Changes in the Poaceae pollen season in Gothenburg
(1979-2012) and the synchronization between
pollen season and flowering phenology
Hanna Nomoto
Examensarbete Botanik
30 hp
Institutionen för Biologi och
miljövetenskap
1
Göteborgs Universitet
HT 13
Index
Sammanfattning på svenska......................................................................................................................................... 4
Keywords ........................................................................................................................................................................ 5
Introduction ................................................................................................................................................................. 6
Plant phenology ........................................................................................................................................................ 6
Poaceae pollen season................................................................................................................................................. 6
Observed changes in pollen season and meteorological parameters influencing it ....................................... 6
Flowering phenology and pollen season ............................................................................................................... 7
Aim .............................................................................................................................................................................. 8
Background .................................................................................................................................................................. 9
Anemophily................................................................................................................................................................ 9
Phenology................................................................................................................................................................... 9
Basic Poaceae morphology ........................................................................................................................................ 9
Roots ........................................................................................................................................................................ 9
Culm ........................................................................................................................................................................ 9
Inflorescence.............................................................................................................................................................10
Anthesis ....................................................................................................................................................................10
Anther dehiscence ..................................................................................................................................................10
Poaceae pollen production.......................................................................................................................................11
Pollinosis ..................................................................................................................................................................11
Materials ......................................................................................................................................................................12
Alopecurus pratensis (meadow foxtail).....................................................................................................................12
Dactylis glomerata (cock’s foot) ................................................................................................................................12
Deschampsia cespitosa (tufted hair-grass) .................................................................................................................12
Deschampsia flexuosa (wavy hair-grass) ...................................................................................................................12
Festuca pratensis (meadow fescue) ..........................................................................................................................12
Festuca rubra (red fescue).........................................................................................................................................12
Lolium perenne (perennial rye-grass) .......................................................................................................................12
Molinia caerulea (purple moor-grass)......................................................................................................................13
Poa pratensis (smooth meadow grass)....................................................................................................................13
Methods .......................................................................................................................................................................13
Location....................................................................................................................................................................13
Part one – Changes in the pollen season and meteorological parameters influencing it ............................13
Pollen data..............................................................................................................................................................13
Meteorological data..................................................................................................................................................14
Statistical analysis...................................................................................................................................................14
Part two – Comparing local flowering phenology with the pollen season 2013 ..........................................15
2
Phenological data ....................................................................................................................................................15
Pollen data..............................................................................................................................................................16
Analysis .................................................................................................................................................................16
Geographical position of locals .................................................................................................................................17
Results ..........................................................................................................................................................................18
Significant changes in temperature and precipitation 1979-2012 ...................................................................18
Temperature ............................................................................................................................................................18
Precipitation ............................................................................................................................................................19
Changes in pollen season 1979-2012 ...................................................................................................................20
The effect of meteorological parameters on the pollen season in Gothenburg, Sweden, during 19792012 ...........................................................................................................................................................................22
The beginning of the pollen season (BPS) .................................................................................................................22
Day when the 1st pollen grain is found .....................................................................................................................23
Pollen peak (PP) ....................................................................................................................................................24
End of pollen season (EPS) ....................................................................................................................................25
Duration.................................................................................................................................................................26
Days with pollen amounts exceeding 80 grains/m3 ..................................................................................................28
Total pollen amount ................................................................................................................................................29
Flowering phenology and pollen season 2013 ...................................................................................................31
Discussion...................................................................................................................................................................32
Atmospheric Poaceae pollen trends .......................................................................................................................32
Changes in climate in Gothenburg ......................................................................................................................32
Temperature.............................................................................................................................................................32
Heat accumulation ..................................................................................................................................................33
Precipitation .............................................................................................................................................................34
Environmental factors in combination ...............................................................................................................35
Why is so much of the variation in pollen counts not explained? ..................................................................36
Previous studies.......................................................................................................................................................37
Land use and cultivation ........................................................................................................................................38
Definition of the main pollen season ..................................................................................................................38
Pollinosis ..................................................................................................................................................................38
Phenology.................................................................................................................................................................38
Pollen season and weather 2013 ..............................................................................................................................38
Flowering phenology vs. pollen season .......................................................................................................................39
Conclusion ..................................................................................................................................................................41
Acknowledgements ..................................................................................................................................................43
References...................................................................................................................................................................44
3
Sammanfattning på svenska
I denna studie har jag undersökt vilka förändringar som har skett i gräspollensäsongen under 1979-2012,
och hur meteorologiska variabler påverkar utvecklingen av pollensäsongen. Syftet var att öka kunskapen
om hur pollen-säsongen påverkas av pågående klimatförändringar. Även blomnings-fenologin hos nio
vanliga Poaceae arter (Alopecurus pratensis, Dactylis glomerata, Deschampsia cespitosa, Deschampsia flexuosa, Lolium
perenne, Festuca pratensis, Festuca rubra, Molinia caerulea, Poa pratensis) observerades och jämfördes med
pollensäsongen 2013. Detta gjordes i syfte att undersöka hur väl den lokala blomningen synkroniserar med
de uppmätta pollenhalterna, och hur detta det skiljer sig mellan arter.
Pollen-data från 1979-2012 användes för att undersöka förändringar i pollensäsongens start, slut,
varaktighet och intensitet. Temperatur- och nederbörds data samt värmeackumulering i form av GDH
(Growth Degree Hours) användes sedan i regressioner mellan meteorologiska parametrar och
pollensäsongen.
Minst två lokaler i Göteborg valdes ut för fenologi-studien. Observationer av blomnings-fenologin
utfördes varannan dag (bortsett från Festuca pratensis som observerades mer sällan) och start-, slut- samt
full-blomning beräknades för att sedan jämföra med pollensäsongens utveckling.
Resultaten visar att pollensäsongen i Sverige har förändrats dramatiskt under 1979-2012. En förlängning
av pollensäsongen på en månad, fyra gånger fler dagar med pollen-halter överstigande 80 pollenkorn/m3
och en fördubbling av den totala pollen-summan har skett på 30 år. Detta beror delvis på
klimatförändringar, eftersom temperatur och nederbörd utan tvekan influerar på pollensäsongen, men
förmodligen har även de förändringar i markanvändningen som skett de senaste 60 åren i Sverige samt
ökade kvävenedfall till följd av trafikutsläpp påverkat förlängningen och intensifieringen av
pollensäsongen.
Den lokala blomningsfenologin hos de studerade Poaceae arterna matchar pollensäsongen väl.
Fullblomningen av Dactylis glomerata och Poa pratensis avspeglas tydligt i pollenkurvan i form av en
pollenpeak. Även fullblomningen av Lolium perenne och Festuca prantensis reflekteras i en senare pollenpeak.
Detta antyder att dessa fyra arter producerar mycket pollen, och eftersom de alla är kända för att orsaka
pollenallergi, så är de till stor del de ansvariga arterna att orsaka gräspollenallergi.
Studien visar att med fortsatt ökad temperatur och nederbörd i Sverige, så kommer pollensäsongen
troligtvis att ytterligare förlängas och intensifieras. Detta skulle inte enbart innebära en förvärrad situation
för gräspollenallergiker, utan dessutom medföra en förändring i Poaceae ekologi som kan leda till en
förändring i skördar av viktiga grödor (majs, ris och vete tillhör alla familjen Poaceae).
Observationer av lokal blomnings-fenologi hos Poaceae kan troligtvis med mer kunskap och utvecklade
metoder användas som komplement för dagens dyra pollenmätningar och leda till förbättrade
pollenprognoser. Resultaten antyder att Dactylis glomerata, Poa pratensis och Lolium perenne tillhör de stora
pollen-producenterna i Sverige och därmed är några av de vikigaste arterna ur allergi synpunkt.
4
Abstract
Phenology observations of Poaceae are valuable in many aspects. First, they work as indicators to detect
temporal changes that can be due to climate change. Additionally, information on temporal differences
between species is important to better understand their ecology. By knowing which species are the major
contributors of pollinosis, which is a severe health problem, it is possible to improve the situation for
allergy sufferers.
Several studies have reported changes in the Poaceae pollen season caused by climate change. Yet,
depending on geographical location, results differ and therefore observations on smaller geographical
scales are necessary. When present, temporal changes in the pollen season will most probably concern the
human population, not only by pollinosis, but also by influencing the harvest of important crops such as
maize and wheat.
This study is separated in two parts, where the first one aims to detect possible changes in the Swedish
pollen season from 1979-2012, and which variables that are responsible for these potential changes.
Regressions between the meteorological variables and the pollen season were made to discover how
temperature and precipitation influence the pollens season. In the second part, the flowering phenology of
nine common Swedish Poaceae species was observed and compared with the pollen season of 2013, to
discover how the flowering phenology matches the pollen season curve.
The results confirm that the Swedish Poaceae pollen season has changed drastically to become prolonged
and more intense. The duration of the pollen season is prolonged by one month, the number of days with
pollen amounts exceeding 80 grains/m3 has become four times higher, and the pollen index has almost
doubled in 30 years. The variables that mainly influence the pollen season are the mean temperature in
April and accumulated spring temperature (Growth Degree Hours). The changes in the pollen season are,
at least partly, due to climate change. The local flowering phenology matches the pollen season well.
Dactylis glomerata, Poa pratensis, Lolium perenne and Festuca pratensis seem to be the major pollen producers,
since full-flowering of these species was clearly reflected in the pollen season curve as pollen peaks. The
flowering of Poaceae is clearly temporally separated between species.
The results show that the pollen season may be even more prolonged and intensified if temperatures and
precipitation continue to increase in Sweden. This will result in more severe consequences for pollinosis
sufferers and changes in the ecology of Poaceae. The study also found that phenological studies can
probably be used as a component in the basis for pollen forecasts, and that four common Poaceae species
probably are mainly responsible for the airborne Poaceae pollen in Sweden.
Keywords
Flowering phenology, Poaceae, atmospheric pollen season, trends in the grass pollen season, meteorological
variables, climate change
5
Introduction
Climate change and global warming are nowadays frequently mentioned in media, politics and not least, in
science.
Overall the global surface temperature has increased with 0.74 ᵒ C the past 100 years, and the rate of
warming is increasing (IPCC 2007). The severity of climate change has been even more acknowledged
after alarming reports of extreme weather events causing humanitarian disasters around the world, which
many scientists suggest are partly due to climate change (IPCC 2012). Ecosystems and species have been
observed to respond to the changing climate and new conditions, the question is how far species
adaptability reach when the rate of climate change increases, and what the consequences will be. Plants are
sessile and are therefore sensitive to climate change in particular.
Plant phenology
Plant phenology observations have been important ever since man started farming land, since knowledge
of when flowering, pollination and seed-set took place was, and still is, crucial to predict and improve the
crops yield. With the increased awareness of climate change, plant phenology studies have increased in
both frequency and status. Phenology studies can provide excellent bio-indicators of climate change. By
knowing when a plant species normally enter a phenophase, temporal changes can be detected. And by
knowing which biotic or abiotic variables that influence the phenology, these potential changes in
phenology could be explained. Furthermore, if changes in phenology can be understood, predictions can
be made of how the phenology will develop and what consequences this would have in the future when
climate change is continuing.
Poaceae pollen season
The pollen season is a crucial part of the phenology of plants and does not only determine the
reproduction and continuity of plants but also have a large impact on human health. In Europe pollinosis
is a major health problem, and in Sweden 25% of the population suffers from pollinosis (Sahlgrenska
University Hospital 2013), mainly induced by the family of Poaceae and the order of Fagales. The symptoms
of pollinosis differ in severity, but can gravely lower life quality both physically and psychologically
(Laforest 2005).
Additionally, Poaceae is probably the most important angiosperm family from an economical and
agricultural point of view since the world’s most important food sources, maize (Zea mays), rice (Oryza) and
wheat (Triticum spp.), all belong to the Poaceae family.
There is no doubt about the importance of Poaceae in many aspects and therefore the pollen season of
Poaceae is of great interest to study. Potential changes in the pollen season of Poaceae will not only have
consequences on the reproduction of Poaceae but will also concern the human population.
Observed changes in pollen season and meteorological parameters influencing it
6
Several previous studies have observed changes in plant phenology in Europe (Sparks & Carey 1995,
Chmielevski & Rötzer 2001, Fitter & Fitter 2002, Van Vliet et al. 2002, Menzel et al. 2006, Jato et al. 2009,
Dahl et al. 2013, amongst many others). Observations from all over Europe overall show changes toward
advanced flowering, pollen season and seed set.
An extensive phenology study including many species and countries (Chmielevski & Rötzer 2001) showed
that in the Baltic Sea region (where Sweden is included) the start of plant growth is overall advancing with
4.3 days/decade and the growth season is extended by 5.9 days/decade. The trends were explained to be
caused by increased temperatures during late winter and early spring. Another study, including 21
countries and 125 000 observations in Europe, observed an advance in leafing, flowering and fruiting due
to an increase in monthly temperature in 78% of the 542 plant species included (Menzel et al. 2006). The
advance of flowering of Poaceae due to climate change has also been confirmed by IPCC in the chapter
“8.2.7 Aeroallergens and disease” in the fourth assessment report from 2007.
Many studies agree that temperature, precipitation and day length are the main responsible meteorological
variables that influence the pollen season. Especially increased temperatures have a significant effect on
pollen season and flowering phenology (Sparks et al. 2000, Chmielevski & Rötzer 2001, Van Vliet et al.
2002, Badeck et al. 2004, Green et al. 2004, García-Mozo et al. 2008, Jato et al. 2009, Recio et al. 2010),
mainly by advancing the pollen season. When knowing which meteorological variables that influence the
pollen season and how, it is possible to discuss and understand the future development of the pollen
season with ongoing climate changes. More profound studies also contribute to the possibility of
improving annual pollen season forecasts.
Flowering phenology and pollen season
There are several studies that aim to improve the pollen forecasts in different ways. Since phenology
studies lately are understood to be useful, there is an interest in understanding how local flowering
phenology synchronizes with measured airborne pollen. Studies of the match or mismatch between pollen
season and local flowering phenology, in order to improve and better understand pollen forecasts have
taken place all over Europe (Jato et al. 2001, Estrella et al. 2005, Tormo et al 2011). Complementing the
airborne pollen counts with phenological observations would improve the understanding of which species
that contribute to the highest amounts of pollen and the temporal distribution of pollen dispersal among
species and thus improve the interpretation of the pollen curve. It will also make it possible to understand
the influence of long-distance traveling pollen grains on the pollen season curve. Thus, by this information
the taxonomical and geographical interpretation of the annual pollen curve could be improved. It will also
serve to find out to what extent phenological records and local observations could be used when
predicting and analyzing the pollen season.
Despite the fact that many exhaustive studies concluded a general European trend of an advanced Poaceae
pollen season start, a deeper analyze of previous studies shows that there are clear differences between
regions and countries in the trends of pollen season and the meteorological variables influencing it. The
differences are too large to make any general conclusions over widespread areas. Therefore it is important
to make observations in how phenology and pollen season have developed on smaller geographic scales.
7
It is also necessary to study if other parts of the pollen season than start date, such as peak and end dates
and amounts of pollen have changed. In the majority of previous studies only the pollen start date is
observed and other phases of the pollen season are ignored.
There is a tradition of predicting and analyzing the pollen season based on “generally accepted
knowledge”, but deeper understanding of which, how and to what extent meteorological variables
influence the pollen season is crucial. Even if it is well-known that temperature and precipitation influence
pollen season, more detailed information is necessary to improve the precision of forecasts. What is also
necessary to complement to previous studies is knowledge of how other meteorological parameters than
just average spring temperature influence on the pollen season, such as days with no precipitation, rate of
heat accumulation and minimum/maximum temperatures. In many studies mean spring temperature is
the only parameter used to explain the changes in pollen season.
Since Sweden belongs to the region where the highest rate of change in plant phenology is estimated
(Chmielevski & Rötzer 2001), effects on the pollen season are expected.
Aim
A Swedish study investigating temporal trends, the influence of meteorological parameters and phenology
of the pollen season of Poaceae will provide valuable information for the fields of climate change,
aerobiology and ecology.
The study will be answering following questions:
1.
2.
3.
4.
How has the pollen season in Sweden changed from 1979 to 2012?
How does meteorological parameters influence the pollen season?
Does the local flowering phenology match the pollen season?
How does matching between local flowering phenology and pollen season differ between Poaceae
species?
Finding answers to these questions will increase the opportunities to manage and predict the effect of
climate change on the pollen season of Poaceae, improve the methods of foreseeing the pollen season and
therefore also improving the situation for pollinosis sufferers. It will additionally increase the
understanding of the Poaceae family´s ecology.
8
Background
Anemophily
Anemophily is the most common type of abiotic pollen dispersal, and as all types of abiotic pollen
dispersals it is one-sided (Fægri & van der Pijl 1979). This makes anemophilous plants independent from
biotic vectors that sometimes can be scarce or absent. Abiotic dispersal of pollen is not directed as biotic
dispersal (e.g. entomophily or ornithophily) and therefore a great quantity of pollen has to be produced
for fertilization of an ovule to occur, which makes pollen dispersal a wasteful process with low fertilization
per pollen grain. Every square meter of the plant’s habitat must receive around a million pollen grains to
ensure pollination (Proctor & Yeo 1973). The pollen grains in anemophilous plants are small, smooth and
dry, suggested to be adaptations that alleviate dispersal and decrease the air resistance.
Phenology
Phenology is the study of periodical events and repeated patterns in nature, such as bud formation, pollen
dispersal and seed set in plants. Temporal variation in phenology among species can be explained by
genetic variation as a result of selection pressure (Elzinga et al. 2007), and possibly, epigenetic changes.
Phenology also depends on abiotic and biotic factors, where the most important factors are temperature,
photoperiod and water ability (Dahl et al. 2013). The relative importance of these factors depends on
geographical location, causing temporal variation in phenophases.
Basic Poaceae morphology
The morphology of Poaceae differs between species, but all
grasses consist of a root-system, culm and inflorencence.
Roots
The root-system is an important storage of nutrients for the
plant and is in Poaceae separated in adventitious and seminal
roots (Bell & Bryan 1991). The adventitious roots grow just
below ground while the seminal roots grow deeper in the soil.
The seminary roots are highly branched and are important as
they absorb high amounts of nutrients the first months of
growth, after which they die in perennial grasses (Langer
1972).
Culm
The culm consists of nodes, internodes, leaves and
meristematic tissue situated above the nodes on the axis of
the leaves.
Illustration 1.1 The basic morphology of a typical Poaceae phytomer
The first shoot from a seed is called the parent shoot. Secondary shoots (side shoot) are called tillers and
develop from the axillary buds of the parent shoot. The production of tillers is called tillering. The tillers
generally have the same morphology as the parent shoot and develop roots, leaves, flowers, an apical
9
meristem and daughter tillers (Langer 1972, Bell & Bryan 1991). Multiple shoots can therefore develop
from one seed. Tillers can develop in two ways. One is where the tiller does not break through the parent
shoots leaf sheath (intravaginal development), and another one where the tiller breaks through the sheath
of the parent shoot and grows horizontally (extravaginal development) (Bell & Bryan 1991). The amount
of tillering is genetically controlled but also strongly influenced by environmental factors (Langer 1972).
The tillers of perennial species can be annual and die the same season as they were produced but can also
survive and flower the following year (Langer 1972). The tillers are essential for the persistence of the
Poaceae population, since they represent a great part of the population.
Inflorescence
There are mainly two types of inflorescences, spikes and panicles (Langer
1972). The spike is unbranched, like in Alopecurus pratensis, while the panicle
is branched as Dactylis glomerata. On the axes there are groups of minor
inflorescences called spikelets. The number of flowers in the spikelet differs
between species.
Each flower is protected by a lemma and a palea. The lemma envelopes the
palea, and together with the flower, they are named the floret.
The flower is only visible during anthesis. Since 98% of the species from
Illustration 1.2. The floret of Poaceae
Poaceae are anemophilous (Fægri & van der Pijl 1979), the morphology of
flowers is not selected to attract pollinators. Therefore Poaceae has a clearly reduced perianth (calyx and
corolla) and the flowers lack color and scent. The Poaceae species most often have three anthers, two
stigmas and one ovule, thus one flower produce only one seed. The stigmas are sticky and featherlike to
be able to catch pollen grains as effectively as possible.
Anthesis
Anthesis starts when the anthers and stigmas are mature and pollen is dispersed. Maturation is mainly a
result of heating after floral primordia are initiated. In cool-temperate grasses, vernalization, i.e. exposure
to low temperatures, is often necessary before maturation could be fulfilled. Dispersal takes place when
pollen grains are exposed to the pollination vector, in the case of 98% of Poaceae species, it is wind. The
ending of anthesis is defined as when anthers and stigmas are no longer available to the pollinating vector.
Anther dehiscence
The Poaceae anther consists of four locules. Two bordering locules are separated by a tissue called septum.
The epidermis-part of the septum is called stomium. The pollen grains are protected by a tapetum (which
provides pollen grains with nourishment), middle-layer, endothecum and a cuticula, covering the whole
anther. Anther dehiscence can be separated into two steps;
I.
II.
Disruption of the septum
Opening of the locules by a split in epidermis
10
Disruption of the septum starts with disappearance of thickened
walls in the septum. The septum cells then go through lysis and
are replaced by intercellular spaces, which open up the septum.
The opening of the stomium is triggered by dehydration when the
locule fluid disappears through evaporation and/or reabsorbation
through vascular bundles which induces shrinkage of the apical
region and the locule walls bend outwards (Keijzer et al.1995).
The evaporation is induced by dry conditions outside of the
anther while reabsorbation in vascular bundles is regulated by the
plant (Dahl et al. 2013). In grasses a contributor to anther
dehiscence may be the swelling of pollen grains due to potassium
movements from the fluids from the locules to the pollen grains
(Dahl et al. 2013).
septum
epidermis
Poaceae pollen production
Illustration 1.3. Anther
The number of pollen grains produced per anther and per individual
differs between species in the Poaceae family (de Vries 1973, Subba & Reddi 1986, Prieto et al. 2003, Green
et al. 2004, Nomoto 2013), which could be explained by differences in reproductive strategies and
genetics. Thus the variation in quantities of pollen produced is high and probably also the variation in
when anther dehiscence occurs, is high among species. Based on measurements of anther
length/individual and geographical distribution of twelve common Poaceae species in Sweden, Dactylis
glomerata, Poa pratensis, Festuca pratensis and Alopecurus pratensis are suggested to be among the species mainly
responsible for pollinosis induced by Poaceae in Sweden (Nomoto 2013).
Pollinosis
The symptoms of pollinosis are both physical (itching, watery, sneezing, blocked nose, swollen and itchy
eyes, asthma and eczema) and psychological (fatigue and even depression; Calderon et al. 2009,
Vårdguiden 2013, Kiotseridis et al. 2013). Pollinosis decreases life-quality, and if you are unlucky and
allergic to more than one pollen type, the symptoms of pollinosis can last from early spring until late
autumn. The number of pollinosis sufferers has increased dramatically in Sweden (Vårdguiden 2013) and
in the rest of Europe (D’ Amato et al.1998) during the last 40 years. There are yet no studies giving an
ultimate explanation of this increase, but the dominating theory is changed conditions during infancy, that
are not optimal for the immune system to develop tolerance.
11
Materials
Alopecurus pratensis (meadow foxtail)
Alopecurus pratensis is very common in Sweden and occurs in all types of landscapes, only lacking in upland
forest areas (Blomgren et al. 2011). Alopecurus pratensis is more common in southern than in northern
Sweden and grows on all types of cultivated soil. It used to be sown as forage. Alopecurus pratensis is known
to induce pollinosis and has a high total pollen production. Alopecurus pratensis is cross-pollinated (Fryxnell
1957).
Dactylis glomerata (cock’s foot)
Dactylis glomerata is very common in Sweden and has traditionally been sown and used as forage. It is a
strong competitor and common on overgrowing pastures and in verges (Blomgren et al. 2011). The
distribution of Dactylis glomerata has increased in Sweden (Blomgren et al. 2011). Dactylis glomerata cause
pollinosis and produce high amounts of pollen. It is known to be cross-pollinated and self-incompatible
(Fryxnell 1957).
Deschampsia cespitosa (tufted hair-grass)
Deschampsia cespitosa is very common in Sweden and its distribution covers all Sweden, except small islands.
Deschampsia cespitosa grows in damp and wet soil and forms big tussocks. The distribution has increased,
probably due to overgrowing of pastures (Blomgren et al. 2011).
Deschampsia flexuosa (wavy hair-grass)
Deschampsia flexuosa is very common in all parts of Sweden. It often grows in dry and sandy soil. In shady
conditions, such as in coniferous forests, it is infertile (Blomgren et al. 2011).
Festuca pratensis (meadow fescue)
Festuca pratensis is very common in Sweden and has traditionally been sown to become forage. The
distribution has increased, which is explained by the change of land use comprising less mowing and
grazing than before, which benefits Festuca pratensis. Festuca pratensis is one of the high pollen producers and
induce pollinosis.
Festuca rubra (red fescue)
Festuca rubra is very common in Sweden and has been sown as ornamental vegetation in verges. It is also
common along grassy sea shores and in meadows (Blomgren et al. 2011). Festuca rubra has long anthers but
do not produce very high total amounts of pollen.
Lolium perenne (perennial rye-grass)
Lolium perenne is common in Sweden and the abundance has increased, which could be explained by a
variety of facts like change of land use and ornamental sowing (Blomgren et al. 2011). Lolium perenne has
12
long anthers and produce high amounts of pollen per anther, but few spikelets which results in a low total
pollen production.
Molinia caerulea (purple moor-grass)
Molinia caerulea is very common in Sweden and grow in robust tussocks on damp, nutrient poor soil
(Blomgren et al. 2011) such as moorland, rocky areas and swamp forest. Molinia caerulea is not known to
induce pollinosis but can produce high amounts of pollen.
Poa pratensis (smooth meadow grass)
Poa pratensis is very common in grasslands in large parts of Sweden. It is sown as an ornamental in verges
and to become forage (Blomgren et al. 2011). Poa pratensis is known to cause pollinosis (Anderson &
Lidholm 2003) but is not one of the highest pollen producers.
Methods
Location
The pollen trap and almost all the localities used in the study are situated in Gothenburg, located on the
west coast of Sweden (Illustration 2.1) and having a typical suboceanic and humid coastal climate. The
average temperature is 7-8 ᵒ C (1961-1990) and the annual rainfall is 800-900 mm (2012).
Part one – Changes in the pollen season and meteorological parameters influencing it
Pollen data
Pollen data based on daily counts from a Burkard seven day recording volumetric spore trap located on a
roof top ca. 35 m above ground level, in Sahlgren’s University Hospital/Östra in Eastern Gothenburg
(57.72; 12.05) were used in this study. The distance to the Botanical Garden of Gothenburg and other
localities for phenology observations is ca. 12 km. The data were analyzed at the Pollen laboratory,
Gothenburg University. Daily Poaceae pollen counts have been recorded at the same location since 1979.
The start, end, pollen peak, duration and intensity of the pollen season were calculated by using data from 1979
to 2012.
Pollen season start (BPS) was defined as when pollen was found in four out of five consecutive days, and the
ending of the pollen season (EPS) was defined as the day when pollen was no longer found in four out of five
consecutive days. The period in-between these dates is the main pollen season (MPS) and its duration is used
in this study. The number of days from beginning of pollen season until the pollen peak and the number
of days from pollen peak until the end of pollen season were also registered. Another way of defining the
beginning of pollen season is the date when accumulated pollen amount increases significantly, in a graph
demonstrated by a sudden strong positive trend. Both methods of calculating the beginning of the pollen
season were tried, but using the latter sometimes made it hard to define the exact start date and this
method is therefore excluded in this paper. As a complement to the beginning of pollen season also the
13
day when the first pollen grain is detected was registered. Pollen peak (PP) was registered when the highest
amount of pollen was measured. Pollen intensity was defined as number of days with pollen amounts exceeding 80
grains/m3 present and also pollen index, the annual pollen sum, was calculated.
Meteorological data
Data series (1979-2013) of temperature and precipitation from the area of Gothenburg from SMHI (Swedish
Meteorological and Hydrological Institute) were used to observe potential temporal changes in climate
from 1979-2012 and to calculate following parameters for each year;
-
Mean temperatures of February, March, April, May, June, July and August
-
Mean temperature of February-April
-
Mean temperature of May- August
-
Minimum temperatures of February, March, April, May, June, July and August.
-
Maximum temperatures of February, March, April, May, June, July and August.
-
Growth degree hours (GDH) - Growth degree hours from 1979-2012 (calculated by Dahl
according to Linvill 1990) were used to calculate heat accumulation from several different periods,
e.g. from the day that grasses start to grow (Poaceae is observed to start growing almost
simultaneously as Salix start to flower, Dahl, pers. comm.), and a record of the flowering of Salix
from 1979-2010 was used) to the beginning of the pollen season /pollen peak. Accumulated
GDH from fixed dates were also used, e.g. from the mean day of growth start to the mean date
of pollen start/peak from all years. The period of accumulated GDH that gave the best response
in the regressions was the accumulated GDH from two weeks before growth start to the 30th of
June (mean day of pollen peak)).
-
Annual precipitation
-
Precipitation in March and April
-
Days with no precipitation in Mars, April, May, June, July and August
-
Accumulated precipitation - Accumulated precipitation was calculated for several time periods
but the period between two weeks before growth start of Poaceae and the 22nd of May (mean date
of pollen season start) gave the best response on pollen season.
Statistical analysis
Meteorological data
Generalized Linear Models were used to calculate the relationship between temperature/precipitation and
years to detect possible trends and significant changes in the climate during the period 1979-2012. The
highest probability level for a result to be regarded as significant was 0.05, and “near significance” was
defined as a probability between 0.05 and 0.1.
Pollen data
14
All parts of pollen season (start date, day when first pollen grain is detected, pollen peak, duration, end of
pollen season, days with pollen amounts exceeding 80 grain/m3 and pollen index) were regressed against
years, to discover potential changes in pollen season 1979-2012.
Regressions between pollen data and meteorological data
Regressions between the pollen season and the meteorological parameters were made to discover if and
how the meteorological variables influence the pollen season. Data were assumed to have a Poisson
distribution (in all cases except days with pollen amounts exceeding 80 grains/m3 and pollen index where
Poisson distribution did not seem to work) since both year and pollen data are counts. The GLM also
gives R2 values. Meteorological parameters that had a significant influence on the pollen season were used
in multiple regressions to detect additive effects on the pollen season.
Part two – Comparing local flowering phenology with the pollen season 2013
Phenological data
Alopecurus pratensis (meadow foxtail), Dactylis glomerata (cock’s-foot), Poa pratensis (smooth meadow grass),
Molinia caerulea (purple moor-grass), Deschampsia flexuosa (wavy hair-grass), Deschampsia cespitosa (tufted hairgrass) Festuca rubra (red fescue) and Festuca pratensis (meadow fescue) were used in this study. These species
were chosen since they are all common in Sweden and many of them are known to be high pollen
producers and, at least the Pooideae members, also pollinosis inducers. At least two fixed localities were
chosen for each species, one where the individuals were exposed to sunlight and one in shade.
The Botanical Garden of Gothenburg, green-house and The Department of Biological and Environmental Sciences/Botany
are two meadows with some diversity of Poaceae.
Margareteberg is a small hill close to a trafficked road with high diversity of grass species, perhaps sowed for
ornamental purposes. Here are localities of Lolium perenne and Festuca rubra found. The Birger Jarl location
has the same conditions but is not on a hill.
Molinia caerulea is found on damp, nutrient poor soil next to downy birch (Betula pubescens) on a hill between
The Botanical Garden of Gothenburg and the conservation area Änggårdsbergen.
The localities at Finnsmossen vary from damp and nutrient poor close to the lake, where Molinia caerulea is
found, to drier areas were the Deschampsia is found.
Observations were made every other day or every day (with some exceptions) and the progression of
flowering was observed. Localities of Festuca pratensis were not found in central Gothenburg but instead
observed outside of Gothenburg, on Tjörn, an island, in scythe-mowed grassland ca. 50 kilometers
northwest of Gothenburg. These latter observations did not take place as often as every other day.
The phenological phases observed in the study were:
Phase 0: No panicle visible
Phase 1: Panicle visible
15
Phase 2: 50 % of all individuals have developed visible anthers
Phase 3: 100 % of individuals have developed anthers
Phase 4: All anthers emptied (dry, crumpled or discolored anthers)
Phase 5: No anthers (after)
Pollen data
The record of daily airborne pollen amounts in Gothenburg 2013, analyzed by the pollen analysis group,
was used to compare with the local flowering phenology observed.
Analysis
The average date of start-, full flowering- and end date of each observed population was calculated as well
as a concluded average date for each species. These data were then compared to the pollen curve of 2013,
to observe if the days when full flowering occurred matched those days with high pollen amounts caught
in the pollen trap. The pollen curve was demonstrated as a graph of daily pollen counts. Meteorological
data were also used as a complement when interpreting the pollen curve.
16
Geographical position of localities
Botanical Garden of
Gothenburg, green
house
(57.6828:11.9522);
Festuca r. (sun),
Deschampsia c. (shade)
Department of
Biological and
Environmental
Sciences
(57.6814:11.9515);
Festuca r. (shadow), Poa p.
(shade+sun), Dactylis g.
(shade+sun), Alopecurus
p. (shadow+sun)
Birger Jarl
(57.6849:11.9242);
Festuca r. (sun), Lolium
p. (sun)
Margareteberg
(57.6879;11.9345)
Lolium p. (sun)
Hill between the
Botanical Garden of
Gothenburg and
Änggårdsbergen
conservation area
(57.6783; 11.9551);
Molinia c. (sun)
Finnsmossen 1
(57.6751; 11.9551)
Molinia c. (shade),
Deschampsia c.
(sun+shadow),
Deschampsia f. (sun+
shade)
Finnsmossen 2
(57.6751; 11.9551)
Deschampsia c. (shadow),
Deschampsia f. (sun+
shade)
The localities of Festuca pratensis are situated on Tjörn (58.0011;11.6413)
Illustration 2.1
17
Results
Significant changes in temperature and precipitation 1979-2012
Temperature
Figure 1.1 The mean temperature in February in Gothenburg
increased during 1979-2012 (Estimate=0.12; Prob>Chi Sq
=0.0292).
Figure 1.2 The maximum temperature in February in Gothenburg
increased during 1979-2012 (Estimate =0.092; Prob>Chi Sq=
0.0303).
Figure 1.3 The mean temperature in March in Gothenburg
increased during 1979-2012 (Estimate=0.072; Prob>Chi Sq=
0.0366) but data are scattered.
Figure 1.4 The maximum temperature of April in Gothenburg
increased during 1979-2012 (Estimate=0.18; Prob>Chi Sq=
0.0040).
Figure1.5 The mean temperature of April in Gothenburg
increased during 1979-2012 (Estimate=0.12; Prob>Chi Sq
=0.0001). The data are less variable, and the trend is clearer than
for any other time period and temperature measure.
Figure 1.6 The minimum temperature of April in Gothenburg
increased during 1979-2012 (Estimate=0.10; Prob>Chi Sq=0.0040,
and if outlier, 1995, is deleted Prob>Chi Sq=0.0002).
18
Figure 1.7 The mean temperature from February – April has
increased in Gothenburg during 1979-2012 (Estimate=0.1;
Prob>Chi Sq=0.0016) but data is scattered and seem to have high
variability.
Figure 1.8 The mean temperature in July has increased in
Gothenburg during 1979-2012 (Estimate =0.08; Prob>Chi
Sq=0.0050).
Figure 1.9 The mean temperature in August has increased in
Gothenburg during 1979-2012 (Estimate=0.09; Prob>Chi
Sq=0.0133).
Precipitation
Figure 1.11 Days with no precipitation in March have increased in
Gothenburg during 1979-2012 (Estimate=0.23; Prob>Chi
Sq=0.0129).
Figure 1.10 Annual precipitation increased in Gothenburg during
1979-2012 (Estimate=5.35; Prob>Chi Sq=0.00165, when outlier
1996 is deleted; estimate 5.29; Prob>Chi Sq=0.0077, when it is
not).
19
Changes in pollen season 1979-2012
Figure 2.1. Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
advance of the BPS (beginning of pollen season) during 1979-2012
is near- significance (Prob>ChiSq=0.0712).
Figure 2.2. Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
registered day when the first pollen grain is detected has advanced
significantly during 1979-2012 (Prob>ChiSq=0.0003).
Figure 2.3. Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
number of days with pollen amounts exceeding 80 grain/m3 has
increased during 1979-2012 (Prob>ChiSq=0.0001). The years 2008
and 2010, that both had 11 days of very high amounts of pollen,
probably contributed to the results.
Figure 2.4. Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The PP
(pollen peak) also show trends of an advance during 1979-2012,
but the result is near-significance only (Prob>ChiSq=0.0839).
Figure 2.5 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
duration of the pollen season were prolonged
(Prob>ChiSq=0.0001) during 1979-2012.
Figure 2.6. Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg The day
when EPS (end of pollen season) occurs were delayed during 19792012 (Prob>ChiSq=0,0002).
20
Figure 2.8 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
total annual amount of pollen during the pollen season increased
during 1979-2012 (Prob>ChiSq= 0.0001
Figure 2.7 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
period between PP (pollen peak) and EPS (end of pollen season)
prolonged (Prob>ChiSq= 0.0001) during 1979-2012. The
prolonged pollen season was due to a prolongation of the later part
of the pollen season since there was no change in the number of
days between BPS (beginning of pollen season) and PP (pollen
peak) during 1979-2012.
Overview of changes in pollen season 1979-2012
The changes in pollen season 1979-2012 were separated in three decades: 1979-1989, 1990-2000 and 2001-2012 (Table 1.1-1.2). The BPS
(beginning of pollen season) and PP (pollen peak) both advanced by around ten days in three decades and the major changes happened the last
decade, 2001-2012, but the results were not significant. The duration prolonged with 31 days. The period PP-EPS (end of pollen season) was
responsible for this, since the BPS and PP advanced with almost the same number of days. EPS was delayed with 22 days, and the mean amounts
of days with pollen amounts exceeding 80 pollen grains/m3 were four times higher during 2001-2012 than during 1979-1989. The total pollen
amount is almost doubled during 2001-2012 compared to the period 1979-1989. Duration, EPS, days with high amounts of pollen and total pollen
amount changed during all decades in contrast to the changes of BPS and PP, where changes took place the last decade.
Table 1.1 Changes in pollen season during three decades (1979-2012).
Pollen season
1979-1989 (mean)
1990-2000 (mean)
2001-2012 (mean)
BPS
146
144
136
Day when 1st pollen grain was registered
137
120
119
PP
186
185
176
Days with pollen amounts >80 grains/ m3
1.6
2.5
5.3
EPS
233
245
256
Duration (days)
87
101
118
BPS-PP (days)
40
41
40
PP-EPS (days)
47
60
80
1043
1369
2061
Total pollen amount (grains)
Table 1.2 Changes in pollen season during three decades (1979-2012).
Pollen season
1979-1989 (mean)
1990-2000 (mean)
2001-2012 (mean)
BPS
26 May
24 May
16 May
Day when 1st pollen grain was registered
17 May
30 April
29 April
4 July
4 July
25 June
1,6
2,5
5.3
21 August
2 September
13 September
Duration (days)
87
101
118
BPS-PP (days)
40
41
40
PP-EPS (days)
47
60
80
1043
1369
2061
PP
Days with pollen amounts >80 grains/ m3
EPS
Total pollen amount (grains)
21
The effect of meteorological parameters on the pollen season in Gothenburg, Sweden, during
1979-2012
The beginning of the pollen season (BPS)
Figure 3.1 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
beginning of the pollen season was negatively related to mean April
temperature (R2=0.34; Prob>ChiSq= 0.0160). The outlier 1991 is
excluded.
Figure 3.2 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
beginning of the pollen season was negatively related to the mean
temperature in May (R2 =0.30; Prob>ChiSq=0.0131).
Figure 3.4 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
beginning of the pollen season was negatively related to annual
precipitation (R2 =0.27; Prob>ChiSq=0.0196). The outlier 2006 is
excluded.
Figure 3.3 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
beginning of the pollen season was negatively related to heat
accumulation from two weeks before growth start – until day 181
GDH, growth degree hours) (R2 =0.41; Prob>ChiSq> 0.0039).
Multiple regressions - Beginning of pollen season
Table 2.1 Explanatory power of combined meteorological
variables on the beginning of the pollen season. Mean temperature
of April and the mean of May provide the best explanation.
22
Variables
R²
p-value
T mean April + annual precipitation
0.45
0.0002
T mean April + T mean May
0.50
0.0001
T mean May+ annual precipitation
0.38
0.0010
T mean May + GDH
0.42
0.0003
GDH + annual precipitation
0.47
0.0002
Day when the 1st pollen grain is found
Outlier 2012 excluded
Figure 3.5. Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The day
when the first pollen grain was detected was negatively related to
the mean temperature in February. (R2 =0.25; Prob>ChiSq=
0.0072).
Figure 3.6. Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The day
when the first pollen grain was detected was negatively related to
the mean temperature in February - April (R2=0.28; Prob>ChiSq=
0.0050).
Figure 3.7. Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The day
when the first pollen grain was detected was negatively related to
the maximum temperature in February (R2=0.27; Prob>ChiSq=
0.0053).
Figure 3.8 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The day
when the first pollen grain was detected was positively related to
the number of days with no precipitation in February (R2=0.26;
Prob>ChiSq= 0.0065).
Figure 3.9. Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The day
when the first pollen grain was detected was negatively related to
the mean temperature in April (R2=0.26; Prob>ChiSq= 0.0065).
Figure 3.10. Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The day
of when the first pollen grain is detected was negatively related to
heat accumulation from two weeks before growth start until 10th of
May (R2=0.21; Prob>ChiSq= 0.0167).
23
Multiple regressions – Day when 1st pollen grain is detected
Table 2.2 Explanatory power of combined meteorological
variables on the day when the first pollen grain is detected. Mean
temperature of April and days with no precipitation in February
provide the best explanation.
Variables
R²
p-value
T mean February + T mean April
0.37
0.0009
T mean February + accumulated precipitation
0.33
0.0038
T mean February+ days with no precipitation February
0.33
0.0057
T mean February -April + accumulated precipitation
0.34
0.0028
T mean February-April + days with no precipitation February
0.35
0.0038
T mean April + accumulated precipitation
0.39
0.0011
T mean April + days with no precipitation February
0.43
0.0007
Accumulated precipitation + days with no precipitation February
0.36
0.0031
Pollen peak (PP)
Figure 3.11. Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
pollen peak was negatively related to the mean temperature in June
(R2=0.21; Prob>ChiSq=0.0423). The outlier 1989 is excluded.
Figure 3.12 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
pollen peak was negatively related to the minimum temperature in
July (R2=0.21; Prob>ChiSq=0.0374).
Figure 3.13 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
pollen peak was negatively related to the amount of precipitation in
March (R2=0.23; Prob>ChiSq= 0.0309).
Figure 3.14 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
pollen peak was negatively related to the mean temperature from
May- August (R2=0.25; Prob>ChiSq=0.0262). The outlier 1989 is
excluded.
24
Multiple regression- Pollen peak
Table 2.3 Explanatory power of combined meteorological
variables on the pollen peak. Mean temperature May-August and
precipitation in March temperature provide the best explanation.
Variables
R²
p-value
T mean June + precipitation March
0.38
0.0009
T mean June + T min July
0.25
0.0065
T mean May-August+ precipitation March
0.45
0.0002
T min July + Precipitation March
0.44
0.0005
End of pollen season (EPS)
Figure 3.15 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
end of the pollen season was positively related to the mean
temperature in April (R2 =0.38; Prob>ChiSq = 0.0002). The outlier
1993 is removed.
Figure 3.16 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
end of the pollen season was positively related to the minimum
temperature in July increases (R2 =0.12; Prob>ChiSq = 0.0331).
Figure 3.17 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
end of the pollen season was positively related to the mean
temperature in April (R2 =0.13; Prob>ChiSq = 0.0283).
Figure 3.18 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
end of the pollen season was positively related to the maximum
temperature in April (R2 =0.34;Prob>ChiSq = 0.0006). The outlier
1993 is removed.
25
Figure 3.19 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
end of the pollen season was positively related to the mean
temperature from February-April (R2=0.13; Prob>ChiSq =
0.0279).
Figure 3.20 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
end of the pollen season was positively related to the amount of
days with no precipitation in March (R2=0.15; Prob>ChiSq =
0.0343).
Multiple regressions – End of pollen season
Table 2.4 Explanatory power of combined meteorological
variables on the end of the pollen season. Mean temperature of
April and the mean temperature and the minimum temperature in
June provide the best explanation.
Figure 3.21 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
end of the pollen season was positively related to the annual
precipitation (R2=0.11; Prob>ChiSq = 0.0398).
Variables
R²
p-value
T mean April+ annual precipitation
0.53
0.0001
T mean April+ days with no precipitation March
0.56
0.0001
T mean April + T min July
0.57
0.0001
T max April + annual precipitation
0.42
0.0005
T max April + days with no precipitation March
0.40
0.0020
T max April + T min July
0.52
0.0001
T min April + annual precipitation
0.21
0.0374
T min April + T min July
0.22
0.0230
Duration
Figure 3.22 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
duration was positively related to the mean temperature in April
(R2=0.5; Prob>ChiSq = 0.0001).
Figure 3.23 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
duration was positively related to the mean temperatures in
February-April (R2=0.22; Prob>ChiSq = 0.0041).
26
Figure 3.25 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
duration was positively related to the annual precipitation (R2 =
0.2; Prob>ChiSq = 0.0085).
Figure 3.24 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
duration was positively related to the mean temperature in April
(R2=0.21; Prob>ChiSq = 0.0048).
Figure 3.26 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
duration was positively related to the maximum temperature in
March (R2=0.16; Prob>ChiSq = 0.0154).
Figure 3.27 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
duration was positively related to the heat accumulation from 1st of
March until day 142 (R2=0.26; Prob>ChiSq = 0.0018).
Multiple regression – Duration
Table 2.5 Explanatory power of combined meteorological
variables on the duration. Mean temperature of April and annual
precipitation provide the best explanation
27
Variables
R²
p-value
T mean April + Annual precipitation
0.59
0.0001
T mean April + T max March
0.55
0.0001
T max April + Annual precipitation
0.37
0.0012
T max April + T max March
0.30
0.0037
T mean February - April + Annual precipitation
0.34
0.0025
GDH + T mean April
0.52
0.0001
GDH + T max April
0.32
0.0033
GDH + T max March
0.31
0.0043
GDH + T mean February – April
0.31
0.0050
Days with pollen amounts exceeding 80 grains/m3
Figure 3.28 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
number of days with pollen amounts exceeding 80 grains/m3 was
positively related to the maximum temperature in March (R2
value=0.14; Prob>ChiSq = 0.0270).
Figure 3.29 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
number of days with pollen amounts exceeding 80 grains/m3 was
positively related to the mean temperature in April (R2=0.24;
Prob>ChiSq = 0.0024). The outlier 1988 is deleted.
Figure 3.30 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
number of days with pollen amounts exceeding 80 grains/m3 was
positively related to the minimum temperature in April (R2=0.24;
Prob>ChiSq = 0.0015).
Figure 3.31 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
number of days with pollen amounts exceeding 80 grains/m3 was
positively related to precipitation in March (R2 =0.17; Prob>ChiSq
= 0.0150). The outliers 1988 and 2012 are deleted.
Multiple regressions – Days with pollen amounts >80
grains/m³
Table 2.6 Explanatory power of combined meteorological
variables on the number of days with pollen amounts exceeding 80
grains/m3. Mean temperature of April and precipitation in March
provide the best explanation.
Figure 3.32 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
number of days with pollen amounts exceeding 80 grains/m3 was
positively related to heat accumulation two weeks from growth
start – until day 181 (R2 =0.13; Prob>ChiSq = 0.0386). The
outliers 1988 and 2012 are deleted.
28
Variables
R²
p-value
T mean April + precipitation March
0.5
0.0001
T mean April + T max March
0.36
0.0016
T max April + precipitation March
0.35
0.0020
T max April + T max March
0.25
0.0145
T max march + precipitation March
0.35
0.0020
Total pollen amount
Also T max April, T mean June, T mean July, T min June and T min August showed significant regressions but the R2 values were very low and
therefore not demonstrated by a graph.
Figure 3.33 Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
total pollen amount was positively related to the mean temperature
in April (R2=0.34; Prob>ChiSq =0.0009).
Figure 3.34. Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
total pollen amount was positively related to the minimum
temperature in April (R2=0.27; Prob>ChiSq =0.0014).
Figure 3.35. Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
total pollen amount was positively related to the mean temperature
in February – April (R2=0.2; Prob>ChiSq =0.0073).
Figure 3.36. Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
total pollen amount was positively related to the annual
precipitation (R2=0.23; Prob>ChiSq =0.0031).
Figure 3.37. Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
total pollen amount was positively related to maximum
temperature in June (Prob>ChiSq =0.0010). R2 value is 0.28.
Figure 3.38. Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
total pollen amount was positively related to the annual
precipitation (R2=0.23; Prob>ChiSq =0.0039).
29
Multiple regressions - Total pollen amount
Figure 2.7 Explanatory power of combined meteorological
variables on the total pollen amount. Mean temperature of April
and precipitation in March provide the best explanation.
Figure 3.39. Airborne grass pollen as registered in a Burkard
volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The
total pollen amount was positively related to the precipitation in
March (R2=0.17; Prob>ChiSq =0.0145). The outlier 2012 is
deleted.
30
Variables
R²
p-value
T mean April + T max June
0.51
0.0001
T mean April + annual precipitation
0.42
0.0003
T mean April + precipitation March
0.56
0.0009
T min April + T max June
0.47
0.0001
T min April + annual precipitation
0.36
0.0015
T min April + precipitation March
0.39
0.0015
T mean February- April + T max June
0.42
0.0003
T mean February-April + annual precipitation
0.34
0.0026
T mean February- April + precipitation March
0.36
0.0012
T max June + annual precipitation
0.38
0.0009
T max June + precipitation March
0.42
0.0003
GDH + T max June
0.36
0.0015
GDH + annual precipitation
0.36
0.0154
Flowering phenology and pollen season 2013
Alopecurus pratensis flowering period (day 150-161)
Poa pratensis flowering period (day 152-181)
Dactylis glomerata flowering period (day 166-177)
Festuca rubra flowering period (day 167-188)
Deschampsia flexuosa flowering period (day 172-188)
Deschampsia cespitosa flowering period (day 173-192)
Festuca pratensis flowering period (day 172-197)
Lolium perenne flowering period (day 175-195)
Molinia caerulea flowering period (day 204-218)
Day of full flowering
Figure 4.1 The graph show the airborne grass pollen 2013 registered in a Burkard volumetric spore trap at SU/Östra sjukhuset,
Gothenburg. The pollen season starts day 140. The increase of pollen counts in the beginning of the pollen season matches the
flowering of Alopecurus pratensis. The full flowering of Dactylis glomerata (172) matches the first pollen peak (day 171-173), and the
full flowering of Lolium perenne and Festuca pratensis (Day 185) matches the second pollen peak (day 188). Pollen is still found after
the flowering period of Molinia caerulea (day 218).
31
Discussion
Atmospheric Poaceae pollen trends
Sweden is one of the countries in Europe that is expected to experience the highest rate of change
towards an earlier spring phenology (Menzel, 2000, Chmielewski & Rötzer 2001, Ahas et al. 2002, Menzel
et al 2006). And there have definitely been dramatic changes in the Poaceae pollen season in Gothenburg.
Previous studies mainly observed an advanced pollen start and peak of the main Poaceae pollen season
(MPS) in Europe (Frenguelli & Bricchi 1998, Rasmussen 2002, Van Vliet et al. 2002, Bertin 2008). These
results are consistent with the results of this study, where this advance is of around ten days (Table 1.11.2). The most striking observations in the present study however, is the prolonged duration of MPS and
an increase in total annual pollen counts as well as the number of days with high pollen counts. In 30
years, the MPS has become more than one month longer. The prolonged pollen season is mainly caused
by a delay of its end; during 1979-1989 the mean end date of the pollen season was the 21st of August,
while during 2001-2012 it was 13th of September, i.e more than three weeks later in 30 years. The pollen
index has almost doubled in 30 years and the number of days with pollen counts exceeding 80 grains/m3
have become four times higher (Table 1.1-1.2).
Changes in climate in Gothenburg
During the last three decades (1979-2012) the spring and summer temperatures have increased
significantly in the Gothenburg area (Figure 1.1-1.9). This is in accordance with climate trends observed in
Europe (Chmielewski & Rötzer 2001, IPCC 2007). The annual rainfall has also increased (Figure 1.101.11). This increment agrees with the trend of increased rainfall in northern Europe (Rossby Centre 2011,
IPCC 2007).
The effects of climate change on pollinosis may be understood when we know to what extent and how
single meteorological variables influence the pollen season.
Temperature
The influence of temperatures in March and April on the grass pollen season has been observed in
previous studies, e.g. that higher temperatures during these months increase the daily amounts of pollen
(Sánches Mesa et al. 2003, Green et al. 2004, Recio et al. 2010) and advance the pollen season (Frenguelli
1989, Antépara et al.1995, Chmielewski & Rötzer 2001, Van Vliet et al. 2002, Kasprzyk & Walanus 2010).
The present study shows that particularly mean temperature in April is important to explain the start,
duration and end of the pollen season, as well as its intensity.
Higher April temperatures significantly advance the date of the beginning of the main pollen season and
when the first pollen grain is detected (Figure 3.1, 3.9). This correlation could be explained by that the
mean date of when Poaceae is observed to begin growth above ground is the 2nd of April, and the following
weeks are therefore critical for the development and survival of the plant. A warm April will accelerate
plant growth and development, as long as the optimal temperature is not exceeded. The consequence is
32
that anther maturity and pollen dispersal will occur earlier. When favorable temperatures are combined
with much precipitation, the conditions for plant growth and development and early flowering will be
even more beneficial. In contrast, low temperatures and drought slow down the development, and the
pollen dispersal start will be delayed.
Another reason for the primary influence of the temperature in April probably is that the temperatures in
February and March, which also influenced the pollen season, but with less explanatory power, include
fewer days with temperatures high enough for plant development to occur. During the days with low (<5
Cᵒ) temperatures no developmental processes that will influence the coming pollen season can take place,
which makes the correlation between the pollen season and the earlier spring months weaker.
The pollen peak is rather influenced by temperatures during late spring and early summer than by early
spring temperatures. Higher temperatures in May-June result in an earlier pollen peak (Figure. 3.11-3.12,
3.14). The Poaceae pollen peak can occur anytime during the period from the middle of June to the
beginning of July. A warm May and June probably accelerates the development of the flowers, and
additionally creates the beneficial dry and warm conditions that trigger anther dehiscence.
The changes in pollen season 1979-2012 occurred at a fairly constant rate, except for the advances in the
beginning of the pollen season and the date of the pollen peak, which have been taken place almost
exclusively the last ten years (Table 1.1-1.2). The beginning of the pollen season and the date of the pollen
peak may be more directly related to spring and early summer temperatures, and may be more sensitive to
the rapid changes in temperature during this period the last ten years.
Heat accumulation
The rate of growth and development is generally related to the rate of heat accumulation. There are
several different ways to calculate this rate, which is often expressed as “Growth Degree Hours” (GDH).
In this study I used a model that originally was used to calculate the start of flowering of apple trees
(Linvill 1990). The model has successfully been applied in previous Scandinavian studies of the start and
course of the pollen season (Andersen 1991, Dahl & Strandhede 1996). In models for calculation of heath
accumulation, you must know the base threshold temperature that has to be exceeded for activation of
enzymes and developmental processes to take place. This base temperature depends on taxonomy and
provenance with regard to longi-/latitude. Thus, two base temperatures were tried out in this study,
namely +2 ᵒC and +5 ᵒC. Using the base temperature +2 ᵒC has turned out to be successful when
calculating the relationship between growth and development for birches and alder. However, in this study
the GDH model based on +5 ᵒC turned out to be superior when calculating this relationship for Poaceae
whereas the use of +2 ᵒC gave ambiguous results.
The good correlation between accumulated GDH from 5 ᵒC and pollen season, and the inferior
correlation between accumulated GDH from 2 ᵒC and pollen season, indicates that plant development of
Poaceae does not occur when temperatures are below 5 ᵒC. The hypothesis is supported by previous
studies that conclude that the minimum temperature required for different species of Poaceae is 5 - 6.5 ᵒC
33
(Beard & Almodares 1980, Üremiş & Uygur 1999, Gorai et al. 2006). When 5 ᵒC is used as the base
threshold temperature, the accumulation of GDH is observed to start to accelerate in the middle of
March, which is also observed to be the period where the germination of grasses occurs to the greatest
extent (Netherlands, Pons 1991). This acceleration coincides with the mean date of two weeks before
Poaceae is observed to grow above ground. During these two weeks before observed growth above ground,
when GDH are starting to accumulate, development may take place underground. If the base temperature
+2 ᵒC is used, irrelevant data will be included. So from now on in this text “accumulated temperature”
refers to the results of the calculation model where 5 ᵒC is used as base temperature.
To find an optimal start date of GDH accumulation is also a dilemma (Rasmussen 2001), and several start
and end dates were examined in this study. The best correspondence on the pollen season was found
when proceeding from the date two weeks before the observed start of the growth above ground of
Poaceae (which varied among years) to the mean date of pollen peak (30th of June). Perhaps proceeding
from the period before growth start gave the best response because the period before growth start is
particularly important. A fixed date is due to be less informative.
Temperature accumulation during spring is positively related to an advanced beginning of the pollen
season and to the date of when the first pollen grain is detected, to a prolonged duration of the pollen
season, to increased number of days with pollen counts exceeding 80 grains/m³ and to an increased
annual pollen sum (Figure. 3.3, 3.10, 3.27, 3.32, 3.38). An increased accumulation rate also resulted in an
earlier pollen peak and a later end date of pollen season, but had low explanatory value.
The accumulated temperature during spring (together with mean April temperature) explained the entire
variation in the pollen season more than any other variable used in this study. Temperature is known to be
one of the most important factors for plant development.
Precipitation
Precipitation obviously influences the pollen season and has also been observed to be important in
previous studies (Green et al. 2004, Recio et al. 2010, Antépara et al. 1995). The explanatory value
however is in general lower than for spring temperatures.
In general particularly precipitation in March seems to influence the pollen season. A high rainfall or
snowfall in March contributes to an earlier pollen peak, more days with high amounts of pollen grains and
a higher pollen index (Figure 3.13, 3.31, 3.39). Water availability is important for developmental processes
before and during growth of Poaceae. The end of pollen season occurs later when the number of days with
no precipitation in March is high (Figure. 3.20). This is perhaps explained by a delay of the whole pollen
season, caused by spring drought.
In general, accumulated precipitation during the entire period from 2 weeks before growth start to 10th of
May did not seem to influence the pollen season as strongly as accumulated March precipitation alone.
Precipitation can be variable in time. Data is likely to be less informative when using a longer
34
accumulation period, since the timing of precipitation with a sensitive development stage is likely to be
less precise.
Environmental factors in combination
High mean temperature in April combined with high precipitation (especially in March) does not only
promote an advance of the beginning of the pollen season but also delay the end of pollen season (R2
0.56), prolong the duration of the pollen season (R2 0.59) and increase the total pollen index (R2 0.56)
(Table 2.4, 2.5, 2.7).
A high temperature and rainfall, resulting in beneficial conditions during spring, may also influence the
pollen season indirectly, not only as obvious as a direct benefit of the early developmental processes
resulting in advance of pollen season start and pollen peak.
As an example, the main reason for the prolonged duration of the pollen season is the delayed end date.
The delayed end of the pollen season is positively correlated to spring temperature, minimum temperature
in July and precipitation. Especially mean temperature in April strongly influences the delay of end date
(Figure 3.15). Beneficial spring conditions (high temperatures and precipitation) favor tiller production as
well as the number of initiated floral primordia. It is known that with increased temperatures, the number
of floral primordia increases (Parker and Botwick 1939). Also the size of the plant (that probably is
positively correlated with beneficial conditions during growth) is positively correlated to the flowering
intensity per plant (De Jong et al. 1986). The production of tillers is higher when the temperature is high
(within the optimum temperature span) and lower (and the mortality higher), when the soil is dry (Langer
1972). Thus, a warm spring with high precipitation results in excellent conditions for tiller production.
Since 60 % of the development of tillers occurs from February –April (Gibson 2009) and the tillers
produced early during the year are the ones that are likely to survive and flower (Langer 1972), beneficial
spring conditions are critical for the quantity of the Poaceae population. Tiller mortality is most frequent
during summer since competition among tillers is high (Gibson 2009). Better conditions during summer
may also lower tiller mortality when competition of limiting factors may not be as high as when conditions
are less beneficial. The culms develop successively, and if there is a higher primordia - and tiller
productivity, new stems can develop during a longer time. This in turn will increase the amount of
airborne pollen later during the year. Furthermore in combination with increased summer temperatures,
maybe a second pollen peak with late species could occur and airborne pollen can be present during a
longer period, explaining the delayed end. It may also be that the increased temperature and rainfall
benefit the late flowering species such as Phragmites australis or Molinia caerulea in particular and therefore it
is mainly the late part of the pollen season that is prolonged; or that stands that were cut during early
summer may grow back to a second flowering during late summer (Calder 1964, Davies 1976). Grasses
usually regrow after cutting, since the meristem tissue is situated close to the ground and protected by leaf
sheaths (Gibson 2009).
If the establishment of new plants, as well as initiation of floral primordia in existing ones, is favored by
high spring temperature and precipitation, this may also explain why the number of days with pollen
counts exceeding 80 grains/m3 and total pollen amount is higher (Figure 3.28- 3.36, 3.38-3.39). An
increased number of flowering stems simply creates higher amounts of pollen.
35
If the spring temperatures instead are low, there are risks of frost damages (Höglind et al. 2012, Kreyling
et al. 2012, Dahl et al.2013). Alternatively, if the amount of precipitation is extremely low, water-stress will
inhibit plant development and tillering. This in turn will lead to a shorter temporal span of the pollen
season; delayed start, earlier end and shortened duration. As an example it is shown in previous studies
that frost damages of Quercus’ catkins results in a delay of pollen season and decrease pollen amounts
(Léon-Ruiz et al. 2011).
Why is so much of the variation in pollen counts not explained?
In this study the factors assumed to affect pollen counts do not explain as much of their variation as
might be expected. Even if some variables clearly have a great influence on pollen season, such as
accumulated spring GDH or mean temperature in April, R2 never exceeds 0.6. This may be explained by
the fact that many factors, not included in this study, also influence the pollen season. These are factors
such as the prevailing weather during pollen dispersal, such as wind speed and direction, the direction of
geostrophic winds and large-scale changes in soil pH (e.g. acidification) and nutrient content in the soil
over time. Especially air pressure, wind speed and the weather conditions related to the direction of the
geostrophic winds are factors that strongly influence the pollen content in the air. Light intensity and
quality probably also influence the pollen season of Poaceae since high light intensity increase the tiller
production and red /far-red light lower tiller production (Gibson 2009), which may influence the pollen
production. The variable relationship between accumulated temperature and the start of the pollen season.
It would be logical to assume that a more or less determined sum of GDH is required for the pollen
season to begin. But the results of the present study show that GDH accumulated from two weeks before
growth starts to the date when pollen season starts, varies between 2406-7510 GDH. This means that one
year the pollen season can start when only 2406 GDH are accumulated, while another year a three times
higher amount of GDH have to be accumulated for the pollen season to start. The variance in GDH
request could possibly be related to when favorable temperatures occur in relation to the requested
photoperiod for primordial initiation or to trigger growth, and/or light quality during the growth of culms.
Furthermore, there are many species included in Poaceae which may differ in response to environmental
factors.
As discussed above, the change towards a more favorable climate for Poaceae may increase the abundancy
and ranges of the important species, as well as favor production of individual plants. Also, rising
carbondioxide levels can have an effect. However, not only climate change is important. Changes in land
use have taken place in Sweden the last 60 years. Land that is no longer cultivated is overgrown by species
such as Dactylis glomerata and Alopecurus pratensis, that are strong competitors in early succession stages
(Nilsdotter- Linde 1992). These species are also high pollen producers. Poaceae species such as Lolium
perenne and Festuca rubra are commonly sown in verges for ornamental purpose in Sweden (Blomgren et al.
2011). This practice may also contribute to the increase of their abundance, as well as the changed
management of these verges. Another trend is the increased atmospheric nitrogen content and downfall
resulting from e.g. traffic emissions, which increase the nitrogen content in the soil and favors tillering in
Poaceae.
36
Especially Dactylis glomerata is interesting, since it is proved to be one of the greatest pollen producers in
studies of phenology related to atmospheric pollen counts and of reproductive output (Nomoto 2013,
present study) combined with the fact that it is nitrogen benefitted (Hejcman et al. 2012) and additionally
is becoming more common in Sweden (Blomgren et al. 2011). This possible increase of competitive grass
species may not only contribute to a prolonged and more intense pollen season, but may also be a threat
to biodiversity, as in the case of Dactylis glomerata (Dainese 2011).
Previous studies
Trends of the Poaceae pollen season differ between geographical areas, according to climate, and to
dominating grass taxa and their ecology. Thus, the way the pollen season has changed, and which factors
that are responsible for these changes, differ between regions. In some studies increased intensity,
advanced pollen season start/peak and prolonged duration of the Poaceae pollen season was also found
(Menzel 2000, Fitter & Fitter 2002, Van Vliet et al. 2002, Bogawski et al. 2012), while others found a
totally opposite situation (Emberlin et al. 1993, González Minero et al.1998, Jato et al. 2009, Recio et al.
2010). Jato et al. (2009) observed lower annual Poaceae counts, fewer days with high amounts of pollen and
shorter pollen season in Galicia (Spain) and also Recio et al. (2010) observed a clear shortening of the
pollen season by delayed start and advanced ending in Malaga (Spain).
Most authors agree in which the parameters that mainly are responsible for potential changes in pollen
season are (temperature, rainfall, wind, increasing carbon dioxide concentration), but depending on latiand longitude, climate change affect these parameters differently. The advance in spring events is more
pronounced in North than in South Europe (Menzel 2000, Chmielewski & Rötzer 2001). In the
Mediterranean area, a decrease in rainfall and increased temperature is observed (IPCC 2007, Giorgi &
Lionello 2008). This new climate cause drought and other unbeneficial conditions for plant survival and
growth, which may explain the shorter and less intense pollen season observed in the Mediterranean. In
rainy and cool years, the pollen season is advanced and the amounts of pollen increased, while years with
higher mean temperatures and low precipitation instead delay and shorten the pollen season and also
contribute to lower annual pollen amounts (Léon–Ruiz et al. 2011).
In North Europe, in contrast, precipitation has increased (Christensen et al. 2001, IPCC 2007, Rossby
Centre 2011, Figure 1.10-1.11) and in combination with increased temperatures (IPCC 2007, Figure 1.11.9) conditions for Poaceae are favorable. This is seen in the results of this study. Many studies show that
precipitation is negatively correlated with high pollen counts and instead cause a delay in pollen season,
depending also on when it falls. This study show trends of increasing precipitation in July resulting in a
decrease of days with high pollen amounts, which is reasonable since rain during this period inhibits
anther dehiscence and wash out already dispersed pollen from the atmosphere.
This study also shows that an increased precipitation in March is positive for the intensity and duration of
the pollen season, while a Spanish study (Recio et al. 2010) found that spring precipitation delays and
shorten the pollen season. This was explained by precipitation being beneficial for vegetative growth,
instead of sexual, so that less pollen was produced. Also, rainfall in spring washes away the light soil that
predominates in Mediterranean mountain slopes, which inhibits plant growth. These are obvious examples
of how results differ due to differences in environmental conditions. But results can also differ without
37
geographical differences. A study from Denmark (Rasmussen 2001) found a nine days’ advance of end
date of the birch (Betula) pollen season. This difference in results could instead be explained by the fact
that the number of mature catkins is predetermined before anthesis. Warm temperatures will empty the
anthers rapidly, whereas in grass, they favor the maturation of a higher number of floral primordia.
Land use and cultivation
Also the development of land use differs between regions. Sweden has overgrowing meadows and
pastures at a larger scale and during a longer period compared with other European countries (Garnier et
al. 2001). This abandonment of cultivated land probably benefits many Poaceae species, at least during early
succession stages.
Definition of the main pollen season
A third factor that could contribute to the differences in results is the way of defining the main pollen
season (Jato et al. 2006). Some studies define the pollen start as when 1% of the total annual pollen
amount is reached others as the first day when 30 grains/m3 (or another concentration) can be measured.
Pollinosis
During the last years with increased intensity of the pollen season, the risk of experiencing more severe
symptoms has also increased since the daily amount of pollen is increasing, and the severity of the
symptoms depends on the amounts of airborne pollen (Domínguez-Vilches et al.1995, D’ Amato et al.
1998, Rapiejko et al. 2007, Kiotseridis et al. 2013). It is reasonable to suggest that one of the reasons for
an increase of people suffering from pollinosis in Europe and Sweden (D’ Amato et al. 1998, Vårdguiden
2013) may be related to the increased number of days of high pollen amounts observed in this study,
although the increment is largely explained by life style factors, such as conditions during infancy, when
tolerance is induced or pollen in combination with emissions in urban areas (D’amato et al. 2001).
If observed climate trends in Gothenburg continue, there will probably be even longer and more intense
pollen season in the future. Pollinosis will simply be more severe for those suffering from Poaceae induced
pollinosis.
Phenology
Pollen season and weather 2013
The pollen season 2013 started the 20th of May, day 140, which is six days later than the mean date of the
last five years (2007-2012). This delay in pollen season start is most probably due to a very cold and dry
spring. The mean temperatures of January-April were all lower than average (1975-2012), especially
March with a mean temperature of -0.7 ᵒC compared to a mean of 2 ᵒC. Additionally the month of
March was very dry with 2.5 mm precipitation, which is the lowest measured in Gothenburg since 1964.
38
These adverse circumstances will, in accordance with the previous results, delay the start of the pollen
season, mainly due to lower than average temperatures in April and low annual precipitation (Figure. 3.1,
3.4). An even larger delay might have been expected, but a higher than average May mean temperature
accelerated the plant development and start of pollen dispersal.
In 2013, the low temperatures and precipitation in spring also decreased the intensity of the pollen season
(Figure 3.28-3.36, 3.38-3.39), also in accordance with previous results. This year, only three days had
pollen amounts exceeding 80 grains/m3, compared with the average 7.5 during the period 2007-2012.
Flowering phenology vs. pollen season
The earliest species coming into flower is Alopecurus pratensis that started to flower four days after pollen
season start, in the locality exposed to sun (Figure 4.1). In the shady locality, plants started flowering
eleven days later. Anthoxantum odoratum is probably responsible for the pollen caught in the trap before the
flowering of Alopecurus pratensis. Anthoxantum odoratum is one of the earliest flowering Poaceae species in
Sweden, and was observed to flower in mid May. It is not included in the study since the population is not
considered large enough to be an important contributor to the annual pollen index.
The mean start of pollen dispersal in Alopecurus pratensis is day 150. Two days later, also Poa pratensis starts
to disperse pollen and pollen counts increased a little (Figure 4.1). The mean date of when Alopecurus
pratensis is in full flowering is day 156. Day 161 is the day when pollen amounts exceed 30 grains/m3, the
threshold that is suggested to trigger moderate symptoms of pollinosis (Kiotseridis et al 2013). At this date
Alopecurus pratensis, Poa pratensis and Dactylis glomerata (in one locality) were observed to flower.
The first pollen peak was observed around day 171-173, which was just after the mean flowering date of
Poa pratensis (day 168) and during the mean flowering date of Dactylis glomerata (172). The overlap of full
flowering of these two high pollen producers is probably an important reason for the clear increase in
pollen counts (demonstrated in the graph by a peak, Figure 4.1). Especially Dactylis glomerata seem to be
responsible, since there is a perfect match between the mean flowering date of Dactylis glomerata and the
peak. Dactylis glomerata is suggested to be the highest pollen producer of pollinosis inducing Poaceae species
in Sweden (Nomoto 2013), based on anther length/individual, and its high pollen production is proved
again in this study. Also Festuca rubra (day 168) and Deschampsia flexuosa (day 172) have started to flower
when the peak is reached and may have contributed to the increased amounts of pollen.
During the first pollen peak, the pollen count reached 89 and 103 grains/m3, but one day later the pollen
counts abruptly decreased from 103 to 15 grains/m3. This could be explained by weather conditions. The
days with high pollen temperature reached 18.3 ᵒ C (89 grains/m3) and 20.8 ᵒ C (103 grains/m3), and there
was little precipitation. One day later, the temperature decreased to 15.4 ᵒ C and anther dehiscence
probably occurs on a smaller scale, which explains the sudden decrease in pollen amount. The following
days the low pollen counts correlate with increased precipitation.
The full flowering of Deschampsia cespitosa (day 182) and D. flexuosa (day 176) or Festuca rubra (day 178) is
not significantly reflected in the graph, even if weather circumstances are beneficial, which implies that
neither D. cespitosa or D. flexuosa produce very high amounts of pollen, although common. An alternative
39
explanation is that large D. cespitosa populations are lacking in the area surrounding Östra sjukhuset, where
the pollen trap is situated.
The full flowering of Lolium perenne and also Festuca pratensis occurred day 185, and probably contributed to
the second pollen peak day 188. The match of flowering to this second pollen peak is not as clear as the
first one. The localities included in this study may be flowering earlier than average since Lolium perenne
was observed to flower later in other localities not included in the study, which may explain the mismatch
of three days.
In previous studies Lolium perenne, unlike Dactylis glomerata and Poa pratensis, did not turn out to be one of
the high pollen producers. It is logical to assume that the second pollen peak is caused by the
simultaneous full flowering of Festuca pratensis, that produce higher pollen amounts than Lolium perenne
(Nomoto 2013), but since there are few localities of Festuca pratensis found in central Gothenburg there are
other possible explanations, such as a large population of Lolium perenne. Lolium perenne is also used in
lawns, where it is cut, but sometimes missed by the mower. Another hypothesis is that the clearly marked
peak in the pollen curve that coincides with the full-flowering of Lolium perenne is not only due to Lolium
perenne, but also to other species, not included in this study, that flower at the same time as Lolium perenne.
This could be species such as Holcus lanatus (Yorkshire-fog). It may also be that Festuca pratensis is more
common closer to Östra sjukhuset, and therefore actually have a significant impact on the pollen curve
contributing to the second pollen peak.
Day 202, the 21st of July, the pollen counts decreased to amounts below 10 grains/m3. This is during a
period where all species in all localities included in this study were observed to not disperse pollen
anymore, except for Molinia caeruela that started to flower day 204.
The mean day of when Molinia caerulea was in full flowering was day 211. There is no noteworthy
reflection in the pollen season curve, neither of start of flowering or full flowering of Molinia caerulea. The
amounts of pollen are decreasing below 10 grains/m3 after the last observed flowering date of Molinia
caerulea. In a previous study (Nomoto 2013) Molinia caerulea was shown not to produce very high pollen
amounts, based on measuring total anther length per individual. But Molinia caerulea produced more pollen
per individual than Lolium perenne. Why the full flowering of Lolium perenne appears to be clearly reflected in
the graph and Molinia caerluea is not can be due to many reasons, some already suggested above.
Even after the last day of flowering of Molinia caerulea, airborne pollen is found. This could be explained by
the late flowering of other Molinia caerulea localities or that populations of e.g. Dactylis glomerata or
Alopecurus pratensis has grown and flower a second time after been cut down earlier during summer. It
could also be due to the flowering of Phragmites australis that was observed to flower in the area of
Gothenburg the turn of the months July/August and is known to be one of the highest pollen producers
of Poaceae, although flowering sometimes seems to fail, or to sometimes be entirely cleistogamic in Sweden
(Nomoto 2013). The flowering of Phragmites australis is not significantly reflected in the pollen curve.
Not surprisingly, the individuals growing in the sunny localities flowered earlier than those in the shady
ones.
40
The good correlation between local flowering phenology and pollen season found in the present study
agrees with some previous studies (Jato et al. 2001), but in contrast, many authors found a temporal
mismatch (Estrella et al. 2006, Tormo et al. 2011, Jato et al. 2001). This mismatch was explained by
influences of long-distance transported pollen caught in the pollen traps and by a possible resuspension,
meaning a temporal gap between the shedding of pollen and its presence in the air. It was also explained
by the fact that in these studies, airborne pollen was measured daily while flowering phenology was
measured less often (often only once a week). In this study, the flowering phenology was observed more
or less every other day, which may have been the crucial detail resulting in the good correlation between
the local flowering phenology and airborne pollen curves. Another crucial factor is that the elevation of
the pollen trap is high enough to represent the regional situation.
Conclusion
Several studies conclude an observed advance in spring phenology and pollen season, foreseeing the most
dramatic changes taken place in Scandinavia (Chmielewski & Rötzer 2001). The results of my study truly
show large changes in pollen season during the last three decades; pollen start and pollen peak are
advanced, ending of pollen season is delayed, days with pollen counts exceeding 80 grain/m3 and pollen
index indices increased. Especially increased temperature in April and accumulated GDH (based on a
threshold temperature of 5 Cᵒ) during spring are important and should therefore be included when
predicting the course and intensity of the pollen season. Precipitation has a clear influence on the pollen
season of Poaceae but overall gave low R2 values, making precipitation an insecure parameter to use alone
when forecasting the pollen season. Before it is used, it is important to identify the developmental stages
when water availability is most important.
The changes in climate are, at least partly, responsible for the observed changes in pollen season, directly
and indirectly. During the last 35 years, temperature and precipitation has increased significantly, which
apparently explains that the presence of atmospheric grass pollen is prolonged and intensified as
compared to the mid-1970’s. However there are many factors not included in this study that probably also
contribute to the changed pattern.
It is important to consider that observed trends in Poaceae pollen season differ between studies, mainly
because of differences depending on geographical location, but also on other factors such as land use and
ways of defining the pollen season. Therefore geographically wide-reaching conclusions of the trends in
pollen season could be to general when the results from Sweden more than once differ from other
European studies.
The changes in pollen season from 1979-2011 are so extensive that it is highly probable that they already
had consequences on both the ecology of Poaceae and the situation for pollinosis sufferers. These
consequences will probably be more severe as climate change is ongoing, and the most drastic scenarios
predicted for the Swedish west coast (Rossby Centre 2011, SMHI 2013) assume an increase of 5-4 ᵒC of
surface temperature and 15% in annual precipitation at 2100.
41
With these climate projections, it seems like the changes in pollen season also will proceed with the same
trends as observed, leading to a more intense and prolonged pollen season which make the situation for
pollinosis sufferers even more difficult, with more severe symptoms. Also, with an increased exposure, the
amount of people affected may increase. The changes in the pollen season of Poaceae concern not only
pollinosis sufferers but the whole ecology of Poaceae. In a broader perspective changes in the Poaceae
pollens season influence the reproduction of some of the economically most important species. A possible
change in harvest yields will be a humanitarian, economical and social matter. Also therefore it is
important to be aware of the changes observed and variables responsible, to be able to predict, understand
and prepare for changes in the reproductive season.
Based on observations of flowering phenology of nine Poaceae species, it can be concluded that the pollen
season, matches the local flowering phenology well for some species and less well for others, and that the
counts registered from the pollen trap indeed reflect the regional situation, as intended. The best match of
the pollen peaks was the overlap of the full flowering of Dactylis glomerata and Poa pratensis and full
flowering of Festuca pratensis and Lolium perenne. This implies that these species are the highest pollen
producers, partly because they produce the most pollen/individual, but also maybe because the
populations of these species are larger and more widely distributed. Additionally Dactylis glomerata, Poa
pratensis, Festuca pratensis and Lolium perenne are all pollinosis inducing and may thus be the major
responsible species for pollinosis induced by Poaceae. Deschampisa flexuosa and D. cespitosa did not seem to
influence the atmospheric pollen counts that much, since full flowering dates are not significantly reflected
in the pollen count curve.
The flowering among species is temporally separated; the first flowering period includes Alopecurus pratensis
and Poa pratensis, while Dactylis glomerata flowers a bit later. Around day 180, Festuca rubra, Deschampsia
flexuosa, Deschampsia cespitosa flowers almost synchronically. Around five days later Festuca pratensis and
Lolium perenne flower. The latest flowering phase includes Molinia caerulea. Poa pratensis, Festuca rubra and
Festuca pratensis seem to have the longest flowering periods.
Since there are only a few records of local flowering phenology from Gothenburg, only one year could be
studied. Yet the information gained from this study may help to improve the interpretation of the pollen
season curve and the phenology of some species of Poaceae. With further knowledge, perhaps more
economically favorable alternatives for making pollen forecasts based on observations of flowering
phenology can be developed. Further studies are also necessary to contribute to deeper knowledge in the
Poaceae ecology, flowering phenology and pollen season.
This study shows that climate change probably will influence the pollen season to become more intense
and prolonged. The most important species influencing the pollen season of Poaceae are Dactylis glomerata,
Poa pratensis Lolium perenne and Festuca pratensis.
Further studies of plant phenology are important to make a record over longer temporal spans. More
detailed studies of variables influencing the pollen season are necessary and also deeper analyzes of what
the changes in pollen season will infer in the future.
42
Acknowledgements
I am very grateful for all the good advice, encouragement and patient guidance of my supervisor Åslög
Dahl.
43
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