic Oscillation (NAO), is a strong determinant of both interannual variation and decadal trends in temperatures and precipitation during winter in northern latitudes, and its recent persistence in one extreme phase may be a substantial component of increases in global temperatures. Hence, the authors investigated the influences of large-scale climatic variability on plant phenology and ungulate population ecology by incorporating the NAO in statistical analyses of previously published data on: (1) the timing of flowering by plants in Norway, and (2) phenotypic and demographic variation in populations of northern ungulates. The authors analyzed 137 time series on plant phenology for 13 species of plants in Norway spanning up to 50 yr and 39 time series on phenotypic and demographic traits of 7 species of northern ungulates from 16 populations in North America and northern Europe spanning up to 30 yr.
The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models. The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the book’s R data package gamair, to enable use as a course text or for self-study.
The occurrence of fruit bodies of larger fungi growing in a coniferous forest in Northern Ireland was recorded from 1974 and the influences of temperature and rainfall on the production of sporocarps examined over a 14-yr-period. The largest sporocarp counts occurred in the autumns following the warmest four summers and the three lowest counts, with one exception, followed cold winters and cold May periods. There was a high statistical correlation between sporocarp counts and the means of average daily temperature for the period 2–4 months prior to recording dates over a 10-yr-period. Sporocarp counts also showed high correlation with rainfall for the period 3–5 months prior to recording; however, rainfall appeared to have little influence on the maximum counts obtained for each year. Correlation values suggested that rainfall for the first 2 d and the 7–14-d-period prior to recording reduced sporocarp production. Values for the maximum sporocarp count per year may be progressively declining.
Procedures are given in the report for determining statistically whether the highest observation, or the lowest observation, or the highest and lowest observations, or the two highest observations, or the two lowest observations, or perhaps more of the observations in the sample may be considered to be outlying observations or discrepant values. Statistical tests of significance are useful in this connection either in the absence of assignable physical causes or to support a practical judgement that some of the experimental observations are aberrant. Both the statistical formulae and illustrative applications of the procedures to practical examples are given, thus representing a rather complete treatment of significance tests for outliers in single univariate samples.
A simple minimization problem yielding the ordinary sample quantiles in the location model is shown to generalize naturally to the linear model generating a new class of statistics we term "regression quantiles." The estimator which minimizes the sum of absolute residuals is an important special case. Some equivariance properties and the joint aymptotic distribution of regression quantiles are established. These results permit a natural generalization to the linear model of certain well-known robust estimators of location. Estimators are suggested, which have comparable efficiency to least squares for Gaussian linear models while substantially out-performing the least-squares estimator over a wide class of non-Gaussian error distributions.
Global climate change impacts can already be tracked in many physical and biological systems; in particular, terrestrial ecosystems provide a consistent picture of observed changes. One of the preferred indicators is phenology, the science of natural recurring events, as their recorded dates provide a high-temporal resolution of ongoing changes. Thus, numerous analyses have demonstrated an earlier onset of spring events for mid and higher latitudes and a lengthening of the growing season. However, published single-site or single-species studies are particularly open to suspicion of being biased towards predominantly reporting climate change-induced impacts. No comprehensive study or meta-analysis has so far examined the possible lack of evidence for changes or shifts at sites where no temperature change is observed. We used an enormous systematic phenological network data set of more than 125 000 observational series of 542 plant and 19 animal species in 21 European countries (1971–2000). Our results showed that 78% of all leafing, flowering and fruiting records advanced (30% significantly) and only 3% were significantly delayed, whereas the signal of leaf colouring/fall is ambiguous. We conclude that previously published results of phenological changes were not biased by reporting or publication predisposition: the average advance of spring/summer was 2.5 days decade−1 in Europe. Our analysis of 254 mean national time series undoubtedly demonstrates that species' phenology is responsive to temperature of the preceding months (mean advance of spring/summer by 2.5 days°C−1, delay of leaf colouring and fall by 1.0 day°C−1). The pattern of observed change in spring efficiently matches measured national warming across 19 European countries (correlation coefficient r=−0.69, P<0.001).