Changes in the Poaceae pollen season in Gothenburg

(1979-2012) and the synchronization between

pollen season and flowering phenology

1

IndexSammanfattning på svenska..................................................................................4Keywords...............................................................................................................5Introduction.........................................................................................................6

Plant phenology..................................................................................................6Poaceae pollen season........................................................................................6Observed changes in pollen season and meteorological parameters influencing it.......................................................................................................................... 6Flowering phenology and pollen season.............................................................7Aim...................................................................................................................... 8

Background.........................................................................................................9Anemophily.........................................................................................................9Phenology...........................................................................................................9Basic Poaceae morphology.................................................................................9

Roots................................................................................................................9Culm................................................................................................................ 9Inflorescence.................................................................................................10

Anthesis............................................................................................................10Anther dehiscence............................................................................................10Poaceae pollen production...............................................................................11Pollinosis...........................................................................................................11

Materials............................................................................................................12Alopecurus pratensis (meadow foxtail)............................................................12Dactylis glomerata (cock’s foot).......................................................................12Deschampsia cespitosa (tufted hair-grass).......................................................12Deschampsia flexuosa (wavy hair-grass)..........................................................12Festuca pratensis (meadow fescue).................................................................12Festuca rubra (red fescue)...............................................................................12Lolium perenne (perennial rye-grass)..............................................................12Molinia caerulea (purple moor-grass)..............................................................13

2

Hanna Nomoto

Examensarbete Botanik30 hp

Institutionen för Biologi och miljövetenskap

Göteborgs Universitet HT 13

Poa pratensis (smooth meadow grass).............................................................13Methods..............................................................................................................13

Location............................................................................................................13Part one – Changes in the pollen season and meteorological parameters influencing it.....................................................................................................13

Pollen data.....................................................................................................13Meteorological data.......................................................................................14Statistical analysis.........................................................................................14

Part two – Comparing local flowering phenology with the pollen season 2013.......................................................................................................................... 15

Phenological data..........................................................................................15Pollen data.....................................................................................................16Analysis..........................................................................................................16Geographical position of locals.....................................................................17

Results................................................................................................................18Significant changes in temperature and precipitation 1979-2012...................18

Temperature..................................................................................................18Precipitation..................................................................................................19

Changes in pollen season 1979-2012...............................................................20The effect of meteorological parameters on the pollen season in Gothenburg, Sweden, during 1979-2012...............................................................................22

The beginning of the pollen season (BPS).....................................................22Day when the 1st pollen grain is found..........................................................23Pollen peak (PP)............................................................................................24End of pollen season (EPS)............................................................................25Duration.........................................................................................................26Days with pollen amounts exceeding 80 grains/m3.......................................28Total pollen amount.......................................................................................29

Flowering phenology and pollen season 2013..................................................31Discussion..........................................................................................................32

Atmospheric Poaceae pollen trends.................................................................32Changes in climate in Gothenburg...................................................................32Temperature.....................................................................................................32Heat accumulation............................................................................................33Precipitation.....................................................................................................34Environmental factors in combination.............................................................35

3

Why is so much of the variation in pollen counts not explained?.....................36Previous studies................................................................................................37Land use and cultivation...................................................................................38Definition of the main pollen season................................................................38Pollinosis...........................................................................................................38Phenology.........................................................................................................38

Pollen season and weather 2013...................................................................38Flowering phenology vs. pollen season.........................................................39

Conclusion.........................................................................................................41Acknowledgements...........................................................................................43References.........................................................................................................44

Sammanfattning på svenskaI 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

4

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.

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.

5

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

Introduction

Climate change and global warming are nowadays frequently mentioned in media, politics and not least, in science.

6

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

7

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

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

8

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.

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.

9

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. How has the pollen season in Sweden changed from 1979 to 2012?2. How does meteorological parameters influence the pollen season?3. Does the local flowering phenology match the pollen season?4. 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.

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.

10

Illustration 1.1 The basic morphology of a typical Poaceae phytomer

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.

RootsThe 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).

CulmThe culm consists of nodes, internodes, leaves and meristematic tissue situated above the nodes on the axis of the leaves.

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 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

11

Illustration 1.2. The floret of Poaceae

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.

InflorescenceThere 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 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. Disruption of the septum

12

Illustration 1.3. Anther

II. Opening of the locules by a split in epidermis

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).

Poaceae pollen production

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

13

septum

epider

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.

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

14

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 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

15

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 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)

16

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 dataGeneralized 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

17

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 dataRegressions 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 hair-grass) 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.

18

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 visiblePhase 1: Panicle visiblePhase 2: 50 % of all individuals have developed visible anthersPhase 3: 100 % of individuals have developed anthersPhase 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.

19

Geographical position of localities

20

Department of Biological and Environmental Sciences (57.6814:11.9515); Festuca r. (shadow), Poa p. (shade+sun), Dactylis g. (shade+sun),

Botanical Garden of Gothenburg, green house (57.6828:11.9522); Festuca r. (sun), Deschampsia c.

Hill between the Botanical Garden of Gothenburg and Änggårdsbergen conservation area (57.6783;

Birger Jarl (57.6849:11.9242); Festuca r. (sun), Lolium p. (sun)

Margareteberg (57.6879;11.9345) Lolium p. (sun)

Illustration 2.1Results

Significant changes in temperature and precipitation 1979-2012

Temperature

-8

-6

-4

-2

0

2

4

6

8

Tmea

n Fe

brua

ry

1980 1984 1988 1992 1996 2000 2004 2008 2012

Year

Figure 1.1 The mean temperature in February in Gothenburg increased during 1979-2012 (Estimate=0.12; Prob>Chi Sq =0.0292).

0

2

4

6

8

10

Tmax

Feb

ruar

y

1980 1984 1988 1992 1996 2000 2004 2008 2012Year

Figure 1.2 The maximum temperature in February in Gothenburg increased during 1979-2012 (Estimate =0.092; Prob>Chi Sq= 0.0303).

-2

0

2

4

6

Tmea

n M

arch

1980 1984 1988 1992 1996 2000 2004 2008 2012Year

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.

21

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)

3

4

5

6

7

8

9

10

11Tm

ean

Apr

il

1980 1984 1988 1992 1996 2000 2004 2008 2012Year

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.

6

8

10

12

14

16

18

20

22

Tmax

Apr

il

1980 1984 1988 1992 1996 2000 2004 2008 2012Year

Figure 1.4 The maximum temperature of April in Gothenburg increased during 1979-2012 (Estimate=0.18; Prob>Chi Sq= 0.0040).

-2

-1

0

1

2

3

4

5

6

Tmin

Apr

il

1980 1984 1988 1992 1996 2000 2004 2008 2012Year

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).

-1

0

1

2

3

4

5

6

Tmea

n Fe

b-A

pr

1980 1984 1988 1992 1996 2000 2004 2008 2012Year

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.

14

15

16

17

18

19

20

21

22

Tmea

n Ju

ly

1980 1984 1988 1992 1996 2000 2004 2008 2012Year

Figure 1.8 The mean temperature in July has increased in Gothenburg during 1979-2012 (Estimate =0.08; Prob>Chi Sq=0.0050).

22

13

14

15

16

17

18

19

20

21Tm

ean

Aug

ust

1980 1984 1988 1992 1996 2000 2004 2008 2012Year

Figure 1.9 The mean temperature in August has increased in Gothenburg during 1979-2012 (Estimate=0.09; Prob>Chi Sq=0.0133).

Precipitation

600

700

800

900

1000

1100

1200

1300

Ann

ual

prec

ipita

tion

1980 1984 1988 1992 1996 2000 2004 2008 2012Year

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).

8101214161820222426

Day

s w

ith n

opr

ecip

itatio

n m

ar

1980 1984 1988 1992 1996 2000 2004 2008 2012Year

Figure 1.11 Days with no precipitation in March have increased in Gothenburg during 1979-2012 (Estimate=0.23; Prob>Chi Sq=0.0129).

Changes in pollen season 1979-2012

120125130135140145150155160165170

BP

S (d

ays

from

1st

of J

anua

ry)

1975 1980 1985 1990 1995 2000 2005 2010 2015Year

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).

0

2

4

6

8

10

12

Day

s w

ith p

olle

n am

ount

s>80

gra

ins/

m3

1980 1984 1988 1992 1996 2000 2004 2008 2012Year

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.

23

60708090

100110120130140150160170

Dur

atio

n (d

ays)

1980 1984 1988 1992 1996 2000 2004 2008 2012Year

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.

9095

100105110115120125130135140145150

Day

whe

n 1s

tpo

llen

grai

n is

det

ecte

d (fr

om 1

st o

f Jan

uary

)

1975 1980 1985 1990 1995 2000 2005 2010 2015Year

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).

155160165170175180185190195200205210

PP

(day

s fro

m 1

st o

f Jan

uary

)

1975 1980 1985 1990 1995 2000 2005 2010 2015Year

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).

210

220

230

240

250

260

270

280

290

300

EP

S (d

ays

from

1st

of J

anua

ry)

1975 1980 1985 1990 1995 2000 2005 2010 2015Year

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 1979-2012 (Prob>ChiSq=0,0002).

20

30

40

50

60

70

80

90

100

110

PP

-EP

S (d

ays)

1980 1984 1988 1992 1996 2000 2004 2008 2012Year

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.

600

1000

1400

1800

2200

2600

3000

Tota

lpo

llen

amou

nt (g

rain

s)

1980 1984 1988 1992 1996 2000 2004 2008 2012Year

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

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

24

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 season1979-1989 (mean) 1990-2000 (mean)

2001-2012 (mean)

BPS 146 144 136Day when 1st pollen grain was registered 137 120 119PP 186 185 176Days with pollen amounts >80 grains/ m3 1.6 2.5 5.3EPS 233 245 256Duration (days) 87 101 118BPS-PP (days) 40 41 40PP-EPS (days) 47 60 80Total pollen amount (grains) 1043 1369 2061

Table 1.2 Changes in pollen season during three decades (1979-2012).

Pollen season1979-1989 (mean) 1990-2000 (mean)

2001-2012 (mean)

BPS 26 May 24 May 16 MayDay when 1st pollen grain was registered 17 May 30 April 29 AprilPP 4 July 4 July 25 JuneDays with pollen amounts >80 grains/ m3 1,6 2,5 5.3EPS 21 August 2 September 13 SeptemberDuration (days) 87 101 118BPS-PP (days) 40 41 40PP-EPS (days) 47 60 80Total pollen amount (grains) 1043 1369 2061

The effect of meteorological parameters on the pollen season in Gothenburg, Sweden, during 1979-2012

The beginning of the pollen season (BPS)

25

120

125

130

135

140

145

150

155

160B

PS

(day

s fro

m 1

st o

f Jan

uary

)

3 4 5 6 7 8 9 10 11T mean April (C)

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.

120

130

140

150

160

170

BP

S (d

ays

from

1st

of J

anua

ry)

8000 10000 12000 14000 16000

accumulated GDH 2 weeksfrom growh start-day 181

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).

120

130

140

150

160

170

BP

S (d

ays

from

1st

of J

anua

ry)

9 10 11 12 13 14 15T mean May (C)

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).

120

130

140

150

160

170

BP

S (d

ays

from

1st

of J

anua

ry)

600 700 800 900 1000 1100Annual precipitation (mm)

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.

Multiple regressions - Beginning of pollen seasonTable 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.

26

Variables R² p-valueT mean April + annual precipitation 0.45 0.0002

T mean April + T mean May 0.50 0.0001T 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 foundOutlier 2012 excluded

110

115

120

125

130

135

140

145

150

Day

whe

n 1s

tpo

llen

grai

n is

det

ecte

d (d

ays

from

1st

of J

anua

ry)

-8 -6 -4 -2 0 2 4 6 8T mean February (C)

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).

110

115

120

125

130

135

140

145

150

Day

whe

n 1s

tpo

llen

grai

n is

det

ecte

d (d

ays

from

1st

of J

anua

ry)

0 2 4 6 8 10T max February (C)

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).

110

115

120

125

130

135

140

145

150D

ay w

hen

1st

polle

n gr

ain

is d

etec

ted

(day

s fro

m 1

st o

f Jan

uary

)

3 4 5 6 7 8 9 10 11T mean April (C)

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).

110

115

120

125

130

135

140

145

150

Day

whe

n 1s

tpo

llen

grai

n is

det

ecre

d (d

ays

from

1st

of J

anua

ry)

-1 0 1 2 3 4 5 6T mean February - April (C)

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).

27

110

115

120

125

130

135

140

145

150D

ay w

hen

1st

polle

n gr

ain

is d

etec

ted

(day

s fro

m 1

st o

f Jan

uary

)

6 8 10 12 14 16 18 20 22 24 26

Days with noprecipitation feb

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).

110

115

120

125

130

135

140

145

150

Day

whe

n 1s

tpo

llen

grai

n is

det

ecte

d (d

ays

from

1st

of J

anua

ry)

0 20 40 60 80 100 120 140 160

Accumulated precipitation from2 weeks before growth start-10th of May

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).

Multiple regressions – Day when 1st pollen grain is detectedTable 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.

28

Pollen peak (PP)

160

170

180

190

200

210

PP

(day

s fro

m 1

st o

f Jan

uary

)

12 13 14 15 16 17 18 19 20T mean June (C)

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.

160

170

180

190

200

210

PP

(day

s fro

m 1

st o

f Jan

uary

)

0 20 40 60 80 100 120Precipitation March (mm)

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).

160

170

180

190

200

210

PP

(day

s fro

m 1

st o

f Jan

uary

)

10 11 12 13 14 15 16 17 18T min July (C)

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).

160

170

180

190

200

210

PP

(day

s fro

m 1

st o

f Jan

uary

)

13 14 15 16 17 18T mean May - August (C)

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.

Multiple regression- Pollen peakTable 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.

29

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

Variables R² p-value

30

T mean June + precipitation March 0.38 0.0009

T mean June + T min July 0.25 0.0065T mean May-August+ precipitation March 0.45 0.0002

T min July + Precipitation March 0.44 0.0005

End of pollen season (EPS)

210

220

230

240

250

260

270

280

290

300

EP

S (d

ays

from

1st

of J

anua

ry)

3 4 5 6 7 8 9 10 11T mean April (C)

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.

210

220

230

240

250

260

270

280

290

300

EP

S (d

ays

from

1st

of J

anua

ry)

-2 -1 0 1 2 3 4 5 6 7 8 9 10T min April (C)

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).

210

220

230

240

250

260

270

280

290

300

EP

S (d

ays

from

1st

of J

anua

ry)

10 11 12 13 14 15 16 17 18T min July (C)

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).

210

220

230

240

250

260

270

280

290

300

EP

S (d

ays

from

1st

of J

anua

ry)

6 8 10 12 14 16 18 20 22T max April (C)

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.

31

210

220

230

240

250

260

270

280

290

300E

PS

(day

s fro

m 1

st o

f Jan

uary

)

-1 0 1 2 3 4 5 6T mean February - April (C)

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).

210

220

230

240

250

260

270

280

290

300

EP

S (d

ays

from

1st

of J

anua

ry)

500 600 700 800 900 1000 1100 1200 1300Annual precipitation (mm)

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).

210

220

230

240

250

260

270

280

290

300

EP

S (d

ays

from

1st

of J

anua

ry)

8 10 12 14 16 18 20 22 24 26

Days with noprecipitation in March

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.

Duration

60

80

100

120

140

160

Dur

atio

n (d

ays)

3 4 5 6 7 8 9 10 11T mean April (C)

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).

60

80

100

120

140

160

Dur

atio

n (d

ays)

-1 0 1 2 3 4 5 6T mean February - April (C)

Figure 3.23 Airborne grass pollen as registered in a Burkard volumetric spore trap at SU/Östra

32

Variables R² p-value

T mean April+ annual precipitation 0.53 0.0001T 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 0.0005T 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 0.0374T min April + T min July 0.22 0.0230

sjukhuset, Gothenburg. The duration was positively related to the mean temperatures in February-April (R2=0.22; Prob>ChiSq = 0.0041).

60

80

100

120

140

160

Dur

atio

n (d

ays)

6 8 10 12 14 16 18 20 22T max April (C)

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).

60

80

100

120

140

160

Dur

atio

n (d

ays)

2 3 4 5 6 7 8 9 10 11 12T max March (C)

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).

60

80

100

120

140

160

Dur

atio

n (d

ays)

500 600 700 800 900 1000 1100 1200 1300Annual precipitation (mm)

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).

60

80

100

120

140

160D

urat

ion

(day

s)

3000 4000 5000 6000 7000 8000

Accumulated GDH from1st of March- day 142

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

33

Variables R²p-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 0.0037T 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

0

2

4

6

8

10

12

Day

s w

ith p

olle

n am

ount

s >8

0 gr

ains

/ m3

2 3 4 5 6 7 8 9 10 11 12T max March (C)

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).

0

2

4

6

8

10

12

Day

s w

ith p

olle

n am

ount

s>80

gra

ins/

m3

-2 0 2 4 6 8 10T min April (C)

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).

0

2

4

6

8

10

12

Day

s w

ith p

olle

n am

ount

s>80

gra

ins/

m3

8000 10000 12000 14000 16000

Accumulated GDH 2 weeksfrom growh start - day 181

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.

0

2

4

6

8

10

12

Day

s w

ith p

olle

n am

ount

s >8

0 gr

ains

/ m3

3 4 5 6 7 8 9 10 11T mean April (C)

Figure 3.29 Airborne grass pollen as registered in a Burkard volumetric spore trap at SU/Östra sjukhuset, Gothenburg. The number of days with

34

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.

0

2

4

6

8

10

12

Day

s w

ith p

olle

na a

mou

nts>

80 g

rain

s/ m

3

20 40 60 80 100 120Precipitation march (mm)

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.

Variables R² p-valueT mean April + precipitation March 0.5 0.0001

T mean April + T max March 0.36 0.0016T max April + precipitation March 0.35 0.0020

T max April + T max March 0.25 0.0145T 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.

600

1000

1400

1800

2200

2600

3000

Tota

lpo

llen

amou

nt (g

rain

s)

3 4 5 6 7 8 9 10 11T mean April (C)

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).

600

1000

1400

1800

2200

2600

3000

Tota

lpo

llen

amou

nt (g

rain

s)

-1 0 1 2 3 4 5 6T mean February - April (C)

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).

35

600

1000

1400

1800

2200

2600

3000To

tal

polle

n am

ount

(gra

ins)

14 16 18 20 22 24 26T max June (C)

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.

600

1000

1400

1800

2200

2600

3000

Tota

lpo

llen

amou

nt (g

rain

s)

-2 0 2 4 6 8 10

T min April (C)

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).

500

1000

1500

2000

2500

3000

Tota

lpo

llen

amou

nt (g

rain

s)

500 600 700 800 900 1000 1100 1200 1300Annual precipitation (mm)

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).

500

1000

1500

2000

2500

3000

Tota

lpo

llen

amou

nt (g

rain

s)

8000 10000 12000 14000 16000

Accumulated GDH 2 weeksfrom growh start - day 181

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).

500

1000

1500

2000

2500

3000

Tota

lpo

llen

amou

nt (g

rain

s)

20 40 60 80 100 120Precipitation march (mm)

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.

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.

Variables R² p-value

36

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 0.0003T mean February-April + annual 0.34 0.0026

precipitationT 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

37

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).

Discussion

38

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)

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 1.1-1.2). 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 1.10-1.11). 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

39

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 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

40

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 (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

41

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 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

42

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).

43

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.

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

44

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.

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).

45

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 lati- and 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 1.1-1.9) 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 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

46

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.

47

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.

48

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 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

49

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.

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.

50

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.

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

51

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.

52

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.

Acknowledgements

I am very grateful for all the good advice, encouragement and patient guidance of my supervisor Åslög Dahl.

53

References

Ahas R. et al. (2002) “Changes in European spring phenology” Int J. Climatol 22: 1727-1738

Andersen T.B (1991) “A model to predict the beginning of the pollen season” Grana 30: 269-275

Andersson K, Lidholm J (2003) Characteristics and immunobiology of grass pollen allergens. Int Arch Allergy Immunol. 130: 87-107.

Antépara I. et al. (1995) “Pollen allergy in the Bilbao area (European Atlantic seaboard climate): pollination forecasting methods” Clin exp allergy 25: 133-140

Badeck F.W. et al. (2004) “Responses of spring phenology to climate change” New Phytologist 162 : 295–309

Baumann U. et al. (2000) “Self-incompability in grasses” Annals of Botany 85: 203-209

Beard J.B & Almodares A. (1980) “Minimum temperature requirements for seed germination of turfgrasses” Texas turfgrass research no. PR-3667-3678: 13-15

Bell A.D & Bryan A. (1991) “Plant form: an illustrated guide to flowering plant morphology” Chapter: ”Grass morphology” Published in the United States by Oxford University Press, New York

Bertin R. (2008) “Plant phenology and distribution in relation to recent climate change” J torrey bot soc 135: 126-146.

Blomgren et al. (2011). ”Bohusläns flora”. Lund Föreningen Bohusläns flora.

Bogawski et al. (2012) “Recent climate change affects the course of pollen seasons of herbaceous taxa in Poznan (Poland), 1996-2011” Poster

Calder D.M (1964) “Flowering behavior of populations of Dactylis glomerata under field conditions in Britain” J. Appl. Ecol. 2:307-320

Calderon et al. (2009)” Allergen injection immunotherapy for seasonal allergic rhinitis” The Cochrane Library 2009, John Wiley & Sons, Ltd. Issue 1

Chmielevski & Rötzer (2001) ”Response of tree phenology to climate change across Europe” Agr Forest Meteorol 108: 101-112

Christensen J.H et al (20012) “A synthesis of regional climate change simulations – a Scandinavian perspective” Geophys res let 28:(6) 1003-1006

Clément C. et al.(1999) “Anther and pollen – from biology to biotechnology” Springer - verlag Berlin Heidelberg

54

D’Amato G.S et al (2001) ”The role of outdoor air pollution and climatic changes on the rising trends in respiratory allergy” Respir. Med 95: 606-611

D’Amato, G. S. (1998). "Pollen-related allergy in Europe." Allergy 53: 567-578.

Dahl Å. & Standhede S.O (1996) “Predicting the intensity of the birch pollen season” Aerobiologia 12: 97-106

Dahl Å & Fredriksson M. (1996) “The timetable for development of maternal tissues sets the stage for male genomic selection in Betula pendula (Betulaceae)” AmJBot 83:895-902

Dahl. Å. et al (2013) Chapter 3 “The onset, course and intensity of the pollen season” In “Allergenic Pollen- A review of the production, release, distribution and health impacts”, Editors Mikhail Sofiev, Karl- Christian Bergmann Springer Dordrecht Heidelberg New York London.

Daines M. (2011) “Impact of land use intensity and temperature on the reproductive performance of Dactylis glomerata populations in the southeastern Alps” Plant Ecol 212: 651–661

Davies I. (1976) “Developmental characteristics of grass varieties in relation to their herbage production” J. agric. Sci., Camb. 87: 33-38

De Jong T.J et al.(1986) “Flowering behaviour of the monocarpic perennial Cynoglossum officinale l.” New Phytol. 103: 219-229

De Vries, A. P. H. (1973). "Some aspects of cross-pollination in wheat (Triticum aestivum L.)3. Anther length and number of pollen grains per anther." Euphytica 23: 11-19.

Domínguez-Vilches E. et al. (1995) “Airborne pollen concentrations, solid particle content in the air and allergy symptoms in Córdoba (Spain)” Aerobiologia 11:129-135

Elzinga J.A et al. (2007) “Time after time: flowering phenology and biotic interactions” Trends Ecol. Evol. 8: 432-439

Emberlin et al. (1993) “Annual variations in grass pollen seasons in London 1961-1990: trends and forecast models” Clin exp allergy 23: 911-918

Estrella N. et al. (2006) “Integration of flowering dates in phenology and pollen counts in aerobiology: analysis of their spatial and temporal coherence in Germany (1992–1999)” Int J Biometeorol 51:49–59

Fægri. L. & Pijl, K. (1979). ”The principles of pollination ecology”,Chapter 6.1 Windpollination, anemophily, London, Pergamon press. 3rd edition.

Fitter A. H. & Fitter R. S. R. (2002) ”Rapid changes in flowering time in British plants” Science 296: 1689-1691

Fort F. et al. (2012) “Root and leaf functional trait relations in Poaceae species: implications of differing resource- acquisition strategies” Journal of plant ecology 1-9

Frenguelli G. & Bricchi E. (1998) “The use of phenol-climatic mode for forecasting the pollination of some arboreal taxa” Aerobiologia 14: 39-44

Frenguelli G. et al. (1989) “A predictive study on the beginning of the pollen season for Gramineae and Olea europea L.” Aerobiologia 5:64-70

Fryxell A. Paul (1957). “Mode of reproduction in higher plants” The botanical review 23: 135-234.

García-Mozo H. et al. (2008) “Predicting the start and peak dates of the Poaceae pollen season in Spain using process-based models” Agr Forest Meteorol 149: 256-262

Garnier E. et al. “Assessing the effects of land-use change on plant traits, communities and ecosystem functioning in grasslands: a standardized methodology and lessons from an application to 11 European sites” Annals of Botany 99: 967–985

Gibson, D. J. (2009). Grasses & grassland ecology chapter 3 “Ecological morphology and anatomy” New York, Oxford University press.

55

Giorgi F. & Lionello P. (2008) “Climate change projections for the Mediterranean region” Global Planet Change 63: 90–104

González Minero et al.(1998) “Airborne grass (Poaceae) pollen in southern Spain. Results of a 10-year study” Allergy 53: 266-274.

Gorai M. et al. (2006) “Seed germination characteristics of Phragmites communis; effects of temperature and salinity” Belg J Bot 139:78-86

Green B.J et al. (2004) “Atmospecric Poaceae pollen frequencies and associations with meteorological parameters in Bribane, Australia: a 5 year record 1994-1999” Int J Biometeorol 48: (4) 172-178

Hejcman M. et al. (2012) “Response of plant species composition, biomass production and biomass chemical properties to high N, P and K application rates in Dactylis glomerata- and Festuca arundinacea-dominated grassland” Grass Forage Sci 67: 488–506

Höglind M et al (2012) “Assessing uncertainties in impact of climate change on grass production in Northern Europe using ensembles of global climate models” Agr Forest Meteorol. 170: 103-113

IGER Science Group “How grass grows” Factsheet produced by the Iger grassland development centre Institute of Grassland & Environmental Research, Plas Gogerddan, Aberystwyth, Ceredigion

IPCC (2012) “Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation” Special Report of the Intergovernmental Panel on Climate Change I and II of the Intergovernmental Panel on Climate Change (Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen,M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK, and New York, NY, USA, 582 pp Cambridge University press.

IPCC, Climate Change 2007: Working Group I: The Physical Science Basis FAQ 3.1

IPCC, Fourth assessment report 2007 Working Group II: Impacts, Adaptation and Vulnerability chapter 12.2.1, 9.6.2,

Jato V. et al. (2001) “Phenological behaviour of Quercus in Ourense (NW Spain) and its relationship with the atmospheric pollen season” Int J Biometeorol 46:176–184

Jato et al. (2006) “May the definition of pollen season influence aerobiological results?” Aerobiologia 22: 13–25

Jato V. et al. (2009) “Poaceae pollen in Galicia (N.W. Spain): characterization and recent trends in atmospheric pollen season” Int J Biometeorol 53: 333-344

Kasprzyk I. & Walanus A. (2010) “Description of the main Poaceae pollen season using bi-Gaussian curves, and forecasting methods for the start and peak dates for this type of season in Rzeszów and Ostrowiec Św. (SE Poland)” J. Environ. Monit 12: 906–916

Keijzer C. J. et al. (2005) “The Mechanics of the Grass Flower: Anther Dehiscence and Pollen Shedding in Maize” Annals of Botany 78: 15–21

Kiotseridis H. et al (2013) ”Grasspollen allergy in children and adolescents-symptoms, health related quality of life and the value of pollen prognosis” Clin Transl Allergy 3: 19

Kreyling et al. (2012) “Geographic origin and past climatic experience influence the response to late spring frost in four common grass species in central Europe” Ecography 35: 268–275

Laforest L. (2005)”Quality of life during pollen season in patients with seasonal allergic rhinitis with or without asthma” Int Arch Allergy Immunol 136: 281-286

Langer R.H.M (1972) “How grasses grow” Studies in biology no 34 Published by: Edward Arnold London

56

León- Ruiz E.et al. (2011) "Study of Poaceae phenology in a Mediterranean climate. Which species contribute most to airborne pollen counts?" Aerobiologia 27: 37.

Liem A .S.N (1980) “Effects of light and temperature on anthesis of Holcus Lanatus, Festuca Rubra and Poa annua” Grana 19: 21-29

Linvill D.E (1990) “Calculating chilling hours and chill units from daily maximum and minimum temperature observations” Hort science 25:14-16

Menzel A. (2000) “Trends in phenological phases in Europe between 1951 and 1996” Int J Biometeorol 44: 76-81

Menzel A. et al. (2006) “European phenological response to climate change matches the warming pattern” Global change biol 12: 1969-1976

Nilsdotter- Linde N.(1992) “Fröblandningar för slåtter och bete” Ekologiskt lantbruk 13

Nomoto H. (2013) ”Pollen production and phenology in 12 species from the Poaceae family - a study of 12 common Poaceae species in Sweden” University of Gothenburg

Parker M. W. & Borthwick H. A (1939) “Effect of variation in temperature during photoperiodic induction upon initiation of flower primordia in biloxi soybean” Botanical Gazette 101: 145-167

Pons T . L (1991) “Germination and mortality of seeds in a chalk-grassland flora” Journal of Ecology 79: 765-780Prieto- Baena J. et al. (2003) ”Pollen production in the Poaceae family” Grana 42:3 153-159

Proctor M. Yeo P. (1973) “The pollination of flowers” Chapter 8 “Pollination by wind and water” editors Huxley J, Gilmour J. Davies M. Mellanby K. William Collins Sns &Co London

Rapiejko et al. (2007) “Threshold pollen count necessary to evoke allergic symptoms” Otalaryngol Pol. 61: 591-594

Rasmussen A. (2002) “The effects of climate change on birch pollen season in Denmark” Aerobiologia 18: 253-265

Recio M. et al. (2010) “Influence of temperature, rainfall and wind trends on grass pollination in Malaga western Mediterranean coast)” Agr Forest Meteorol 150: 931-940

Rossby Centre (2011), climate science for the Swedish meteorological institute “Sveriges klimat i framtiden”

Sahlgrenska Univesrity Hospital (2013) Göran Samuelsson “Våren är här – liksom pollenallergin”

Sánchez Mesa J.A et al. (2003) “Characteristics of grass pollen seasons in areas of southern Spain and the United Kingdom” Aerobiologia 19: 243-250

Sparks T. H & Carey P. D. (1995) “the responses of species to climate over two centuries: an analysis of the marsham phenological record, 1736-1947” Journal of Ecology 83: 321-329

Sparks T.H. et al. (2000) “An examination of the relationship between flowering times and temperature at the national scale using long-term phenological records from the UK” Int J Biometeorol 44: 82–87

Statistics Sweden, Health and Medical care 1980–2005.

Subba Reddi C & Reddi N.S (1986). "Pollen production in some anemophilous angiosperms." Grana 25: 55-61.

Tooke F. & Battey N.H “Temperate flowering phenology” Jour Exp Bot 11: 2853–2862

Tormo R. et al. (2011) “Phenological records as a complement to aerobiological data” Int J Biometeorol 55:51–65

57

Üremiş I & Uygur F.N (1999) “Minimum, optimum and maximum germination temperatures of some important weed species in the C ̧ukurova Region of Turkey” Tükiye Herboloji Dergisi 2: 1-12

Van Vliet A. et al. (2002) ”The influence of temperature and climate change on the timing of pollen release in the Netherlands” Int. J. Climatol. 22: 1757–1767

Vårdguiden Västra Götalandsregionen (2013) “Pollenallergi”

Illustrations:

1.1 Page 11 Background FAO-Corporate document repository “Pasture –cattle-cocnut-systems! (1995) pp 683 RAP Publication regtional office for Asia and Pacific.

1.2 Page 12. Published on the website of V plants a virtual herbarium of the Chicago region from the book “Plants of the Chicago region” 4th edition by Swink F. &Wilhelm G. (1994) Indianapolis Indiana academy of science

58