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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
method and cubic splines with 8 knots, was performed with the residuals from
model shown in Fig. 1B as response (23, 24). In a separate analysis (SI Text),
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.
1. Menzel A, Sparks TH, Estrella N, Koch E, Aasa A, Ahas R, Alm-Ku¨ bler K, Bissolli P,
Braslavska´ O, Briede A, et al. (2006) Glob Change Biol 12:1969 –1976.
2. Stenseth NC, Mysterud A, Ottersen G, Hurrell JW, Chan KS, Lima M (2002) Science
3. Walther GR, Post E, Convey P, Menzel A, Parmesan C, Beebee TJC, Fromentin JM,
Hoegh-Guldberg O, Bairlein F (2002) Nature 416:389 –395.
4. Menzel A, Fabian P (1999) Nature 397:659.
5. Bradley NL, Leopold AC, Ross J, Wellington H (1999) Proc Natl Acad Sci USA 96:9701–9704.
6. Fitter AH, Fitter RSR (2002) Science 296:1689 –1691.
7. Lavoie C, Lachance D (2006) Am J Bot 93:512–516.
8. Primack D, Imbres C, Primack RB, Miller-Rushing AJ, Del Tredici P (2004) Am J Bot
9. Jonze´ n N, Linde´ n A, Ergon T, Knudsen E, Vik JO, Rubolini D, Piacentini D, Brinch C, Spina
F, Karlsson L, et al. (2006) Science 312:1959 –1961.
10. Gange AC, Gange EG, Sparks TH, Boddy L (2007) Science 316:71.
11. Wood SN (2006) Generalized Additive Models: An Introduction with R (Chapman and
Hall/CRC, Boca Raton, FL).
12. Post E, Stenseth NC (1999) Ecology 80:1322–1339.
13. Ku¨ es U, Liu Y (2000) Appl Microbiol Biotechnol 54:141–152.
14. Ovaska JA, Nilsen J, Wielgolaski FE, Kauhanen H, Partanen R, Neuvonen S, Kapari L,
Skre O, Laine K (2005) Ecol Stud 180:99 –115.
15. Eveling DW, Wilson RN, Gillespie ES, Bataille A (1990) Mycol Res 94:998 –1002.
16. Intergovernmental Panel on Climate Change (2007) Climate Change 2007: Impacts,
Adaptation and Vulnerability. Contribution of Working Group II to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change, eds Parry ML,
Canziani JP, Palutikof JP, van der Linden PL, Hanson CE (Cambridge Univ Press,
Cambridge, UK).
17. Grubbs F (1969) Technometrics 11:1–21.
18. Hurrell JW (1995) Science 269:676 679.
19. Stenseth NC, Ottersen G, Hurrell JW, Mysterud A, Lima M, Chan KS, Yoccoz NG,
Adlandsvik B (2003) Proc R Soc London Ser B 270:2087–2096.
20. Liu RY (1988) Ann Stat 16:1696–1708.
21. Mammen E (1993) Bootstrap and wild bootstrap for high dimensional linear models in
resampling. Ann Stat 21:255–285.
22. Stige LC, Ottersen G, Brander K, Chan KS, Stenseth NC (2006) Mar Ecol Prog Ser
23. Koenker R (2006) QUANTREG: Quantile Regression. R package (Vienna University,
Vienna), Version 4.01.
24. Koenker R, Bassett G (1978) Econometrica 46:33–50.
25. Pinheiro JC, Bates DM (2002) Mixed-Effects Models in S and S-PLUS (Springer, New
26. Pinheiro JC, Bates D, DebRoy S, Sarker D (2006) nlme: Linear and nonlinear mixed
effects models. R package (Vienna University, Vienna), Version 3.1-73.
www.pnas.orgcgidoi10.1073pnas.0709037105 Kauserud et al.
... Boddy et al., 2014), mostly in Europe (e.g. Kauserud et al., 2008;Büntgen et al., 2012a;Andrew et al., 2018) and North America (Diez et al., 2013). These studies consensually showed significantly delayed fruiting patterns of fungi during the 20th century in both nemoral and boreal regions. ...
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... These authors evidenced a 30 d fruiting shift of early-fruiting species with no shift for late-fruiting species. Interestingly, and even if averaged values have to be considered with caution because of the above-mentioned methodological biases, we observed a fruiting shift at the secular scale of similar amplitude to previous studies investigating the same question at decennial scales (Gange et al., 2007;Kauserud et al., 2008;Moore et al., 2008). This finding suggests that either most phenological shifts are recent and similar in duration across Europe, or changes in the Mediterranean area during recent decades were less pronounced than in temperate regions. ...
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... fungal fruitbodies, mushrooms) in many societies and, especially, in the Mediterranean basin (Boa, 2004;Palahí et al., 2009). Given that different fungal species and functional groups are driven differently by climatic, nutritional, or biotic factors, it is expected that different fungal guilds also react differently to changes in the environmental conditions (Kauserud et al., 2008;Diez et al., 2013;Bennett and Classen, 2020;Collado et al., 2019). Thus, mycorrhizal and saprotrophic fungi productivity may be affected differently by climate change and potentially further feed back into climate change impacts. ...
... In line with the findings reported by Salerni et al. (2002), Kauserud et al. (2008), Büntgen et al. (2013) andÁ greda et al. (2015), we found that the relationships between environmental variables and fungal productivity, as well as the importance given to each predictor in the models, varied depending on the fungal trophic strategy. Such variation reflects differences in the fruiting phenology of the species in each functional group and their ecological requirements (Diez et al., 2013;J. ...
Fungi are responsible for many of the processes that occur in natural ecosystems and largely determine forest ecosystem dynamics, such as the ability of trees to access limiting nutrients and sequester carbon. Understanding and predicting climate change impacts on fungal dynamics over large scales is key in order to gain further insights into the effects of global change on natural ecosystem functioning and related ecosystem services. In this study, we use predictive models based on machine learning algorithms to estimate, in a spatially explicit way, the historical and future (1976-2100) evolution of mycorrhizal and saprotrophic macrofungal productivity in Mediterranean forest areas under climate change scenarios. The greatest changes in total productivity, as well as mycorrhizal fungi, are predicted to occur in subalpine and montane pine forests, where fungal productivity is estimated to decrease, and will be more pronounced under climate change scenarios with higher expected increase in temperature. In contrast to mycorrhizal species, saprotrophic fungi could benefit from pronounced changes in climate and increase their productivity in supra-and mesomediterranean regions at mid-range elevations. Moreover, we estimated that fungal productivity has also changed historically in some scattered areas where changes in climate over the years may have led to a decrease in productivity. This study contributes to raising awareness on the need for anticipating potential global change impacts on this key element of ecosystem functioning, and for deploying possible management policies oriented toward maintaining the important role of fungal productivity in both climate change mitigation and adaptation.
... GAMs, unlike LMs, are data-driven rather than model-driven, and they allow the form of response curves to be determined from data rather than fitting an a priori parametric model, which is limited in its available shape of response. This flexibility and applicability have led to the use of GAM in a wide range of research areas, including climate change (Aalto et al., 2013;Grieve et al., 2017;Kauserud et al., 2008), ecology (Fisher et al., 2018;Pedersen et al., 2019), human health and diseases (Dominici et al., 2002;Jain et al., 2019;Ravindra et al., 2019), etc. ...
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Global climate change, expected to be one of the most severe challenges that human beings have ever encountered, has had far-reaching impacts on ecosystems and humans, among which the potentially increasing chance of violent conflict has raised attention recently. However, several years of research have produced no consensus regarding whether climate variability affects the risk of armed conflict and how it may affect conflict. In this study, we built a geographically disaggregated method to explore the relationship between climate variability from normal climate conditions and armed conflicts both on a local and regional scale. With the 10,993 conflict records acquired in 25 African countries over 16 years from 2000 to 2015, we estimated the effects of temperature and wet day variability on conflicts in agricultural and non-agricultural areas, respectively, on gridded 1° resolution. The results showed that deviations from the normal climate have a systematical impact on the risk of conflict: The risk of violence rises with increasing deviations from the temperature norms in both non-agricultural and agricultural areas. Regarding the rainfall variability, in non-agricultural areas, the risk of violence grows with increasing anomalous wet days, either more or fewer days than the annual average, while in agricultural areas, increases in violence risk only exhibit under the impact of fewer wet days than the annual average. We expect these findings would provide empirical support for policymakers and relevant organizations who need to prepare additional law enforcement and/or peacekeeping resources when climatic anomalies are detected.
... On the one hand, earlier vegetative onset in the spring increased calf weight and survival, likely through both mother and calf access to high-quality forage (Bårdsen and Tveraa 2012;Tveraa et al. 2013). However, warmer autumn and winter temperatures delay or compress the mushroom fruiting season, affect distribution of spores, and change mushroom abundance in Fennoscandia, an important food source for reindeer during the autumn, which may contribute to reduced female herd condition and consequently female reproductive success (Collado et al. 2019;Kauserud et al. 2008Kauserud et al. , 2010Kauserud et al. , 2011Paoli et al. 2019). Therefore, earlier spring onset and later permanent ground snow coverage for the winter without significantly warmer temperatures in the autumn are likely important for counteracting negative density-dependent effects on maternal fitness, reproductive success, and calf survival and body mass (Bonenfant et al. 2009). ...
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Global temperatures are increasing, affecting timing and availability of vegetation along with relationships between plants and their consumers. We examined the effect of population density, herd body condition in the previous year, elevation, plant productivity and phenology, snow, and winter onset on juvenile body mass in 63 semi-domesticated populations of Rangifer tarandus throughout Norway using spatiotemporal generalized additive models (GAMs) and varying coefficient models (VCMs). Optimal climate windows were calculated at both the regional and national level using a novel nonlinear climate window algorithm optimized for prediction. Spatial and temporal variation in effects of population and environmental predictors were considered using a model including covariates decomposed into spatial, temporal, and residual components. The performance of this decomposed model was compared to spatiotemporal GAMs and VCMs. The decomposed model provided the best fit and lowest prediction errors. A positive effect of herd body condition in the previous year explained most of the deviance in calf body mass, followed by a more complex effect of population density. A negative effect of timing of spring and positive effect of winter onset on juvenile body mass suggested that a snow free season was positive for juvenile body mass growth. Our findings suggest early spring onset and later winter permanent snow cover as reinforcers of early-life conditions which support more robust reindeer populations. Our methodological improvements for climate window analyses and effect size measures for decomposed variables provide important contributions to account for, measure, and interpret nonlinear relationships between climate and animal populations at large scales.
... The period of highest fungal production during the study comprised the months of September to May and it was not limited to autumn, resulting in a delay to the winter months that joined the traditional periods of autumn and spring. This fact could be explained by climate change that, as well as delaying the appearance of ectomycorrhizal fruiting bodies in forest areas dominated by deciduous trees [24,64], could delay the fructification towards the first weeks of winter in forests with holm oak dominance [25,65]. On the other hand, the PCA results for the analyzed weeks showed four separated groups with a greater similarity of species, which seemed to indicate that the formation of carpophores was not restricted to a certain time of the year, but neither could it be included in a single fruiting period. ...
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The methodology used for the determination of macrofungal diversity in Mediterranean areas differs in the time of sampling and the number of years displayed, making it difficult to compare results. Furthermore, the results could be refuted because the studies are being conducted over an insufficient number of years or without considering the variation of the meteorological conditions from one year to the next and its effects on fruiting time, which might not fit the sampling. In order to optimize field work on fungal fruiting in Mediterranean environments dominated by holm oak (Quercus ilex L.), a weekly field analysis of macrofungal diversity from February 2009 to June 2013 was carried out in a Mediterranean holm oak forest in the middle-west of the Iberian Peninsula. The results revealed that fruiting bodies appeared throughout the year and that there was a delay in autumn fruiting, overlapping with spring. All this seems to indicate that weekly collection throughout the year and for a period of two years could be sufficient to estimate the macrofungal biodiversity of this ecosystem.
... Seasonality of airborne spores from other fungal genera are relatively less well documented. There is, however, an enhanced understanding of spore seasonality for a class of fungi known as basidiomycetes, where there are visible fruiting bodies to mark the sporulation of fungi (Kauserud et al., 2008;Sato et al., 2012). ...
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Fungal spores make up a significant proportion of organic matter within the air. Allergic sensitisation to fungi is associated with conditions including allergic fungal airway disease. This systematic review analyses outdoor fungal spore seasonality across Europe and considers the implications for health. Seventy-four studies met the inclusion criteria, the majority of which (n = 64) were observational sampling studies published between 1978 and 2020. The most commonly reported genera were the known allergens Alternaria and Cladosporium, measured in 52 and 49 studies, respectively. Both displayed statistically significant increased season length in south-westerly (Mediterranean) versus north-easterly (Atlantic and Continental) regions. Although there was a trend for reduced peak or annual Alternaria and Cladosporium spore concentrations in more northernly locations, this was not statistically significant. Peak spore concentrations of Alternaria and Cladosporium exceeded clinical thresholds in nearly all locations, with median peak concentrations of 665 and 18,827 per m³, respectively. Meteorological variables, predominantly temperature, precipitation and relative humidity, were the main factors associated with fungal seasonality. Land-use was identified as another important factor, particularly proximity to agricultural and coastal areas. While correlations of increased season length and decreased annual spore concentrations with increasing average temperatures were reported in multi-decade sampling studies, the number of such studies was too small to make any definitive conclusions. Further, up-to-date studies covering underrepresented geographical regions and fungal taxa (including the use of modern molecular techniques), the impact of land-use and climate change will help address remaining knowledge gaps. Such knowledge will help to better understand fungal allergy, develop improved fungal spore calendars and forecasts with greater geographical coverage, and promote increased awareness and management strategies for those with allergic fungal disease.
Technical Report
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In this report, we summarize the current state of knowledge and best estimates of how climate change is expected to impact Norwegian forest ecosystems from now to the year 2100
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Lichen collected worldwide for centuries have resulted in millions of specimens deposited in herbaria that offer the potential to assess species boundaries, phenotypic diversification, ecology, and distribution. The application of molecular approaches to historical collections has been limited due to DNA fragmentation, but high-throughput sequencing offers an opportunity to overcome this barrier. Here, we combined a large dataset of ITS sequences from recently collected material and historical collections, obtained through Sanger, 454, or Illumina Sequencing, to test the performance of ITS barcoding in two genera of lichenized Basidiomycota: Cora and Corella. We generated new sequence data for 62 fresh specimens (from 2016) and 274 historical collections (collected between 1888 and 1998), for a dataset of 1325 sequences. We compared various quantitative approaches to delimit species (GMYC, bPTP, ASAP, ABGD) and tested the resolution and accuracy of the ITS fungal barcoding marker by comparison with a six-marker dataset. Finally, we quantitatively compared phylogenetic and phenotypic species delimitation for 87 selected Cora species that have been formally described. Our HTS approach successfully generated ITS sequences for 76% of the historical collections, and our results show that an integrative approach is the gold-standard for understanding diversity in this group.
Introduction: Pollen and fungal spore concentrations in outdoor air are partly dependent on atmospheric conditions. Since the climate is changing, there is a growing body of research on the effects of climate change on aeroallergens. The present article provides a rapid review of this literature, highlighting the points of agreement, but also drawing attention to the main mistakes to be avoided. State of art: For pollen, the prevailing view is that rising temperatures lead to an earlier start to the pollen season, a longer season, increased allergenic potential and higher concentrations. However, there are exceptions: what is true for one taxon, in one place and at one time, can almost never be generalised. For fungal spores, it is even more difficult to state universal rules. Perspectives: Four priorities can be set for future research: (1) to look for trends only on sufficiently long series and not to neglect possible trend reversals; (2) to give priority to the local scale and the separate consideration of the various pollen and mycological taxa; (3) not to limit oneself to temperature as an element of explanation, but also to consider the other elements of the climate; (4) not to try to explain any evolution in the abundance or seasonality of aeroallergens by climate change alone. Conclusions: Many more analytical studies giving precedence to observation over reasoning are still required, without any preconceptions, before it is possible to synthesise the impacts of climate change on pollen and, even more so, on fungal spores.
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The impact of the environment on interannual variability in fish recruitment has proven difficult to establish empirically, and environment-recruitment correlations have often been found to break down when more data become available. This may suggest that the statistical models have failed to capture the essential explanatory variables, or that environment-recruitment relationships are non-stationary, and thus actually change. The present paper explores the effect of climate, measured by the North Atlantic Oscillation (NAO), on the recruitment of North Atlantic cod Gadus morhua. The literature on the topic is reviewed and compared with results from a new analysis, in which data from all 22 main stocks are combined in 1 overall model. Results of the new analysis demonstrate (i) a geographic pattern in the effect of the NAO on recruitment, which resembles the geographic pattern of the correlation between the NAO and sea surface temperature, and (h) trends in recruitment levels as well as in the effects of climate. These trends are not fully explainable through changes in spawning stock biomass. Summarizing the old and new insights, we arrive at the following general conclusions: NAO affects cod recruitment through local environmental variables such as sea temperature, salinity, oxygen, turbulence and advection. Cod recruitment is density-dependent, although the new analysis does not unequivocally support the existence of general patterns of density-dependent climate effects. There are trends in cod recruitment and in the relationship between climate and recruitment, possibly caused by demographic changes in the cod stocks (e.g. fishing-induced) and changes in the biotic or abiotic environment (regime shifts).
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.
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.
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).
Linear Mixed-Effects * Theory and Computational Methods for LME Models * Structure of Grouped Data * Fitting LME Models * Extending the Basic LME Model * Nonlinear Mixed-Effects * Theory and Computational Methods for NLME Models * Fitting NLME Models