Thesis
Ecology and genetics of the rare plant "Aster amellus" L. in a fragmented landscape
MAYOR, Romain
Abstract
La fragmentation du paysage a été reconnue comme l'une des forces agissant négativement sur la biodiversité. L'isolement peut perturber l'échange de gènes entre les populations, favorisant des déséquilibres de Hardy-Weinberg et augmentant ainsi dramatiquement des risques de chute de "fitness" par consanguinité. Dans le but de mettre en évidence une potentielle érosion génétique d'espèces en situation de fragmentation, nous avons étudié 31 populations du rare "Aster amellus" L. 7 marqueurs microsatellites analysés sur 2600 individus ont montré des fortes situations de "drift" et de consanguinité dans les populations isolées, ceci notamment dû à un flux de gènes usuels limité à une distance de 30m. La dépression de "fitness" par consanguinité a été détectée, ces effets se sont révélés être purement génétiques, puisque 865 relevés de végétation nous ont permis de contrôler sur des possibles artefacts écologiques. Ceci implique une menace de déclin des populations en situation de fragmentation.
Reference
MAYOR, Romain. Ecology and genetics of the rare plant "Aster amellus" L. in a fragmented landscape. Thèse de doctorat : Univ. Genève, 2008, no. Sc. 3996
URN : urn:nbn:ch:unige-6328 DOI : 10.13097/archive-ouverte/unige:632
Available at: http://archive-ouverte.unige.ch/unige:632
Disclaimer: layout of this document may differ from the published version.
1 / 1 UNIVERSITÉ DE GENÈVE FACULTÉ DES SCIENCES Département de botanique et Prof. R. Spichiger de biologie végétale Dr. D. Aeschimann
Ecology and Genetics of the Rare Plant Aster amellus L. in a Fragmented Landscape
Thèse
présentée à la Faculté des Sciences de l’Université de Genève pour obtenir le grade de Docteur ès sciences, mention biologie
par
Romain Mayor
de
Genève (GE)
Thèse - 3996 -
Genève 2008
La Faculté des sciences, sur le préavis de Messieurs R. SPICHIGER, professeur ordinaire et directeur de thèse (Département de botanique et de biologie végétale), D. AESCHIMANN, docteur et codirecteur de thèse (Département de botanique et de biologie végétale), M. FISCHER, professeur (University of Bern, Institute of Plant Sciences, Bern, Switzerland) et de Madame C. LAMBELET, docteur (Conservatoire et Jardin botaniques de la Ville de Genève, Chambésy, Suisse) autorise l’impression de la présente thèse, sans exprimer d’opinion sur les propositions qui y sont énoncées.
Genève, le 30 juin 2008 Thèse - 3996 -
Contents
Chapter 1 General Introduction 5
Chapter 2 Identification and characterization of eight 19
microsatellite loci in Aster amellus L. (Asteraceae)
Chapter 3 Inbreeding and inbreeding depression in the rare perennial 27
Aster amellus L. in natural conditions with biotic and
ecological interactions
Chapter 4 Effects of distance, density and population size on 51
gene flow in the rare Aster Amellus L.
in a fragmented landscape
Chapter 5 Effects of ecology and genetic among fragmented 75
populations of the long-lived Aster amellus L. in
experimental conditions with biotic and abiotic treatment
Chapter 6 Synthesis 97
Résumé 102
References 107
Remerciements 117
Appendix 119
Chapter 1
General Introduction
5 CHAPTER 1
This thesis deals with the effects of fragmentation on a rare plant species. We will first introduce the theoretical background and present general aspects of the study, thus giving the reader the option to go directly to the synthesis in the final chapter. The corpus of the thesis
(Chapters 2 to 5) itself presents important information including graphics and details of procedures. Complementary information, provided in the introduction, can help to understand the corpus, but care has been taken to avoid overlap of contents.
From Sadi Carnot to fragmentation
In the last 20 years, there has been an increasing interest in the population biology of wild species to assess the threats induced by human activity. Indeed, since the thermodynamic treatises of Sadi Carnot (1824), it has expanded from a rural lifestyle to an urban and industrialised one. Extensive farming has been replaced by intensive farming: traditional, fragmented, agricultural land has been grouped to form extended areas without natural habitats, and semi-natural habitats have been abandoned. This has had three evident effects.
The first is the destruction of habitat leading a given population directly to its death. The second is the reduction of habitat size. The third is the fragmentation of remnant populations.
We will focus on the effects of fragmentation which, as we will see, are intimately bound to the first two effects. Other consequences of the industrial revolution, such as pollution, are not considered in this study.
Fragmentation
Fragmentation is the process whereby one unified entity is divided into smaller entities. For a group of individuals called a population, living in a given landscape and participating together in reproduction, fragmentation of the landscape has two major impacts. First, one big population breaks up into several small populations which form a metapopulation; second,
6 GENERAL INTRODUCTION individuals previously “connected” in the reproduction process become isolated, impeding in this way the maintenance of panmixia.
Small and isolated populations are more susceptible to perish through stochastic processes such as environmental perturbation, demographic change, and genetic drift (Shaffer
1987). They have to face a diminution of gene flow and therefore an increase of inbreeding and the perpetuation of genetic drift without possible rescue.
In order to tackle the fragmentation issue, we studied a rare plant whose populations are like islands surrounded by intensive farming and urban areas.
Inbreeding
Inbreeding is caused either by autofecondation or by non-random association of conspecifics in the reproduction process (Keller & Waller 2002). As a general consequence, an increasing number of individuals in the subsequent generations become homozygote at different loci.
From generation to generation, the number of homozygote loci increases at the individual level and thus at the population level.
In order to assess inbreeding in small populations, we analysed the mean observed heterozygosity of the populations with 7 microsatellite markers, and related it to census sizes of the populations. Microsatellites are small regions in the DNA where base pairs are repeated in sequences of 1bp to 4bp. They are neutral codominant markers, thus ideal for studying inbreeding, drift and gene flow (Estoup et al. 1998; Ouborg et al. 1999; Ross et al. 1999).
Drift
Drift is a stochastic process in which the set of alleles of a population at generation t0 is progressively lost in subsequent generations (Ellstrand & Elam 1993). The reason for this is that in the reproduction process, one individual gives only half of its alleles to one offspring.
7 CHAPTER 1
Drift is less visible in large populations where the initial diversity of alleles at t0 is nearly preserved because the possibility for an individual to give more than half of its alleles is increased by the number of fecundation events. Moreover if a certain allele is rare in a given population (large or small), its chance of being transmitted to the next generation is enhanced if it is carried by a larger number of individuals, i.e. in larger populations. This leads us to the concept of purge (see section on purge).
In order to assess genetic drift in small populations, we analysed the average gene diversity of populations with seven microsatellite markers and related it to census population sizes.
Inbreeding depression and drift depression
The threat of inbreeding is inbreeding depression. Three cases of inbreeding depression are postulated: the lethal recessive case, ovedominance, and partial dominance. The lethal recessive case is characterised by a strong selection on inbred genotypes which give no chance of survival. Overdominance is the case where homozygotes are less fit than heterozygotes; this permits survival of inbred individuals, but with a genetic load implying a constant pressure on the individual’s fitness. Partial dominance is the case where slightly or mildly deleterious recessive alleles are partially masked by dominant alleles (Charlesworth &
Charlesworth 1987; Leberg & Firmin 2008).
Drift depression occurs when the variability of alleles is diminished through genetic drift, thus decreasing the capacity of a population to adapt itself to changing conditions
(Barrett & Kohn 1991) (see section on purge). Moreover, loss of alleles could lead to inbreeding by drift (Jacquard 1968; Glémin 2003) resulting in cases similar to those of inbreeding.
8 GENERAL INTRODUCTION
In order to detect inbreeding depression in natural conditions and in controlled ex situ conditions, we measured fitness traits of plants and related them to the level of inbreeding measured with microsatellite markers.
Purge
A purge is the possibility of a given population to eliminate deleterious alleles. It may simply happen by genetic drift (case were drift has a positive effect) or by inbreeding (Glémin 2003).
In both cases, small populations have a greater chance to experience a purge. The reason for this is that deleterious alleles are better conserved in large populations, either through lack of drift or through maintenance of deleterious alleles among heterozygous individuals. Purge may be viewed as a possible rescue for small populations, but it should not be thought of as having a generally positive effect. A good example of this is the maintenance of high polymorphism of melanin genes in insect species (True 2003); the debate about the role played here by selection is ongoing, but variability seems to be important in this case although a morph is not always advantageous. In our study, we did not test whether a purge occurred, but we introduced this term because a purge may lead to failure in the detection of inbreeding effects in small populations. Moreover we were faced with a confusing pattern between purge and maternal effects in ex situ experiments (see Chapter 5).
Gene flow
Gene flow is the process whereby genes “travel” throughout an entire population or among the populations of a metapopulation. In plants, gene flow is mediated by pollen dispersal, seed dispersal (Levin & Kerster 1974) and clonal propagation (Johansson 1993; Johansson &
Nilsson 1993). A panmixia situation is produced by gene flow between individuals of a population. Any interruption in gene flow causes a rift in panmixia. The longer the
9 CHAPTER 1 interruption, the more we loose panmixia. One current rule says that one migrant per generation is sufficient to maintain panmixia in a metapopulation (Wright 1931). However simulations showed that 5 to 20 migrants are not sufficient to reach a Hardy-Weinberg equilibrium between populations (Lacy 1987) . Rupture of gene flow increases situations of genetic drift and in this way prevents rescue by heterosis.
In this study, we analyse how gene flow is influenced by fragmentation parameters: isolation by density in terms of number of surrounding individuals around one individual and number of surrounding populations around one population, isolation by size of populations in terms of the number of counted individuals in a given site and isolation by distance.
Ecological parameters
We have seen how genetic parameters can influence the fate of populations; however fate of populations can also be driven by physical abiotic or biotic pressures. This is what we call environmental stochasticity (see section on fragmentation). In plant species, ecological parameters are extremely important because fate lies there: to stay in place alive or to die.
Playing an evident role among fitness traits of plants, ecological parameters clearly act as a noise relative to inbreeding effects, and a measured negative fitness trait in small populations could be due to inadequate habitat conditions. Typically, a clearing that closes under pressure from lignified species will create shade which is disadvantageous for herbaceous plants and this inadequate environment will cause deleterious fitness traits in herbaceous plants.
In order to control but also to assess the effects of ecological parameters on measured fitness traits in natural conditions and in ex situ experiments, we used ecological factors of
Landolt (1977) based on 865 square-metre vegetation surveys ; other ecological parameters were altitude and vegetation height.
10 GENERAL INTRODUCTION
Studied species
We studied Aster amellus L. (AA) from the Asteraceae family for different reasons. First, it belongs to the angiosperm group of the plant kingdom. Angiosperms present several characteristics which are ideal for the study of inbreeding in natural and experimental conditions. They are easy to recognize and their taxonomic situations are in most cases well defined. Because of their stationary property, counting them and assessing their ecological parameters was simple.
Reproductive systems of plants present a great variety of cases ranging from apomixes to complete outbreeding. Reproduction could be monitored via caging experiments. Moreover, plant behaviour was controlled through experimental conditions where plants were submitted to various controlled biotic and/or abiotic pressures. Second, AA is a rare plant living in calcareous grasslands which are typically fragmented habitats through change in land use
(Zoller & Wagner 1986b; Zoller & Wagner 1986a; Stöcklin et al. 2000; Köhler et al. 2005).
Indeed populations of AA are like islands in an intensive agricultural landscape, forming an ideal model to study fragmentation effects.
Distribution of the species
Aster amellus is a central-European species. Its north-east distribution limit is West Siberia by the Tobol river (57°14’N/67°2’E) and its south-west limit is South-West France in the Tarn
(44°5’N/3°5’E). The distribution area is interrupted by the Alps (Jäger 1992) (Fig. 1). Our study is situated in the western range of the species (Fig 1).
11 CHAPTER 1
Fig. 1 Area distribution of Aster amellus L. ( ____ ), A. amelloides Bess. ( _|___|_ ) and A. ibericus M. Bieb. ( _||___||_ ) (Jäger 1979). 1: 57°14’N/67°2’E, +- Aleksandrovska, E Tyumen (Russia); 2: 52°38’N/16°30’E, +- Poznan (Poland); 3: 42°N/21°27’E, Skopje (Macedonia); 4: 49°N/12°E, +- Regensburg (Germany); 5: 51°18’N/12°E, +-Leipzig (Germany); 6: 48°20’N/2°50’E, +- Montereau, S Paris (France); 7: 44°5’N/3°5’E, Millau, E Toulouse (France); 8: Extreme SW and NE studied populations limiting the study area: 45°57’N/5°54’, +- Seyssel (France), S Genève and 47°32’N/8°10’E, +- Brugg, NW Zürich (Switzerland).
Ecology of the species
Aster amellus is a concurrent-stress-strategic species (Frank & Klotz 1990). It grows in warm hilly to montane areas, in limestone substrates with carbonated to eutrophic humus, normally on basic to neutral pH, in dry soils (mesophilic to xerophilic species); the substrate materials are gravel, calcareous sand, clay, loess and silts. AA is a heliophilous to semi-heliophilous species growing in grasslands, clearings, edges, slopes, waysides and clear forest (Rameau et al. 1989) . It belongs to several phytosociological groups: Festuco-Brometea (Mesobromion),
Geranion sanguinei, Berberidenalia, Quercion pubescenti-petraeae, Cephalanthero-fagion,
Erico-pinetelia (Molinion pinion) (Rameau et al. 1989; Delarze et al. 1998; Oberdorfer et al.
2001). The Flora of SSSR (Tamamshyan 1959) indicates the same types of biotopes. In our
12 GENERAL INTRODUCTION study we were mainly concerned with Geranion sanguinei and Molinion pinion, but variants as listed above were also observed.
Morphological descriptions with data measured in natural conditions
AA is a hemicryptophyte perennial plant with (1)-5-(30) cm rhizomes (observed). One genet produces (1)-3-(68) straight pubescent stems to a height of (3)-35-(83) cm. From the ground to the terminal capitulum (with bracts under the capitulum), a stem produces (3)-21-(43) leaves which are rough like a cat’s tongue, with a lanceolated to oval shape and a dark greenish colour. Inferior leaves are attenuated by a petiole and measure (1.7)-6-(15.1) cm length (with petiole) and (0.6)-1.7-(4) cm width. One stem produces (1)-4-(34) capitula with lilac ligulated female flowers and yellow tubulous flowers, producing 0.1 to1.5 mm-wide dark-brown pubescent cypselae, weighing (0.1)-0.47-(1.2) mg with a whitish to yellowish pappus of around 4 mm length. As vegetative parts, AA also produces (1)-3-(22) rosettes with
(2)-5-(12) long leaves attenuated by a petiole and measuring (1)-7.6-(17.7) cm length (with petiole) and (0.6)-2.2-(5.2) cm width. We observed these values in in situ conditions, but some experimental conditions showed a drastic increase in plant production, for example stems carrying more than 400 capitula (Chapter 5).
Reproduction
Aster amellus is known to be entomogame but self-compatibility was mentioned (see Chapter
3 for details). Seeds are mainly dispersed by wind but only over short distances (see Chapter
4) and seed banks are known to be transient (Thompson et al. 1997; Cerabolini et al. 2003)
(see Chapter 5). Clonal propagation is possible by production of lateral ramets, but was not found to be high except in certain situations. For example, in one site we collected three separate DNA samples in a 50-cm-long patch filled with ramets separated from each other by
13 CHAPTER 1 less than 5 cm where we observed no microsatellite variation; Bonnier & Douin (1990) also signalled multiplication by the underground system. In our study we considered AA as an outbreeder and we collected DNA from sufficiently separated individuals.
Biotic interaction
The biotic sphere of AA individuals encompasses surrounding conspecifics, surrounding plant species, parasitic strikes and herbivory (see Chapter 3), mycorhizal associations, and pollinators. Münzbergová (2006) studied the effect of the ploidy level of Aster amellus on plant–herbivore interaction. Mycorhizal association is under study (Hanka Plachá). In our study, 238 plant species were in competition with Aster amellus.
Overview of measurements
Between September 2003 and 2007 we worked with 19 to 31 populations of Aster amellus along the Jura (France–Switzerland). Populations were situated in three main regions, Upper
Savoy, the Geneva Basin and Aargovia, and in one small region, Le Landeron (see Chapter 3 for map).
Population size
In the 31 studied sites, we counted the number of individuals at each 2m/2m square of the populations (see Chapter 4 for details) and we calculated the total population size. Each count was geopositioned in order to obtain one map of the density of individuals per population.
Genetic parameters
Two thousand six hundred individuals from 31 populations were analysed through seven microsatellite markers developed at the Botanical Garden of Geneva (Chapter 2; eight
14 GENERAL INTRODUCTION markers are presented but only seven were finally used). We defined the level of inbreeding per population by the mean observed heterozygosity and average gene diversity per population (Chapter 3). Moreover we calculated the Fst matrix of populations and kinship coefficients of individuals, and other related statistics (spatial autocorrelation, Sp statistics)
(Chapter 4). Finally we calculated estimators of population sizes with a linkage disequilibrium method and with a coalescent approach (Chapter 4).
Ecological parameters
Eight hundred and sixty-five vegetation records were made in 31 populations in order to assess ecological parameters of the populations (see Chapter 3 for method and table of population characteristics). We defined the humidity, luminosity and nutritive substances of the sites. Moreover we used the herbaceous vegetation height which we measured in each site and the altitude of sites as other environmental covariates.
Growth experiment
We set up ex situ experiments in different soil conditions as an abiotic treatment and under competition with a perennial herb, Bromus erectus, as a biotic treatment. Around 400 plants from 23 to 31 populations were used in each treatment, resulting in a total of 1200 plants
(with individuals in the benign conditions) (Chapter 5). Moreover we set up one in situ experiment in order to obtain a treatment integrating all natural pressures, but growth was very low and mortality very high so this experiment could not be compared with ex situ experiments.
15 CHAPTER 1
Measurement of fitness
Between 2003 and 2005 we collected cypselae in 19 to 31 populations and measured germination rates in chamber conditions and in in situ conditions.
Between 2005 and 2007, we followed 1600 individuals marked with stakes in 31 populations. We noted whether they were in reproductive or in vegetative form. In 2005 and
2006, we measured phenotypic plant traits: number of rosettes, number of stems, number of leaves, length and width of leaves, number of capitula, number of cypselae, weight of cypselae, and germination rate (2005) (Annex 2). We noted also the number of parasited leaves and the number of infected cypselae (see Chapter 3). We calculated cumulative vegetative production, reproductive production, reproductive output, and cumulative parasitic strikes.
In our ex situ experiment we measured the same fitness traits as in natural conditions, except for the germination rate and parasitic strikes (Chapter 5) (Annex 3).
Structure of the thesis
Chapter 2 presents the characterisation of eight microsatellite markers with technical indications and linkage disequilibrium tests.
Chapter 3 assesses the effect of population size on the levels of inbreeding and genetic drift. We then analyse the effect of inbreeding depression on separate and cumulative fitness traits measured in natural conditions. Ecological parameters were analysed as covariates with heterozygosity and their interactions were also tested.
Chapter 4 analyses the effect of fragmentation parameters (isolation by density, isolation by population size, isolation by distance) on gene flow within and between populations. Census sizes of populations are also compared with effective sizes inferred from molecular markers.
16 GENERAL INTRODUCTION
Chapter 5 analyses the effect of inbreeding depression on separate and cumulative fitness traits in ex situ conditions with biotic and abiotic treatments. We monitored the effects of ecological parameters in source populations. We also analyse the effect of inbreeding depression on the germination rate in in situ conditions and in chamber conditions using cypselae collected over three years; ecological influences and maternal effects are examined.
Finally we present one small seed stock experiment which was carried out to assess the viability of seeds in natural conditions.
Chapter 6 summarizes our findings and conclusions, and is appended with a French summary.
17
Chapter 2
Identification and characterization of eight microsatellite loci in Aster amellus L. (Asteraceae)
with Y. Naciri
Molecular Ecology Notes (2007) 7, 233-235
19 CHAPTER 2
Abstract
Eight polymorphic microsatellites were developed in the perennial herbaceous Aster amellus
L. (Asteraceae), and characterized on three populations from France and Switzerland. The number of alleles ranged between 4 and 30 depending on the locus, and mean number of effective alleles was 5.8. The average gene diversity equalled 0.744 (range: 0.419-0.957) and the overall differentiation was found significant (θ = 0.092, P <0.01). Three loci displayed significant heterozygote deficiencies, which might indicate the presence of null alleles.
Amplifications were detected on eight loci in Aster alpinus L.
Keywords: Aster amellus L., Aster alpinus L., microsatellite, fitness, population genetics, conservation.
20 CHARACTERIZATION OF MICROSATELLITE LOCI
Aster amellus L. is a perennial species widespread in central Europe. It is locally endangered in Switzerland (Moser et al. 2002). Fragmented landscape and abandoned management are suspected to be the main reasons for the observed population decrease. One consequence of such a decrease is the perturbation of the breeding system (Widén 1993). Microsatellite markers were developed in order to analyse inbreeding rate and to infer the species genetic structure at a regional scale.
Estoup and Martin’s protocol (1996) was used for microsatellite isolation and characterisation. Total DNA extraction was achieved following Doyle and Doyle’s protocol
(1987). DNA was digested using HaeIII, RsaI and AluI (Qbiogen), and the mung bean exonuclease was used to fill out sticky ends. Digested fragments were ligated into pUC19/SmaI/BAP plasmid following the manufacturer’s protocol (Amersham Pharmacia) for blunt-end cloning. DH5α competent cells (Invitrogen) were transformed using the ligation product. Two libraries were necessary to obtain a sufficient number of usable loci. 10,000 clones were pricked out in total and transferred on Hybond-N+ membranes (Amersham).
(TCT)8, (AAT)8, (AG)10 and (AC)10 DIG labelled oligonucleotides were used as probes for the hybridization phase and positives clones were detected with the anti-DIG-AP antibody
(Roche Molecular).
A total of 174 positive clones were found and sequenced and 99 contained at least one microsatellite motif. We found 22 Poly(A) (9≤n≤64), 13 (CA)n (7≤n≤24), seven (GA)n
(9≤n≤53), four (TAA)n (13≤n≤19), two (GAA)n (7≤n≤9), one (CTAA)11, four perfect compound motifs (15≤n≤32), 27 imperfect motifs (8≤n≤37), and 19 compound imperfect motifs (13≤n≤22).
We selected 18 loci, for which unlabelled reverse and fluorescent forward primers were designed manually because most of the microsatellite flanking regions were located in areas composed of repeated but imperfect motifs (Genebank accessions DQ514673-DQ514680 and
21 CHAPTER 2
DQ534911-DQ534920). Nonetheless, we checked for the absence of dimer formation in all pairs of primers with OLIGO 4.0-s® software (National Bioscience Inc.). Preliminary tests allowed discarding 10 loci, because of non specific amplifications or high suspicion for null alleles. The eight remaining loci were screened on ABI377 automated sequencer and scored using GENSCAN ANALYSER® software version 3.1 (Applied Biosystems). 90 individuals from three large populations of Aster amellus L. (Pâquis: Haute-Savoie, and Sergy: Bassin
Genevois, France; Nätteberg: Argovie, Switzerland) were DNA extracted using an enzymatic method modified from Manen et al. (2005): after 6h incubation leading to complete cell walls digestion, 2μl of AGOWA® magnetic particles with 100μl of AGOWA® binding buffer were used to isolate DNA from cell components. The DNA, fixed on magnetic particles, was washed two times and then eluted in 60μL TE PH8. PCR were made using the QIAGEN®
Multiplex kit which provides a Multiplex PCR Master Mix already containing HotStartTaq®
DNA polymerase, a multiplex PCR buffer at 6mM MgCl2, dNTPs and a factor MP which improves annealing and elongation. The following conditions were applied: 15 min at 95°, 35 cycles composed of 30 s at 94°, 90 s at annealing temperature (Tm, Table 1), 60 s at 72°, followed by 30 min of final extension at 60°. Multiplexing was performed using a set of three primer pairs at 58°C annealing and a set of five primer pairs at 62°C annealing (Table 1).
PCRs were conducted in a final volume of 5μL with 2.5μL Master Mix, 0.5μL primers mix
(10μM each in initial volume), 1μL H2O and 1μL DNA. The results were analysed using
FSTAT 2.9.3. (Goudet 2001) . Bonferroni corrections were carried out in multiple tests for adjusting the nominal levels.
A high polymorphism was found with a number of alleles ranging from 4 (Aam.J15) to 30
(Aam.D10) overall individuals (Table 1). Allelic richness per population was very close to the census number of alleles due to the balanced sampling (Rs range: 2.0-21.7). Mean number of
22 CHARACTERIZATION OF MICROSATELLITE LOCI effective allele was 5.8 (Ne range: 1.7-15.9), indicating that about half of the alleles were rare or not frequent.
Positive and significant deviations from Hardy-Weinberg Equilibrium were found for three loci (P = α/8, Table 1): Aam.H231 for Pâquis, Aam.B3 for Pâquis and Nätteberg and
Aam.D10 for Pâquis and Sergy. Aam.D10 is a long microsatellite (GA)38, for which the presence of multiple Stutter bands could have hidden the presence of a nearby allele. Such misleading records would result in heterozygotes underestimation, but do not explain that a non significant Fis was found in Nätteberg. The presence of null alleles is therefore suspected for these three loci at least in some populations. Negative deviations from HWE were detected for Aam.J15 but with non significant Fis. The systematic excess in heterozygotes found for this locus could be due to undetected amplification artefacts or to balanced selection.
The overall differentiation was equal to θ = 0.092 (P <0.01). Linkage disequilibrium was not detected at the 5% level in any of the three populations (P < 0.05/28), which indicates physical independence of the eight loci.
The eight loci were tested separately on Aster alpinus L. All loci gave successful amplifications except Aam.A12 for which two individuals did not amplify (Table 2). The number of alleles ranged between one and seven. Although some loci showed secondary peaks (Aam.A12, Aam.H231, Aam.B3, Aam.D10, Aam.J15, Aam.G431), amplifications were detected within the allele size range of A. amellus or in the very close vicinity, except for
Aam.J15 (Table 2).
The microsatellite loci described here will be used to assess the genetic differentiation of
Aster amellus L. at a regional scale and to address the question of the effect of fragmentation on genetic diversity.
23 CHAPTER 2
Acknowledgements
We wish to thank S. Caetano and R. Niba for technical assistance, Drs. D. Aeschimann and C.
Lambelet for having provided financial support, C. Habashi as well as I. & T. Lindenberger for their help in the field and Prof. R. Spichiger for his support.
24 CHAPTER 2
Table 1 Characteristics of eight microsatellite loci in Aster amellus L. tested on three populations from France: Pâquis (Upper Savoy), Sergy (Geneva Basin) and Switzerland: Nätteberg (Aargovia). Tm, annealing temperature in °C; Nb , sample size; Nat , total number of alleles overall populations; Pop , studied populations; Na , number of alleles within populations; Rs , allelic richness per population; Ne , effective number of alleles per population; Ho , observed heterozygotie; He , expected heterozygotie; Fis, significant value are indicated with *, ** and ***, α being equal to 0.05, 0.01 and 0.001 respectively, with adjusted nominal levels P = α/8.
Motif GenBank Primers Dye T m Size range Nb N at Pop N a R s N e H o H e Fis 5' 3' ns Aam .F58 (GT)11 DQ514673 GATAGAGTGTTTGTCTGTGAGTG VIC 58 74-88 30 13 Pâquis 8 7.9 4.627 0.733 0.798 0.081 5'TGTGGAACCCCTAAGCCG3' 30 Sergy 8 8 6.294 0.967 0.853 -0.133ns 30 Nätteberg 8 8 3.6 0.733 0.734 0.002ns 5' 3' ns Aam .A12 (TAA)18 DQ514674 GGCATAAAAACATTCCTATACG NED 58 97-139 29 12 Pâquis 6 6 2.339 0.586 0.583 -0.006 5'ATTCAATTAGTTTCCATATCCC3' 30 Sergy 8 7.8 3.939 0.667 0.76 0.123ns 30 Nätteberg 11 10.9 5.921 0.833 0.845 0.014ns 5' 3' ** Aam .H231 (TG)11AAAT(TG)4 DQ514675 TGAACATGATAATGATGAGGATG 6-FAM 62 138-148 30 5 Pâquis 4 4 2.171 0.3 0.553 0.457 5'ACCAAAATTCTTATAACACCTTC3' 30 Sergy 4 4 1.917 0.333 0.489 0.318ns 29 Nätteberg 4 4 2.726 0.621 0.645 0.037ns 5' 3' * Aam .B3 (CTAA)10 DQ514676 TAGTGAAATAATGTGATACTACTCC NED 62 133-173 30 10 Pâquis 4 4 2.62 0.4 0.629 0.417 5'GTTTGAACCAATGGAAATCCTGC3' 30 Sergy 8 7.9 5.202 0.7 0.814 0.181ns 28 Nätteberg 6 6 3.246 0.321 0.712 0.548*** 5' 3' *** Aam .D10 (GA)38 DQ514677 AAATGATTTGTGTGGTGCG VIC 62 135-195 30 30 Pâquis 21 20.7 15.929 0.7 0.957 0.269 5'GTTTATCTGTTAAAGTGACTGG3' 29 Sergy 18 17.7 10.383 0.517 0.927 0.442*** 29 Nätteberg 22 21.7 14.017 0.931 0.945 0.015ns 5' 3' ns Aam .A415 (AT)19 DQ514678 CCAGAAGAAGATTACATAAGAGTG 6-FAM 58 173-209 29 19 Pâquis 12 11.9 8.087 0.862 0.892 0.034 5'TCAATAGTGTGTTTATTTGCAAGC3' 29 Sergy 17 16.8 7.787 0.759 0.889 0.147ns 30 Nätteberg 18 17.8 15 0.966 0.949 -0.019ns 5' 3' ns Aam .J15 (TAA)7AAA(TAA)5 DQ514679 TGGAAATTATAGAGCCTATCAAGCAG NED 62 197-206 29 4 Pâquis 2 2 1.788 0.517 0.447 -0.157 AGTGATGTTTA 5'TCTCCGGCTATCAATCCTCTTTTGC3' 30 Sergy 4 3.9 1.706 0.533 0.419 -0.273ns ns (TAA)8 29 Nätteberg 3 3 2.069 0.759 0.522 -0.455 5' 3' ns Aam .G431 (TAA)15 DQ514680 CTATCCTACACTAACAATCCACT 62 262-304 30 13 Pâquis 9 8.9 6.316 0.733 0.858 0.145 5'CATCTTCCCTCTCTTAACCTAC3' VIC 30 Sergy 13 12.7 7.692 0.767 0.887 0.135ns 29 Nätteberg 7 7 3.832 0.793 0.751 -0.056ns
Table 2 Amplification of the eight microsatellite loci in 5 individuals of Aster alpinus L.. Aam .F58 Aam .A12 Aam .H231 Aam .B3 Aam .D10 Aam .A415 Aam .J15 Aam. G431 Size range in A. 74-88 97-139 138-148 133-173 135-195 173-209 197-206 262-304 amellus Alleles found in A. 70 97, 118, 140, 144, 133, 165, 145, 149, 171, 173, 200, 203, 262, 277, alpinus 130, 139 146, 150 169 153,165, 169, 177, 187, 224 280, 292 173,185 189, 193 Successful 53 5 5 5555 amplifications
Chapter 3
Inbreeding and inbreeding depression in the rare perennial Aster amellus L. in natural conditions with biotic and ecological interactions
27 CHAPTER 3
Abstract
Reduction in population size can lead to inbreeding, thus negatively affecting plant fitness traits. Under natural conditions, ecological factors may also play a key role in measured fitness traits, leading to misinterpretation of results. Several studies have clearly shown inbreeding effects due to small population size, but inbreeding depression in natural conditions has not always been proved. Environmental variables and biotic interactions could be reasons for non-detection of deleterious effects due to inbreeding. Strategies of the species should also be considered for better comprehension of results.
To assess the effects of inbreeding, we studied 31 populations of the infrequent Aster amellus in natural conditions. (1) Using microsatellite markers, we found that the effect of population size on heterozygosity and gene diversity was high. (2) We found significant inbreeding depression in the germination rate and reproductive output when taking into account ecological factors and biotic interactions. (3) Vegetation height had a positive effect on plant productivity but a negative effect on total fitness. (4) Biotic interactions were higher at higher altitude and so in less fragmented landscape. (5) More outbred populations responded better to potential seed feeder.
Aster amellus plants were found to be highly affected to inbreeding and drift caused by small populations and consequently showed clear signs of inbreeding depression. Pressure caused by surrounding competition diminished reproduction of the species but enhanced vegetative maintenance.
Key words: Aster amellus, fragmentation, inbreeding, drift, inbreeding depression, population size, ecological parameters, competition, stress, strategy, parasite strikes, biotic interactions, microsatellite, germination rate, cumulative production, reproductive output, fitness
28 INBREEDING IN NATURAL CONDITIONS
Introduction
Deterministic effects such as overkill, habitat destruction, fragmentation, and introduced species lead to a reduction in, and isolation of, populations (Hedrick et al. 1996) . Small and isolated populations are stochastically more susceptible to genetic drift and inbreeding, which may lead to inbreeding depression (Ellstrand & Elam 1993). Inbreeding is characterized by an excess of homozygotes in a given population. Overdominance and/or partial dominance will then affect the fitness components (Charlesworth & Charlesworth 1987; Keller & Waller
2002). Drift is characterized by a loss of allelic diversity in each generation we might see a loss of alleles due to non-reproductive individuals or death. Drift may enhance a more rapid fixation of deleterious alleles and so negatively affect fitness.
Several studies have shown the negative effect of low population size, genetic drift, and inbreeding on fitness traits (Reed & Frankham 2003; Reed 2005; Leimu et al. 2006), but this is not the case in certain species or for certain fitness components (Lammi et al. 1999;
Luijten et al. 2000; Hooftmann & van Kleunen 2003; Bachmann & Hensen 2007). Beneficial purging in these small populations together with a more favourable environment are possible reasons for the contrasting results. Methodology, for example choosing whether to count genets or ramets for perennial species could also explain failure to detect inbreeding depression (Pluess & Stöcklin 2004).
Parasite strikes are known to be lower in fragmented populations (Kruess &
Tscharntke 2000) and so could counterbalance negative effects due to inbreeding (Colling &
Matthies 2004). However, disappearance of parasitic interaction might decrease responses to stochastic parasite strikes enhanced by specific year conditions (see (Hoehn 2006) for an example of drought-enhancing fungal pathogens) and so could submit small and fragmented populations to hazardous strikes that are not controlled, either by a loss of recent contact with the pest or by inbreeding depression.
29 CHAPTER 3
Long-lived perennial species invest more in survival than in reproduction if conditions are not suitable (Crawley 1985). A study on Senecio jacobea L. showed increased mortality in individuals producing more capitula (Gillman & Crawley 1990) . Hartemink et al. (2004) also showed that short-lived perennials tended to produce as many flowering parts as possible and long-lived perennials, such as Succisa pratensis Moench, minimized their reproductive output in stress conditions. As our studied plant was a long-lived species we were attentive to production of plants.
Seed mass is known in certain species to influence germination and subsequent stages in early life (Castro 1999). Seed mass embed maternal effects (ecological and genetic) and embryonic genetic (Esau 1977), a more recent study on Arabidopsis suggest that maternal sporophyte and endosperm only worked in determining seed mass, without embryonic activity
(Jofuku et al. 2005). Control of seed numbers and seed mass is also known to be intrinsically linked (Primack 1987), and dependent to resource availability (Stephenson 1981). Deleterious genetic controls on either mass, number, and/or their interactions, may be linked to inbreeding. We focused one part of our analysis on these relationships.
Since 1950 intensive agricultural practices have modified the landscape structure, by a deterioration of the environmental matrix. Use of the calcareous grasslands of south-west central Europe became economically non-viable (Köhler et al. 2005) and was therefore progressively abandoned (Stöcklin et al. 2000). Thanks to recent conservation management efforts, fragments of calcareous grassland are now maintained. Habitats are no longer abandoned, but are nevertheless always small and fragmented and could be compared to small islands among intensive agricultural areas (Stöcklin et al. 2000).
To assess the effect of this currently fragmented landscape on genetic and fitness components, we studied 31 populations of the perennial Aster amellus in calcareous grasslands along the Jura between Upper Savoy (France) and Aargovia (Switzerland). We
30 INBREEDING IN NATURAL CONDITIONS wanted to answer the following questions: (1) Are the heterozygosity and gene diversity of the populations affected by population sizes? (2) Are specific seed (cypselae in our case) components (mass, number, germination, and their relationships) affected by inbreeding of the populations? (3) Are populations affected by inbreeding depression with respect to cumulative production, reproductive output and total fitness? (4) How do ecological parameters and parasite infections interact with our findings? (5) Are parasite interactions affected by inbreeding of the populations?
Materials and Methods
Study species
Aster amellus L. is a central-European species. Its north-east distribution limit is west Siberia by the Tobol river (57°14’N/67°2’E) and its south-west limit is south-west France in the Tarn
(44°5’N/3°5’E). The distribution area is interrupted by the Alps (Jäger 1979). The species grows on limestone and dry soil, in warm hilly to warm montane belts. It is heliophilous to semi-heliophilous, growing in grasslands, clearings, open forests, edges, slopes and waysides
(Rameau et al. 1989).
Aster amellus is a perennial, hemicryptophyte species with a concurrent-stress strategy
(Frank & Klotz 1990). It overwinters under the soil surface, buds appear in March and differentiate into rosettes or stems throughout spring. (Münzbergová 2006) indicates 8 cm between ramets of a genet, but our observations indicate (1)-5 cm-(30) and we use this average distance for our measurements. A genet can produce (1)-3-(68) ramets, a stem (1)-4-
(34) capitula and an infructescence (6)-64-(146) cypselae (measures 2005-2006, on 1600 plants, in 31 populations along the Jura). A cypsela is a heterocarpous fruit containing one seed coated by the ovarian walls and the calyx; the all forming one unique structure. This is usually confused with the seed, but in our sense it is not equivalent because a developed fruit
31 CHAPTER 3 doesn’t mean that it contains a developed seed. Flowering time is in August-September, followed by fructification in October-November. Seeds are trichometeochore and partially ethelochore (Müller-Schneider 1986). Without wind, with 0.8 m/s fall speed and 0.35 m capitulum height, we can calculate a seed dispersion distance of 0.44 m and with a 27 m/s wind speed; seeds could reach up to 12 m (Tackenberg 2001). Aster amellus is an entomogame generalist; during our observation in the field, we saw three Apidae species
(Apis, Ceratina, Epeolus), six Syrphidae species and Lepidoptera. Literature indicates self- incompatibility of the flowers (Kovanda 2005; Münzbergová 2006), which was confirmed with an ex situ experiment involving caging capitula (unpublished data), but intracapitulum or intercapitulum fecundation was not tested. Seeds stock is normally transient (Thompson et al.
1997; Cerabolini et al. 2003) (see Chapter 5).
On vegetative parts, we saw insect herbivory traces, leaf miners, mushrooms
(Ramularia, Cerospora, Phoma, Phyllostica, (determination by Adrien Bolet)), Orthoptera eggs and Gasteropoda. Mammalian herbivores feed on capitula, whereas cypselae are eaten by Coleophora obscenella Herrich-Schäffer (Baldizzone & Tabell 2002). We frequently saw a fluorescent orange larva, which was potentially a seed feeder, Diptera, from the cecidomyiid group (Mark Shaw pers. com.). However, we obtained only a larval form, so we were not in a position to clearly assess the species. We maintained it as a potential seed feeder and use
“orange larva” in the text to indicate the species. From a caged growth of collected capitula, we obtained one Euderus sp. (determination by Hannes Baur), which parasitizes Lepidoptera larvae.
32 INBREEDING IN NATURAL CONDITIONS
Study sites
In 2005, we investigated 31 populations of Aster amellus in the Jura (Switzerland-France) along a 246 km transect, distributed in three main regions: Upper Savoy, the Geneva Basin and Aargovia, and one small region, Le Landeron (Neuchâtel) (Fig. 1).
Fig. 1 Distribution of the 31 studied populations across the four study regions, situated between the 45°/47°N latitude and the 5°/8°E longitude. The two distal populations were separated by 246 km. We provide also the Swiss distribution of Aster amellus (in dark grey on the general map). Please refer to Table 1 for code and coordinates of populations. CRSF, Centre of the Swiss Flora Network.
In September 2005, we counted separately flowering ramets and reproductive genets
(ramets separated by less than 5 cm were considered as one genet (see description)) with hand-counters in each 2x2 m squares of the populations. All surfaces were georeferenced with
33 CHAPTER 3 the help of a Trimble GeoXT® GPS, working with ARCPAD software version 6.03 (ESRI) and connected to a Hurricane Antenna. Coordinates were then corrected by postprocessing using GPSCORRECT software and a fixed antenna based in Geneva (data collected by the
Geneva measurement service). With the positioning material, we built a 10/10 m virtual grid and marked physical points with wooden stakes at each grid node. In total, 865 such points were marked on in the entire set of 31 populations (minimum 10 points per population). At these specific points, in addition to the traditional count (see above), we inventoried the
Table 1 Population parameters of the 31 studied populations, classified by population size in number of vegetative + reproductive genets (POP SIZE) (we provide also number of stems (STEM)). Ecological parameters according to Landolt (1977) are luminosity (LUM), humidity (HUM) and nutritive substances (SUB). We measured also vegetation height (VEGH). Genetically parameters are mean observed heterozygosity (HET) and average gene diversity (GD). Z is the altitude from the see level, X and Y are projected Swiss coordinates. We provide population name (POP NAME), code of the populations (see Fig. 1) and region of origin (REG): AR, Aargovia; GB, Geneva Basin; US, Upper Savoy; LAN, Le Landeron. POP NAME CODE REG X Y Z STEM POP SIZE LUM HUM SUB VEGH HET GD Bois de la Grille BG GB 496267 118871 389 51 21 3.481 2.441 2.407 41.0 0.493 0.473 Crottes CR GB 510938 111803 437 52 71 3.280 2.491 2.395 49.1 0.426 0.521 Hermance HER GB 508619 127495 411 69 101 3.560 2.102 2.256 34.4 0.636 0.601 Chanières 2 CH2 GB 492785 115573 405 116 141 3.488 2.263 2.363 30.8 0.499 0.591 Chanières 1 CH1 GB 492555 115911 406 43 161 3.497 2.338 2.260 29.9 0.650 0.647 Bouffard BOU GB 491008 117114 390 118 181 3.429 2.614 2.388 26.9 0.564 0.586 Rebaterre REB US 474430 101064 458 154 206 3.399 2.374 2.516 29.5 0.566 0.621 Champcoquet 2 CC2 GB 487857 111460 393 252 239 3.444 2.297 2.242 34.3 0.586 0.586 Trembley TR US 480310 101431 493 150 368 3.439 2.421 2.292 27.5 0.513 0.592 Repentance REP GB 488022 110854 385 130 480 3.457 2.375 2.320 28.2 0.566 0.615 Vuache VU US 482799 102088 679 506 521 3.507 2.169 2.244 35.1 0.489 0.584 Franclens FRA US 475644 100057 475 271 610 3.430 2.513 2.422 36.1 0.604 0.648 Schihalden 4 SC4 AR 654491 258515 455 737 695 3.695 2.065 2.260 23.4 0.660 0.687 Schihalden 1 SC1 AR 654100 258579 446 258 871 3.396 2.185 2.142 32.6 0.634 0.683 Allondon 2 AL2 GB 488978 117406 375 1084 871 3.628 2.079 2.215 35.6 0.544 0.557 Allondon 1 AL1 GB 489072 119824 404 1530 901 3.608 2.049 2.223 42.2 0.582 0.635 Aecheberg 3 AE3 AR 646279 252779 497 209 925 3.640 2.276 2.226 30.9 0.596 0.575 Frût FRU US 474100 100387 441 255 1015 3.610 2.301 2.399 21.7 0.693 0.717 Hundruggen HU AR 651133 258663 525 224 1029 3.500 2.297 2.145 23.1 0.672 0.718 Landeron 2 LAN2 LAN 571499 212302 495 4826 1041 3.440 1.931 2.033 34.3 0.572 0.668 Sparberg SP AR 654462 264432 553 617 1097 3.421 2.362 2.250 35.1 0.718 0.746 Champcoquet 1 CC1 GB 487560 111326 374 1335 1592 3.419 2.382 2.314 34.3 0.688 0.676 Schihalden 2 SC2 AR 654232 258681 474 1919 1701 3.607 2.188 2.292 37.2 0.657 0.698 Eggberg 2 EG2 AR 642580 251690 569 2250 1809 3.515 2.123 2.470 44.9 0.665 0.729 Thoiry TH GB 486211 121926 656 2073 2679 3.649 1.961 2.199 24.2 0.600 0.695 Landeron 1 LAN1 LAN 571530 213088 614 6332 3260 3.613 2.054 2.214 38.8 0.556 0.666 Eggberg 1 EG1 AR 642924 251600 532 2547 4820 3.583 2.137 2.183 30.0 0.616 0.682 Sergy SE GB 487918 124683 659 2287 7194 3.482 2.021 2.223 26.6 0.678 0.728 Pâquis PA US 480162 90570 615 986 8294 3.494 2.036 2.248 26.3 0.617 0.673 Hessenberg HES AR 649952 261086 505 4484 15350 3.495 2.299 2.126 22.9 0.690 0.758 Nätteberg NAT AR 649659 260664 484 9751 30927 3.547 2.244 2.137 24.7 0.689 0.749 number of vegetative genets. This was then used to estimate the number of vegetative individuals in every 4 m2 plot by using a binomial function, y=a*x2+b*x+c, where y is the number of vegetative individuals and x is the number of reproductive individuals. We then
34 INBREEDING IN NATURAL CONDITIONS calculated the total number of individuals per population. At the marked points we also made, during July 2006, 1 m2 presence/absence vegetation records, following the New Binz nomenclature (Aeschimann & Burdet 1994). Landolt (1977) values were used in a weighted average formula according to Diekmann (2003) for calculation of the ecological parameters.
In the years 2005 and 2006, we also measured the vegetation height by measuring the size of representative grass in the 1 m2 plots (Table 1 for population characteristics).
Plants survey and measurements
In September 2005, we precisely marked with 10 cm nails one reproductive and one vegetative plant on a 1 m2 plot at each of the 865 sampling points. We also mapped these plants on a 20/20 cm grid (Fig.2).
Five millimetre-diameter pieces of leaf 2(m) were collected from the two marked 1(m) individuals and from an additional
individual outside the 1 m2 plot, but within 2 the 4 m2 square around the physical point. 1 These leaf materials were directly placed in
1.5 ml tubes filled with silicagel. DNA
3 20(cm) extractions were done using an enzymatic
Fig. 2 Sampling scheme: in the 1 m2 we did vegetation method coupled with magnetic beads records, marking and mapping studied individuals 2 with the help of a 20/20 cm grid; in the 4 m we (Manen et al. 2005). We analysed a total of counted the reproductive and vegetative genets; we defined a genet as a group of ramets separated from each other by less than 5 cm. Filled squares, stakes; 2600 individuals using seven microsatellite filled circles, DNA sampling; 1, marked reproductive individual; 2, marked vegetative individual; 3, markers, as described in Mayor & Naciri additional non-marked reproductive individual outside 2 the 1 m area for microsatellite analysis only. (2007) (locus AamF58 was abandoned
35 CHAPTER 3 because of problems in reading it in certain populations). We calculated mean multilocus heterozygosity per individual and then per population, as a measure of inbreeding (Galeuchet
2005a). We obtained average gene diversity with ARLEQUIN 3.01 (Excoffier 2006) .
Between the 1st and 15th September 2005 and 2006 we measured and counted on the marked plant: number of rosettes and/or stems; width and length of the biggest leaf; size of the biggest stem. We calculated leaf area, as an elliptic form.
Between the 20th and 30th October 2005 and 2006, we counted the number of initiated capitula (i.e. with presence of pappus) on the biggest stem and on the totality of an individual’s stems. We counted, separately, the small and badly developed units, and good ones. We collected one fully developed infructescence per marked plant and dried it for three weeks at room temperature. Cypselae were separated into four groups per capitulum using two criteria: width (≥1 mm, <1 mm) and presence or absence of infection. They were then counted and weighted per group, and cypsela mass was calculated. Number of orange larvae and Coleophora cocoons per capitulum were also counted. In April 2006, the previous year’s non-infected seeds were sown in Petri dishes filled with a 7 g agar in 1 l H2Od mix and placed for three weeks in a germination chamber at a 14/10H and 25/20° day/night regime. Presence of a radicle was counted as germination, even if subsequent development failed.
Between the 1st and 15th September 2006, we classified marked plants as reproductive or vegetative. We counted the number of leaves of the largest rosette for vegetative individuals and of the highest stem for reproductive individuals (from the base to the terminal capitulum with bracteoles). Additionally, we counted the number of leaves with herbivory traces and the number of leaves with all kinds of parasite attacks including herbivory. In 2007 we surveyed, for one more year, the stages of plants, so in total, we made three years of population observations.
36 INBREEDING IN NATURAL CONDITIONS
Cumulative fitness traits
We use four different kind of cumulative responses. Firstly we calculated the vegetative production from vegetative plants as the product of number of leaves, area of leaves and number of rosettes. Secondly we calculated the reproductive production from reproductive plants as the product of number of leaves, area of leaves, number of stems, number of reproductive stems’ capitula and number of cypselae per capitulum; this will not represent the final reproduction but what the plant invests in reproduction. Thirdly we calculated reproductive output as the product of the rate of stems, rate of reproductive stems’ capitula, rate of developed cypselae and germination rate; this gave a weight to plants which produced highly but poorly, this was because, in the field, we observed a general decrease in quality of cypselae in high/poorly reproductive plants. Fourthly, a cumulative parasite strike was obtained by pooling the rate of infected vegetative and reproductive leaves and rate of developed infected cypsela over two years of sampling.
Total fitness
To take into account the stages of plants (vegetative, reproductive), we calculated a transient- fertility matrix per population that used a stage-fate-fertility table as input data. Stage represented the stage in the earliest year, and fate the stage in the subsequent year; fertility was the sum of reproductive output (see above) between the earliest year stage and the subsequent year stage. A reproductive individual took the mean of population reproductive output over years 2005–2006 and a vegetative individual took a zero value. These matrices were then used to calculate lambda of the populations, which was here naturally biased because we used only the transient-fertility matrix without taking into account population size change, and without seedling survival. However, this will give a view of the total fitness over three years and also the stable stages situation in percentage of reproductive or vegetative
37 CHAPTER 3 individuals. Matrixes were obtained with R software (Stubben & Milligan 2007) and are based on the works of: Caswell (2001), Morris & Doak (2002).
Data analysis
We used a type IV mixed model, based on restricted maximum likelihood (REML) (Bates
2007). The type IV model had the advantage of being adapted to unbalanced data sets. For all models we used population identity and population identity x year of sampling interaction as random factors. We firstly constructed a full model with all interactions and then non significant terms were drooped, provided that this didn't positively perturb the Akaike information criterion (AIC). The tests of significance used the Monte-Carlo procedure with
10000 Markov chains.
Firstly, we analysed the effects of altitude, luminosity, humidity, nutritive substances, vegetation height, heterozygosity, and years of sampling on square root number of cypselae, log10 cypsela mass, germination rate of developed and no infected cypselae and square root number of orange larvae per capitulum. We added as covariates log10 cypsela mass, square root number of cypselae and infection rate of developed cypselae. For germination rate we also did one analysis with average gene diversity instead of heterozygosity.
Secondly, we analysed the effects of ecological parameters (as above) and heterozygosity on square root cumulative vegetative production, cumulative log10 reproductive production, square root reproductive output and cumulative parasite strike.
All analyses were done using the R statistical project (R Development Core Team
2007).
38 INBREEDING IN NATURAL CONDITIONS
Results
General observations
Between the years 2005 and 2007, 97% of the marked plants had survived. Cypselae <1 mm did not germinate. Thirty-two percent of the cypselae that were non-infected and ≥1mm germinated. In averaging our 865 vegetation records, we found 22 plant species per square meter and in total 238 plant species were recorded (Annex 1).
Inbreeding and drift
Heterozygosity and average gene diversity were positively related to population size.
(R2=0.36, p<0.001; R2=0.63, p<0.001; Fig. 3).
Fig. 3 Population size (reproductive individuals + vegetative individuals) related to mean observed heterozygosity and average gene diversity. We present R2 coefficient of regression and p value: *** P<0.001.
Cypsela components
Cypsela numbers per capitulum were influenced positively by cypsela mass (maternal component) and negatively by heterozygosity of the population and by their interaction (Table
2, Fig. 4E). Cypsela mass must be viewed here as representing the general condition of the
39 CHAPTER 3 plant, so when cypsela mass was low (i.e., bad condition), capitula had lower numbers of cypselae.
Cypsela mass was influenced by the number of cypselae per capitulum, luminosity and heterozygosity and their three-way and two-way interactions (Table 2). We saw that with a large number of cypselae in the capitulum, high heterozygote populations performed better in the output cypsela mass (Fig. 4F).
Germination rate of non infected and developed cypselae was influenced positively by more heterozygote populations (Table 2) and also highly by average gene diversity (Table 2,
Fig. 4G). Luminosity, vegetation height, and cypsela mass were also implicated in the capacity for germination with possible interactions between them (Table 2). Interaction with gene diversity showed complex situations depending on luminosity, allelic diversity and the general condition of the maternal plant (embedded by the cypsela mass) (Fig. 4H).
Table 2 Linear model type IV analysis of cypsela components (cypsela number, cypsela mass, germination rate of non- infected and developed cypselae, and number of orange larvae per capitulum) against population parameters: altitude, luminosity, nutritive substances, vegetation height, heterozygosity (and average gene diversity (GD) for a second analysis of germination rate, instead of heterozygosity as independent variable) and year of sampling; we add in the model as covariates: log10 cypsela mass, square root number of cypselae and rate of developed infected cypselae. Non-significant terms were dropped (dr), provided that this didn't positively perturb the Akaike information criterion (AIC). Model use population identity (31 groups) and population identity x year of sampling (62 groups) as random effects. We present t values and p levels of significance. + p<0.1; * p<0.05; ** p<0.01; *** p<0.001; na, not available. Cypsela Cypsela Germination Germination No. of orange number mass rate rate (GD) larvae Source of variation t t t t t Altitude dr dr dr dr 5.098 *** Luminosity dr -2.784 ** 1.969 + 4.167 *** 2.186 * Humidity dr dr dr dr dr Nutritive substances dr 2.174 * dr dr dr Vegetation height dr dr 1.563 1.566 dr Heterozygosity -4.04 *** -2.551 * 1.738 + 3.972 *** -0.8 Cypsela mass -2.375 * na 3.262 ** 4.803 *** -9.392 *** Cypsela number na -3.128 ** dr dr 5.883 *** Cypsela infection -3.039 *** na -6.351 *** -6.308 *** 2.562 * Luminosity x heterozygosity dr dr -1.627 -3.856 *** dr Vegetation height x heterozygosity dr dr -1.638 -1.736 + dr Cypsela mass x luminosity dr na -3.105 ** -4.657 *** dr Cypsela mass x vegetation height dr dr -2.676 * -2.205 + dr Cypsela mass x heterozygosity 2.833 ** na -2.992 ** -4.609 *** dr Cypsela number x luminosity na 2.64 ** dr dr dr Cypsela number x heterozygosity na 3.426 *** dr dr dr Cypsela infection x heterozygosity dr na dr dr -2.34 * Cypsela mass x vegetation height x heterozygosity dr dr 2.726 ** 2.35 * dr Cypsela mass x luminosity x heterozygosity dr dr 2.86 ** 4.484 *** dr Year dr 4.392 *** na na -3.177 ** No. of observations 1607 1679 781 781 1607 AIC 4071 -1183 -50.63 -55.17 2738
40 INBREEDING IN NATURAL CONDITIONS
Fig. 4 A) and B) Effects of vegetation height (competition) on cumulative vegetative production and reproductive output. C) Effects of heterozygosity on cumulative reproductive production. D) Two-way interaction between heterozygosity and luminosity affecting reproductive output. E) Two-way interaction between heterozygosity and cypsela mass acting on cypselae number. F) Two-way interaction between heterozygosity and cypsela number on cypsela mass. G) General effect of gene diversity on germination rate. H) Three-way interaction between gene diversity, luminosity and cypsela mass on germination rate. We present standard errors; significance levels of the relations are given in Table 2 and 3.
Plant production, reproductive output and total fitness
Cumulative vegetative production was only influenced by the vegetation height parameter
(p<0.05, Table 3). Higher vegetation favourised the vegetative plants production (Fig. 4A).
41 CHAPTER 3
Cumulative reproductive production was increased by nutritive substances (p<0.01,
Table 3), and decreased for higher heterozygote populations (p<0.05, Fig. 4C).
Reproductive output as a final fitness measure, was negatively influenced by vegetation height and humidity (p<0.05, Table 3, Fig. 4B). It was positively related to heterozygosity but with an interaction with luminosity; in poor luminosity, more heterozygous populations were less fit. (p<0.05, Table 3, Fig. 4D).
Table 3 Mixed linear model type IV analysis of cumulative vegetative and reproductive fitness traits, and parasitic interaction (pooled over years) against ecological parameters, mean heterozygosity of the populations, years of sampling and their interactions. Non-significant terms were dropped (dr), provided that this didn't positively perturb the Akaike information criterion (AIC). Model use populations (31 groups) and years x populations interaction (62 groups) as random effects. We present t values and p levels of significance: + p<0.1; * p<0.05; ** p<0.01; *** p<0.00. na, not available. Vegetative production Reproductive production Reproductive output Parasite intercation Source of variation t t t t Altitude dr dr dr -1.848 + Luminosity 1.0943 1.159 2.199 * dr Humidity 0.166 dr -2.32 * dr Nutritive substances 0.1232 2.501 ** dr dr Vegetation height 2.2516 * dr -2.382 * -2.504 * Heterozygosity -0.1666 -1.966 * -2.409 * dr Heterozygosity x luminosity dr dr 2.451 * dr Altitude x vegetation height dr dr dr 2.738 * Year dr 5.435 *** 5.64 *** na No. of observations 1382 1381 1393 4849 AIC 9263 5923 -3030 -5841
From projection matrices we obtained lambda values ranging from 1.02 to 1.67 and the stable reproductive stage which ranged from 30% to 95%. Linear models taking into account altitude, Landolt’s ecological parameters, and heterozygosity showed effects on lambda values at the 0.05 level of significance, negatively with altitude (here the effect of cypsela infection), negatively with vegetation height (effect of competition) and positively with nutritive substances and no effect of heterozygosity. The stable reproductive stage was marginally, negatively significant at the 0.1 p level with humidity. The stable vegetative stage was significantly, at the 0.05 p level, positively related with humidity. Took alone in the model, humidity was significantly at the 0.01 p level, related to reproductive and vegetative stable stages, respectively negatively and positively, with R2=0.29.
42 INBREEDING IN NATURAL CONDITIONS
Biotic interactions with respect to parasite strikes
We saw a highly negative effect of infected developed cypselae on number of cypselae per capitulum (p<0.001, Table 2) which may indicate a possible adaptive trait, when populations with a rate of infection lowered their cypsela number in response to parasite infection. The number of developed infected cypselae acted negatively on the germination rate of non- infected and developed cypselae (p<0.001, Table 2). We were not able to tell whether plants expressly inhibited their germination to diminish a local density of conspecific in an effort to control parasite infestation over a long time scale, or simply because of invisible infection
(the first stage of parasite larvae in the fruit).
The number of orange larvae per capitulum interacted strongly with cypsela number, mass, and infection (Table 2), and was influenced negatively with heterozygosity under an interaction with the cypsela infection rate (p<0.05, Table 2, Fig. 5A). We saw a higher number of orange larvae per capitulum in populations at higher altitude (p<0.001, Table2), this was in line with cumulative parasite strikes (p<0.1, Table 2). Altitude, in this last case, also interacted with vegetation height (p<0.05, Table 2, Fig. 5B).
Fig. 5 A) Two-way interaction between infection rate of cypselae per capitulum and heterozygosity of the populations, on number of orange grubs per capitulum over years 2005 and 2006 (Table 2). B) Effect of two ways interaction between altitude and vegetation height of the populations on cumulative infection (rate of vegetative and reproductive parasitized leaves, rate of developed and infected cypselae pooled over years 2005 and 2006) (Table 3).
43 CHAPTER 3
Discussion
Inbreeding and drift in small populations
The population size had a positive effect on the heterozygosity of the population indicating the risk of inbreeding depression in small populations of the self-incompatible Aster amellus.
Average gene diversity was also highly influenced by population size submitting small populations to a second genetic risk of extinction by drift to possible rapid fixation of deleterious alleles.
Similar results were found in the self-incompatible Succisa pratensis Moench and
Arnica montana L. in studies involving allozyme markers and Fis as inbreeding coefficient
(Vergeer et al. 2003; Luijten et al. 2000). With microsatellites markers, the geitonogamous
Silene flos-cuculi L. (Galeuchet et al. 2005a), showed an effect on heterozygosity with an interaction between altitude and population size, and a marginal effect on gene diversity with population size.
Inbreeding depression at the cypsela level: number, mass, germination
We saw a negative relationship between the number of cypselae per capitulum and heterozygosity. In the literature we found no case where heterozygosity positively increased the number of fruits under natural conditions. Significant positive relationship was found with
Fis inbreeding coefficient for Silene flos-cuculi L. (Hoehn 2006), which corroborates our results. However, the number of fruits was positively correlated with heterozygosity or gene diversity in studies involving greenhouse conditions (Galeuchet et al. 2005b), or with other population parameters such as population size (Colling & Matthies 2004). Seeds in our case were, potentially, directly linked to the number of fruits, because we were in the presence of cypsela-type fruits (one fruit = one potential seed). Therefore, a high number of cypselae had to be an important fitness trait to maintain reproductive output, but could be a drawback under
44 INBREEDING IN NATURAL CONDITIONS stress conditions. It is known that high rates of seed production could negatively affect seed size (Primack 1987); in contrast, our study found a general tendency in which larger cypsela sets were correlated with greater cypsela mass (this effect due to the general conditions of maternal plants). However, examining the relationship curve between these two traits showed that after about 72 cypselae per capitulum, cypsela mass had a tendency to diminish, but looking at the scatter plot, this was with a level of uncertainty (Fig. 6).
With respect to Fig. 4F, it appeared that in
the case of high cypsela content per
capitulum, larger heterozygote populations
performed better in output cypsela mass,
indicating possible better control of cypsela
numbers, under various natural stress
conditions, which implied better cypsela
mass control and higher germination rate
(Fig. 4G). This could be seen as a cascade Fig. 6 Quadratic curve of order 2 representing relationship between cypsela mass and number of cypselae per capitulum (averaged by years and populations). We effect, in which one defective fitness trait present coefficient of relatedness and degree of significance. *** p<0.001. (here, input of a number of cypselae) dramatically affects subsequent linked traits (see (Gonzáles et al. 2007) for a good example).
Germination rate was positively influenced by both heterozygosity and gene diversity of the populations, indicating high inbreeding and drift depression on this important fitness trait. Germination rate was also positively influenced by cypsela mass, implying non- controlled maternal effects (i.e. not controlled by Landolt’s ecological factor and our genetic measurement) on this fitness trait. Interactions between non-controlled maternal effects
(included in the cypsela mass), ecological parameters and gene diversity indicated that even with high maternal effect, specific ecological parameters and drift (or inbreeding) still
45 CHAPTER 3 influenced germination capacity and that these parameters were intrinsically linked. For example heterozygosity in interaction with cypselae number influenced cypsela mass (see above).
Effects on cumulative plant production, reproductive output and total fitness
Production and reproductive output of Aster amellus populations showed corroborating results according to the descriptive ecology of the species. Plants did not support humidity well and performed better in sunnier conditions. Moreover, the stress-competitive strategy adopted by
Aster amellus was also underlined by the deleterious effect of competition in reproductive output (stress characteristic), but with high production of vegetative and reproductive characteristics in the competitive situation. This permits maintenance of the species, at least in vegetative form, in the case of high competitiveness. This agrees with maintenance of the populations in the Geneva basin from 1850 to 2005 (Lambelet-Haueter et al. 2006), and the very low percentage of individual deaths during the three years of the survey. Site managers in Aargovia region noted that, before management of the sites, Aster amellus was not easily visible (reproductive form), even in Nätteberg (the biggest population) (pers. com.). Finally, we showed that the species responded well to nutritive substances and so was also in line with the stress type species, which can perform in a wide range of ecological conditions, but are restricted to harsh areas by competition pressure. It is important here to underline the strategy of the species, because Aster amellus is not always seen as an endangered species, for example the Swiss red data list considers AA to be not threatened (Moser et al. 2002), because active management, these last years, in the north-west part of Switzerland, was favoured localization of the species. Red data lists have a lack of population dynamic knowledges
(Moser et al. 2002) and are mainly based on presence/absence of a species in a given area, and less on population size or inbreeding possibly caused, in this case, by a lower reproductive
46 INBREEDING IN NATURAL CONDITIONS output due to great competition pressure induced by 50 years of the site having been abandoned.
Heterozygosity of the populations in natural conditions diminished the cumulative reproductive production in natural conditions. In exchange, reproductive output was positively linked to heterozygosity. Higher production of plants in the long-lived Scorzonera humilis L. was linked to lower reproductive output (Colling et al. 2002) and suggest that long- lived species had to balance their production to maintain correct reproductive output in stress conditions (like competition), so more heterozygous populations might have better performed in this balancing capacity. Other ecological conditions, like more nutritive substances, favoured cumulative reproductive production but did not act negatively on reproductive output, indicating that production should not be negative when plants were in a free competition situation.
When luminosity is poor, less heterozygous populations performed better in reproductive output (Fig. 4D). This suggested possible adaptation of fragmented populations submitted to forest canopy shade. In checking populations involved in the low luminosity x high heterozygosity interaction, we found a greater content of more than 30 cm lignified plants (survey not presented) than in bright luminosity x high heterozygous populations; abandoned agricultural practices or lake of management were also observed at these sites. We suggested that small and low heterozygous populations that for a long were subjected to shade pressure and so might have been better adapted to this ecological parameter, than bigger and more heterozygous populations that have recently been subjected to shade conditions. More advanced studies show that conspecifics from woodland areas were more efficient in experimental shade conditions than genotypes belonging to clearing habitats and vice-versa
(Gravuer et al. 2005).
47 CHAPTER 3
Heterozygosity of the populations was not related to total fitness of the plants, taking into account stage forms (here vegetative, reproductive). The future of the populations appears to be mainly driven by the competitive-stress strategy of the species with negative fitness in cases of increasing competition and positive fitness bonded to more nutritive substances. However, our three years of study was apparently insufficient to detect mortality events with respect to long persistence of the species. The study by Hoehn (2006), which used five years of survey in a plant with higher turnover, found only a tendency of lower lambda with respect to population size; genetic effect was not tested. Moreover, we remarked that between 2005 and 2006 heterozygous populations showed a lower percentage of reproductive plants, but this was no longer the case in taking in account the 2006 to 2007 transition stage.
So our three years of survey were probably also insufficient to correctly assess the stable stage situation in natural conditions for a long-lived plant.
Biotic interactions with respect to parasite strikes
Higher cumulative parasitic interactions were seen at higher altitude; this was not corroborated by other studies (Galeuchet 2003; Wettstein & Schmid 1999). The different populations were situated at altitudes that were not sufficiently different and were, furthermore, at the same vegetation level (hilly), which may have prevented detection of a negative effect. We suspect that the effect of the surrounding environmental matrix was greater. Low altitude sites were to a greater extent surrounded by intensive agriculture and and urban areas with lower natural habitat heterogeneity, which act negatively on the diversity of insects species (Clark & Samways 1997; Wettstein & Schmid 1999; Di Giulio et al.
2001). Plant species richness also positively influences insect diversity (Siemann 1998). In our study, altitude was positively correlated with plant species richness (R2=0.14, P<0.05).
48 INBREEDING IN NATURAL CONDITIONS
In line with cumulative parasite strikes, we found a higher number of orange larvae at higher altitude. Orange larvae was also related to the number of developed infected cypselae and indicated that we were in the presence of a high potential cypsela feeder. Orange larvae were also related to the number of cypselae per capitulum and indicated a potential choice by the insect to lay eggs in plants with higher cypsela content. The rate of developed and infected cypselae was negatively related to cypselae number per capitulum: potential adaptation of
Aster amellus populations in response to infection might be possible here. In line with this, we found an interaction between the rate of infections and heterozygosity of populations, with a lower content of orange larvae per capitulum in cases of higher heterozygosity. Cassinello et al. (2001) found more Gazella cuvieri parasitized by nematodes, when there were more inbred individuals. So inbreeding depression could, in our case, also act in the failure of a correct response to cypsela parasite strikes.
Conclusions
We found strong inbreeding and drift in small populations of Aster amellus, which may complicate the future of these populations in response to environmental change and other stochastic phenomena.
Depression due to inbreeding and also to drift has already acted on a crucial fitness trait: the germination rate. Possible failures in the control of cypsela number by more inbred populations in natural conditions could be implicated in failure of correct germination. At a higher level, reproductive output was also affected by inbreeding depression. As we allowed for abiotic and biotic factors, it appears that these negative effects were not artefacts but were really due to inbreeding.
Ecological factors clearly also played a role and the competition of surrounding species was negatively related to fitness, implying constant pressure on the species and
49 CHAPTER 3 indicating that management of sites is a first key to maintaining Aster amellus. Gottfried &
Ellenberg (1994) showed the importance of mowing late in the season (November). Köhler et al. (2005) indicated relative resistance of the species to fire management. Stress strategic species are favoured by grazing and fire management (Moog et al. 2005).
Parasite strikes mainly occurred at higher altitude, indicating less fragmentation of the surrounding environmental matrix at higher altitude. Less inbred populations responded better to the presence of potential cypsela feeders. We think more investigation is needed to clearly assess this relationship.
Long-lived species should be surveyed for more than three years to correctly assess the stable stage situation and to take into account the death of individuals, which was very low during our survey.
Acknowledgements
We thank Markus Fischer for his advice; owners and managers for allowing us access in the sites; Denis Jordan, Patrice Prunier and Pilippe Druart for help in the population localization;
C. Schneider for botanical assistance; Ivo and Theresa Lindenberger and Christine Habashi for their help in measurement of plants; Sophie Dunand-Martin and Catherine Lambelet for providing us the ex situ conditions for germination tests; Domain of Nature and Landscape of
Geneva (DNP) for providing financial support; David Aeschimann and Rodolphe Spichiger for their support; Dominique Krayenbühl and Patsy Gasperetti for English corrections.
50
Chapter 4
Effects of distance, density and population size on gene flow in the rare Aster Amellus L. in a fragmented landscape
51 CHAPTER 4
Abstract
Gene flow is an important process to maintain panmixia within populations and metapopulation. We studied 31 populations of the rare perennial Aster amellus in three main regions along the Jura (France and Switzerland). We analysed 2600 individuals through 7 microsatellite markers. We focused our analyses on the effects of fragmentation on gene flow.
We found increasing genetic differentiation (Fst) with distance, smaller population size, lower density of surrounding populations and isolation of subpopulations. Analyses of kinship coefficients revealed high structure below 2 m and end of structure above 30 m. Interruption of the gene flow was found from 1 to 10 km within regions. Within populations, Sp statistics ranged from 0.0027 to 0.0757 and were influenced by the density of individuals at a 20 m radius.
As population size seems to be an important parameter for a good comprehension of the gene flow process, we tested two estimators of population size based on molecular markers. Ne obtained by linkage disequilibrium method was very close to census data (R2 =
0.82). As an ancestral population size estimator, theta (Θ) obtained by a coalescent-based approach was less related (R2 = 0.56) than Ne to census size and therefore indicated change between past and present population size.
In this study, we found a high effect of fragmentation on gene flow either between or within populations implying a high effect of genetic drift. Aster amellus populations should therefore be treated with care by conservation managements.
Key words: gene flow, isolation, density, population size, distance, fragmentation, metapopulation, Aster amellus, structure, linkage disequilibrium, drift, conservation
52 GENE FLOW IN FRAGMENTED LANDSCAPE
Introduction
Gene flow between populations and genetic structure of populations are factors that influence their fitness. The current theory developed by (Wright 1931) states that one migrant per generation should counteract inbreeding effect in small populations. Newman and Tallmon
(2001) showed experimentally that one migrant considerably decreases inbreeding depression in the annual Brassica campestris (rapa). However, simulation models developed by Lacy
(1987) showed that 5 to 20 migrants per generation are not sufficient to counteract these effects. Moreover, Galeuchet et al. (2003; 2005b) showed negative effects of isolation on fitness parameters in Silene flos-cuculi L. where populations were isolated by a distance of more than 300 m.
Gene flow is the movement of genes and in plant populations it is mediated by seeds, pollen (Levin & Kerster 1974) or clonal dispersal (Johansson 1993; Johansson & Nilsson
1993). Environmental features such as physical barriers or wind directions (Johnson et al.
2006) and population characteristics such as density of individuals (Mustajärvi et al. 2001;
Rognli et al. 2000) influence gene flow.
Distance between populations is certainly the most important factor influencing gene flow (Rognli et al. 2000). Fragmentation of the landscape could also limit migration between populations (Rocha & Aguilar 2001), favouring isolation by limiting the size and number of populations in a specific area.
Gene flow studies involve either direct methods such as measurement of pollinator flight distances and follow-up of pollen fluorescent markers or indirect methods such as the use of neutral markers. The former are particularly difficult to undertake. They usually underestimate gene flow because pollinators carry pollen from different plants at the same time and it is not easy to determine exactly the origin of the pollen that fecundates each flower (Levin 1981; Schaal 1980). The use of pollen fluorescent markers should take into
53 CHAPTER 4 account the carry-over effect, but it remains a photographic view of dispersal which does not take into account flow from generation to generation. Neutral gene markers are therefore a better way to assess gene flow within and between populations. In many cases, microsatellite markers are good tools as proposed by (Ouborg et al. 1999).
For analysing the data, we used two approaches: (1) the most traditional one was based on calculation of Fst values, i.e. parameters that in giving the level of differentiation between populations give an idea of the gene flow between them; (2) the other was based on the kinship coefficient which assesses the gene flow within populations (Loiselle et al. 1995;
Escudero et al. 2003). We used Fst for analysis at the population or subpopulation level and the kinship coefficient for analysis at the individual level.
A second important parameter in studies on inbreeding effect in populations is the effective size of the populations called Ne (Schwartz et al. 1998). Small populations are more susceptible to inbreeding depression (Ellstrand & Elam 1993) and therefore need particular interest in conservation issues. Certain organisms are difficult to count in the field or counts are destructive (Criscione & Blouin 2005), thus molecular methods inferring size of populations are important tools. Because of their stationary properties, plants can easily be counted in the field and therefore could provide a good basis for comparison of results obtained by molecular markers with an empirical count which is needed to assess the theory developed (Wang 2005).
As estimator of population size, we used theta (Θ) inferred by coalescence and Ne from the linkage disequilibrium (LD) method. Theta infers the former size and NeLD (used with non-linked neutral markers from individuals collected at the same time or more properly at the same generation) informs us on actual effective size of a population. Ne should not necessarily represent the total of individuals in a population, but should indicate the number of individuals which participate together at a given time in the reproduction process;
54 GENE FLOW IN FRAGMENTED LANDSCAPE moreover depending on how Ne is calculated, it could also reflect genetic history of a population.
Central-European calcareous grasslands have experienced increased fragmentation since 1950 due to change of agricultural practices (Stöcklin et al. 2000). They are now like islands in urban and intensive agricultural regions. To assess the effect of fragmentation on gene flow, we studied 31 populations of the infrequent Aster amellus along the Jura in France and in Switzerland. We wanted to answer the following questions: (1) Do fragmentation parameters like distance, density and size of populations influence gene flow between populations? (2) Do the populations show a genetic structure and at what scale? (3) Does the density of individuals influence the within-population gene flow? (4) How well are population sizes estimated via microsatellite variation related to census population size?
Methodology
Study species
The hemicryptophyte long-lived Aster amellus is a central-European species present between west Siberia and western France (Jäger 1979). It lives in calcareous Molinio-pinion or
Geranion-sanguinei (Rameau et al. 1989). Clonal propagation is possible but restricted to the very close vicinity of the germination point. Indeed we observed in the field an average of 5 cm between ramets from a genet. Aster amellus is trichometeochore (wind dispersion by hairs) and partially ethelochore (human dispersion by cultivation) (Müller-Schneider 1986).
Without wind, for 0.8 m/s fall speed and 0.35 m capitulum height, we can calculate a seed dispersion distance of 0.44 m and with a 27 m/s wind speed, seeds could go as far as 12 m
(Tackenberg 2001). Seed stock is known to be transient (Thompson et al. 1997; Cerabolini et al. 2003) but two years of seed stock experiment showed high survival of buried seeds
(Chapter 5). The species is pollinated by generalist insects. We observed in the field three
55 CHAPTER 4
Apidae species (Apis, Ceratina, Epeolus), six Syrphidae species, Lepidoptera and flies from the Tachinidae group. Aster amellus is mainly self-incompatible (Kovanda 2005;
Münzbergová 2006), but Frank and Klotz (1990) indicated the possibility of self- compatibility, probably by geitonogamie.
Study sites and counts of individuals
Fig. 1 Presentation of population constellations of the 31studied populations, in their respective region (“big Asters”). “Small Asters” are the surrounding populations of studied populations, obtained by local botanical database. Dot-dashed rectangle was the delimitation we used in Geneva Basin for analysis comparing the regions. We provide in lat./long. the northern (SP; Sparberg, near Brugg in Switzerland)) and southern (PA: Pâquis, near Seyssel in France) population coordinates. On the Switzerland map we have in dark-grey the Swiss distribution of Aster amellus (furnished by the CRSF: Centre of the Swiss Flora Network). See Table 1 for population codes and coordinates.
56 GENE FLOW IN FRAGMENTED LANDSCAPE
In September 2005, we investigated 31 populations (Fig. 1) of Aster amellus along the Jura in three main regions: Upper Savoy (US, France), the Geneva Basin (GB, France–Switzerland) and Aargovia (AR, Switzerland) and in one small area, Le Landeron (LAN, Switzerland). The most distant populations were separated by 246 km.
As distance is known as a strong parameter influencing gene flow, we tried to suppress its effect by excluding a total of four populations from within-region analyses (population code for excluded populations: CR, HER, LAN1, LAN2). In this way, we obtained 3 regions with the same area (around 100 km2) and same mean distance between populations: 6.8 km in
GB, 7 km in US and 7.6 km in AR (Fig. 1).
Table1 Population characteristics: population names and respective identity code; REG, region of origin (AR, Aargovia; GB, Geneva Basin; US, Upper Savoy); D, density of individuals with corrected surfaces (concave polygon and without vegetation inclusions); NO, number of sampling points (times 3 for number of sampled individuals); *, radius distances around sampling points or around populations. Switzerland No. of surrounding No. of individuals coordinates populations POPULATION NAME CODE REG X Y 4 m2 10 m* 20 m* 30 m* Total D m2 1 km* 5 km* 10 km* NO Aecheberg 3 AE3 AR 646279 252779 27 579 899 925 925 3.79 3 22 47 10 Allondon 1 AL1 GB 489072 119824 11 170 367 540 901 0.46 1 16 31 15 Allondon 2 AL2 GB 488978 117406 8 132 283 396 871 0.35 1 12 29 20 Bois de la Grille BG GB 496267 118871 6 21 57 57 21 0.53 1 6 14 10 Bouffard BOU GB 491008 117114 4 37 106 155 181 0.17 1 12 28 15 Champcoquet 1 CC1 GB 487560 111326 12 208 606 1022 1592 0.72 3 4 23 22 Champcoquet 2 CC2 GB 487857 111460 7 77 115 145 239 0.44 3 5 22 10 Chanières 1 CH1 GB 492555 115911 14 100 148 161 161 0.84 4 12 26 10 Chanières 2 CH2 GB 492785 115573 6 41 68 70 141 0.24 4 12 22 10 Crottes CR GB 510938 111803 7 25 29 56 71 0.22 1 2 2 10 Eggberg 1 EG1 AR 642924 251600 14 340 1073 1958 4820 1.06 4 17 32 35 Eggberg 2 EG2 AR 642580 251690 13 300 568 702 1809 0.97 4 17 32 25 Franclens FRA US 475644 100057 7 114 286 351 610 0.37 1 18 37 15 Frût FRU US 474100 100387 16 125 260 416 1015 0.41 4 13 36 15 Hermance HER GB 508619 127495 8 59 99 101 101 0.44 1 1 2 10 Hessenberg HES AR 649952 261086 22 522 1568 2673 15350 1.42 4 21 54 70 Hundruggen HU AR 651133 258663 23 331 687 763 1029 1.75 2 23 56 15 Landeron 1 LAN1 NE 571530 213088 12 266 526 758 3260 0.82 2 3 7 36 Landeron 2 LAN2 NE 571499 212302 10 358 861 1011 1041 1.16 3 5 7 10 Nätteberg NAT AR 649659 260664 30 880 2692 5149 30927 2.89 4 22 59 104 Pâquis PA US 480162 90570 11 168 480 890 8294 0.44 2 7 24 110 Rebaterre REB US 474430 101064 14 175 206 206 206 1.29 3 14 36 10 Repentance REP GB 488022 110854 16 206 430 480 480 1.17 3 4 23 10 Schihalden1 SC1 AR 654100 258579 22 501 839 872 871 2.32 7 19 54 10 Schihalden2 SC2 AR 654232 258681 23 547 1288 1672 1701 2.27 6 20 52 10 Schihalden4 SC4 AR 654491 258515 21 227 343 348 695 1.14 6 13 51 10 Sergy SE GB 487918 124683 11 131 427 809 7194 0.34 6 14 23 120 Sparberg SP AR 654462 264432 15 131 267 339 1097 0.47 4 20 43 23
To assess the effect of isolation by density of populations we asked local botanical databases for the coordinates of Aster amellus populations: the Alpine National Botanical
57 CHAPTER 4
Conservatory of France and the Centre of the Swiss Flora Network (Fig. 1). Then we counted the number of surrounding populations from studied populations at different radius distances
(Table 1).
To assess the effect of population sizes and density of individuals respectively on between- and within-population gene flows, we counted reproductive individuals (each ramet separated by less than 5 cm was considered as one genet) at each 2m/2m square of the populations and geopositioned each count on a GeoExplorer® GPS (Trimble, Sunnyvale,
USA) connected to a hurricane antenna functioning with ARCPAD 6.0.3 software
(Environmental Science Research Institute (ESRI), Redlands, USA) (Fig 2). To take into account vegetative individuals as well, we counted at specific sampling points (see DNA collection) non-reproductive individuals and estimated for each 2m/2m count the number of total individuals with a binomial function. Geopositioned points were postprocessed with
GPScorrect™ software (Trimble) and with data collected by the Geneva office of measurement services. We calculated total population sizes and obtained the number of individuals at different radius distances from sampling points (see DNA collection) with a buffer procedure in ARCGIS 9.1 software (ESRI) (Table 1).
Fig. 2 Map of individual counts geopositioned (example for the Pâquis (PA) population). The grid is a 2m/2m grid. Filled squares: sampling points marked with stakes at approximately ten meters from each other, where we collected DNA on three individuals and where we counted the reproductive and vegetative individuals (one genet was defined as a group of ramets separated by less than 5 cm from each other). The points represent counts of reproductive individuals only.
58 GENE FLOW IN FRAGMENTED LANDSCAPE
DNA collection
Following (Epperson & Li 1996), we sampled three individuals as much as possible at each intersection of a 10m/10m lattice. In September 2005, we marked with 865 stakes (minimum
10 per population) visible reproductive individuals at the nearest intersection of a 10m/10m lattice (Fig. 2). At these sampling points, we collected DNA from the marked reproductive individuals and on two additive individuals, one situated within a 1m/1m square centred on the marked points and the other situated between this first square and a second 2m/2m square.
For spatial autocorrelation analysis we obtained in the order 1, 2 and 10 m distance classes followed by consecutive 10 m distance classes; maximal distance class was 350 m. We sampled 5 mm diameter pieces of leaf and stocked them directly in 1.5 ml tubes filled with silicagel. DNA was extracted following an enzymatic method (Manen et al. 2005). Seven microsatellite markers were used (Mayor & Naciri 2007) (the AamF58 marker was deleted due to a reading problem: some individuals showed more than two picks on the electropherogram) and in total 2600 individuals were analysed.
Fst analysis
We used Fst analysis to determine effects of isolation (by density of surrounding populations), population size and distance on gene flow at the population and subpopulation level.
To test the effect of isolation by density and of the size of populations on Fst, we conducted a first analysis where Fst were averaged within regions by pairs of populations for each population; in this way, we expected to cancel the effect of distance, because average distance between populations in the regions was nearly identical between the different regions
(see study sites). We called this analysis Fst[average].
As constellations of populations within the regions were not identical between regions, we searched thoroughly for a possible background effect of distance. We used intraregional
59 CHAPTER 4
Fst and distance matrices without averaging the population pairs and constructed two new matrices. We constructed a population density matrix and a population size matrix by summing of population pairs, respectively the number of populations surrounding them at a 5- km radius and their population sizes. We called this analysis Fst[matrix].
We tested effects of isolation on Fst[average] and Fst[matrix] by population density, population size, distance and their possible interactions with the linear model.
Moreover, in order to assess the effect of continuous versus discontinuous populations on gene flow, we constructed a new Fst matrix in order to consider subpopulation structures.
We considered individual groups separated by more than 30 m as discontinuous subpopulations. Continuous subpopulations were delimited using TESS 1.1 (Chen 2007) which uses a Bayesian clustering algorithm. Parameters were 12000 for running sweeps (number of
MCMC repetitions) and 1200 for burnin sweeps (transient period, noise to remove), Ψ=0.6
(interaction parameters for weight of spatial coordinates). Using an admixture model, the maximal number of clusters was set on ten (this to avoid groups with too low a number of individuals) and ten runs were done per metapopulation. We related Fst of continuous and discontinuous subpopulations to the geographical distances separating each group. We called this analysis Fst[subpop]. We calculated all Fst values using ARLEQUIN 3.01 (Excoffier
2006) which involves an AMOVA method for codominant markers, with 10000 permutations for the significance test.
Kinship coefficient analyses
We used kinship coefficient (K) (Loiselle et al. 1995) and derived statistics to understand the pattern of gene flow at the individual level.
Firstly, in order to assess the presence of structure, we calculated K between individuals using a multipopulation approach. K were then attributed to distance classes
60 GENE FLOW IN FRAGMENTED LANDSCAPE following the sampling scheme. 10000 permutation tests of spatial coordinates were done per distance class and confidence intervals were calculated by jacknifing over loci. This was presented with a spatial autocorrelogram and represented the general pattern of dispersal within populations.
Secondly, in order to assess effects of density and population size on within- population gene flow, we calculated the Sp statistic developed by Vekemans & Hardy (2004;
Vekemans & Hardy 2004), using their proposed formula: Sp = blog/(K1-1), where blog was the slope of the regression of K on the logarithmic distance and K1 the kinship coefficient of the first distance class. Calculated Sp were then related with a linear model to population size and to density at various scales (at radius distances of 1, 10, 20 and 30 m around studied individuals).
Thirdly in order to obtain the spatial structure at the regional scale, we conducted K analyses separately in the Aargovia and Geneva Basin regions. Each region was considered as one big metapopulation. Autocorrelograms were obtained using arbitrary distance classes.
From the individual kinship coefficient matrices (between pairs of individuals in their corresponding regions), we calculated average K between pairs of proximal individuals (i.e. three individuals per sampling point) and K of each individual (averaged with all its corresponding pairs in its region). We called them respectively K[proxy] and K[overall].
Kinship coefficients, regression slopes, permutation tests and jacknife over loci were obtained with SPAGEDI 1.2 (Hardy & Vekemans 2002).
Migration and population size analyses
Numbers of migrants (M) between populations and theta (Θ) estimator of population sizes were evaluated with MIGRATE 2.3 (Beerli & Felsenstein 1999) using a maximum likelihood and coalescent theory-based approach (Excoffier 2006). We assumed here a single stepwise
61 CHAPTER 4 mutation model with a constant mutation rate between loci. We ran 10 short Monte Carlo chains with 50000 trees sampled at each run and 3 long chains with 500000 trees sampled at each run. Two analyses were done, one in the Aargovia region and the other in the restricted
Geneva Basin region. M values were then related to isolation in terms of number of surrounding populations and to population sizes.
A second estimator of population sizes (Ne) was obtained with NESTIMATOR (Peel et al. 2004} which uses a linkage disequilibrium method (LD). NeLD and theta (Θ) were then related to real population sizes (count of individuals); NeLD and population sizes were transformed with the common logarithm.
As all analyses use multilocus methods, we performed a preliminary linkage disequilibrium test with ARLEQUIN 3.01 (Excoffier 2006): all loci were independent from each other
(Chapter 2).
Results
Fst analysis
In increasing order of genetic dissimilarity, we had continuous subpopulations, discontinuous subpopulations and populations. Their mean Fst values were respectively 0.0093, 0.03 and
0.14. Figure 3A clearly shows the differences between continuous (open circles) and discontinuous (triangle) subpopulations
All pairs of Fst values among populations were significant under a permutation test.
Average Fst by pair of populations in each region was 0.051 (±1 se: 0.004) for Aargovia
(AR), 0.087 (±1 se: 0.0025) for Upper Savoy (US) and 0.119 (±1 se: 0.009) for the Geneva
Basin (GB) (with the 2 excluded populations in GB: 0.147 (+-1 se: 0.012)). They were significantly different from each other under a Tukey’s honestly-significant-difference test
62 GENE FLOW IN FRAGMENTED LANDSCAPE
(AR–GB, p<0.001; AR–US, p<0.01; US–GB, p<0.05, Fig. 3C). The number of populations in
AR, US and GB was respectively 50, 29 and 23 representing the degree of isolation and was therefore a highly putative variable explaining differences in Fst values between regions.
Fig. 3 A) Quadratic curve of distance effect on Fst. Fst[subpop] matrix contained continuous subpopulations defined with TESS 1.1 (Chen 2007), discontinuous subpopulations defined by a gap between individuals of more than 30 m and all studied populations. B) Effect on Fst[matrix] of two-way interaction between distance separating two populations and the sum of their population sizes (pop. size). Each matrix was calculated separately by region and then pooled in one analysis. See Table 2 for the level of significance. C) Effect on Fst[average] of studied regions (see text for significance). Within regions, Fst matrices were averaged by population. AR, Aargovia; US, Upper Savoy; GB, Geneva basin.
Average Fst per pair of populations in each region (Fst[average] ) was negatively correlated with the number of surrounding populations in a 5-km radius from studied populations (R2=0.62, p<0.001, Table 2, Fig. 4A). It was also the case with population sizes
(R2=0.4, p<0.001, Table 2, Fig. 4B). The whole linear model took into account the number of surrounding populations at a 5-km radius, population sizes and distances between populations explained 74% of the variation of Fst values (R2=0.74, p<0.001, Table 2). As expected, the parameter of distance didn’t influence the model (p>0.1, Table 2, Fig. 4C).
63 CHAPTER 4
Fst[matrix] analyses (variable not averaged over pairs in the matrix) showed a background effect of distances (R2=0.05, p<0.05, Table 2, Fig. 4F) due to different constellations of populations between regions. In this case, population sizes and distances
Table 2 Effects on Fst of isolation by density, population size, distance and their possible interactions. Isolation by density corresponded to the number of surrounding populations at a 5-km radius. Fst[average] was the average Fst by pair of populations in the matrix and Fst[matrix] using the entire matrix without averaging (see text). We present a complete linear model with all parameters tested together, and a single linear model of each parameter tested separately. We provide the degree of freedom (df), the t value (t), the R2 coefficient of relatedness, and the p levels of significance: * p<0.05; ** p<0.01;*** p<0.001. dr, dropped; na, not available. Fst[average] Fst[matrix] Whole model Separated effects Whole model Separated effects Source of variation df t R2 df t R2 df t R2 df t R2 Density -5.469 *** 25 -6.328 *** 0.62 -7.561 *** 113 -10.24 *** 0.48 Population size -2.465 * 25 -4.107 *** 0.4 1.993 * 113 -6.815 *** 0.29 Distance -1.056 25 -0.703 0.02 3.095 ** 113 2.366 * 0.05 Population size x distance dr na -2.513 * na 23 *** 0.74 110 *** 0.61
Fig. 4 A-F) Separated effects of density on Fst in terms of number of surrounding populations at a 5-km radius, population size and distance. Fst[average] : matrices of Fst calculated within regions and then averaged by population. Fst[matrix]: matrices of Fst calculated within regions and not averaged by pair of populations; population size and density matrices were calculated by summation of population pairs. R2 coefficient of relatedness and p level of significance are presented in Table 2. ns p>0.1; * p<0.05, ** p<0.01, *** p<0.001.
64 GENE FLOW IN FRAGMENTED LANDSCAPE
interacted (p<0.05, Table2, Fig 3B) and isolation by density remained a strong variable working alone and negatively on Fst values (p<0.001, R2=0.48, Table 2, Fig 4D).
Kinship coefficient analyses
Fig. 5 A) Spatial autocorrelogram of within-population kinship coefficient (overall populations) on distance classes. Black points: p <0.05 under 10000 permutation tests; white points: p >0.05. Confidence intervals were obtained by jacknifing over loci (distance classes containing more than 10000 pairs, had too small confidence intervals for representation). A dashed line represents the slope of regression. B) The spatial autocorrelogram with kinship coefficients was calculated separately within regions: black points and a dashed slope of regression for the Geneva Basin (GB); white points and a dotted regression slope for Aargovia (AR). All points were significant at p <0.05 under 10000 permutation tests. For confidence intervals, see A). C-D) Comparison of Aargovia (AR) and Geneva Basin (GB) kinship coefficients obtained by considering each region as one metapopulation. K[proxy]: kinship coefficient averaged by pair of proximal individuals (three pairs per sampling point). K[overall]: kinship coefficient averaged for each individual with all its corresponding pairs.
65 CHAPTER 4
The general pattern of gene flow at the within-population level (Fig. 5A) with respect to distance classes showed a significant positive structure below 30 m and a significant negative structure above 100 m (i.e. individuals significantly negatively related). Higher structure was found below 2 m.
Comparisons of K[proxy] between Aargovia and the Geneva Basin showed more closely related individuals in the Geneva Basin (Fig. 5C). The situation of each individual with respect to all other individuals in their corresponding regions (K[overall]) showed fewer unrelated individuals in Aargovia than in the Geneva Basin (Fig. 5D). Spatial autocorrelograms showed a general end of structure at 1 to 5 km in Aargovia and at 5 to10 km in the Geneva Basin (Fig. 5B); Sp was respectively 0.0048 and 0.0114, implying a higher regression slope in the Geneva Basin.
Fig. 6 Effect of individuals density at different spatial scales on Sp statistic. Lower Sp indicated better gene mixing. Density was calculated with different radii around the studied individuals and averaged for each population. Represented functions were smoothed with a cubic form. Table 3 gives levels of significance of the total linear model and separate effects.
66 GENE FLOW IN FRAGMENTED LANDSCAPE
Table 3 Effects on Sp statistic of the density of individuals at various spatial scales (averaged by population): 1-m radius, 10- m radius , 20-m radius , 30-m radius, total population size (POPS). We present the whole linear model with all parameters tested together and the single linear model of each parameter tested separately. We provide the degree of freedom (df), the t
value (t), the R2 coefficient of relatedness and the p levels of significance: + p<0.1; * p<0.05; ** p<0.01;*** p<0.001. Sp statistic Whole model Separated effects Individual density df t R2 df t R2 1-m radius 0.288 29 -2.016 + 0.13 10-m radius 2.107 * 29 -3.55 ** 0.3 20-m radius -3.444 ** 29 -4.819 *** 0.45 30-m radius 2.668 * 29 -4.845 *** 0.45 Population size -2.796 ** 29 -5.311 *** 0.49 25 *** 0.67
Calculated Sp for each studied population ranged from 0.0027 to 0.0757. Sp was related to population sizes (p<0.01, Table 3) and was influenced by density of individuals at radii of 10, 20 and 30 m but not at a 1-m radius (Table 3, Fig. 6).
Migration and population sizes
Mean number of immigrants and emigrants were respectively 1.03 to 2.24 and 0.91 to 2.41 for Aargovia, and 0.63 to 2.06 and 0.8 to 1.68 for the Geneva Basin (Table 4). Immigration and emigration were clearly related with population size (R2= 0.66, p<0.001; R2=0.5, p<0.001; Fig. 7A-B). Immigration and emigration were also related to the number of surrounding populations within a 5-km radius (R2=0.25, p<0.05, Fig. 7C; R2=0.2, p<0.05).
As estimators of population size, both theta (Θ) and Ne from the linkage disequilibrium method explain in average 50% of the population census size (R2=0.51, p<0.001; R2=0.56, p<0.001 (analysis with 21 populations)). If we deleted populations with no upper bound in the linkage method, the relation became higher with R2=0.82 and p<0.001
(analysis with 18 populations) or R2=0.78 and p<0.001 (analysis with 27 populations) (Fig
7D).
67 CHAPTER 4
Fig. 7 A-C) Population sizes and number of surrounding populations related to the mean of immigrants and emigrants per population calculated separately in Aargovia and the Geneva Basin regions. D) Census population sizes related to the linkage disequilibrium estimator of population sizes (NeLD). We present the R2 coefficient of relatedness and levels of significance: * P<0.05; *** P<0.001.
68 GENE FLOW IN FRAGMENTED LANDSCAPE
Table 4 Census population sizes and their corresponding size estimators. POP, population abbreviations; PS, census population sizes; Theta, likelihood-coalescent estimators; IM, mean number of immigrants; EM, mean number of emigrants; Ne, effective population size estimators with the linkage disequilibrium method and corresponding inferior (Neinf) and superior (Nesup) bounds. NA, not available. POP PS Θ IM EM Ne Neinf Nesup BG 21 0.41 0.92 1.29 6 5 9 CR 71 NA NA NA 28 18 48 HER 101 NA NA NA 66 33 442 CH2 141 0.90 1.03 0.89 42 28 76 CH1 161 1.60 1.08 0.89 30 22 49 BOU 181 0.55 0.63 1.07 56 33 135 REB 206 NA NA NA 16 12 24 CC2 239 0.75 0.99 0.97 32 23 60 TR 368NANANA9458210 REP 480 0.96 0.67 0.80 173 55 NA VU 521NANANA423160 FRA 610 NA NA NA 49 36 76 SC4 695 1.37 1.07 1.48 40 29 61 AL2 871 0.54 0.66 0.81 42 32 58 SC1 871 1.44 1.36 1.02 615 98 NA AL1 901 0.89 1.25 1.31 57 40 92 AE3 9251.101.030.917638615 FRU 1015 NA NA NA 158 90 511 HU 1029 1.23 1.20 1.32 97 63 192 LAN2 1041 NA NA NA 255 73 NA SP 1097 1.64 1.42 1.21 4481 407 NA CC1 15920.891.180.93201106971 SC2 17011.141.221.065234100 EG2 18091.211.431.418064105 TH 2679 1.47 1.67 1.50 452 319 738 LAN1 3260 NA NA NA 158 114 247 EG1 48201.141.431.97190141279 SE 7194 1.73 2.06 1.68 1857 1063 6236 PA 8294 NA NA NA 454 354 613 HES 15350 1.81 2.22 1.83 820 529 1700 NAT 30927 1.97 2.24 2.41 1235 810 2450
Discussion
In this paper, we used a range of powerful methods to determine the effects of several fragmentation parameters on gene flow at different levels, going from analyses at the within- population level to analyses between populations and between regions. In all cases, we found that the gene flow was negatively affected by the fragmentation parameters: isolation by decrease of density, isolation by decrease of population size and isolation by distance. Census population size was in strong correlation especially with the effective size obtained with the
69 CHAPTER 4 linkage disequilibrium method (NeLD), indicating a high genetic drift situation. Theta, as an ancestral measure of population size, was also correlated with census size, but less than NeLD and so indicated possible change of population size due to fragmentation.
Gene flow and the effects of distance
The quadratic curve (Fig. 3A) showed a non-linear form of gene dispersal with low genetic differentiation below 100 m, an increase in genetic differentiation starting at around 1 km and stabilisation of genetic differences after 10 km at a 1.49 Fst value. A similar pattern has been observed for other taxonomic groups (e.g. fishes in (Hänfling & Weetman 2006)). Start of significant genetic differentiation after 1 km in Aargovia and after 5 km in the Geneva Basin as shown by the spatial autocorrelation analysis of regions was also in line with it.
Observations of honeybees permitted to assess a flight distance of 1.5 km in average and a maximal distance of 10 km from the source (Steffan-Dewenter & Kuhn 2003). We conclude for a general end-of-dispersal pattern where pollen may not reach a population after 5 km.
The within-population analysis of spatial autocorrelation (Fig. 5A) showed two important patterns of gene dispersal strongly linked to biological aspects of the species. First, a high degree of relatedness was shown below 2 m, bound to low seed rain dispersal distance
(see Study species), and pollination events more intense at low distances: 4 m for bumblebees and butterflies (Schmitt 1980), 5 m in an ex situ experiment with pollinators of Carduus nutans L. (Smyth & Hamrick 1987). Second, end of structure was observed above 30 m, indicating more related individuals below this distance and therefore a possible second step of pollination events bound to partial flight distances of pollinators as seen also by Schmitt
(1980) with fluorescent dye. As our study implicated only nuclear markers collected on adult individuals, this is physically not possible to distinguish between seeds and pollen movement.
However in congruence with the Fst analyses (Fig. 3A) it appear that discontinuous
70 GENE FLOW IN FRAGMENTED LANDSCAPE subpopulations are more differentiated than continuous ones separated by the same distance, suggesting more a rupture of pollen flow than an rupture of seed dispersion. Moreover, in comparison with simulations proposed by Heuertz et al. (2003) a high diminution of kinship coefficient at lower distance classes, followed by a shallower shape of the curve, producing a convex (concave upwards curve) function, indicates in our case, a major contribution of seeds between 0-10 m, followed at greater distances by a higher pollen contribution. In accordance with the simulation where σp ≈ 10σs, we could expect σs = 30 m/10 m = 3 m, which correspond to the shape of the within population autocorrelogram (σs = seed dispersal distance and σp = pollen dispersal distance). The change of points not significant to points significantly negative at distances of 30-100 m (Fig. 5A) indicate the disruptive nature of the flow of gene linked to pollen movement.
Analysis of the spatial autocorrelograms at different spatial scales allows us to answer many questions about gene flow and to distinguish between current gene dispersion distance and long dispersion distance. There is no single correct spatial scale for studies, instead, a multiscale approach is essential to correctly understand patterns of dispersion (Levin 1992;
Fenster et al. 2003).
Gene flow and effects of density
Fst analyses clearly showed a difference in populations, both between and within the regions, with a higher genetic differentiation between populations in areas with sparser populations.
The pattern of spatial autocorrelograms comparing regions confirms this result. In comparison to Aargovia, the Geneva Basin showed a higher degree of relatedness at small spatial distances, followed by a higher negative degree of relatedness at higher distances, indicating more isolated individuals in the area with sparser populations. In addition, the slope used to calculate Sp was two times higher in the Geneva Basin, resulting in a lower Sp in the denser
71 CHAPTER 4 region. Sp is typically lower in dense situations (Vekemans & Hardy 2004). Analyses of intraregional kinship coefficients matrices were consistent and the individuals of populations in Aargovia were significantly less related at the small spatial scale (2 m) and less unrelated at the large spatial scale (10 km) than the individuals from the Geneva Basin.
Analysis of Sp statistics showed that the within-population gene flow was positively influenced by an increasing density of individuals and this more particularly in 20- to 30-m radius circles (Fig. 6), thus indicating the importance of a high density of individuals on this spatial scale to maintain as much as possible pollinators’ flights, typically by pollen carry- over. (Fukue et al. 2007) found in the same sense in a tree species more pollen donors in higher density plots.
According to our results, we believe that there is a general positive effect of density of individuals on gene flow at the population and regional levels, with lower gene dispersal distances but with higher frequencies in denser situations, implying a diminution of the relatedness coefficient and therefore a more efficient mixing of genes.
Gene flow and gaps between individuals
Comparison between discontinuous and continuous subpopulations showed that individual fragmentation had an impact on differentiation between demes of individuals. After 50 m, a gap between individuals allows a genetic differentiation process. Limitation of current gene dispersion, high below 2 m and low between 10 and 30 m, certainly explains this rupture of connectedness in discontinuous subpopulations.
Population sizes
Census population sizes had a high impact in the observed pattern of gene dispersal.
Migration process, differentiation of populations and degree of relatedness were in
72 GENE FLOW IN FRAGMENTED LANDSCAPE congruence with it. Migration events increase with size of populations. Similar results were found in (Hansen et al. 2007) with estimators of population sizes.
Moreover population sizes interact with other parameters like distance between populations and could in certain cases be a compensatory factor decreasing a negative isolation effect. That pollination event depended on distance and population size was shown in Silene alba (Miller) Krause by a direct method (Richards et al. 1999).
Molecular estimators of population size were good estimators, especially Ne obtained with the linkage disequilibrium (LD) method. In deleting populations without upper confidence limits around NeLD, we probably discarded populations with specific historical properties and complicated allele arrangements which need more individuals to be correctly tested (England et al. 2006). Lower effective size found for all populations compared to realistic count (Table 4) is certainly explained by the fact that we were not in panmixia situations; presence of structure (Fig 5A) may explain this bias. Another explanation could be the presence of past bottlenecks, masked at present by recent management. Bottlenecks and subpopulation structure are violations of correct NeLD estimation (Hill 1981; Wang 2005) .
High correlation between NeLD and census population size indicated high drift situations because NeLD with neutral markers is highly influenced by genetic drift (Hill
1981). Furthermore theta as an ancestral estimator of population size showed a lower correlation with census population size than with the NeLD estimator and therefore could indicate a change of population size between past and present. High actual drift situations and changes in population sizes should be seen as a clear effect of the fragmentation process and should be considered as a high threat to natural or semi-natural plant populations. In parallel to this study, we found negative drift effects in Aster amellus with respect to the germination rate (Chapter 3).
73 CHAPTER 4
Conclusions
Fragmentation processes like gaps between individuals, reduced population sizes and reduced density act negatively on gene flow either among or within populations. Although the fragmentation process does not increase the spatial distance between individuals or populations, it does however lower the chance of direct contacts by gene flow between remote individuals because it becomes more difficult for pollinators to fly across; the same applies to gene carry-over via seed or pollen dispersal.
According to our results, management at the regional spatial scale should include at least one large population in the centre of a 100 km2 square to maintain a reservoir of genes and to maintain possible migration events at longer distances. Isolated populations should be reconnected to the whole population system by increasing their size and/or by creating new neighbour populations. Seed transport from local populations could also be a management solution. At the within-population level, maintaining current gene flow between demes should be done by limiting separation of demes below 30 m. Enhancing density of individuals from remote demes should also facilitate gene flow. Sites management should allow for germination of seed stock (Chapter 5) and therefore providing alleles possibly eliminated by drift.
Acknowledgements
We thank Markus Fisher, Florence Noel and Nathalie Machon for comments on earlier version ;Yamama Naciri for her help in microsatellite development; Jean-François Manen for methods of DNA extraction; Catherine Lambelet and David Aeschimann for financial support; Rodolphe Spichiger for his support.
74
Chapter 5
Effects of ecology and genetics among fragmented populations of the long-lived Aster amellus L. in experimental conditions with biotic and abiotic treatment
75 CHAPTER 5
Abstract
Small and fragmented populations are submitted to inbreeding and face the risk of depression in fitness traits. We studied 31 populations of the endangered long-lived Aster amellus from fragmented grasslands along the Jura (France-Switzerland). We compared F1 generations from F0 seeds belonging to natural populations, through three experimental treatments which expressed a gradually lower fitness in the sense: full earth > benign > competition. Our results suggest that what might be attributed to a purge is rather associated with a loss of maternal effects. Ecological factors of source populations were highly implicated in cumulative vegetative production and reproductive output of F1 experimental plants. This reinforced a maternal effect hypothesis. In parallel, we conducted in situ sowing with a competition removal treatment. We found significant inbreeding effect on germination rate irrespective of treatment, but number of survivor fell down and inbreeding effect was no more found after two years of surveys. Germination rate of total fruits measured over three years of sampling, showed high interactions between ecological parameters and heterozygosity of the populations. From a seed stock experiment, we found survival possibility of seeds in natural conditions. Our study showed a complex interaction in determination of germination rate of total cypsela set; inbreeding was acting in this process, facing populations to an additional risk of extinction, cumulated with stochastic year conditions and ecology of the populations.
Phenotypic observed traits were highly influenced by maternal effect with continuity of ecological observed effect in natural population on fitness of F1 plants; a loss of inbreeding effect in stress conditions indicated a possible breaking of link between a diminishing maternal effect and overdominance pattern.
Key words: Aster amellus, fragmentation, inbreeding depression, ecological parameters, abiotic stress, biotic stress, competition, Bromus erectus, maternal effect, purge, germination rate, cypsela, seed, seed stock
76 INBREEDING IN EXPERIMENTAL CONDITIONS
Introduction
Change in land use during the last century induced fragmentation of landscape and reduction of semi-natural grasslands in central-Europe (Zoller & Wagner 1986b; Zoller & Wagner
1986a; Stöcklin et al. 2000; Köhler et al. 2005).
Small and isolated populations are more susceptible to environmental and demographic stochasticity threats. After reduction and isolation of a population, we might also find inbreeding depression (Fischer & Matthies 1998b; Fischer & Matthies 1998a;
Galeuchet et al. 2005b).
Inbreeding depression is characterised by a non-random association of individuals which increases the degree of relatedness between them (Keller & Waller 2002). Three cases of inbreeding depression are distinguishable: lethal recessive, partial dominance and overdominance. Lethal recessive is characterised by a strong selection of inbred genotypes and gives no chance of survival. Overdominance is the case where homozygotes are less fit than heterozygotes; this permits survival of inbred individuals but with a genetic load implying a constant pressure on individual fitness. Partial dominance is the case where slightly or mildly deleterious recessive alleles are partially masked by dominant alleles.
(Charlesworth & Charlesworth 1987; Leberg & Firmin 2008).
Experimental growing is a keystone to understand inbreeding phenomena in small and fragmented populations. Various abiotic and biotic treatments permit to compare how plants from small and big populations react against differential environmental conditions (genetic plasticity) and how ecological parameters from source populations (maternal effect) influence results with respect to various conditions. Purge may also react differentially against various treatments.
77 CHAPTER 5
Purge is the process where deleterious alleles are eliminated by selection (Leberg &
Firmin 2008). Purge is mainly observed with highly deleterious alleles and overdominance, if present, should be less or not purged (Hedrick 1994; Boakes & Wang 2005).
Plasticity is the response of a species to various conditions. Differences between geographical sources against various treatment conditions on phenotypic traits reflect geographical plasticity (Berg et al. 2005). Plasticity could have genetic sources (Schlichting
1986) and inbreeding could lead to differences in plasticity responses between small populations and big populations (Fischer et al. 2000).
Maternal effect is one of the most important influences on offspring phenotype
(Bernardo 1996) . It is viewed as the potential of a source plant to predict the environment of its offspring (Marshall & Uller 2007). Maternal effects could be of three origins: cytoplasmic
(maternal organelles), parental endospermic (mainly affected by the maternal part), and maternal phenotypic (maternal environment and/or genetic) (Roach & Wulff 1987). If seed mass influences germination rate, we could account for possible maternal effect (Oostermeijer et al. 1994).
We studied 31 populations of the infrequent Aster amellus along the Jura (France-
Switzerland), where habitats show a reduction of size since 1950 (Stöcklin et al. 2000).
Populations remain fragmented in sizes and degree of isolation, with various effects on gene flow and population fitness traits in natural conditions (Chapter 3 and 4). We submitted the F1 generation reproduced by the F0 generation, originating from natural populations, to various experimental treatments. Plants were studied in ex situ conditions with different qualities of earth to test abiotic influences and with competition of grass to test biotic pressure. Moreover we set up an in situ experiment to test effect of competition versus competition removal in natural conditions. We also did a small seed stock experiment to assess viability of seeds in natural conditions.
78 INBREEDING IN EXPERIMENTAL CONDITIONS
We asked ourselves the following questions: 1) Was there inbreeding depression in experimental conditions? 2) Was inbreeding depression associated to experimental abiotic or biotic pressure? 3) Was there an effect of population components other than inbreeding such as ecological parameters or maternal components on fitness components? 4) Was there possibility of seed stocks production in natural conditions?
Methodology:
Population parameters
Table 1 Population parameters of the source F0 seeds. POP, population code; X/Y, east and north Switzerland projected coordinates; LUM, luminosity; SUB, nutritive substances; VEGH, vegetation height; HET, observed heterozygosity; CYPNO, number of cypselas per capitulum; CYPM, cypsela mass; CYPINF, cypsela infection rate per capitulum. We provided also years of sampling. Population POP X Y 2003 2004 2005 LUM SUB VEGH HET CYPNO CYPM CYPINF Aecheberg3 AE3 646279 252779 NO NO YES 3.64 2.23 30.85 0.60 77.00 3.20 0.02 Allondon1 AL1 489072 119824 NO YES YES 3.61 2.22 42.24 0.58 72.14 3.63 0.05 Allondon2 AL2 488978 117406 NO YES YES 3.63 2.21 35.63 0.54 70.81 3.62 0.25 Grille BG 496267 118871 NO YES YES 3.48 2.41 40.95 0.49 75.06 4.96 0.01 Bouffard BOU 491008 117114 YES YES YES 3.43 2.39 26.94 0.56 61.34 6.17 0.00 Champcoquet1 CC1 487560 111326 NO YES YES 3.42 2.31 34.26 0.69 61.34 4.46 0.03 Champcoquet2 CC2 487857 111460 YES YES YES 3.44 2.24 34.33 0.59 68.89 5.79 0.00 Chanières1 CH1 492555 115911 YES YES YES 3.50 2.26 29.90 0.65 48.33 3.55 0.02 Chanières2 CH2 492785 115573 NO NO YES 3.49 2.36 30.77 0.50 72.00 5.11 0.00 Crottes CR 510938 111803 YES NO YES 3.28 2.40 49.05 0.43 65.56 5.83 0.00 Eggberg1 EG1 642924 251600 YES NO YES 3.58 2.18 30.01 0.62 58.29 4.55 0.30 Eggberg2 EG2 642580 251690 YES YES YES 3.51 2.47 44.85 0.66 56.37 5.40 0.47 Franclens FRA 475644 100057 YES YES YES 3.43 2.42 36.10 0.60 66.52 4.57 0.05 Frût FRU 474100 100387 YES YES YES 3.61 2.40 21.66 0.69 66.31 6.20 0.04 Hermance HER 508619 127495 YES YES YES 3.56 2.26 34.38 0.64 74.95 5.32 0.00 Hessenberg HES 649952 261086 NO NO YES 3.49 2.13 22.91 0.69 59.56 4.83 0.15 Hundruggen HU 651133 258663 NO YES YES 3.50 2.15 23.10 0.67 58.33 5.77 0.01 Landeron1 LAN1 571530 213088 YES YES YES 3.61 2.21 38.79 0.56 69.18 3.43 0.49 Landeron2 LAN2 571499 212302 YES YES YES 3.44 2.03 34.32 0.57 73.95 4.42 0.41 Nätteberg NAT 649659 260664 YES YES YES 3.55 2.14 24.70 0.69 59.03 5.46 0.17 Pâquis PA 480162 90570 YES YES YES 3.49 2.25 26.28 0.62 65.96 5.10 0.09 Rebaterre REB 474430 101064 YES YES YES 3.40 2.52 29.48 0.57 71.24 6.12 0.03 Repentance REP 488022 110854 YES NO YES 3.46 2.32 28.20 0.57 60.60 5.54 0.00 Schihalden1 SC1 654100 258579 NO NO YES 3.40 2.14 32.55 0.63 59.30 5.03 0.02 Schihalden2 SC2 654232 258681 NO YES YES 3.61 2.29 37.24 0.66 65.75 7.03 0.02 Schihalden4 SC4 654491 258515 NO NO YES 3.70 2.26 23.38 0.66 62.18 7.49 0.00 Sergy SE 487918 124683 YES YES YES 3.48 2.22 26.63 0.68 65.46 3.52 0.28 Sparberg SP 654462 264432 NO YES YES 3.42 2.25 35.08 0.72 59.93 4.67 0.18 Thoiry TH 486211 121926 YES YES YES 3.65 2.20 24.16 0.60 64.69 4.50 0.07 Trembley TR 480310 101431 YES YES YES 3.44 2.29 27.53 0.51 61.35 4.76 0.28 Vuache VU 482799 102088 YES YES YES 3.51 2.24 35.13 0.49 68.96 3.21 0.25
We selected randomly 31 populations along the Jura in three main regions (6-11 populations):
Upper Savoy (France), Geneva basin (France Switzerland), Aargovia (Switzerland) and 2 populations in one small area: Le Landeron (Switzerland). 246 km separated the two distal
79 CHAPTER 5 populations. We analysed 2600 individuals through seven microsatellite markers and calculated mean heterozygosity of the populations (Chapters 2 and 4). We measured ecological parameters in the 31 populations with a total of 865 vegetation records and associated it to Landolt (1977) factors and ; by his way we obtained nutritive substances and luminosity of population sources (see Chapter 3) (see Chapter 3). We also measured vegetation height by measuring height of grass species surrounding the studied individuals.
(Table 1)
Studied species
The long-lived Aster amellus L. is a central-European species belonging to dry grasslands and to the forest edge of hilly vegetation (Rameau et al. 1989). It is normally self incompatible
(Kovanda 2005; Münzbergová 2006), but (Frank & Klotz 1990) indicated the possibility of self-pollination. AA has a stress strategic comportment (Frank & Klotz 1990) (Chapter 3).
Seed stock was reported as transient (Thompson et al. 1997; Cerabolini et al. 2003). AA has different growing potentials depending on ecological components, with low growth in the presence of low nutritive substances and a vegetative comportment under high competition
(Chapter 3). Adult plants produce one or more basis rosettes and/or one to several stems; all ramets regrouped in small clonal patches. One stem could produce one to several capitula
(403 was maximum observed in experimental conditions). With Asteraceae we are obliged to count fruits instead of seeds and one fruit, named a cypsela (heterocarpous fruit = calyx + ovarian walls), contains potentially one seed. Cypsela number can vary widely around a mean of 64 units per capitulum (in natural conditions, maximum observed in experimental conditions was 403), with higher production in terminal capitulum (pers. obs.). Cypsela feeders were also reported such as Coleophora obscenella (Lepidoptera) (Baldizzone &
80 INBREEDING IN EXPERIMENTAL CONDITIONS
Tabell 2002) and potentially a Diptera of the cecidomyiid group (Mark Shaw communication).
2003 sampling
Between end October and early November 2003 we collected one capitulum per 30 randomly selected individuals in each of 19 studied populations. Cypselae were dried for three weeks, then counted and weighed at the nearest tenth of milligram. Then we calculated individual cypsela mass. In December 2003, seeds were sown in pots, recovered by 2 mm of earth. Pots were arranged in an external layer at the botanical garden of Geneva. We counted germinations from March to July 2004.
2004 sampling and in situ experiment and 2005-2006 ex situ experiment
Between the 20th October and the 5th November 2004 we collected all capitula from seventy- seven 1 m2 plots belonging to 23 populations. Cypselae were dried for three weeks at room temperature, then well mixed and divided into 9 lots of 100 for each plot. Each lot was weighed at the nearest tenth of a milligram.
In December 2004, five lots were sown in a 100 m2 area in the biggest studied population (Nätteberg, Aargau). The experimental scheme was semi-randomized with one lot per block (five blocks). Each lot was sown in a 20x20 cm square divided into two parts, one where vegetation was kept and the other where it was discarded. Fifty cypselae were sown one by one with a clamp in the part with a vegetation cover and 50 were thrown and covered with 2 mm of site earth in the part without vegetation. Because of bad weather conditions
(snow), cypselae of the last block were all sown with the second method. We counted germination and subsequent plantlets in April 2005, June 2005 and September 2005, 2006 and
2007 with the help of 4x4 cm mesh grid. In the last count, all plantlets were discarded.
81 CHAPTER 5
In March 2005 the four other lots were sown in Petri dishes filled with a 7 g agar in 1 l
H2Od mix and placed for 3 weeks in a germination chamber at a 14/10H and 25/20° day/night regime. After 3 weeks, germinations were counted. We transplanted five plantlets of each source plot into 4x4x4 cm multipot trays in a humus-sand soil mix and arranged them in an external layer. After this three-month acclimatization period, plants were transplanted into the final experimental conditions: a layer filled to 50 cm with a mix of 1/3 gravel and 2/3 of rich soil earth; each plant was separated from the other by 20 cm. The experiment was arranged in a semi-randomized structure of five blocks so as to control the effects of the presence of a tap on one side of the experimental layer. First measurements were done after 3 months under experimental conditions (October 2005) (i.e. after 6 months of seedling life). We counted number of rosettes, number of leaves of the principal rosettes and measured width and length of the biggest leaf. After 15 months of experimental conditions (October 2006) (i.e. after 18 months of plant life), we classed individuals in vegetative or reproductive forms. Individuals were assigned as producer of rosettes if supernumerary rosettes were present. For the reproductive individuals, we counted number of total capitula and harvested the terminal capitulum of the principal plant axes. After 3 weeks of drying at room temperature, cypselae were counted and weighed at the nearest tenth of a milligram.
2005 sampling and 2006-2007 ex situ experiment
Between the 20th October and the 1st November 2005 we collected 865 capitula from previously marked plants belonging to 31 populations (see Chapter 3). Then we replicated the same protocol as the 2005-2006 ex situ experiment up to the acclimatization phase, except that we transplanted two seedlings per family for 450 maternal plants (minimum of 10 maternal plants per population). After the three months of acclimatization, we transplanted plants into 11.7 cm diameter, 20 cm deep pots in a 1/3 sand and 2/3 grass earth mix. In each
82 INBREEDING IN EXPERIMENTAL CONDITIONS family, one seedling was subjected to a competition treatment. For this treatment, we surrounded the Aster amellus seedlings with 16 three-month-old transplanted seedlings of
Bromus erectus grass (origin UFA: Switzerland producer of seeds). Pots were randomised and arranged in an outside layer at the Geneva Botanical Garden. Two measurement periods were done in October 2006 and 2007, following the 2005-2006 experiment (see above).
Cumulative vegetative production and reproductive output:
Cumulative vegetative production was calculated using data of the first measurement (i.e. after 6 months of seedling life), as the product between leaf area (calculated as an ellipse), number of leaves and number of rosettes.
Reproductive output was calculated using data of the second measurement (i.e. 18- month old plants) as the product between numbers of total capitula produced per plant, and total cypsela mass per capitulum.
Seed stock
With the rest of the cypselae collected in 2004, we did twenty-one lots of 100 cypselae. We put them in nylon bags and buried them at 5 cm in 11 of the study sites, with two bags per sites (one site only with one bag). Start of experiment was in December 2004 and bags were collected at the end of years 2005 and 2006. Cypselae were then tested for germination rate in standardized laboratory conditions (see above).
Data analysis
We first analysed the in situ experiment with ANOVA type I sum of square. We analysed effects of nutritive substances, vegetation height, mean cypsela mass of maternal plants and heterozygosity of source populations and date of counts, block structure and treatments
83 CHAPTER 5
(vegetation removal) on counts of germinations and subsequent plantlets. Main effects were analysed against population identity. Fixed factors (date, block, treatment) and their interactions with population parameters were analysed against their respective interactions with population identities. Population identities and their interactions with fixed factors were tested against residual error.
Secondly we analysed effects of nutritive substances, vegetation height, cypsela mass of maternal plants and heterozygosity of the populations and years of sampling and their full possible interactions on germination rate of total cypsela set (with infected and undeveloped cypselae) over 2003, 2004 and 2005 ex situ sowing. Here we used a type IV linear mixed model based on restricted maximum likelihood (REML) (Bates 2007). We used population identity and its interaction with years of sampling as random factors. The tests of significance used the Monte-Carlo procedure with 10000 Markov chains.
Thirdly we analysed luminosity, vegetation height, nutritive substances and heterozygosity of the populations and treatments on square root reproductive output, square root vegetation production, square root cypsela number, log10 cypsela mass, producer of rosettes, stage (reproductive, vegetative after 18 months of plant life) and survival. We pooled data of the 2005-2006 ex situ experiment with those of the 2006-2007 ex situ experiment and considered the 2005-2006 experiment as a full earth treatment, the 2006-2007 pots without competition as a benign condition and 2006-2007 pots with Bromus erectus competition as a competition treatment The block structure of the 2005-2006 experiment showed a positive effect in direction of the perturbation source, but no interaction with other tested parameters was detected. Therefore blocks were excluded from the analysis. We used ANOVA type I sum of square. We treated population identity as error term for main effect, and population identity x treatments interaction as error term for population identity, treatments and interactions between treatments and population parameters.
84 INBREEDING IN EXPERIMENTAL CONDITIONS
Fourthly we analysed cypsela output mass and the number of cypselae per capitulum of plants studied in the 2006-2007 experiment only. We tested effects of population’s heterozygosity and of treatment and added in the model log10 maternal cypsela mass, square root number of maternal cypselae and maternal cypsela infection rate. We used a mixed linear model (as above) and treated population identity and population identity x treatment interaction as error terms. Non significant terms were drooped, provided that this didn't positively perturb the Akaike information criterion (AIC).
Results
In situ sowing experiment
A) All dates B) April 2005 June 2005 Sept. 2005 Source of variation df ms F p Variation df ms F p ms F p ms F p SUB 1 2.54 0.25 SUB 1 0.36 0.10 1.59 0.43 0.65 0.19 VEGH 1 50.04 5.00 * VEGH 1 10.66 3.09 + 19.31 5.25 * 12.98 3.78 + CYPM 1 55.06 5.50 * CYPM 1 22.87 6.63 * 30.92 8.40 ** 16.35 4.77 * HET 1 71.51 7.14 * HET 1 23.29 6.75 * 22.96 6.24 * 24.56 7.16 * POP 18 10.01 13.35 *** POP 18 3.45 3.03 3.68 4.13 *** 3.43 4.35 *** COMP 1 297.02 157.15 *** Residuals 360 1.14 0.89 0.788 SUB:COMP 1 0.55 0.29 VEGH:COMP 1 4.03 2.13 Sept. 2006 Sept. 2007 CYPM:COMP 1 12.57 6.65 * Variation df ms F p ms F p HET:COMP 1 2.57 1.36 SUB 1 0.05 0.03 0.48 0.41 POP:COMP 18 1.89 2.52 *** VEGH 1 10.90 6.92 * 8.46 7.24 * DATE 3 265.7 428.55 *** CYPM 1 1.88 1.19 0.57 0.48 SUB:DATE 3 0.19 0.31 HET 1 5.55 3.52 + 2.09 1.79 VEGH:DATE 3 0.46 0.74 POP 18 1.58 1.55 + 1.17 1.30 CYPM:DATE 3 3.55 5.73 ** Residuals 360 1.02 0.9 HET:DATE 3 1.98 3.19 * POP:DATE 54 0.62 0.83 BLOC 4 73.85 34.51 *** Table 2 ANOVAs of parameters from 23 Aster amellus populations and SUB:BLOC 4 1.55 0.72 experimental components on number of germinations and/or plantlets in in situ VEGH:BLOC 4 1.54 0.72 CYPM:BLOC 4 2.24 1.05 sowing experiment. A) All counts together, with removal competition (COMP) as HET:BLOC 4 3.38 1.58 treatement and control of block (BLOC) effects and date of counts (DATE) and POP:BLOC 72 2.14 2.85 *** maternal cypsela mass (CYPM) to take into account heterogeneity of cypselas COMP:DATE 3 27.96 93.20 *** SUB:COMP:DATE 3 0.38 1.27 quality. B) Decomposition with count of germinations and/or plantlets at each VEGH:COMP:DATE 3 0.06 0.20 sampling date as response variable. Population parameters are nutritive CYPM:COMP:DATE 3 0.95 3.17 * substances (SUB), vegetation height (VEGH), heterozygosity (HET). We HET:COMP:DATE 3 0.27 0.90 POP:COMP:DATE 54 0.3 0.40 present degree of freedom (df), mean squares (ms), F ratio (F) and p values: Residuals 2488 0.75 +P<0.1; * P<0.05; ** P<0.01; ***P<0.001.
After 30 months of survey, plantlets were very small with around 2-4 leaves, with width not more than 1 cm, and were not able to produce stems and this probably for at least two more
85 CHAPTER 5 years (pers. obs.). This contrasted with open air ex situ experiments where plants were able to be reproductive after 18 months.
Fig. 1 A) Effects of heterozygosity across years of sampling on germination rate (Table 3 for level of significance). B) Effects of years and their respective precipitation on cypsela mass. C) Effects of three way interactions between years, soil nutritive substances and heterozygosity on germination rate (Table 3 for level of significance). D) Effects of dates and treatments (vegetation removal (NOVEG), in situ canopy (VEG) and cumulative VEG+NOVEG (TOTAL)) on germination and plantlet counts from in situ experiment; experiment started with 100 sowed cypselae per plot in December 2004. We present p level of significance where heterozygosity affected germination or plantlet counts: – p>0.1; + p<0.1; * p<0.05; (Table 2). na, not available.
Germination peak occurred six months after sowing, in June 2006. Germination was positively related to heterozygosity in the first 12 months after sowing whether there was competition or not (p<0.05 for March, June and September 2005, except competition situation
86 INBREEDING IN EXPERIMENTAL CONDITIONS in June 2005 marginally significant p<0.1, Table 2A, Fig. 1D). After that, a high sink of subsequent plantlets led to failure in detecting heterozygosity effect (p<0.1 in September 2006 and p>0.1 in 2007, table 2B, Fig. 1D). Presence of surrounding vegetation in the competition treatment had a highly negative effect on germination rate but this was compensated by a lower death rate throughout the years (interaction COMP:DATE, p<0.001, Table 2A, Fig.
1D). No competition by heterozygosity interaction was found here. As ecological sources involving effects on germination rate, both vegetation height and nutritive substances of the originating populations were significant (p<0.05, Table 2A). Cypsela mass affected also positively germination and was moreover in interaction with competition and date components (respectively p<0.05, p<0.05, p<0.01, Table 2A).
Germination rate in ex situ condition over the years
Table 3 Linear mixed model type IV testing effects of mean nutritive substances (SUB), mean vegetation height (VEGH), maternal cypsela mass (CYPM), mean heterozygosity (HET) of the populations on germination rates. We used years of sampling (YEAR) as treatment effect and population identity (POP) and interaction between population identity and years of sampling (POPxYEAR) as random terms for the whole model. We provide number of observations, numbers of groups of the random factors and the Akaike information criterion (AIC). We present student t value and p value (inferred with 10000 MCMC). ** p<0.01; *** p<0.001. Source of variation t p (continued) t p SUB 4.671 *** SUBxVEGHxYEAR 2005 3.698 *** VEGH 4.288 *** SUBxCYPMxYEAR 2004 3.48 *** CYPM 7.42 *** SUBxCYPMxYEAR 2005 5.632 *** HET 4.524 *** VEGHxCYPMxYEAR 2004 3.473 *** YEAR 2004 2.756 ** VEGHxCYPMxYEAR 2005 4.717 *** YEAR 2005 3.889 *** SUBxHETxYEAR 2004 2.939 ** SUBxVEGH -4.334 *** SUBxHETxYEAR 2005 3.979 *** SUBxCYPM -7.514 *** VEGHxHETxYEAR 2004 3.032 ** VEGHxCYPM -6.423 *** VEGHxHETxYEAR 2005 3.637 *** SUBxHET -4.582 *** CYPMxHETxYEAR 2004 3.562 *** VEGHxHET -4.122 *** CYPMxHETxYEAR 2005 5.516 *** CYPMxHET -7.209 *** SUBxVEGHxCYPMxHET -6.213 *** SUBxYEAR 2004 -2.816 ** SUBxVEGHxCYPMxYEAR 2004 -3.519 *** SUBxYEAR 2005 -3.926 *** SUBxVEGHxCYPMxYEAR 2005 -4.814 *** VEGHxYEAR 2004 -2.965 ** SUBxVEGHxHETxYEAR 2004 -3.091 ** VEGHxYEAR 2005 -3.654 *** SUBxVEGHxHETxYEAR 2005 -3.688 *** CYPMxYEAR 2004 -3.43 *** SUBxCYPMxHETxYEAR 2004 -3.632 *** CYPMxYEAR 2005 -5.529 *** SUBxCYPMxHETxYEAR 2005 -5.647 *** HETxYEAR 2004 -2.869 ** VEGHxCYPMxHETxYEAR 2004 -3.517 *** HETxYEAR 2005 -3.932 *** VEGHxCYPMxHETxYEAR 2005 -4.594 *** SUBxVEGHxCYPM 6.509 *** SUBxVEGHxCYPMxHETxYEAR 2004 3.578 *** SUBxVEGHxH 4.175 *** SUBxVEGHxCYPMxHETxYEAR 2005 4.708 *** SUBxCYPMxHET 7.355 *** No. of observations 1492 VEGHxCYPMxHET 6.1 *** No. of groups for POP & POPxYEAR 31 & 73 SUBxVEGHxYEAR 2004 3.017 ** AIC -1568
87 CHAPTER 5
Heterozygosity of the populations was positively related to germination rate, but years and ecological parameters of the populations and maternal cypsela mass were implicated in this relation by forming interactions (Table 3, Fig. 1A and 1C). Different precipitation conditions over the years largely influenced year’s cypsela mass (Fig. 1B). This had an impact on germination rates, and 2003 extreme conditions led to no detection of inbreeding depression in using total cypsela set (Fig. 1A).
Treatments effects on fitness in ex situ conditions:
Fig. 2 A-H) Effects of two way interactions between treatments (dark grey: full earth 2005-2006 experiment; grey: benign conditions (plants in pots, 2006-2007 experiment); light grey: competition (plants in pots with Bromus erectus competition, 2006-2007 experiment) and ecological parameters (measured vegetation height and Landolt’s nutritive substances) of source populations on cumulative fitness parameters and on specific fitness traits (levels of significance are given in Table 4).
88 INBREEDING IN EXPERIMENTAL CONDITIONS
Experimental treatments had high effects on number of cypselae, stages of plants, survival, vegetative production and reproductive output. The order of positive influences was full earth treatment > benign conditions > competition treatment. However cypsela mass was not perturbed in case of competition treatment versus benign conditions and was negatively related to the full earth treatment. Heterozygosity of the populations wasn’t highly bound to the response variables. However interaction with treatment in the cypsela number per capitulum indicated a plastic response bound to heterozygosity (p<0.001, Table 4, Fig. 3A).
Moreover production of rosettes was negatively related to heterozygosity (p<0.001, Table 4,
Fig. 3B). Interactions between treatments and ecological parameters on response variables
(Table 4) indicated plastic responses depending on ecological source of the populations.
These interactions were highly dependent on the full earth treatment (Fig. 2A-F). However in the case of vegetation height x treatment interaction, cypsela mass was negatively affected by the competition treatment x high vegetation interaction (Fig 2G). One hundred percent of plants survived in the full earth treatment, nutritive substances positively affected survival in case of benign and competition situation (Fig. 2H).
Table 4 Effects of luminosity (LUM), nutritive substances (SUB), vegetation height (VEGH), heterozygosity (HET), treatment (TREAT) (full earth, competition and benign conditions), population identity (POP) and their interactions on reproductive output, cumulative vegetative production, and seperated fitness traits, from ex situ experiments. We present mean square (ms), degree of freedom (df) and p levels of significance. + p<0.1; * p<0.05; ** p<0.01; *** p<0.001. Effects are graphically represented in Fig. 2 and Fig. 3. Reproductive Vegetative Cypsela Cypsela mass Rosette Survival Stage output production number production Source of variation df ms ms ms ms ms ms ms LUM 1 99485 128 12.75 0.0837 0.052 0.064 0.776 + SUB 1 108991 918 * 0.75 0.0009 0.102 0.601 * 0.015 VEGH 1 1067 32 0.1 0.012 0.109 0.145 0.103 HET 1 6891 48 2.08 0.033 4.632 *** 0.104 0.003 POP 26 43628 *** 180 *** 5.31 *** 0.0525 ** 0.226 0.092 + 0.227 ** TREAT 2 2838452 *** 11798 *** 267.6 *** 0.9473 *** 14.948 *** 0.91 *** 4.039 *** TREAT:LUM 2 7516 44 0.03 0.1047 * 0.057 0.024 0.623 ** TREAT:SUB 2 22856 * 84 * 0.11 0.0954 * 0.004 0.244 * 0.076 TREAT:VEGH 2 28571 * 110 * 4.3 ** 0.0762 * 0.082 0.079 0.154 TREAT:HET 2 7562 35 5.55 *** 0.0055 0.257 0.059 0.025 TREAT:POP 43 6997 ** 23 0.67 0.0215 0.212 0.055 0.088 Residuals ms 4396 39 0.73 0.0185 0.183 0.053 0.082 Residuals df 962 1145 976 976 1134 1210 1134
89 CHAPTER 5
Fig 3 A) Effect of treatments x heterozygosity interaction on residual number of cypselae per capitulum (Table 4). B) Effect of heterozygosity on side rosettes production in the ex situ experiments (individuals with supplementary rosettes took a 1 value and those without production of side rosettes took a 0 value) (Table 4). C) Effect of maternal cypsela mass x heterozygosity interaction on number of cypselae per capitulum produced in the 2006-2007 ex situ experiment (Table 5). D) Effect of maternal cypsela mass on output cypsela mass of the 2006-2007 ex situ experiment (Table 5).
Cypsela components
Maternal cypsela mass had a high effect on cypsela yield and the cypsela mass of the 2006-
2007 ex situ experiment (p<0.001, Table 5, Fig. 3D). Moreover maternal cypsela mass was in
90 INBREEDING IN EXPERIMENTAL CONDITIONS interaction with heterozygosity with respect to the cypsela yield (p<0.05, Table 5, Fig. 3C).
Cypsela yield was positively affected by the number of cypselae in the maternal source plants and negatively by cypsela infection rate of the maternal plants (p<0.001, p<0.05, Table 5).
Table 5 Mixed linear type IV models of effects of heterozygosity, number of cypselae of maternal plants, number of cypselae of studied plant, mass of maternal plant cypsela , mass of studied plant cypsela , rate of infected cypselae from maternal plant, treatment and their interactions on cypsela number and cypsela mass from 2006-2007 ex situ experiment. Models use population identity and population identity x treatemant interaction as error terms. Non significant terms were dropped (dr), provided that this didn't positively perturb the Akaike information criterion (AIC). We present t values and p level of significance: + p<0.1; * p<0.05; ** p<0.01; *** p<0.001. na, not available. F1ex situ 2006 generation from F0 2005 in situ marked plants Offspring cypsela number Offspring cypsela mass Source of variation tt Heterozygosity -1.929 + dr Maternal cypsela number 3.54 *** dr Offspring cypsela number na dr Maternal cypsela mass -2.677 ** 3.44 *** Offspring cypsela mass dr na Maternal cypsela infection rate -2.409 * dr Treatement 12.006 *** dr Heterozygosity x offspring cypsela number na dr Heterozygosity x maternal cypsela mass 2.445 * dr Heterozygosity x offspring cypsela mass dr na No of groups for population 31 31 No of groups for population x treatment 58 58 No of observations 665 730 AIC 1671 -900.6
Seed stock
Table 6 Germination rate of experimental seed stock Seeds buried for one or two years were stayed one or two years in in situ conditions compared to initial germination rate of the same source seeds able to germinate nearly as well as seeds stayed three months in ex situ conditions. We present also code of population origin. na, not available. stayed 3 months in laboratory conditions ex situ in situ Population code 2004 2005 2006 AL2 7 8 8 (Table 6). FRU 54 49 na HU 61 52 53 LAN1 14 12 16 NAT 38 37 3 PA 25 19 26 SE 36 28 27 SP 29 27 26 TH 52 16 15 TR 45 17 11 VU 16 9 8
91 CHAPTER 5
Discussion
Treatment effect
Experimental treatments had a high effect on phenotypic and fitness characteristics, either in in situ conditions with respect to germination rate or in ex situ conditions with respect to single or cumulative traits. In general, the response was positive with better conditions.
However survival of plantlets in in situ conditions was not influenced by the vegetation removal treatment because plantlets benefited from the canopy protection, during warm time, in the no vegetation removal treatment. Moreover the cypsela mass observed in the full earth treatment was lower than in competition or benign conditions, suggesting a surcharge of cypsela production. Nevertheless the reproductive output remained better in the full earth treatment and with no observed death after 15 months of experiment.
Inbreeding depression
Inbreeding depression in ex situ conditions was practically not observed in the F1 generation up to the cypsela mass output. However we found an interaction heterozygosity x full earth treatment, where heterozygosity influenced negatively the number of produced cypsela per capitulum. This heterozygosity influence was also observed in field conditions (Chapter 3), but was interpreted as a regulation for a correct cypsela mass output and this relation wasn’t more respected in ex situ conditions. We think that observed inbreeding effect on cypsela number in natural conditions, was also observed in the full earth treatment through a latent effect, where maternal effect was carried over up to the reproductive phase of experimental F1 plants. This is a confounding situation because non observed inbreeding effect in experimental conditions against observed inbreeding effect in natural conditions should be attributed to a purge. We were here probably more concerned by a maternal carry over, where initial maternal components of cypselae gave a differential signal to the plants from different
92 INBREEDING IN EXPERIMENTAL CONDITIONS origins; as growth was faster in the full earth treatment, this input effect was more pronounced on final architecture of plants than in other conditions. In brief, slower growing conditions acted more efficiently in eliminating maternal effect.
Nevertheless, with respect to the producer of rosettes, the negative direction imposed by more outbreed populations in the sense of lower production was still carried over in all treatments, but this was however more observed during the first measurement (after 3 months of experiment) and so suggested also maternal effect. However selection could act to optimize maternal fitness by producing smaller offspring, thus sacrificing offspring fitness (Einum &
Fleming 2000). Overdominance seems so to be in interaction with maternal component, and a
“wash” of maternal effects, through controlled experimental conditions, seems to break the link between phenotypic maternal effect and overdominance. Swindell and Bouzat (2006) , also reported disappearance of inbreeding depression through stressful conditions in
Drosophila experiments, but this occurred with successive generations under the same treatment conditions and was interpreted as a purge of deleterious alleles. Koelewijn & van
Damme (2005) found with Plantago coronopus L., as in our study high effects of inbreeding in field and disappearance of effects in glasshouse conditions and interpreted it by a stronger effect of genetic load in natural environments; they found also a strong relation between seed mass and the fitness of offspring.
The loss of inbreeding effect after 2 years of plantlets survey in in situ experiments indicated that inbreeding was more likely pronounced in germination capacity; after that, ecological pressures were so high that inbreeding remained negligible. The low growth and high mortality of offspring in natural conditions were also observed in Orso (2007).
93 CHAPTER 5
Ecological influences
Ecological parameters of the populations were more related to our experimental treatments than heterozygosity. Nutritive substances of the source populations influenced fitness positively and vegetation height rather negatively. This was also observed in natural conditions (Chapter 3) and so suggested here maternal effects bound to ecological conditions of source populations. Maternal effect related to maternal provenance was also found in another study (Castro 1999). There is a yet no proof about the way maternal sporophytic or gametophytic genes could affect phenotype architecture of offspring plants, but recent studies showed evidences of epigenetic factors involved in the germination process (Adams et al.
2000; Evans & Kermicle 2001) . Finally interactions between ecological sources and treatment indicated ecological plasticity.
Seed stock
Our finding that seeds could stay in sites in dormant phase was not confirmed by literature
(Thompson et al. 1997; Cerabolini et al. 2003) where seeds are known as transient. This contrasts also with high observed germination rates in our in situ germination experiment, where we could see few subsequent germinations after one year and no subsequent germinations after two years. With respect to previous tests, where we confronted sets of 100 cypselae of the same origin to various chamber conditions we found no germination at 6°, low germination at a 12°/16° regime, and high germination in conditions involving at minimum a
20° part in the regime. We suggest that colder situations induced by the burying of cypselae blocked the germination. This has an importance in the conservation of Aster amellus as well as in the understanding of life cycles of the species. It suggests that small sites progressively closed by grasses or lignified species could be rescued after a reopening of the vegetation, by germination of stocked seeds which didn’t germinate because of unsuitable conditions in the
94 INBREEDING IN EXPERIMENTAL CONDITIONS past. This is perhaps a fine-tuned effect but is still important to understand possible maintenance of the species during the last century, because species with possible seed stocks are less susceptible to disappearance (Stöcklin & Fischer 1999).
Global view with respect to germination rate
The model with the full of interactions we obtained in parameters influencing germination rate was principally due, of fact that we were confronted to use the total of cypselae (with infected and undeveloped cypselae) in calculation of the germination rate. The total set of cypselae is bound to a high number of biotic and abiotic parameters. The greater the number of variables influencing a response, the greater the possible interactions found throughout the range of variables. The reason for this is that two variables influencing a response without interaction can enter in interaction through a third variable. In our case ecologically controlled parameters, maternal cypsela mass, year of sampling and heterozygosity formed a complex structure determining germination rate. As inbreeding was included in this relation, we concluded that inbreeding takes part in the possible forces working for the determination of the germination rate. This imposes a genetic load on the species and small populations should be treated with care. Keller et al. (2002) equally found inbreeding effects of varied magnitude depending on ecological parameters.
Conclusions
F1 seedlings and subsequent life forms up to the first F1 cypsela output were more influenced by ecological parameters of source populations than by inbreeding. Observed effects in natural conditions were reported in experimental conditions. However observed inbreeding effects in natural conditions tended to disappear in our experimental conditions. That this observed loss was higher in more difficult growing conditions and that ecological parameters
95 CHAPTER 5 of source populations highly influenced fitness suggested that we were in a maternal effect situation and not in a purge process. Purge might nevertheless have been present in the germination process and so we were in the situation of lethal recessive inbreeding.
Subsequent experiments on Aster amellus, but perhaps more widely also on long-lived plant species taken out of their natural environment, should be attentive to maternal effects, which should be firstly “washed” with a longer term survey or by using subsequent generations.
Masked seed stocks of Aster amellus might buffer local disappearance of the species.
Inbreeding depression acted in the first life stage, i.e. on the germination rate, and was bound to ecological parameters in the determination of the future of populations with respect to the capital process of germination.
Acknowledgements
We thank Frédéric Bieri, Robert Braito and their colleagues from the Botanical Garden of
Geneva for having provided us material and help in ex situ experiments; Joëlle Orso for her help in the in situ experiment; Johanna Lindenberger and Jean-Christian Krayenbühl for having provided us logistic support in Aargovia; David Aeschimann and Catherine Lambelet for having provided financial support; Dominique Krayenbühl for English corrections.
96
Chapter 6
Synthesis
Résumé
97 CHAPTER 6
Since the industrial revolution, fragmented landscape has increased. Plant populations are dispersed through the landscape and certain species have become rare and remain isolated in an island complex. Small populations are stochastically more susceptible to disappearance through environmental perturbation, demographic change and/or genetic causes.
We studied the rare, long-lived Aster amellus living in remnants of the calcareous grasslands of central-Europe, focussing our study on the effects of fragmentation on genetic and fitness characteristics.
In Chapter 2 we presented eight microsatellite markers. Because of reading problem with locus AamF58, only seven were used in our study. No linkage disequilibrium was found in any of three populations tested, which indicated physical independence of the loci.
In Chapter 3, we documented high inbreeding and drift situations with respect to population size, respectively R2=0.36 and R2=0.63. Moreover, inbreeding and drift depression were found with respect to the capital process of the germination rate. This implied a negative effect of inbreeding on reproductive output. Cumulative production of plants was mainly influenced by ecological parameters, with a positive effect due to surrounding plant competitiveness (measured with vegetation height) and with soil nutritive substances. On the other hand, competition had a negative effect on reproductive output and also on total fitness
(taking into account three years of stage survey). As with other biotic pressure, we found a negative effect from seed feeders. The marginal effect of inbreeding was found on number of a potential seed feeder (orange larvae found in the capitula) and suggests less resistance to parasite strikes in highly inbred populations. In conclusion, populations of Aster amellus are threatened by the fragmentation process, either by inbreeding depression in small populations or by competition pressure due to the abandonment of semi-natural habitats.
In Chapter 4 we found high effects of fragmentation parameters on gene flow within and among populations. We first showed an increasing genetic differentiation with distance
98 SYNTHESIS between populations. In the middle term, gene flow appears to be totally interrupted starts at 5 km of distance. Then we discarded the distance effect and showed a highly negative effect of isolation by density of surrounding populations (R2=0.62) and a relatively negative effect of isolation by population size (R2=0.4). At the within-population level we found a strong structure below 2 m and end of structure above 30 m. This confirmed the dispersal characteristics of the species and its pollination system: low seed rain distances and pollinator comportment. The structure of populations was lower at higher densities of individuals; starts at a 10-m radius around studied individuals, an increase of the conspecific density, increased gene flow. Comparisons between theta (ancient size of population inferred by coalescence) and NeLD (linkage disequilibrium method in order to estimate effective size) with census population sizes, showed respectively relative (R2=0.51) and high degree (R2=0.82) of relatedness. This indicates a change in population sizes from the past to the present and a high drift situation in the present. In conclusion, gene flow is disturbed by fragmentation parameters. In this way small and isolated populations of Aster amellus experience a high drift situation.
In Chapter 5, ex situ experiments showed the high impact of treatment on plants, with greater fitness in the full earth treatment and less fitness in the competition treatment in comparison to benign conditions. Inbreeding depression was not detected in ex situ conditions. However, we found an interaction between the treatment in full earth and heterozygosity of source populations. The effect of inbreeding observed in the full earth treatment was equivalent in direction to what we observed in natural conditions (lower cypsela production in more heterozygous populations). That we did not observe this effect in benign conditions or in competition treatment suggested that observed effects in natural conditions disappear more rapidly in harder experimental conditions. We interpreted this result not as a purge resultant, but as the progressive disappearance of maternal effects
99 CHAPTER 6 accumulated in the cypselae. Moreover, ecological parameters of source populations were highly implicated in the observed fitness in experimental conditions, again with a more pronounced effect in the full earth treatment and with identical effects to natural conditions.
The germination rate from natural seed sources sown in in situ or ex situ conditions was in both cases influenced negatively by inbreeding depression. The germination rate of cypselae collected over three years in natural populations was influenced by several ecological parameters, maternal effects and inbreeding depression. These parameters together formed complex interactions, acting together in this way on the fitness of plants. In conclusion, in our ex situ experiments we found a replication of effects on fitness observed in natural conditions.
Harder experimental conditions showed fewer replication effects and suggested that maternal effects had different impacts depending on the growing conditions. The complex interaction that we found in determination of the germination rate showed that processes that determine the threats of populations are nested and that inbreeding is involved.
In the present context of knowledge our finding showed a strong relationship with the size of populations and heterozygosity or gene diversity of the populations; and this with precise counts of the individuals and with neutral gene markers (microsatellite). It makes a final point on the question: is there inbreeding or drift in small plant populations? Until know correlations were low and majority of used markers were alloenzymes. From the second question, is there any inbreeding depression within natural plant populations? We confirmed it with respect to germination rates and so we were in line with three meta-analysis papers regrouping a high number of population viability studies. Moreover we proved it in controlling ecological parameters and so we were in line with recent studies. Although inbreeding depression was poor in controlled experimental conditions, but we didn’t measure the germination rate; present knowledge in ex situ conditions is vague and results presented in the literature are contradictory. We think that experiments should take more than one
100 SYNTHESIS generation in account to “wash” maternal effect and controlled pollination within offspring of the same populations should be done to avoid heterosis. In response to the third questions: is there any diminished gene flow in fragmented population? We found a rift of gene flow with isolation and population sizes parameters. This is probably easily comprehensible and evident but was until now not clearly showed with neutral gene markers, we are here in line with direct observation of pollinator flight which is known to be perturbed with fragmentation parameters. Moreover we assessed a population structure at 30 m and therefore showed that fragmentation is a multi-scale problematic. A simple rupture of 30 m between conspecific is a start of fragmentation.
Conclusions
The fragmentation of landscape induced by human activity had a high effect on inbreeding level, on drift level, on fitness, and on gene flow in the studied populations of Aster amellus.
Ecological parameters were also strong and negative fitness was induced by competition from the surrounding vegetation. Other biotic disturbance such as the presence of seed feeders also negatively influenced fitness.
Ex situ experiments showed the presence of maternal effects and further experiments on the species should take note of this. Inbreeding depression was less in ex situ conditions than in natural conditions. Effects of ecological parameters of population sources showed a continuum between our observations in natural conditions and our observations in ex situ conditions.
Sites should be correctly mown, grazed or burned (Gottfried & Ellenberg 1994; Köhler et al. 2005; Moog et al. 2005) to prevent high competition pressure. Germinations of viable seeds observed in our seed stocks experiment, could also emerge after a reopening of the vegetation and so could provide alleles possibly purged by drift.
101 CHAPTER 6
In order to maintain gene flow, conservation management should maintain one large population at the centre of a 100 km2 square. The size of small populations should be enhanced and density in the number of surrounding populations increased. At the within- population level, demes should not be separated by more than 30 m and the density of demes should be sufficiently high: 150–200 individuals separated by a distance of two to 10 m.
Enhancing gene flow will provide some new alleles in small populations and so counteract situations of high genetic drift.
Through our study we were able to notice a very deteriorate environment and situations where populations were cloistered on the edge of a slope, a piece of clearing completely neglected or surrounded by houses, indeed even near new constructions. Our findings prove many negative effects due to the fragmentation on plant fitness and on gene flow between plants. Therefore it appears necessary to take urgent measurements with respect to Aster amellus and the vegetal and animal communities sharing its biotope. The preservation of wild species is important for the maintenance of biodiversity and therefore for the ecosystem stability which surrounds us.
Résumé
Cette thèse étudie l’écologie et la génétique d’une plante rare, Aster amellus, dans un paysage fragmenté. Depuis la révolution industrielle le paysage a été sérieusement modifié, les zones d’agriculture extensive ont été transformées en zones d’exploitation intensive ; les habitats semi-naturels ont été abandonnés et les habitats naturels ont drastiquement diminué en surface. Ce processus a induit une fragmentation des populations d’espèces sauvages. La fragmentation a deux impacts majeurs sur les populations. En premier une grande population se scinde en plusieurs petites populations formant une metapopulation. Deuxièmement « la connexion » entre les individus d’une population est perturbée, typiquement chez les plantes
102 RÉSUMÉ par une rupture d’événements de pollinisation. Les petites populations sont plus susceptibles de disparition lors d’événements stochastiques: perturbations environnementales, changements démographiques et dérives génétiques. De plus le flux de gènes est perturbé, provoquant ainsi des déséquilibres d’hétérozygotie et favorisant des situations de dérive génétique en entravant le secours par hétérosis. Plusieurs études ont montré un effet négatif de la consanguinité sur les espèces sauvages, mais les paramètres écologiques n’ont pas toujours
été contrôlés. D’autres études n’ont montré aucun effet négatif. Pourtant des publications regroupant plusieurs études montrent un effet général négatif de la consanguinité. C’est dans ce contexte que nous avons essayé d’identifier pour une plante rare les effets de la fragmentation.
Aster amellus est une plante d’Europe centrale, à long cycle de vie, vivant dans les prés secs et calcaires, le long des pentes à orientation SW, colonisant les clairières, lisières, friches et bords de chemins. Elle pratique principalement la fécondation croisée par l’intermédiaire de syrphidés, abeilles, mouches et papillons ; la multiplication végétative est possible mais n’a été observée que sur des petites distances. La dispersion des graines se fait principalement par le vent, mais à de faibles distances du fait de la hauteur des plantes (35 cm) et du poids des graines (0.47 mg). Le stock de graines est connu pour être transitoire
(germination dans les 12 mois après la mise à graine), mais nos résultats ont montré la possibilité d’avoir des graines vivantes après deux ans passés dans la terre.
Nous avons étudié 31 populations d’Aster amellus le long du Jura (France-Suisse), entre la Haute-Savoie et le canton d’Argovie sur un transect de 246 km. Nous avons relevé la taille des populations par comptage, effectué 865 relevés de végétation pour définir les paramètres écologiques des populations, analysé 2600 individus sur 7 marqueurs microsatellites pour définir les paramètres génétiques. Dans des conditions naturelles, nous avons étudié la fitness de 1600 plantes marquées avec des jalons, pendant 3 ans (2005-2006-
103 CHAPTER 6
2007). De plus 1200 plantes ont été suivies sur une génération dans des conditions ex situ avec un traitement en pleine terre, un traitement avec compétition d’une graminée: Bromus erectus, et une condition bénigne (plantes en pot sans traitement spécifique). Nous avons
également établi une culture in situ dans le but d’obtenir un troisième traitement englobant les conditions naturelles, mais la croissance était très lente et la mortalité très forte.
Nous avons démontré que les populations de faible taille influencent positivement la consanguinité et la dérive génétique.
Nous avons trouvé une forte dépression due à la consanguinité sur le taux de germinations des cypsèles (la cypsèle est un fruit hétérocarpien contenant potentiellement une graine) récoltées en nature et semées en conditions ex situ et in situ. Il en résulte un effet positif des hétérozygotes dans la capacité reproductive des plantes (produit de traits de fitness reproductifs). Un calcul du taux de germination basé sur le set total de cypsèles (avec infectées et mal développées) collectées sur les sites pendant les années 2003, 2004 et 2005, a montré que la consanguinité était imbriquée avec d’autres variables dans la détermination du taux de germination, comme les paramètres écologiques et les effets maternels. Parmi les paramètres écologiques, la taille de la végétation environnante influence positivement la production des plantes notamment dans la production végétative, par contre elle influence négativement la capacité reproductive et la fitness totale (basée sur 3 ans de suivis des stades reproductifs ou végétatifs des plantes marquées). Les attaques parasitaires influencent aussi la fitness totale et un fort lien a été trouvé entre l’altitude et les attaques parasitaires; ceci parce que les sites plus élevés bénéficient d’un meilleur contact avec la matrice environnementale et donc fournissent de meilleures niches pour les parasites. Un effet marginal négatif des hétérozygotes sur la présence d’un mangeur de graines potentiel (une larve orange observée très souvent dans les capitules), suggère une meilleure réaction des hétérozygotes contre les parasites.
104 RÉSUMÉ
Le suivi de la croissance de plantes en conditions ex situ n’a pas montré beaucoup d’effets liés à la consanguinité, excepté une interaction lors du traitement en pleine terre où les plantes provenant de populations plus hétérozygotes produisaient moins de cypsèles, reproduisant ainsi un effet déjà observé en nature. De même les paramètres écologiques des populations sources ont influencé les résultats des cultures de manière plus prononcée en pleine terre, avec des effets sur la fitness identiques à ce que nous avons pu mesurer en nature.
Il semble donc que des effets maternels sont impliqués dans nos mesures de fitness et que des conditions de culture plus dures (compétition) ont tendances à faire disparaître ces effets.
Nous avons démontré que plusieurs paramètres de fragmentation perturbent le flux de gènes à l’intérieur et entre les populations; ces paramètres sont la distance, la densité en individus ou en nombre de populations sur une surface donnée, et la taille des populations.
Nous avons défini à l’intérieur des populations une structure maximum de 30 m à l’intérieur de laquelle les individus sont génétiquement plus proches, pour des raisons liées au comportement des pollinisateurs, ainsi qu’une forte structure génétique en dessous de 2 m, liée a une faible distance de pluie de graines. Des comparaisons entre les dénombrements d’individus et des estimateurs de tailles de population, respectivement theta (Θ) un estimateur de taille ancienne inféré par des approches de coalescence et NeLD, un estimateur de taille efficace récente calculé par la méthode du déséquilibre de liaison, ont montré des changements entre les tailles anciennes et actuelles et un fort lien avec des situations de dérive génétique actuelles.
En conclusions, les petites populations d’Aster amellus expérimentent de fortes situations de consanguinité et de dérive génétique. Ceci a un impact négatif sur le taux de germinations et la capacité reproductive (produit de traits de fitness reproductifs). Les facteurs biotiques, soit la compétition et les attaques parasitaires, ont un impact négatif sur la fitness totale des plantes. Les suivis de croissance des plantes en conditions ex situ ont montré la
105 CHAPTER 6 présence d’effets maternels qui répliquent des effets déjà observés en nature, mais qui ont tendance à disparaître en fonction du traitement. Le flux de gènes à l’intérieur des populations ou entre les populations est fortement perturbé par les paramètres de fragmentation, il en résulte une perte de panmixie et une augmentation des situations de consanguinité.
Les sites d’Aster amellus doivent être correctement entretenus par fauche, pâturage ou feux (Gottfried & Ellenberg 1994; Köhler et al. 2005; Moog et al. 2005) afin d’éviter une trop forte pression de compétition. L’entretien d’un site abandonné peu possiblement faire émerger des anciennes graines qui n’auraient pas germé à cause de conditions non idéales et ainsi apporter des nouveaux allèles possiblement perdus par dérive génétique. Pour maintenir le flux de gènes, il faudrait au minimum maintenir une grande population au centre d’un carré de
100 km2. Les petites populations doivent être agrandies et le nombre de populations environnant les petites populations devrait être augmenté. A l’intérieur des populations, des groupes d’individus ne devraient pas être séparés par plus de 30 m de distance et la densité à l’intérieur des groupes doit être suffisamment élevée: 150 à 200 individus séparés les uns des autres par des distances de 2 à 10 m.
A travers notre étude nous avons pu constater un environnement très dégradé et des situations où les populations étaient recluses sur un coin de pente, un bout de clairière à l’abandon ou entourées par des villas, voire proches de nouvelles constructions en cours. Nos résultats prouvent plusieurs effets négatifs liés à la fragmentation sur la fitness des plantes et le flux de gènes entre les plantes. Il nous semble donc nécessaire de prendre des mesures urgentes à l’égard de l’Aster amellus et des communautés végétales et animales qui forment son biotope. La préservation des espèces sauvages est importante pour le maintien de la biodiversité et donc de la stabilité des écosystèmes qui nous entourent.
106 REFERENCES
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115
REMERCIEMENTS
Remerciements
Je remercie vivement Rodolphe Spichiger pour avoir permis la réalisation de ce travail au sein du Conservatoire botanique de Genève. J’ai pu travailler dans un cadre agréable avec une grande variété de ressources à ma disposition. Un grand merci à David Aeshimann pour avoir encadré ce travail et pour m’avoir laissé une grande liberté, j’ai pu aussi compter sur son appui dans les moments durs. Merci à Catherine Lambelet pour avoir mis à disposition les outils liés à la conservation ex situ. Je profite aussi pour saluer ici Daniel Jeanmonod qui m’a accepté comme assistant aux travaux pratiques de botanique systématique, dans le cadre de ces enseignements.
Je voudrais aussi remercier David Galeuchet, Sophie Hoehn et Judit Lienert pour m’avoir fourni leurs manuscrits de thèse qui m’ont servit de ligne de conduite le long de ce travail. Je profite ici pour remercier Markus Fischer pour avoir donné à travers ces travaux un véritable influx sur la manière d’étudier la biologie des plantes, il m’a donné aussi de la confiance et de bons conseils dans la phase terminale du travail. Merci aussi à Nathalie Machon et Florence Noel pour leurs commentaires sur le Chapitre 4.
Merci aux gestionnaires et propriétaires des sites qui nous ont laissé travailler sur les sites. Je remercie particulièrement les « Vieux » de l’AGEO-Aargau qui ont bien soignés les jalons posés sur les sites (pas un de moins !). Merci au domaine nature et paysage de Genève (DNP) pour avoir financé les déplacements sur les sites.
Merci à AGIS (Aargauisches Geographisches Informationssystem) et DCMO (La Direction Cantonale de la Mensuration Officielle) pour m’avoir fourni les orthophotos des sites d’études et les fichiers Rinex de postcorrections GPS. Merci aussi au CRSF (Centre du Réseau Suisse de Floristique) et au CBNA (Conservatoire Botanique National Alpin) pour les coordonnées de populations d’Aster amellus.
Un grand merci à Frédéric Bieri qui m’a donné un sacré coup de mains dans l’élaboration des cultures ex situ ; ainsi qu’à Robert Braito pour nous avoir laissé une grande liberté de travail et pour son regard sympathique à l’égard de ces Aster amellus. Merci à Sophie Dunand- Martin pour m’avoir initié aux techniques in vitro; je profite ici, pour remercier toutes les aides provenant des jardiniers et personnel annexé. Merci aussi à Joëlle Orso pour son aide apportée sur l’expérience in situ. Je remercie Laurent Burgisser pour l’élaboration de certains matériels de terrain.
Un grand merci aussi à Jean-François Manen pour son aide au laboratoire de génétique : « Vous aller voir les gars, pas de problème, je place là un bout d’alu sur le fusible et ichte- bau », mais surtout j’ai pu apprendre avec lui à extraire l’ADN avec des enzymes qui délave les Jeans et y’a rien à redire car ça « crachait » bien sur les photos. Merci à Yamama Naciri pour son initiation aux techniques de développement de microsatellite et à Sofia Caetano pour les bons trucs de laboratoire : « Avec cette technique, tu peux faire 3 gels par jour ! ». Je profite ici pour remercier tout le personnel adjoint au laboratoire de génétique.
Pour les botanistes, je dois beaucoup à Christian Schneider qui m’a montré comment on déterminait une plante à l’état végétatif. Merci aussi à Patrice Prunier, Denis Jordan et Philippe Druart pour les localisations des populations d’Aster amellus.
117 REMERCIEMENTS
Je profite aussi de remercier ici mes amis du groupe Cycloproduction avec qui on a fait de bonnes pauses fondues, que ce soit dans un îlot de verdure urbain ou dans la Sylve jurasienne. Merci à Pascal Martin pour ces conseils en statistiques, ainsi qu’à Raphaël Spadazzi pour m’avoir conduit en voiture sur le terrain quand j’avais le pied cassé !
Un grand merci à Johanna Lindenberger et Jean-Christian Krayenbühl pour le logement et les accommodations lors des missions en Argovie ainsi que pour les bons conseils en statistiques. Merci aussi à Ivo et Teresa Lindenberger pour les coups de main sur le terrain. Merci aussi à Dominique Krayenbühl pour les corrections de l’Anglais.
Un grand merci à Christine Habashi qui m’a donné de l’aide sur le terrain, mais surtout pour sa patience et son écoute dans les moments un peu durs, merci aussi d’avoir gardé nos enfants quand j’étais sur le terrain. Voilà Layla le travail de Papa est terminé! et toi Sylvain tes sourires depuis le berceau m’ont bien aidé lors de la phase terminale du travail.
118 APPENDIX
Appendix 1 Table of vegetation records with number of occurrence per sites (code of population in Chapter 3) and for the whole sites (TOT). Grey line gives the number of recorded plots, total number were 857/865, 8 plots were not refound mainly because of vandalism. SPECIES TOT AE3 Al1 AL2 BG BOU CC1 CC2 CH1 CH2 CR EG1 EG2 FRA FRU HER HES HU LAN1 LAN2 NAT PA REB REP SC1 SC2 SC4 SE SP TH TR VU Abies alba Mill. 1 1 Acer campestre L. 16 2 2 1 2 1 8 Acer pseudoplatanus L. 44 1 2 1 3 6 1 13 5 10 2 Achillea millefolium L. 32 1 3 2 3 1 2 5 3 4 8 Agrimonia eupatoria L. 82521 1 4 13 9 1 1 3126 2851161 Allium carinatum L. s.str. 3111 Anacamptis pyramidalis (L.) Rich. 19 1 1 1 1 7 1 1 2 2 1 1 Anthericum ramosum L. 410 11 7 17 8 8 8 12 60 13 24 5 3 93 10 9 92 17 13 Anthyllis vulneraria L. s.str. 101 1 1 1 7 4 1 5 4 12 1 1 7 20 34 2 Aquilegia vulgaris L. 3 3 Arabis hirsuta (L.) Scop. 32 1 Arctostaphylos uva-ursi (L.) Spreng. 3 3 Artemisia campestris L. 22 Asperula cynanchica L. 340 1 5 4 7 6 5 3 4 29 9 3 1 7 52 10 66 28 4 2 1 1 49 1 23 10 9 Asperula tinctoria L. 7 7 Aster amellus L. 857 10 15 20 10 15 22 10 10 10 10 35 23 15 15 10 70 15 36 10 104 110 10 10 10 10 10 116 22 64 15 15 Berberis vulgaris L. 28 412111 Blackstonia perfoliata (L.) Huds. 32 1 3 1 1 2 9 5 5 1 2 2 Botrychium lunaria (L.) Sw. 22 Brachypodium pinnatum (L.) P. Beauv. 560 7 11 16 10 9 21 8 1 8 9 21 21 1 15 9 10 2 17 18 107 10 7 3 9 8 106 16 55 10 15 Brachypodium sylvaticum (Huds.) P. Beauv. 52 3 Briza media L. 19922 2115 65 22327 262121 846062542 Bromus erectus Huds. s.str. 810 10 15 20 10 12 18 5 10 9 9 35 19 14 14 10 69 15 36 8 100 104 9 10 10 10 10 108 21 62 13 15 Buphthalmum salicifolium L. 138 9 57 14 46 2 4 3 2 1 Bupleurum falcatum L. s.str. 133 9 13 21 3 39 23 20 4 1 Calamagrostis epigejos (L.) Roth 826 Campanula glomerata L. s.str. 51 3 6 1 3 20 17 1 Campanula rotundifolia L. 119 1 4 1 8 5 12 9 3 28 7 4 3 2 18 2 11 1 Carex caryophyllea Latourr. 28744161 11561113751428 8 3116445 3643348 Carex flacca Schreb. 701 9 4 10 5 14 21 10 10 10 9 33 9 14 12 9 68 15 5 98 93 8 10 10 10 6 105 18 47 15 14 Carex montana L. 563 7 1 6 10 8 19 7 9 3 20 6 4 1 57 14 32 8 92 59 10 8 10 6 97 20 19 15 15 Carex panicea L. 21 85111311 Carex spicata Huds. 11 Carex tomentosa L. 174 10 5 5 2 1 17 1 1 34 70 10 5 8 5 Carlina acaulis L. s.str. 11 2 9 Carlina vulgaris L. 122 1 1 3 6 4 1 2 13 1 1 14 10 16 22 9 1 1 6 3 1 6 Carpinus betulus L. 24 1 2 2 1 2 13 2 1 Castanea sativa Mill. 3 21 Centaurea jacea L. s.str. 271 5 5 4 1 6 3 4 7 18 8 10 4 27 4 17 33 34 2 2 6 5 38 11 12 1 4 Centaurea scabiosa L. s.str. 103 6 15 14 8 20 6 12 1 1 6 5 9 Centaurium erythraea Rafn 17 3 1 1 1 4 1 4 1 1 Cephalanthera longifolia (L.) Fritsch 20 11 215 1 Cephalanthera rubra (L.) Rich. 1 1
APPENDIX
Appendix 1 (Continued) SPECIES TOT AE3 Al1 AL2 BG BOU CC1 CC2 CH1 CH2 CR EG1 EG2 FRA FRU HER HES HU LAN1 LAN2 NAT PA REB REP SC1 SC2 SC4 SE SP TH TR VU Cirsium acaule Scop. 64 1 3 3 1 27 3 16 3 2 5 Cirsium arvense (L.) Scop. 2 2 Cirsium tuberosum (L.) All. 172 8 15 51 8 61 5 7 17 Cirsium vulgare (Savi) Ten. 1 1 Clematis vitalba L. 4 11 11 Cornus sanguinea L. 10631 2 3 2 1 5 9 7 9 2 1 6 1664 5 2 14152 Corylus avellana L. 16 1 1 2 1182 Cotoneaster tomentosus Lindl. 3 11 1 Crataegus laevigata (Poir.) DC. 22 Crataegus monogyna Jacq. 110 1 2 3 4 2 1 2 15 3 1 2 3 7 1 27 7 2 5 1 13 3 5 Cuscuta epithymum (L.) L. 1 1 Dactylis glomerata L. 421 2 28 1 2 1 3 44212 Danthonia decumbens (L.) DC. 12 84 Daphne laureola L. 1 1 Daucus carota L. 34 2 1 6 2 4 2 1 1 5 10 Dianthus carthusianorum L. s.str. 3 3 Digitalis grandiflora Mill. 22 Epilobium dodonaei Vill. 12 12 Epipactis atrorubens (Hoffm.) Besser 24 31 125 1 2 Epipactis helleborine (L.) Crantz 8 71 Epipactis palustris (L.) Crantz 12 1 6 2 2 1 Equisetum arvense L. 11 74 Equisetum telmateia Ehrh. 40 10 10 2 10 8 Erigeron annuus (L.) Pers. s.str. 413 Euonymus europaeus L. 41 1 2 Euphorbia amygdaloides L. 11 9 1 1 Euphorbia cyparissias L. 400 311 9 4 54 35 3113 63810 78440 10 8 2821239 1 Euphorbia dulcis L. 211 Euphorbia verrucosa L. 171 4 3 23 35 8 64 4 4 5 11 1 9 Euphrasia rostkoviana Hayne s.str. 75 3 2 18 5 41 6 Fagus sylvatica L. 18 2 15 1 9 Festuca ovina L. 314 10 13 6 6 11 6 9 1 1 27 16 4 7 6 20 10 25 2 44 10 5 3 5 5 3 6 7 29 3 14 Festuca pratensis Huds. s.str. 2 11 Filipendula vulgaris Moench 2 2 Fragaria vesca L. 92 16 Fragaria viridis Duchesne 8 71 Frangula alnus Mill. 31 1 9 2 12 1 5 1 Fraxinus excelsior L. 27 2 1 1 1 1 2 2 12 4 1 Fumana procumbens (Dunal) Gren. & Godr. 36 14 18 4 Galium aparine L. 11 Galium boreale L. 2 2 Galium mollugo L. 40 4 14 2 9 1 5 1 1 3 Galium pumilum Murray 18 8 1 1 2 6 Galium verum L. s.str. 158 5 10 4 1 2 6 1 1 2 6 5 1 6 57 3 40 8 Genista pilosa L. 20 7427
APPENDIX
Appendix 1 (Continued) SPECIES TOT AE3 Al1 AL2 BG BOU CC1 CC2 CH1 CH2 CR EG1 EG2 FRA FRU HER HES HU LAN1 LAN2 NAT PA REB REP SC1 SC2 SC4 SE SP TH TR VU Genista sagittalis L. 77 1 1 37 38 Genista tinctoria L. 512 9 12 19 6 14 17 5 8 4 9 16 12 13 8 47 91 44 7 7 6 88 4 46 12 8 Gentiana germanica Willd. 16 16 5 2 1 1 Geranium sanguineum L. 34 7 10 6 3 8 Globularia bisnagarica L. 144 1 1 1 1 2 3 1 1 22 3 19 18 2 4 31 5 29 Globularia cordifolia L. 1 1 Gymnadenia conopsea (L.) R. Br. 99 1 1 1 10 6 3 3 4 2 16 2 4 19 11 1 4 1 1 1 1 1 6 Gymnadenia odoratissima (L.) Rich. 14 51 7 1 Hedera helix L. 64 1 1 2 4 3 3 1 2 1 37 9 Helianthemum canum (L.) Baumg. 1 1 Helianthemum nummularium subsp. obscurum (Celak.) Holub 206 2 12 18 3 4 2 1 24 6 8 11 2 2 60 50 1 Helictotrichon pratense (L.) Besser 1 1 Helictotrichon pubescens (Huds.) Pilg. 36 7 15 11 1 1 1 Helleborus foetidus L. 33 Hepatica nobilis Schreb. 3 21 Hieracium laevigatum aggr. 9 9 Hieracium murorum L. 105 1 4 3 2 2 1 1 3 1 1 3 2 26 1 37 1 14 2 Hieracium pilosella L. 142 3 2 2 1 1 3 2 3 3 2 1 38 2 1 1 41 34 2 Hieracium piloselloides Vill. 11 1 73 Hippocrepis comosa L. 464 6 2 6 6 9 11 6 9 8 4 8 12 8 9 7 55 14 16 48 57 1 7 6 8 5 85 11 32 4 4 Hippocrepis emerus (L.) Lassen 15 1 11 3 Hippophaë rhamnoides L. 11 Hypericum perforatum L. s.str. 72 3 4 1 5 16 1 1 10 1 1 1 1 1 4 9 13 Hypochaeris maculata L. 24 11 5 863 Ilex aquifolium L. 3 3 Inula conyza DC. 10 2 1 1 2 1 1 2 Inula salicina L. 132 5 15 1 15 8 6 7 10 9 7 5 7 2 1 2 10 5 6 11 Juglans regia L. 91 71 Juniperus communis L. s.str. 97 1 3 4 3 27 2 14 13 4 15 8 2 1 Knautia arvensis (L.) Coult. 112 2 3 8 6 4 1 4 16 2 32 7 5 7 3 1 2 8 1 Knautia dipsacifolia Kreutzer s.str. 532 Koeleria macrantha (Ledeb.) Schult. 5 32 Koeleria pyramidata (Lam.) P. Beauv. 141 4 3 1 1 1 1 8 1 8 1 31 30 1 6 6 10 6 15 5 2 Laburnum anagyroides Medik. 5 14 Laserpitium latifolium L. 2 2 Laserpitium prutenicum L. 10 91 Lathyrus pratensis L. 40 1 10 2 13 2 4 8 Leontodon hispidus L. s.str. 192 3 5 3 2 4 13 9 74 13 3 3 1 2 2 29 7 17 2 Leucanthemum vulgare aggr. 50 1 1 1 4 4 2 2 2 18 4 2 1 4 4 Ligustrum vulgare L. 114 2 1 1 2 3 2 2 3 1 1 4 12 13 4 3 1 41 2 16 Linum catharticum L. 2816 238435131826543710 5930521532182073 Linum tenuifolium L. 81 2 1 2 13 10 35 11 1 2 4 Listera ovata (L.) R. Br. 1 1 Lithospermum officinale L. 1 1
APPENDIX
Appendix 1 (Continued) SPECIES TOT AE3 Al1 AL2 BG BOU CC1 CC2 CH1 CH2 CR EG1 EG2 FRA FRU HER HES HU LAN1 LAN2 NAT PA REB REP SC1 SC2 SC4 SE SP TH TR VU Lolium perenne L. 1 1 Lonicera periclymenum L. 1 1 Lonicera xylosteum L. 911124 Lotus corniculatus L. 557 10 4 14 3 10 8 7 8 3 3 18 13 10 13 5 51 13 21 74 72 8 9 8 9 7 79 15 38 14 10 Lotus maritimus L. 232 5 12 17 3 1 2 1 8 7 6 48 64 18 7 4 1 11 13 4 Luzula multiflora (Retz.) Lej. 1 1 Medicago falcata L. 92511 Medicago lupulina L. 30 6 1 5 2 1 1 3 11 Medicago sativa L. 1 1 Melampyrum cristatum L. 28 7 16 311 Melampyrum pratense L. 34 12841 Melica nutans L. 31 2 Melittis melissophyllum L. 16 5 2 1 2 6 Mercurialis perennis L. 11 Molinia arundinacea Schrank 485 1 1 2 9 15 22 10 10 5 10 7 15 11 8 67 15 93 61 9 10 1 2 54 17 5 15 10 Myosotis stricta Roem. & Schult. 321 Odontites luteus (L.) Clairv. 43 6 1 20 16 Onobrychis viciifolia Scop. 23 21 21 1 1 1 6 13 13 Ononis repens L. 91 3 2 5 3 3 2 3 1 24 1 1 24 16 3 Ononis spinosa L. s.str. 108 1 1 1 8 9 3 9 18 6 13 4 5 1 15 3 2 9 Ophrys araneola Rchb. 11 Ophrys holosericea (Burm. f.) Greuter s.str. 2 11 Orchis ustulata L. 211 Origanum vulgare L. 104 11 1 12 23 6 3 3 5 1 5 6 15 13 Orobanche gracilis Sm. 15 2 1 4 1 1 1 2 2 1 Orobanche teucrii Holandre 1 1 Parnassia palustris L. 2 2 Peucedanum carvifolia Vill. 2 2 Peucedanum cervaria (L.) Lapeyr. 408 4 1 10 10 15 22 10 10 10 9 35 17 15 10 45 7 9 4 63 10 10 5 2 5 28 14 15 13 Peucedanum oreoselinum (L.) Moench 76 10 2 21 43 Picris hieracioides L. s.str. 641 1 Pimpinella saxifraga L. 236 1 2 7 1 1 3 5 1 18 12 2 49 36 4 2 6 47 4 35 Pinus sylvestris L. 62 1 15 4 1 1 15 4 16 1 1 3 Plantago lanceolata L. 80 1 2 3 7 5 3 7 1 1 22 28 Plantago media L. 169 1 9 1 5 8 6 5 2 7 4 1 9 2 11 1 27 1 4 8 4 18 26 7 2 Plantago serpentina All. 16 3 1 5 4 3 Platanthera chlorantha (Custer) Rchb. 11 Poa angustifolia L. 80 3 12 2 1 2 6 18 1 5 11 3 1 4 4 1 6 Poa compressa L. 14 1310 Polygala amarella Crantz 15 1 35 2 2 1 1 Polygala comosa Schkuhr 121 2 6 1 8 3 5 7 3 6 10 5 19 1 1 26 1 15 2 Polygonatum odoratum (Mill.) Druce 30 6 2 3 8 1 1 6 1 2 Populus alba L. 512 2 Populus tremula L. 56 8 8 12 4 1 4 1 1 1 4 1 1 1 3 3 3
APPENDIX
Appendix 1 (Continued) SPECIES TOT AE3 Al1 AL2 BG BOU CC1 CC2 CH1 CH2 CR EG1 EG2 FRA FRU HER HES HU LAN1 LAN2 NAT PA REB REP SC1 SC2 SC4 SE SP TH TR VU Potentilla erecta (L.) Raeusch. 116 3 15 7 3 32 7 1 11 8 4 16 9 Potentilla neumanniana Rchb. 295 7 13 1 2 1 5 10 7 5 4 20 21 37 70 1 1 5 45 3 36 1 Prunella grandiflora (L.) Scholler 574 8 10 18 8 7 6 2 27 1 6 13 8 64 15 6 94 89 2 8 8 10 4 77 16 45 10 12 Prunella laciniata (L.) L. 36 1 1 20 1 13 Prunella vulgaris L. 3 12 Prunus avium L. 5 41 Prunus mahaleb L. 5 21 2 Prunus spinosa L. 109 2 5 1 2 1 1 11 8 1 2 19 1 9 3 4 2 4 22 2 9 Pteridium aquilinum (L.) Kuhn 1 1 Pyrus pyraster Burgsd. 3 12 Quercus pubescens Willd. 208 2 1 1 1 2 1 9 2 55 1 93 38 2 Quercus robur L. 11934 35 7 9 5 5 93 9 1 1 4 92 1 23 2 5 1 4 31 Rhamnus cathartica L. 31 1 1 Rhinanthus alectorolophus (Scop.) Pollich 211 Rhinanthus minor L. 16 1 1 13 1 Robinia pseudoacacia L. 72221 Rosa canina L. 44 4 1 2 1 1 1 3 2 8 5 1 14 1 Rosa pimpinellifolia L. 1 1 Rubus fruticosus aggr. 13 1 1 1 1 3 1 5 Salix caprea L. 2 2 Salvia pratensis L. 118 4 1 2 1 11 9 6 26 6 1 7 1 1 6 2 8 24 2 Sanguisorba minor Scop. s.str. 489 9 1 3 10 2 2 2 32 12 2 2 6 46 11 29 99 63 1 9 10 10 73 10 33 5 7 Scabiosa columbaria L. s.str. 169 3 2 1 5 4 4 2 5 5 3 22 6 5 58 11 1 1 8 2 14 4 3 Schoenus nigricans L. 2 2 Securigera varia (L.) Lassen 38 10 3 1 8 5 6 3 2 Sedum sexangulare L. 8 26 Senecio erucifolius L. 47422312148311 2 6 241 Serratula tinctoria L. s.str. 14 9 23 Seseli annuum L. s.str. 10 19 Sesleria caerulea (L.) Ard. 12 1 38 Silaum silaus (L.) Schinz & Thell. 11 Silene nutans L. s.str. 26 2 1 5 5 7 6 Silene vulgaris (Moench) Garcke s.str. 2 11 Sorbus aria (L.) Crantz 61 2 4 1 3 3 1 23 2 16 3 2 1 Stachys officinalis (L.) Trevis. s.str. 49 1 2 20 24 2 Stachys recta L. s.str. 11 1 12 1 6 Succisa pratensis Moench 124 1 3 1 15 5 3 4 1 51 3 1 6 7 7 1 13 2 Tamus communis L. 22 Tanacetum corymbosum (L.) Sch. Bip. 18 11 7 Taraxacum officinale aggr. 1 1 Teucrium chamaedrys L. 466 11 18 1 6 2 5 1 8 10 9 10 1 9 33 2 8 5 70 77 2 5 110 5 55 3 Teucrium montanum L. 135 2 2 1 4 1 1 1 60 45 1 13 1 3 Thesium linophyllon L. 10 10 Thesium pyrenaicum Pourr. 10 271
APPENDIX
Appendix 1 (Continued) SPECIES TOT AE3 Al1 AL2 BG BOU CC1 CC2 CH1 CH2 CR EG1 EG2 FRA FRU HER HES HU LAN1 LAN2 NAT PA REB REP SC1 SC2 SC4 SE SP TH TR VU Thymus pulegioides L. s.str. 2422715 122 3112611824256 62654076 Tofieldia calyculata (L.) Wahlenb. 4 4 Tragopogon pratensis subsp. minor (Mill.) Hartm. 1 1 Tragopogon pratensis subsp. orientalis (L.) Celak. 13 1 2 1 1 1 3 1 2 1 Trifolium campestre Schreb. 1 1 Trifolium medium L. 76 1 4 2 1 4 1 15 8 2 1 1 14 5 7 3 4 1 1 1 Trifolium montanum L. 147853116312 1753 2 119 2 181427 Trifolium ochroleucon Huds. 2 2 Trifolium pratense L. s.str. 22 1 5 2 2 1 11 Trifolium repens L. s.str. 6 6 Trifolium rubens L. 16 7 3 1 1 4 Trisetum flavescens (L.) P. Beauv. 651 Veronica spicata L. 22 Veronica teucrium L. 9621 Viburnum lantana L. 51 1 5 1 4 1 1 16 1 1 4 1 10 4 1 Vicia cracca L. s.str. 37 3 2 10 1 3 1 2 1 1 2 10 1 Vicia hirsuta (L.) Gray 14 1 6 6 1 Vincetoxicum hirundinaria Medik. 43 1 9 22 1 2 3 1 4 Total number of species 238 49 65 60 48 50 63 59 46 49 43 84 85 53 75 48 78 55 87 36 104 109 53 43 54 66 63 127 67 108 59 61
APPENDIX
Appendix 2 Table of in-situ measurements of the 31 populations (population codes in Chapter3), first lines give the means of measurement and second lines give the standard errors. We present firstly the 2005 counts of genets, followed by the morphological measurements of marked genets pooled over years 2005 and 2006 (germination rate only in 2005) , finally we present stages of marked individuals over years 2005(05)-2006(06)-2007(07), because of vandalism and strong management with gyrogrinding we lose all stakes in BG and SC2 in 2007. repro, reproductive genets; veget, vegetative genets. In situ measurements AE3 AL1 AL2 BG BOU CC1 CC2 CH1 CH2 CR EG1 EG2 FRA FRU HER HES HU LAN1 LAN2 NAT PA REB REP SC1 SC2 SC4 SE SP TH TR VU No. of vegetative genets per 4m2 21.8 3.3 3.7 1.8 2.5 4.2 2.8 11.8 2.5 4.0 9.7 4.1 5.1 11.7 4.3 15.6 19.6 1.9 1.5 20.8 6.0 9.8 11.5 15.9 6.2 7.0 7.9 8.5 3.8 6.5 2.1 4.50 1.12 0.64 0.44 0.63 0.58 1.08 3.47 1.17 0.84 1.66 1.05 1.14 1.88 1.15 1.83 4.59 0.44 0.37 1.65 0.53 2.95 1.64 2.20 1.78 1.47 0.80 1.85 0.48 0.96 0.64 No. of reproductive genets per 4m2 5.30 7.87 4.35 3.90 1.87 8.18 4.50 2.50 3.80 2.60 4.57 9.00 2.27 4.13 3.40 6.06 3.33 9.81 8.90 9.61 4.59 4.60 4.20 5.70 16.80 14.40 2.85 6.22 3.85 2.73 2.93 1.16 2.59 0.86 0.74 0.17 1.07 1.12 0.52 0.92 0.43 0.65 1.94 0.33 0.75 0.58 0.86 0.68 1.57 1.55 0.90 0.48 0.70 0.73 1.16 3.91 3.63 0.20 1.21 0.39 0.32 0.49 No. of total genets per 4m2 27.1 11.1 8.0 5.7 4.3 12.4 7.3 14.3 6.3 6.6 14.3 13.1 7.3 15.9 7.7 21.7 22.9 11.7 10.4 30.5 10.5 14.4 15.7 21.6 23.0 21.4 10.7 14.7 7.7 9.2 5.0 5.28 3.27 1.20 1.15 0.69 1.21 2.06 3.71 1.79 0.87 2.17 2.41 1.28 2.28 1.53 2.28 5.01 1.75 1.71 2.11 0.83 3.40 1.76 2.44 5.27 4.88 0.87 2.45 0.73 1.16 0.97 Rate of reproductive genets per 4m2 0.22 0.77 0.59 0.74 0.59 0.63 0.67 0.27 0.71 0.41 0.40 0.75 0.38 0.32 0.52 0.32 0.25 0.85 0.86 0.34 0.48 0.47 0.28 0.27 0.72 0.62 0.42 0.46 0.56 0.34 0.70 0.04 0.06 0.05 0.06 0.08 0.04 0.08 0.07 0.08 0.07 0.04 0.05 0.04 0.06 0.07 0.02 0.06 0.03 0.03 0.02 0.02 0.09 0.04 0.05 0.05 0.05 0.03 0.06 0.03 0.04 0.07 No. of ramets (repro + veget genet) 3.13 8.65 5.12 9.91 5.09 2.59 4.33 6.15 2.87 4.20 4.05 7.70 3.63 2.63 3.38 2.87 2.85 10.46 9.35 3.31 2.72 7.20 7.18 3.13 3.98 4.27 2.96 3.14 3.32 2.55 5.52 0.34 1.21 0.78 1.63 0.59 0.27 0.69 0.92 0.38 0.60 0.27 0.57 0.53 0.17 0.53 0.14 0.31 1.14 1.18 0.16 0.13 1.51 1.12 0.28 0.49 0.56 0.12 0.28 0.16 0.18 0.75 No. of rosettes (veget genet) 3.07 3.60 1.80 3.30 2.67 2.47 2.71 5.21 2.06 2.95 3.24 5.22 2.70 2.42 2.88 2.44 2.24 2.10 4.77 2.51 2.17 2.20 5.46 3.10 2.94 2.94 2.26 2.58 2.28 2.81 2.58 0.41 0.58 0.20 0.70 0.60 0.34 0.76 1.26 0.28 0.52 0.39 1.11 0.75 0.23 0.76 0.13 0.31 0.26 1.22 0.16 0.11 0.30 1.04 0.37 0.38 0.61 0.13 0.31 0.18 0.34 0.33 No. of leaves (veget genet) 5.10 5.50 5.70 6.67 5.22 4.67 5.17 4.73 5.00 5.00 6.27 4.43 6.15 5.94 5.13 5.42 6.41 5.33 5.00 5.45 5.33 6.33 5.00 4.55 6.50 5.75 5.82 5.48 5.19 5.33 5.25 0.41 0.65 0.40 0.21 0.52 0.26 0.83 0.38 0.37 0.44 0.49 0.48 0.56 0.36 0.52 0.18 0.25 0.50 0.45 0.14 0.14 1.05 0.39 0.39 0.60 0.45 0.20 0.27 0.21 0.56 0.37 No. of parasited leaves (veget genet) 2.80 1.25 1.10 3.33 1.56 1.33 2.00 0.64 2.11 1.22 2.73 1.29 3.54 2.11 1.25 1.57 1.55 1.78 0.80 1.87 2.54 2.33 1.64 1.09 3.00 2.50 2.63 1.90 2.04 3.17 2.13 0.29 0.63 0.59 0.88 0.34 0.30 0.86 0.20 0.39 0.22 0.29 0.47 0.42 0.35 0.56 0.15 0.35 0.57 0.37 0.12 0.15 0.56 0.25 0.41 0.63 0.82 0.23 0.26 0.29 0.54 0.30 Rate of parasited leaves (veget genet) 0.58 0.22 0.15 0.51 0.30 0.28 0.35 0.14 0.41 0.28 0.47 0.33 0.58 0.36 0.23 0.31 0.24 0.32 0.19 0.35 0.48 0.38 0.39 0.24 0.45 0.42 0.44 0.35 0.39 0.57 0.42 0.07 0.09 0.07 0.13 0.08 0.06 0.10 0.05 0.06 0.08 0.05 0.14 0.06 0.06 0.10 0.03 0.06 0.09 0.09 0.02 0.03 0.07 0.08 0.10 0.08 0.13 0.03 0.05 0.05 0.07 0.06 No. of leaves with herbivory (veget genet) 2.50 1.00 0.50 2.50 1.56 0.90 0.83 0.36 2.11 1.11 1.68 1.14 3.46 1.00 0.63 1.14 1.05 1.22 0.20 0.88 2.18 2.00 0.93 0.91 2.30 2.13 2.04 1.68 1.15 2.50 0.63 0.27 0.71 0.22 0.85 0.34 0.25 0.31 0.15 0.39 0.26 0.28 0.46 0.46 0.24 0.38 0.14 0.28 0.55 0.20 0.10 0.15 0.63 0.25 0.34 0.58 0.77 0.22 0.28 0.22 0.50 0.18 Rate of leaves with herbivory (veg genet) 0.52 0.17 0.07 0.38 0.30 0.19 0.17 0.08 0.41 0.26 0.24 0.30 0.57 0.16 0.11 0.22 0.16 0.21 0.05 0.16 0.42 0.33 0.22 0.21 0.32 0.34 0.33 0.30 0.23 0.47 0.13 0.07 0.10 0.03 0.13 0.08 0.05 0.06 0.03 0.06 0.08 0.04 0.14 0.07 0.04 0.06 0.03 0.05 0.08 0.05 0.02 0.03 0.09 0.07 0.09 0.06 0.11 0.03 0.05 0.04 0.09 0.04 Length of leaves (veget genet) 9.38 9.89 7.82 8.83 7.12 7.59 8.71 7.38 7.92 8.09 10.21 9.85 7.24 7.79 9.12 6.49 7.45 8.93 10.19 8.03 5.96 8.20 7.39 7.72 8.48 7.88 7.66 7.49 7.57 7.55 10.35 0.38 0.98 0.60 0.84 0.70 0.37 0.56 0.57 0.77 0.64 0.40 1.05 0.53 0.60 0.59 0.18 0.37 0.49 0.80 0.18 0.13 0.71 0.34 0.64 0.73 0.76 0.19 0.32 0.26 0.43 0.72 Width of leaves (veget genet) 2.60 2.52 2.02 2.42 2.04 2.00 2.31 2.02 2.31 2.48 2.53 2.66 2.15 2.69 3.11 1.79 2.30 2.46 2.47 2.14 1.78 2.71 2.32 2.18 2.24 2.35 2.22 2.21 2.12 2.47 2.54 0.12 0.20 0.12 0.21 0.18 0.07 0.19 0.14 0.17 0.15 0.10 0.22 0.14 0.15 0.21 0.04 0.11 0.13 0.17 0.04 0.03 0.20 0.09 0.17 0.19 0.18 0.04 0.07 0.06 0.17 0.12 Area of leaves (veget genet) 19.6 20.8 13.5 17.9 12.8 12.5 16.8 12.7 15.4 17.0 21.5 23.6 13.4 18.3 23.5 9.8 14.4 18.3 21.0 14.5 8.8 18.8 13.8 14.8 16.6 15.4 14.2 13.7 13.4 15.5 21.5 1.50 3.49 1.80 3.18 2.11 0.98 2.53 1.64 2.48 2.36 1.51 3.71 1.78 2.24 2.93 0.49 1.36 1.91 2.85 0.60 0.30 2.92 1.13 2.25 3.12 2.66 0.60 0.90 0.78 1.83 2.21 No. of ramets (repro genet) 3.15 9.97 6.82 12.7 6.24 2.71 5.19 7.00 3.45 5.33 4.56 8.32 4.39 2.89 3.71 3.30 3.83 13.03 11.56 4.05 3.17 10.20 9.75 3.17 4.74 5.53 3.40 3.97 3.85 2.41 7.03 0.48 1.44 1.09 2.05 0.76 0.41 0.95 1.33 0.61 0.98 0.35 0.64 0.73 0.26 0.73 0.24 0.59 1.39 1.47 0.25 0.21 2.21 2.21 0.44 0.77 0.82 0.17 0.48 0.22 0.21 1.05 No. of rosettes (repro genet) 1.73 3.87 2.98 7.96 3.92 0.96 2.54 4.90 1.14 2.62 1.92 3.45 2.48 1.26 1.75 1.68 1.74 1.54 2.37 2.16 1.41 6.32 7.69 1.56 2.09 3.32 1.43 2.21 1.43 0.84 3.54 0.45 0.77 0.72 1.53 0.68 0.39 0.88 1.26 0.37 0.67 0.26 0.49 0.72 0.25 0.68 0.17 0.34 0.28 0.53 0.21 0.18 1.70 2.11 0.32 0.59 0.71 0.11 0.43 0.17 0.18 0.82 No. of stems 1.42 6.11 3.84 4.71 2.32 1.76 2.65 2.10 2.32 2.71 2.65 4.86 1.91 1.63 1.96 1.62 2.09 11.49 9.19 1.89 1.76 3.88 2.06 1.61 2.65 2.00 1.97 1.76 2.41 1.57 3.49 0.10 0.98 0.53 1.00 0.31 0.16 0.21 0.34 0.40 0.49 0.24 0.46 0.17 0.15 0.24 0.11 0.40 1.31 1.29 0.11 0.09 0.96 0.53 0.26 0.49 0.30 0.11 0.21 0.15 0.14 0.53 Stalk height 41.9 53.1 41.7 52.9 26.5 30.8 37.9 28.2 35.6 49.6 37.6 49.8 42.8 36.4 44.2 26.1 30.6 49.1 47.4 34.4 26.9 38.6 29.1 38.3 40.1 34.1 31.1 39.7 33.8 28.5 45.2 2.52 2.07 1.50 2.78 1.22 1.22 1.93 1.60 2.10 2.95 1.11 1.49 2.23 3.73 2.37 0.80 2.28 1.33 1.82 0.69 0.63 2.74 2.53 2.83 2.31 2.01 0.64 2.07 0.73 2.13 1.44 No. of stalk leaves 21.1 25.3 26.5 25.5 22.3 22.4 24.1 18.1 23.3 23.8 19.4 20.4 21.6 22.0 26.0 17.5 18.6 22.6 26.4 18.9 21.2 27.9 20.8 18.6 24.8 18.3 20.6 20.8 21.6 18.9 22.2 1.37 1.20 0.95 1.98 0.90 0.91 0.86 1.70 1.62 2.37 0.54 0.78 1.13 2.30 2.25 0.60 1.87 0.74 1.09 0.47 0.57 2.07 1.47 1.65 1.88 2.90 0.44 1.62 0.52 1.21 0.79 No. of parasited stalk leaves 4.71 4.74 4.68 2.00 4.26 2.65 3.36 2.33 1.60 3.36 6.74 7.29 6.71 6.50 2.83 4.35 3.13 6.13 6.27 4.50 4.10 4.93 1.33 5.00 5.30 6.14 5.21 4.13 6.27 4.10 3.79 1.38 0.52 0.67 0.38 0.87 0.53 0.80 0.55 0.50 0.79 0.55 0.52 1.07 1.88 0.59 0.40 0.74 0.50 0.79 0.31 0.28 1.38 0.42 0.98 1.45 1.24 0.34 1.14 0.41 0.54 0.52 Rate of parasited stalk leaves 0.22 0.20 0.18 0.08 0.19 0.12 0.14 0.13 0.07 0.14 0.35 0.37 0.31 0.26 0.11 0.25 0.17 0.28 0.24 0.24 0.19 0.17 0.06 0.26 0.19 0.30 0.25 0.20 0.29 0.24 0.18 0.06 0.02 0.03 0.01 0.03 0.02 0.03 0.03 0.02 0.03 0.03 0.03 0.05 0.06 0.02 0.02 0.03 0.02 0.03 0.02 0.01 0.05 0.02 0.04 0.04 0.06 0.01 0.05 0.02 0.05 0.03
APPENDIX
Appendix 2 (Continued) In situ measurements AE3 AL1 AL2 BG BOU CC1 CC2 CH1 CH2 CR EG1 EG2 FRA FRU HER HES HU LAN1 LAN2 NAT PA REB REP SC1 SC2 SC4 SE SP TH TR VU No. of stalk leaves with herbivory 3.14 1.63 1.96 1.36 3.21 1.17 1.86 1.22 1.00 2.91 5.47 5.46 4.41 1.83 1.92 3.15 1.50 3.43 3.53 2.67 2.93 3.93 1.17 4.71 4.30 4.86 3.57 3.25 2.87 3.67 2.53 1.67 0.45 0.32 0.34 0.88 0.35 0.52 0.28 0.37 0.73 0.56 0.58 1.05 0.66 0.50 0.37 0.53 0.37 0.58 0.26 0.26 1.25 0.40 1.06 1.59 1.37 0.27 1.06 0.26 0.51 0.53 Rate of stalk leaves with herbivory 0.14 0.07 0.07 0.06 0.14 0.05 0.08 0.07 0.04 0.12 0.28 0.27 0.21 0.07 0.07 0.18 0.08 0.16 0.14 0.14 0.14 0.14 0.05 0.24 0.15 0.23 0.18 0.15 0.13 0.21 0.12 0.07 0.02 0.01 0.01 0.03 0.01 0.02 0.02 0.01 0.02 0.03 0.03 0.05 0.02 0.02 0.02 0.03 0.02 0.02 0.01 0.01 0.04 0.01 0.04 0.05 0.06 0.01 0.04 0.01 0.05 0.03 Length of stalk leaves 7.35 7.27 6.84 6.58 5.60 6.03 6.08 5.38 5.68 6.22 6.53 8.10 5.50 5.39 6.63 5.09 5.74 7.09 7.44 6.25 4.90 5.90 5.15 5.87 6.19 5.98 5.98 5.99 6.05 5.15 6.95 0.38 0.25 0.18 0.22 0.15 0.19 0.19 0.22 0.30 0.31 0.17 0.24 0.20 0.46 0.31 0.11 0.32 0.14 0.25 0.11 0.08 0.45 0.21 0.29 0.34 0.36 0.08 0.28 0.10 0.27 0.23 Width of stalk leaves 1.87 2.31 2.09 1.96 1.74 1.60 1.63 1.52 1.65 1.80 1.66 2.25 1.62 1.64 2.10 1.30 1.62 2.17 2.23 1.64 1.51 2.20 1.73 1.53 1.86 1.84 1.75 1.73 1.73 1.78 1.97 0.09 0.09 0.07 0.07 0.06 0.06 0.07 0.07 0.09 0.11 0.05 0.07 0.07 0.12 0.13 0.03 0.10 0.05 0.11 0.03 0.03 0.17 0.09 0.08 0.11 0.11 0.03 0.08 0.03 0.09 0.09 Area of stalk leaves 11.2 13.7 11.5 10.3 7.8 7.8 7.9 6.6 7.7 9.2 8.9 14.9 7.3 7.9 11.4 5.4 7.8 12.6 13.4 8.4 6.1 11.4 7.1 7.3 9.6 8.9 8.6 8.6 8.6 7.7 11.1 1.08 0.94 0.56 0.62 0.42 0.49 0.54 0.51 0.72 0.98 0.47 0.83 0.54 1.21 1.16 0.22 0.82 0.54 1.07 0.29 0.20 1.47 0.59 0.72 1.10 0.98 0.25 0.76 0.28 0.71 0.84 No. of grazed stems 0.38 0.16 0.19 1.32 0.36 0.04 0.24 0.33 0.05 0.09 0.22 0.12 0.13 0.43 0.16 0.15 0.00 0.41 0.03 0.13 0.14 0.00 0.19 0.11 0.04 0.05 0.12 0.10 0.23 0.11 0.68 0.11 0.07 0.07 0.59 0.11 0.03 0.12 0.13 0.05 0.06 0.07 0.05 0.07 0.10 0.12 0.04 0.00 0.22 0.03 0.02 0.03 0.00 0.11 0.06 0.04 0.05 0.03 0.07 0.06 0.05 0.24 No. of develloped capitulums per ramet 2.23 3.50 1.81 4.16 2.91 1.46 2.50 0.81 2.95 3.30 2.43 4.73 2.24 2.94 3.60 1.46 1.85 3.78 5.45 2.03 1.96 7.85 1.48 2.81 5.64 3.10 2.28 1.80 2.52 1.64 2.10 0.39 0.57 0.22 0.50 0.30 0.14 0.22 0.18 0.32 0.37 0.24 0.84 0.34 0.43 0.54 0.09 0.22 0.35 0.90 0.12 0.09 1.64 0.18 0.41 0.79 0.55 0.15 0.25 0.16 0.18 0.41 No. of undevelloped capitulums per ramet 1.38 3.34 1.58 0.53 0.20 1.15 0.12 0.81 0.18 0.00 2.80 4.76 1.76 0.42 0.28 0.65 0.67 2.25 2.45 1.23 0.24 0.11 0.00 0.96 1.09 0.19 1.49 2.16 1.72 0.93 2.85 0.28 0.53 0.20 0.53 0.11 0.17 0.08 0.19 0.11 0.00 0.32 0.71 0.38 0.15 0.18 0.08 0.22 0.21 0.41 0.10 0.03 0.06 0.00 0.51 0.74 0.09 0.11 0.32 0.15 0.18 0.39 No. of total capitulms per ramet 3.62 6.84 3.39 5.00 3.11 2.60 2.62 1.62 3.14 3.30 5.22 9.25 4.00 3.36 3.88 2.12 2.52 6.03 7.90 3.27 2.20 7.96 1.48 3.77 6.73 3.29 3.76 3.94 4.21 2.57 4.95 0.42 0.80 0.23 0.74 0.31 0.21 0.26 0.20 0.32 0.37 0.37 0.86 0.52 0.44 0.52 0.11 0.28 0.37 0.84 0.15 0.10 1.64 0.18 0.51 1.14 0.58 0.17 0.30 0.19 0.26 0.45 Rate of develloped capitulums per ramet 0.63 0.53 0.54 0.96 0.95 0.63 0.98 0.53 0.95 1.00 0.50 0.49 0.60 0.88 0.93 0.75 0.81 0.60 0.60 0.67 0.91 0.97 1.00 0.87 0.93 0.96 0.64 0.49 0.64 0.70 0.43 0.07 0.05 0.05 0.04 0.03 0.05 0.02 0.10 0.03 0.00 0.04 0.05 0.07 0.04 0.05 0.03 0.05 0.03 0.07 0.02 0.01 0.02 0.00 0.06 0.04 0.02 0.02 0.05 0.03 0.06 0.06 No. of develloped capitulums per genet 2.38 10.37 4.11 10.4 5.00 1.87 4.65 1.29 5.82 4.96 5.34 14.04 3.24 3.48 5.08 1.89 3.06 23.11 24.39 3.12 2.91 23.5 1.71 4.04 12.32 4.81 3.96 1.96 4.82 2.34 2.63 0.40 3.49 0.86 2.03 0.84 0.21 0.57 0.40 1.35 0.87 0.86 3.07 0.62 0.50 0.82 0.19 0.66 3.85 6.25 0.31 0.26 7.71 0.24 0.93 2.48 0.98 0.55 0.32 0.58 0.41 0.50 No. of undevelloped capitulums per genet 2.19 14.24 5.00 0.58 0.29 1.44 0.42 1.52 0.41 0.00 5.84 13.80 2.47 0.58 0.32 1.03 1.15 16.31 8.68 1.80 0.32 0.22 0.00 1.73 1.82 0.19 2.39 2.82 3.20 1.43 7.07 0.52 3.41 0.71 0.53 0.16 0.22 0.26 0.34 0.18 0.00 0.89 2.07 0.57 0.19 0.18 0.16 0.41 2.37 1.49 0.21 0.04 0.11 0.00 0.98 1.29 0.09 0.28 0.47 0.34 0.31 1.45 No. of total capitulms per genet 4.7 24.9 9.1 11.7 5.3 3.3 5.1 2.8 6.3 5.0 11.1 27.4 5.7 4.1 5.6 3.0 4.2 39.4 33.1 5.0 3.2 23.7 1.7 5.8 14.1 5.0 6.2 4.8 8.0 3.8 9.9 0.63 5.62 1.35 1.91 0.83 0.32 0.70 0.60 1.34 0.87 1.30 3.25 0.95 0.51 0.77 0.29 0.89 4.83 6.74 0.41 0.27 7.70 0.24 1.30 2.96 1.01 0.61 0.49 0.69 0.54 1.48 Rate of develloped capitulums per genet 0.61 0.41 0.43 0.96 0.95 0.65 0.96 0.44 0.92 1.00 0.49 0.43 0.62 0.87 0.87 0.74 0.84 0.53 0.58 0.66 0.91 0.97 1.00 0.86 0.93 0.97 0.63 0.47 0.62 0.68 0.36 0.07 0.04 0.05 0.04 0.03 0.04 0.02 0.09 0.03 0.00 0.03 0.05 0.06 0.04 0.06 0.03 0.05 0.03 0.07 0.02 0.01 0.02 0.00 0.06 0.04 0.02 0.02 0.05 0.03 0.06 0.05 No. of ≥1mm cypselae 28.4 41.7 33.9 70.9 53.9 38.4 55.9 31.1 61.9 58.9 36.2 42.6 44.8 53.3 56.6 35.3 44.6 33.7 45.1 37.0 51.3 60.1 49.7 35.9 50.2 50.8 29.1 35.8 37.9 39.6 30.0 6.16 4.14 3.92 2.59 2.98 3.07 2.93 3.78 3.31 3.86 2.20 2.75 3.87 3.39 5.50 1.51 3.41 2.51 5.92 1.32 1.26 2.39 3.81 4.29 4.00 4.18 1.63 3.66 2.03 3.57 5.43 No. of infected cypselae 1.15 3.10 16.76 0.76 0.00 1.57 0.00 0.94 0.00 0.11 15.99 26.12 2.65 2.00 0.00 8.42 0.52 33.15 29.45 9.28 5.43 1.81 0.00 1.05 1.45 0.00 17.54 10.60 4.29 17.16 17.18 0.77 0.97 3.61 0.53 0.00 1.15 0.00 0.66 0.00 0.08 2.08 2.66 1.73 0.98 0.00 1.30 0.38 2.52 4.86 1.06 0.83 1.09 0.00 0.73 1.35 0.00 1.28 2.62 0.90 3.68 3.92 No. of total cypselae 77.0 72.1 70.8 75.1 61.3 61.3 68.9 48.3 72.0 65.6 58.3 56.4 66.5 66.3 74.9 59.6 58.3 69.2 74.0 59.0 66.0 71.2 60.6 59.3 65.8 62.2 65.5 59.9 64.7 61.4 69.0 3.62 2.28 2.09 2.79 1.88 1.67 2.08 3.16 2.55 4.05 1.51 1.86 2.33 2.87 3.98 1.16 2.86 1.71 3.09 0.90 0.97 1.99 2.59 2.39 2.88 2.71 1.10 1.98 1.50 1.77 1.83 Mass ≥1mm cypsela mg 0.60 0.46 0.53 0.51 0.67 0.56 0.64 0.45 0.54 0.61 0.60 0.62 0.58 0.70 0.61 0.64 0.65 0.44 0.58 0.73 0.58 0.65 0.62 0.61 0.83 0.83 0.54 0.62 0.64 0.58 0.46 0.04 0.03 0.03 0.02 0.03 0.02 0.03 0.03 0.03 0.04 0.02 0.04 0.03 0.03 0.04 0.02 0.04 0.02 0.04 0.02 0.01 0.04 0.04 0.04 0.04 0.05 0.01 0.03 0.02 0.03 0.02 Mass not infected cypsela mg 0.31 0.37 0.40 0.48 0.58 0.44 0.54 0.34 0.49 0.51 0.45 0.51 0.48 0.60 0.53 0.48 0.52 0.32 0.47 0.55 0.50 0.58 0.52 0.51 0.70 0.69 0.38 0.44 0.47 0.48 0.36 0.03 0.03 0.03 0.03 0.04 0.03 0.03 0.02 0.03 0.03 0.02 0.05 0.03 0.04 0.04 0.02 0.04 0.02 0.04 0.02 0.01 0.05 0.04 0.05 0.04 0.07 0.01 0.04 0.02 0.04 0.04 Mass total cypsela mg 0.32 0.36 0.36 0.50 0.62 0.45 0.58 0.36 0.51 0.58 0.45 0.54 0.46 0.62 0.53 0.48 0.58 0.34 0.44 0.55 0.51 0.61 0.55 0.50 0.70 0.75 0.35 0.47 0.45 0.48 0.32 0.04 0.03 0.03 0.02 0.03 0.03 0.04 0.03 0.03 0.04 0.02 0.03 0.03 0.04 0.04 0.02 0.04 0.02 0.04 0.02 0.01 0.04 0.03 0.05 0.04 0.06 0.01 0.04 0.02 0.04 0.03 Germination rate ≥1mm cypselae 0.57 0.19 0.24 0.12 0.34 0.29 0.28 0.15 0.26 0.12 0.34 0.21 0.20 0.56 0.51 0.34 0.35 0.14 0.09 0.52 0.29 0.35 0.39 0.33 0.67 0.50 0.21 0.36 0.45 0.14 0.05 0.08 0.06 0.05 0.04 0.06 0.05 0.06 0.07 0.06 0.04 0.05 0.06 0.05 0.05 0.09 0.03 0.07 0.04 0.06 0.02 0.02 0.07 0.09 0.08 0.03 0.05 0.02 0.06 0.04 0.05 0.02
APPENDIX
Appendix 2 (Continued) In situ measurements AE3 AL1 AL2 BG BOU CC1 CC2 CH1 CH2 CR EG1 EG2 FRA FRU HER HES HU LAN1 LAN2 NAT PA REB REP SC1 SC2 SC4 SE SP TH TR VU Germination rate total cypselae set 0.12 0.12 0.05 0.11 0.24 0.17 0.20 0.08 0.22 0.10 0.19 0.07 0.15 0.46 0.34 0.16 0.27 0.02 0.02 0.27 0.23 0.32 0.29 0.24 0.46 0.41 0.09 0.21 0.22 0.12 0.04 0.04 0.03 0.01 0.03 0.04 0.04 0.04 0.03 0.06 0.04 0.03 0.03 0.04 0.06 0.07 0.02 0.06 0.01 0.01 0.02 0.02 0.07 0.07 0.08 0.05 0.07 0.02 0.05 0.03 0.05 0.02 Germination rate not infected cypselae 0.12 0.13 0.13 0.11 0.24 0.17 0.20 0.08 0.22 0.10 0.24 0.16 0.15 0.46 0.34 0.19 0.27 0.03 0.03 0.31 0.23 0.32 0.29 0.25 0.48 0.41 0.11 0.24 0.24 0.13 0.04 0.04 0.04 0.03 0.03 0.04 0.04 0.04 0.03 0.06 0.04 0.04 0.05 0.04 0.06 0.07 0.02 0.06 0.01 0.02 0.02 0.02 0.07 0.07 0.09 0.05 0.07 0.02 0.05 0.03 0.05 0.02 No. of orange grubs per capitulum 1.17 0.94 1.48 0.18 0.10 0.24 0.14 0.05 0.26 0.00 0.88 1.61 1.03 0.43 0.05 0.95 0.25 2.96 2.29 0.87 0.45 0.05 0.00 0.04 0.00 0.05 1.78 1.09 2.46 0.65 3.06 0.41 0.26 0.36 0.10 0.07 0.09 0.10 0.05 0.17 0.00 0.17 0.31 0.34 0.14 0.05 0.19 0.11 0.46 0.82 0.10 0.08 0.05 0.00 0.04 0.00 0.05 0.20 0.30 0.27 0.23 0.62 No. of Coleophora grubs per capitulum 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.52 0.00 0.00 0.00 0.06 0.00 0.23 0.19 0.10 0.06 0.00 0.00 0.04 0.00 0.00 0.10 0.04 0.01 0.06 0.00 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.13 0.00 0.00 0.00 0.02 0.00 0.06 0.15 0.03 0.02 0.00 0.00 0.04 0.00 0.00 0.02 0.03 0.01 0.04 0.00 Rate of repro05-repro06-repro07 0.30 1.00 0.95 NA 0.63 0.67 0.83 0.36 0.88 0.50 0.66 0.81 0.56 0.07 0.36 0.45 0.27 0.89 0.92 0.41 0.54 0.64 0.22 0.20 NA 0.20 0.65 0.20 0.86 0.46 0.75 0.15 0.00 0.05 NA 0.11 0.11 0.11 0.15 0.13 0.17 0.08 0.07 0.13 0.07 0.15 0.06 0.12 0.05 0.08 0.05 0.05 0.15 0.15 0.13 NA 0.20 0.06 0.08 0.05 0.14 0.11 Rate of repro05-repro06-veget07 0.40 0.00 0.00 NA 0.16 0.05 0.08 0.18 0.00 0.10 0.03 0.06 0.19 0.43 0.27 0.04 0.07 0.04 0.00 0.06 0.20 0.18 0.00 0.20 NA 0.00 0.13 0.04 0.02 0.38 0.00 0.16 0.00 0.00 NA 0.09 0.05 0.08 0.12 0.00 0.10 0.03 0.04 0.10 0.14 0.14 0.02 0.07 0.03 0.00 0.02 0.04 0.12 0.00 0.13 NA 0.00 0.04 0.04 0.02 0.14 0.00 Rate of repro05-veget06-veget07 0.30 0.00 0.05 NA 0.05 0.14 0.00 0.36 0.00 0.30 0.06 0.03 0.13 0.29 0.00 0.21 0.07 0.04 0.00 0.10 0.17 0.00 0.33 0.50 NA 0.40 0.06 0.44 0.02 0.00 0.13 0.15 0.00 0.05 NA 0.05 0.08 0.00 0.15 0.00 0.15 0.04 0.03 0.09 0.13 0.00 0.05 0.07 0.03 0.00 0.03 0.04 0.00 0.17 0.17 NA 0.24 0.03 0.10 0.02 0.00 0.09 Rate of repro05-veget06-repro07 0.00 0.00 0.00 NA 0.16 0.14 0.08 0.09 0.13 0.10 0.26 0.10 0.13 0.21 0.36 0.30 0.60 0.00 0.08 0.43 0.09 0.18 0.44 0.10 NA 0.40 0.16 0.32 0.10 0.15 0.13 0.00 0.00 0.00 NA 0.09 0.08 0.08 0.09 0.13 0.10 0.07 0.05 0.09 0.11 0.15 0.05 0.13 0.00 0.08 0.05 0.03 0.12 0.18 0.10 NA 0.24 0.04 0.10 0.04 0.10 0.09 Rate of veget05-veget06-veget07 0.00 0.50 0.18 NA 0.22 0.35 0.43 0.50 0.33 0.50 0.13 0.10 0.36 0.50 0.33 0.25 0.33 0.42 0.13 0.26 0.52 0.29 0.75 0.30 NA 0.25 0.34 0.76 0.26 0.20 0.40 0.00 0.50 0.12 NA 0.15 0.11 0.20 0.19 0.21 0.17 0.06 0.10 0.13 0.15 0.17 0.05 0.13 0.10 0.13 0.05 0.05 0.18 0.16 0.15 NA 0.25 0.06 0.11 0.08 0.13 0.16 Rate of veget05-veget06-repro07 0.11 0.00 0.45 NA 0.33 0.35 0.14 0.13 0.33 0.00 0.13 0.20 0.29 0.17 0.00 0.28 0.47 0.13 0.38 0.16 0.14 0.14 0.00 0.30 NA 0.00 0.14 0.12 0.13 0.10 0.00 0.11 0.00 0.16 NA 0.17 0.11 0.14 0.13 0.21 0.00 0.06 0.13 0.13 0.11 0.00 0.05 0.13 0.07 0.18 0.04 0.04 0.14 0.00 0.15 NA 0.00 0.05 0.08 0.06 0.10 0.00 Rate of veget05-repro06-repro07 0.44 0.50 0.36 NA 0.00 0.20 0.43 0.25 0.33 0.10 0.72 0.50 0.07 0.17 0.44 0.37 0.20 0.46 0.50 0.45 0.17 0.14 0.13 0.20 NA 0.50 0.24 0.06 0.29 0.20 0.20 0.18 0.50 0.15 NA 0.00 0.09 0.20 0.16 0.21 0.10 0.08 0.17 0.07 0.11 0.18 0.06 0.11 0.10 0.19 0.05 0.04 0.14 0.13 0.13 NA 0.29 0.06 0.06 0.08 0.13 0.13 Rate of veget05-repro06-veget07 0.44 0.00 0.00 NA 0.44 0.10 0.00 0.13 0.00 0.40 0.03 0.20 0.29 0.17 0.22 0.10 0.00 0.00 0.00 0.13 0.18 0.43 0.13 0.20 NA 0.25 0.28 0.06 0.32 0.50 0.40 0.18 0.00 0.00 NA 0.18 0.07 0.00 0.13 0.00 0.16 0.03 0.13 0.13 0.11 0.15 0.04 0.00 0.00 0.00 0.03 0.04 0.20 0.13 0.13 NA 0.25 0.06 0.06 0.09 0.17 0.16 Rate of reproductive plants 2006 0.80 0.83 0.72 0.65 0.68 0.52 0.70 0.50 0.53 0.55 0.71 0.82 0.57 0.40 0.65 0.48 0.27 0.77 0.75 0.53 0.56 0.70 0.30 0.40 0.55 0.60 0.71 0.18 0.78 0.79 0.71 0.09 0.08 0.08 0.12 0.09 0.08 0.11 0.11 0.12 0.11 0.06 0.06 0.09 0.09 0.11 0.04 0.08 0.05 0.10 0.04 0.03 0.11 0.11 0.11 0.11 0.13 0.03 0.06 0.04 0.08 0.09 Rate of reproductive plants 2007 0.42 0.92 0.90 NA 0.64 0.68 0.79 0.42 0.86 0.35 0.88 0.85 0.53 0.31 0.60 0.70 0.77 0.84 0.95 0.73 0.48 0.61 0.41 0.40 NA 0.56 0.61 0.38 0.75 0.48 0.62 0.12 0.08 0.05 NA 0.09 0.07 0.10 0.12 0.10 0.11 0.04 0.06 0.09 0.09 0.11 0.04 0.08 0.07 0.05 0.03 0.03 0.12 0.12 0.11 NA 0.18 0.04 0.08 0.05 0.11 0.10 Rate of repro-repro 0506 + 0607 0.36 0.78 0.66 0.59 0.48 0.40 0.62 0.31 0.48 0.30 0.52 0.69 0.37 0.18 0.38 0.33 0.20 0.68 0.65 0.34 0.38 0.45 0.16 0.20 0.45 0.29 0.45 0.14 0.55 0.39 0.50 0.08 0.07 0.06 0.12 0.07 0.05 0.08 0.07 0.09 0.07 0.04 0.05 0.06 0.05 0.08 0.03 0.05 0.04 0.08 0.02 0.02 0.08 0.06 0.06 0.11 0.09 0.03 0.04 0.03 0.07 0.07 Rate of repro-veget 0506 + 0607 0.15 0.06 0.03 0.18 0.20 0.11 0.05 0.18 0.15 0.23 0.10 0.07 0.18 0.29 0.23 0.16 0.18 0.01 0.03 0.19 0.15 0.18 0.22 0.25 0.15 0.21 0.13 0.20 0.08 0.12 0.15 0.06 0.04 0.02 0.10 0.05 0.03 0.04 0.06 0.06 0.07 0.03 0.03 0.05 0.06 0.07 0.02 0.05 0.01 0.03 0.02 0.02 0.06 0.07 0.07 0.08 0.08 0.02 0.04 0.02 0.05 0.05 Rate of veget-repro 0506 + 0607 0.26 0.08 0.15 0.06 0.18 0.20 0.13 0.15 0.18 0.15 0.27 0.14 0.18 0.18 0.25 0.26 0.32 0.10 0.20 0.29 0.14 0.21 0.19 0.18 0.10 0.29 0.22 0.14 0.22 0.25 0.17 0.07 0.05 0.04 0.06 0.05 0.04 0.05 0.06 0.07 0.06 0.04 0.04 0.05 0.05 0.07 0.03 0.06 0.03 0.06 0.02 0.02 0.07 0.07 0.06 0.07 0.09 0.02 0.04 0.03 0.06 0.05 Rate of veget-veget 0506 + 0607 0.08 0.08 0.15 0.18 0.14 0.29 0.21 0.33 0.18 0.33 0.11 0.05 0.27 0.36 0.15 0.24 0.30 0.09 0.13 0.18 0.31 0.16 0.43 0.28 0.30 0.21 0.18 0.34 0.14 0.08 0.19 0.04 0.05 0.04 0.10 0.05 0.05 0.07 0.08 0.07 0.08 0.03 0.02 0.06 0.06 0.06 0.03 0.06 0.02 0.05 0.02 0.02 0.06 0.08 0.07 0.11 0.08 0.02 0.05 0.02 0.04 0.05
APPENDIX
Appendix 3A Table of ex-situ measurements from F1 generation (F0 seeds collected in 2004 in 23 natural populations): full earth treatment. First measurement corresponds to 6 months aged seedlings, the
following measurements correspond to 18 months aged seedlings. First lines give the means of measurement and second lines give the standard errors. Population codes in chapter 3. Ex-situ measurements AL1 AL2 BG BOU CC1 CC2 CH1 EG2 FRA FRU HER HU LAN1 LAN2 NAT PA REB SC2 SE SP TH TR VU No. of rosettes (first measurement) 1.07 1.68 1.30 1.40 1.10 1.60 1.20 1.10 1.00 1.08 1.20 1.00 1.20 1.00 1.11 1.29 1.13 1.07 1.29 1.00 1.25 1.20 1.67 0.07 0.22 0.15 0.24 0.10 0.40 0.20 0.10 0.00 0.06 0.20 0.00 0.09 0.00 0.11 0.09 0.09 0.07 0.10 0.00 0.12 0.13 0.33 No. of leaves (first measurement) 15.3 14.8 15.9 16.0 12.3 9.6 14.0 11.8 15.4 17.5 13.0 13.8 12.8 10.2 15.1 19.2 16.7 18.9 14.1 16.3 15.5 18.9 16.2 1.84 1.22 2.99 2.70 1.58 1.12 1.52 1.38 1.50 2.16 3.30 1.29 0.81 1.74 1.31 1.83 1.47 1.30 0.72 1.64 1.02 2.51 1.94 Width of leaves (first measurement) 3.23 3.23 2.79 3.46 2.99 2.98 3.42 3.84 3.42 3.66 3.22 3.22 2.75 3.04 3.25 3.16 4.26 3.50 2.99 3.29 3.40 3.67 3.71 0.24 0.19 0.30 0.09 0.35 0.15 0.42 0.22 0.54 0.24 0.42 0.20 0.13 0.34 0.15 0.17 0.26 0.18 0.14 0.14 0.16 0.40 0.26 Length of leaves (first measurement) 8.11 7.92 7.34 8.76 7.90 8.22 7.82 9.09 8.68 9.73 8.16 8.96 6.80 5.82 9.46 8.54 9.11 9.83 7.98 8.94 9.40 8.33 8.41 0.82 0.46 0.94 0.51 0.73 0.68 0.67 0.80 1.40 0.64 0.91 0.32 0.36 0.47 0.43 0.49 0.61 0.49 0.44 0.55 0.45 1.11 0.55 No. of stems 1.47 2.04 2.60 1.40 2.00 1.40 1.80 2.10 2.20 3.36 1.60 1.76 1.80 1.80 2.06 2.92 3.00 3.07 2.00 2.20 2.75 2.90 2.33 0.27 0.31 0.69 0.40 0.47 0.24 0.73 0.31 0.49 0.43 0.60 0.28 0.20 0.37 0.27 0.38 0.49 0.40 0.20 0.24 0.37 0.66 0.44 Stalk height 60.7 50.8 47.8 39.8 46.3 42.8 60.0 55.6 50.2 61.1 52.6 51.8 46.7 56.4 49.3 52.3 59.7 54.9 48.4 55.3 51.8 51.2 46.5 2.19 2.73 4.50 8.82 1.99 3.43 4.49 1.69 2.56 2.33 2.11 1.92 1.78 5.23 2.13 1.91 2.33 1.86 1.57 1.61 1.65 3.51 2.10 No. of stalk leaves 42.5 37.5 34.5 38.6 39.3 38.2 43.3 37.9 34.0 41.4 44.8 40.5 36.7 34.0 40.7 45.3 42.3 39.0 35.5 42.6 41.3 43.6 43.0 2.06 2.24 3.06 8.74 1.16 3.17 3.25 1.25 2.05 1.69 3.48 1.34 1.29 3.70 1.13 1.57 1.70 1.86 1.41 1.37 1.32 1.59 2.49 Width of stalk leaves 2.43 2.05 2.22 1.38 1.43 1.82 1.68 2.64 2.56 2.50 1.96 1.84 2.06 2.52 1.85 1.74 2.77 1.95 2.00 1.87 2.10 2.14 2.24 0.12 0.13 0.29 0.18 0.17 0.10 0.14 0.19 0.21 0.15 0.12 0.10 0.09 0.30 0.10 0.10 0.20 0.18 0.09 0.12 0.15 0.11 0.15 Length of stalk leaves 7.07 6.28 5.98 4.86 5.17 5.26 5.45 7.27 8.16 7.17 5.64 6.15 6.07 6.46 6.15 5.61 7.27 6.34 6.29 6.15 6.56 6.08 6.37 0.39 0.39 0.74 0.74 0.38 0.53 0.23 0.24 0.80 0.31 0.29 0.23 0.29 0.75 0.26 0.27 0.40 0.45 0.30 0.29 0.27 0.45 0.32 No. of total capitulms per ramet 64.8 80.1 58.3 54.5 43.3 57.6 54.8 82.7 130.4 80.5 85.2 80.5 38.8 36.2 76.9 100.9 76.4 111.9 74.0 104.1 108.5 58.7 116.5 6.76 11.21 18.63 8.97 9.35 16.47 2.84 14.81 31.68 15.24 28.88 9.97 4.29 11.72 10.75 17.91 10.83 20.96 10.77 19.71 18.12 9.95 26.63 No. of total capitulms per genet 80.2 101.9 96.4 63.8 56.3 69.0 82.0 119.8 158.4 131.0 88.4 93.2 53.1 41.6 100.1 167.9 120.7 186.2 93.6 145.8 153.1 107.7 163.9 13.7 14.6 39.6 10.4 11.9 20.4 18.8 27.8 39.4 19.9 30.1 12.0 7.6 12.7 14.5 35.2 14.5 33.2 13.8 30.7 25.7 23.6 37.2 No. of total cypselae 88.1 99.3 91.6 86.5 90.5 72.8 94.0 76.7 111.8 96.8 107.6 88.4 85.4 86.0 93.8 106.1 96.9 80.9 94.2 86.9 102.7 117.7 106.3 2.70 2.73 5.73 7.41 3.72 7.59 5.72 2.94 4.92 3.95 3.72 3.67 2.90 6.30 3.70 3.43 6.59 2.52 3.28 3.01 3.58 4.52 5.15 Mass total cypsela mg 6.04 5.19 4.73 5.55 4.38 5.12 6.81 6.05 7.05 6.32 4.62 5.99 5.34 7.44 4.98 5.08 5.37 5.60 4.80 5.10 5.81 5.93 6.16 0.40 0.35 0.19 1.22 0.48 0.56 0.82 0.62 0.25 0.39 0.44 0.37 0.33 0.88 0.33 0.28 0.41 0.36 0.31 0.30 0.35 0.57 0.28 Producer of supernumerary rosettes 0.07 0.36 0.30 0.40 0.10 0.40 0.20 0.10 0.00 0.08 0.20 0.00 0.14 0.00 0.03 0.29 0.13 0.07 0.23 0.00 0.16 0.20 0.27 0.07 0.10 0.15 0.24 0.10 0.24 0.20 0.10 0.00 0.06 0.20 0.00 0.06 0.00 0.03 0.09 0.09 0.07 0.07 0.00 0.07 0.13 0.12 Survival 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
APPENDIX
Appendix 3B Table of ex situ measurements from F1 generation (F0 seeds collected in 2005 in 31 natural populations) : benign condition. First measurement corresponds to 6 months aged seedlings, the following measurements correspond to 18 months aged seedlings. First lines give the means of measurement and second lines give the standard errors. Population codes in chapter 3. Ex situ measurements AE3 AL1 AL2 BG BOU CC1 CC2 CH1 CH2 CR EG1 EG2 FRA FRU HER HES HU LAN1 LAN2 NAT PA REB REP SC1 SC2 SC4 SE SP TH TR VU No. of rosettes (first measurement) 1.40 1.30 1.17 1.14 1.40 1.07 1.25 1.75 1.86 2.00 1.50 1.10 1.64 1.64 1.00 1.60 1.56 1.33 1.00 1.38 1.89 1.80 1.13 1.33 1.00 1.50 1.85 1.71 2.00 2.80 2.25 0.22 0.21 0.11 0.14 0.27 0.07 0.16 0.75 0.46 0.53 0.25 0.10 0.27 0.24 0.00 0.17 0.44 0.17 0.00 0.15 0.22 0.42 0.13 0.24 0.00 0.34 0.27 0.27 0.22 0.49 0.45 No. of leaves (first measurement) 16.5 16.4 14.9 17.4 16.2 17.4 15.5 15.8 15.1 16.7 15.2 15.6 17.3 17.3 16.4 16.8 15.6 14.7 16.0 17.0 17.5 16.6 16.3 17.3 18.0 17.0 16.7 16.3 15.9 16.3 16.0 0.43 0.97 0.70 1.00 0.90 1.18 0.93 1.44 0.40 1.02 0.72 0.86 0.62 1.13 1.19 0.58 0.75 0.87 4.00 0.59 0.62 0.98 1.01 0.97 0.78 1.15 0.48 0.82 0.51 0.82 0.82 Width of leaves (first measurement) 1.94 2.19 1.79 1.87 1.96 1.66 2.06 2.13 2.19 2.10 1.96 2.31 1.94 1.97 1.76 1.81 1.82 2.02 2.60 1.77 1.89 2.16 1.93 1.84 2.06 2.20 1.92 1.89 1.86 2.13 2.36 0.13 0.15 0.12 0.16 0.09 0.10 0.16 0.22 0.09 0.12 0.14 0.16 0.12 0.12 0.09 0.07 0.16 0.10 0.40 0.05 0.07 0.15 0.06 0.12 0.14 0.10 0.08 0.13 0.09 0.09 0.11 Length of leaves (first measurement) 5.00 5.51 3.98 4.39 4.44 3.98 4.60 5.48 4.67 4.83 4.81 5.18 4.99 5.15 4.31 4.69 4.93 4.40 5.70 4.67 4.72 4.85 4.25 5.29 5.32 5.77 4.24 4.79 4.35 3.82 5.49 0.44 0.60 0.42 0.59 0.41 0.30 0.67 0.48 0.42 0.84 0.54 0.58 0.29 0.34 0.39 0.21 0.76 0.23 0.40 0.25 0.19 0.49 0.33 0.56 0.54 0.58 0.20 0.34 0.22 0.21 0.53 No. of stems 1.00 1.63 1.44 2.40 2.00 2.23 1.88 1.75 1.33 1.29 1.91 2.13 1.83 1.82 1.50 1.90 1.75 1.75 1.00 1.79 2.09 1.38 1.86 1.25 1.67 1.67 1.72 1.79 1.97 1.33 2.25 0.00 0.26 0.29 0.40 0.29 0.38 0.23 0.25 0.21 0.29 0.21 0.44 0.27 0.26 0.29 0.16 0.49 0.37 NA 0.13 0.15 0.26 0.26 0.16 0.24 0.33 0.11 0.21 0.15 0.17 0.53 Stalk height 46.4 52.7 47.8 45.4 40.7 36.6 33.4 44.1 41.0 45.5 41.0 44.6 43.1 48.8 45.3 42.1 41.8 33.9 40.5 42.0 38.6 48.3 32.6 44.4 41.4 40.9 38.6 41.0 37.7 36.9 44.8 3.52 2.73 3.42 1.44 1.54 2.36 2.20 1.39 2.13 4.71 1.66 4.33 2.67 2.66 3.68 1.68 4.56 4.38 NA 1.61 1.59 4.27 2.47 2.69 3.23 3.83 1.02 2.64 1.69 3.21 2.68 No. of stalk leaves 33.8 35.9 36.0 36.8 36.8 42.6 33.5 42.3 37.7 34.3 30.5 34.5 36.3 34.8 40.8 34.2 31.5 28.8 27.0 33.0 37.9 35.8 31.6 34.8 29.9 31.2 32.8 32.1 30.1 31.4 39.5 2.81 2.23 3.06 2.89 2.05 2.13 2.68 2.56 2.22 3.11 1.59 2.83 3.09 2.30 5.60 1.36 1.77 3.66 NA 1.20 1.54 1.50 3.26 2.48 4.29 2.04 1.27 1.08 1.01 2.89 1.76 Width of stalk leaves 1.36 1.44 1.39 1.28 1.19 0.99 1.29 1.13 0.92 1.17 1.24 1.56 1.21 1.26 1.13 1.12 1.25 1.23 1.60 1.10 1.05 1.43 1.19 1.23 1.14 1.38 1.26 1.31 1.14 1.32 1.11 0.05 0.20 0.24 0.08 0.06 0.08 0.07 0.03 0.11 0.09 0.05 0.16 0.07 0.11 0.17 0.05 0.09 0.18 NA 0.06 0.03 0.07 0.10 0.11 0.05 0.17 0.04 0.10 0.05 0.08 0.11 Length of stalk leaves 5.22 4.16 3.81 3.72 3.77 3.85 3.95 4.23 3.23 3.79 4.39 4.48 4.00 4.09 3.73 4.13 4.24 3.63 4.50 4.22 3.37 3.85 3.39 3.88 3.83 4.48 3.98 3.99 3.68 3.93 3.69 0.48 0.32 0.29 0.24 0.20 0.22 0.15 0.10 0.24 0.25 0.23 0.19 0.15 0.28 0.45 0.14 0.40 0.45 NA 0.19 0.12 0.30 0.16 0.29 0.33 0.33 0.11 0.18 0.12 0.29 0.16 No. of total capitulms per ramet 10.2 12.7 9.3 9.2 10.1 6.9 7.3 9.5 11.0 9.9 7.3 7.9 8.8 9.6 12.5 10.0 9.1 7.4 7.0 11.5 8.3 7.8 7.6 13.1 8.6 8.8 9.4 10.2 9.59.39.5 1.35 1.98 1.05 1.46 1.46 1.00 0.92 1.32 2.03 1.81 0.91 2.73 0.98 1.71 2.50 0.85 1.73 2.97 NA 0.90 0.70 1.51 1.00 1.55 1.14 1.25 0.66 1.26 1.09 2.01 1.44 No. of total capitulms per genet 10.2 15.7 12.1 14.4 15.7 11.3 9.8 12.8 14.0 10.4 10.2 11.1 12.3 12.9 14.5 13.2 11.5 9.7 7.0 15.0 12.9 8.6 10.0 13.5 11.3 12.7 12.4 14.2 12.8 10.2 13.4 1.35 1.71 2.74 1.69 2.01 1.21 1.21 1.70 1.83 1.57 0.88 2.52 1.24 1.82 1.50 0.70 1.43 2.81 NA 0.97 0.61 1.53 1.80 1.38 1.08 1.93 0.79 1.50 1.31 1.84 1.07 No. of total cypselae 88.0 77.8 85.8 68.2 62.4 77.0 74.0 65.5 73.2 68.3 73.2 74.0 74.8 73.9 83.5 74.8 68.4 69.9 55.0 76.0 79.2 69.5 66.5 70.5 67.2 66.7 76.3 70.9 73.5 74.8 80.0 5.03 4.14 9.19 3.97 3.15 4.32 3.39 5.80 3.44 4.58 3.23 4.08 5.10 4.42 6.74 2.60 3.93 6.60 NA 2.27 2.82 6.01 4.14 3.09 2.41 3.81 2.10 3.12 2.26 3.00 4.25 Mass total cypsela mg 6.39 6.17 7.03 5.97 6.90 7.33 6.83 6.84 6.66 6.84 8.65 8.10 6.18 5.08 6.41 6.21 7.31 6.26 10.02 6.52 6.87 5.63 6.14 6.14 7.33 6.28 6.57 7.15 6.14 8.16 6.69 0.54 0.75 0.39 0.12 0.39 0.48 0.59 0.14 0.59 0.53 0.32 1.06 0.28 0.52 0.63 0.25 1.02 0.50 NA 0.31 0.32 0.90 0.45 0.57 0.42 0.35 0.26 0.44 0.26 0.50 0.72 Producer of supernumerary rosettes 0.70 0.50 0.55 0.57 0.40 0.27 0.38 0.50 0.57 0.71 0.71 0.30 0.50 0.73 0.50 0.53 0.44 0.44 1.00 0.37 0.49 0.80 0.50 0.67 0.33 0.67 0.49 0.50 0.61 0.80 0.75 0.15 0.17 0.16 0.20 0.16 0.12 0.18 0.29 0.20 0.18 0.13 0.15 0.14 0.14 0.19 0.09 0.18 0.18 0.00 0.08 0.08 0.13 0.19 0.17 0.17 0.21 0.08 0.14 0.08 0.13 0.16 Survival 0.91 0.83 0.85 1.00 1.00 1.00 0.80 1.00 0.88 0.78 0.93 1.00 1.00 1.00 0.80 0.85 0.90 1.00 0.67 0.88 0.93 1.00 1.00 1.00 0.90 0.86 0.84 0.93 0.95 1.000.80 0.09 0.11 0.10 0.00 0.00 0.00 0.13 0.00 0.13 0.15 0.07 0.00 0.00 0.00 0.13 0.06 0.10 0.00 0.33 0.05 0.04 0.00 0.00 0.00 0.10 0.14 0.06 0.07 0.03 0.00 0.13
APPENDIX
Appendix 3C Table of ex-situ measurements from F1 generation (F0 seeds collected in 2005 in 31 natural populations): competition treatement. First measurement corresponds to 6 months aged seedlings, the following measurements correspond to 18 months aged seedlings. First lines give the means of measurement and second lines give the standard errors. Population codes in chapter 3. Ex-situ measurements AE3 AL1 AL2 BG BOU CC1 CC2 CH1 CH2 CR EG1 EG2 FRA FRU HER HES HU LAN1 LAN2 NAT PA REB REP SC1 SC2 SC4 SE SP TH TR VU No. of rosettes (first measurement) 1.10 1.11 1.40 1.14 1.00 1.07 1.00 1.00 1.29 1.14 1.13 1.00 1.31 1.27 1.00 1.18 1.00 1.29 NA 1.17 1.42 1.00 1.38 1.00 1.00 1.00 1.08 1.13 1.26 1.38 1.00 0.10 0.11 0.22 0.14 0.00 0.07 0.00 0.00 0.29 0.14 0.09 0.00 0.17 0.19 0.00 0.09 0.00 0.29 NA 0.08 0.11 0.00 0.38 0.00 0.00 0.00 0.04 0.13 0.13 0.38 0.00 No. of leaves (first measurement) 15.2 12.1 12.2 14.3 14.4 13.0 12.4 11.3 12.7 13.0 12.9 14.7 14.1 11.7 12.1 13.1 14.2 10.9 NA 13.9 13.2 12.4 15.5 12.9 12.4 12.3 13.3 13.4 13.5 14.4 11.5 0.63 1.12 0.42 0.84 1.63 0.57 0.57 0.33 1.06 0.79 0.60 1.08 0.64 0.54 0.77 0.44 0.66 0.96 NA 0.56 0.49 0.69 0.57 0.51 1.01 1.15 0.42 0.61 0.44 0.96 1.32 Width of leaves (first measurement) 1.71 1.88 1.54 1.53 1.82 1.58 1.54 1.37 1.74 1.69 1.59 2.00 1.79 1.77 1.65 1.57 1.66 1.63 NA 1.64 1.63 1.83 1.54 1.51 2.10 1.88 1.76 1.75 1.73 1.79 1.60 0.10 0.20 0.05 0.14 0.08 0.05 0.17 0.03 0.17 0.13 0.10 0.16 0.12 0.08 0.11 0.06 0.10 0.14 NA 0.05 0.06 0.15 0.09 0.16 0.23 0.13 0.05 0.10 0.06 0.17 0.15 Length of leaves (first measurement) 3.64 4.26 3.51 3.71 3.89 3.36 3.20 2.63 3.86 3.34 3.43 4.50 3.44 4.03 3.46 3.68 3.64 3.47 NA 3.77 3.62 3.82 2.99 3.53 3.69 3.60 3.26 3.46 3.55 3.19 3.73 0.35 0.42 0.26 0.50 0.32 0.25 0.30 0.23 0.32 0.27 0.25 0.62 0.28 0.32 0.23 0.17 0.27 0.50 NA 0.18 0.15 0.32 0.28 0.30 0.47 0.19 0.10 0.25 0.16 0.21 0.90 No. of stems 1.00 1.00 1.17 1.00 1.14 1.00 1.00 2.00 1.00 1.00 1.00 1.00 1.18 1.00 1.17 1.12 1.14 1.20 NA 1.13 1.21 1.00 1.00 1.17 1.29 1.00 1.06 1.09 1.07 1.00 1.00 0.00 0.00 0.17 0.00 0.14 0.00 0.00 NA 0.00 0.00 0.00 0.00 0.18 0.00 0.17 0.07 0.14 0.20 NA 0.06 0.09 0.00 0.00 0.17 0.18 0.00 0.04 0.09 0.05 0.00 0.00 Stalk height 30.8 37.5 26.3 27.1 24.1 25.3 25.2 18.5 22.9 26.1 24.3 32.4 27.1 31.8 24.4 27.9 31.6 23.4 NA 24.9 26.1 30.9 19.6 28.3 27.5 22.2 25.2 26.6 25.8 22.5 27.3 1.09 2.04 2.13 3.53 1.91 1.46 1.78 NA 1.83 2.83 2.03 3.71 1.32 1.68 2.25 0.95 1.94 4.97 NA 1.35 1.44 2.13 4.05 2.12 1.18 1.89 0.79 1.55 1.12 1.83 3.06 No. of stalk leaves 24.3 26.3 23.2 22.6 22.9 27.0 24.0 14.0 24.1 23.4 19.7 23.7 23.9 24.5 23.3 25.9 25.0 20.4 NA 22.5 26.2 26.5 23.8 25.3 25.9 20.2 23.3 23.1 23.7 22.8 26.7 1.44 1.93 1.54 1.33 2.40 1.62 2.68 NA 1.37 1.81 1.91 2.65 1.17 1.91 1.76 0.92 1.38 3.56 NA 1.15 1.28 1.46 3.50 1.20 1.84 1.42 0.92 1.44 1.13 1.83 0.33 Width of stalk leaves 1.53 1.20 1.12 1.10 1.20 0.98 1.03 0.90 0.91 1.20 0.91 1.20 1.06 1.18 1.12 0.95 1.13 1.04 NA 0.94 0.98 1.21 0.94 0.97 1.04 1.23 1.05 0.94 0.94 1.15 1.30 0.16 0.11 0.12 0.08 0.10 0.06 0.07 NA 0.07 0.12 0.06 0.15 0.06 0.08 0.05 0.04 0.05 0.12 NA 0.05 0.03 0.10 0.14 0.12 0.07 0.11 0.04 0.07 0.04 0.11 0.25 Length of stalk leaves 5.03 3.70 3.23 3.18 3.57 3.37 3.48 2.60 2.97 3.27 3.46 3.82 3.50 3.67 3.48 3.35 3.71 3.18 NA 3.44 2.95 3.75 2.91 3.03 3.13 4.13 3.25 3.08 3.34 2.98 3.73 0.49 0.12 0.34 0.14 0.29 0.11 0.19 NA 0.23 0.23 0.19 0.30 0.17 0.35 0.28 0.11 0.17 0.34 NA 0.18 0.11 0.27 0.22 0.19 0.23 0.40 0.13 0.20 0.16 0.37 0.37 No. of total capitulms per ramet 3.75 4.50 2.17 3.40 3.00 3.33 3.00 2.00 3.14 3.14 3.14 3.50 3.08 4.00 2.50 3.59 3.50 2.40 NA 3.47 3.67 3.63 3.00 3.67 3.57 3.00 3.41 3.90 3.41 2.33 3.33 0.85 0.29 0.48 0.51 0.44 0.38 0.45 NA 0.59 0.46 0.40 0.43 0.23 0.45 0.50 0.22 0.34 0.68 NA 0.31 0.48 0.32 0.97 0.21 0.69 0.52 0.25 0.59 0.32 0.49 0.33 No. of total capitulms per genet 6.25 4.50 2.33 3.40 3.14 3.33 3.00 3.00 3.14 3.14 3.14 3.50 3.33 4.00 3.00 3.78 3.50 2.80 NA 3.63 4.00 3.63 3.00 3.83 3.86 3.00 3.38 4.10 3.59 2.33 3.33 3.28 0.29 0.42 0.51 0.40 0.38 0.45 NA 0.59 0.46 0.40 0.43 0.33 0.45 0.89 0.23 0.34 0.73 NA 0.29 0.50 0.32 0.97 0.31 0.63 0.52 0.26 0.59 0.41 0.49 0.33 No. of total cypselae 64.7 66.0 53.5 54.4 51.7 62.3 46.2 54.0 56.7 55.7 51.0 55.7 68.3 58.0 70.8 64.7 56.9 56.2 NA 61.2 61.0 59.8 62.0 55.7 61.7 61.8 64.5 62.7 66.1 69.8 70.0 7.42 3.36 4.58 5.90 5.44 2.14 9.17 NA 3.41 5.80 5.16 5.82 3.20 9.38 4.16 2.16 1.84 6.81 NA 2.76 2.68 3.30 5.75 3.36 3.08 6.53 2.33 4.67 3.08 5.95 3.51 Mass total cypsela mg 7.81 7.21 7.48 5.54 7.76 6.93 6.36 7.57 6.64 6.36 7.72 7.54 6.39 7.08 6.25 7.17 9.28 4.88 NA 6.42 6.65 7.58 6.47 5.48 6.33 6.68 6.77 6.11 5.59 8.10 7.40 1.23 0.63 0.77 0.65 0.78 0.47 0.95 NA 0.46 0.47 1.11 1.09 0.43 0.79 0.72 0.25 0.62 0.87 NA 0.43 0.28 0.61 0.57 0.99 0.94 0.65 0.28 0.46 0.27 0.48 0.50 Producer of supernumerary rosettes 0.20 0.22 0.30 0.43 0.44 0.14 0.14 0.67 0.14 0.29 0.27 0.29 0.31 0.18 0.00 0.19 0.00 0.29 NA 0.31 0.34 0.11 0.13 0.11 0.10 0.17 0.13 0.27 0.18 0.38 0.25 0.13 0.15 0.15 0.20 0.18 0.10 0.14 0.33 0.14 0.18 0.12 0.18 0.13 0.12 0.00 0.07 0.00 0.18 NA 0.08 0.08 0.11 0.13 0.11 0.10 0.17 0.05 0.12 0.06 0.18 0.25 Survival 1.00 0.82 0.91 1.00 1.00 0.93 0.88 0.75 0.88 0.78 1.00 0.88 1.00 1.00 1.00 0.93 0.90 0.88 0.00 0.90 0.95 0.90 1.00 1.00 1.00 0.86 0.89 1.00 0.98 1.000.57 0.00 0.12 0.09 0.00 0.00 0.07 0.13 0.25 0.13 0.15 0.00 0.13 0.00 0.00 0.00 0.04 0.10 0.13 0.00 0.05 0.03 0.10 0.00 0.00 0.00 0.14 0.05 0.00 0.03 0.00 0.20
APPENDIX
Appendix 4 Photos gallery.
A) B)
C) D) G)
E)
F) H) J)
I)
A) Undeground part was observed with an average of 5 cm between ramets (showed scale: 1 cm). B) A Epeo- lus working on a flower of Aster amellus, in September (site: Allondon 1). C) Vegetative form. D) Cypsela (scale: mm). E) Germination in June. F) 30 months agged seedlings, in situ experiment, leaf: around 1 cm width. G) Fructification in Oktober. H) Coleophora larva feeding on cypselae. I) Orange larva (cecidomyiid group (Mark Shaw pers. com.)) found in the capitula (scale: mm). J) Euderus sp. (determination by Hannes Baur) parasite insect larvae, were found in a encagement experiment where capitula were placed in paper bags closed by a glass tube (scale: mm).
131 APPENDIX
Appendix 4 (Continued)
A) B)
C) D)
E) F)
A-F) Observed Syrphidae shedding pollen on Aster amellus, along the Jura , France-Switzerland.
132