Linnaeus University DissertationsNr 295/2017

Jon Tinnert

Microevolution in pygmy grasshoppers

linnaeus university press

Lnu.seisbn: 978-91-88357-87-8

Microevolution in pygm

y grasshoppersJon Tinnert

Microevolution in pygmy grasshoppers

Linnaeus University Dissertations No 295/2017

MICROEVOLUTION IN PYGMY GRASSHOPPERS

JON TINNERT

LINNAEUS UNIVERSITY PRESS

Linnaeus University Dissertations No 295/2017

MICROEVOLUTION IN PYGMY GRASSHOPPERS

JON TINNERT

LINNAEUS UNIVERSITY PRESS

Abstract Tinnert, Jon (2017). Microevolution in pygmy grasshoppers. Linnaeus University Dissertations No 295/2017, ISBN: 978-91-88357-87-8. Written in English with a summary in Swedish. Knowledge of how spatiotemporal environmental variation impacts ecological and evolutionary processes and contributes to genetic and phenotypic diversity of natural populations is key to understanding and protecting biological diversity. In this thesis I used pygmy grasshoppers to study how environmental conditions, population dynamics, dispersal and admixture may influence genetic structure and diversity, and to evaluate how functionally important variation may affect the ability of populations to cope with novel and changing habitats.

Analyses of AFLP (Amplified Fragment Length Polymorphism) markers in Tetrix subulata individuals from 20 sampling locations in Sweden showed significant genetic structure and restricted gene flow among populations. Genetic diversity increased with population size and proportion of long-winged dispersive phenotypes on the island of Öland, but not on the mainland.

A contrasting environment comparative approach (CECA) applied to 20 T. undulata populations suggested that processes associated with environmental change differently influence functional and neutral diversity. Long-winged phenotypes were more common in disturbed than in stable habitats, indicative of recent establishment. Color morph diversity was higher in disturbed environments consistent with the notion that polymorphism promotes establishment success. Conversely, neutral diversity (AFLP) was lower in disturbed habitats, pointing to a stronger eroding effect of genetic drift in disturbed compared to stable habitats.

I compared genetic and morphological variation between sympatric populations of the two species. Populations of the generally dispersive T. subulata were genetically less differentiated compared with the more sedentary T. undulata, suggesting that the latter species has been less influenced by the homogenizing effects of gene flow. Non-parallel body size differences pointed to species-specific drivers of morphological change.

Finally, comparisons of reproductive output of T. subulata females that had been experimentally mated with males from the same or from a different population suggested that responses to interbreeding and genetic admixture can differ in direction and magnitude even between populations within a species, and thus influence whether dispersal translates into gene flow.

My thesis emphasizes the complexity of microevolution and illustrates how the effects of different ecological and evolutionary processes can vary according to disturbance regimes and geographic areas, and differ between closely related sympatric species.

Microevolution in pygmy grasshoppers Doctoral dissertation, Department of Biology and Environmental Science, Linnaeus University, Kalmar, 2017 Cover photo: Tetrix subulata, by Jon Tinnert ISBN: 978-91-88357-87-8 Published by: Linnaeus University Press, 351 95 Växjö Printed by: DanagårdLiTHO AB, 2017

Abstract Tinnert, Jon (2017). Microevolution in pygmy grasshoppers. Linnaeus University Dissertations No 295/2017, ISBN: 978-91-88357-87-8. Written in English with a summary in Swedish. Knowledge of how spatiotemporal environmental variation impacts ecological and evolutionary processes and contributes to genetic and phenotypic diversity of natural populations is key to understanding and protecting biological diversity. In this thesis I used pygmy grasshoppers to study how environmental conditions, population dynamics, dispersal and admixture may influence genetic structure and diversity, and to evaluate how functionally important variation may affect the ability of populations to cope with novel and changing habitats.

Analyses of AFLP (Amplified Fragment Length Polymorphism) markers in Tetrix subulata individuals from 20 sampling locations in Sweden showed significant genetic structure and restricted gene flow among populations. Genetic diversity increased with population size and proportion of long-winged dispersive phenotypes on the island of Öland, but not on the mainland.

A contrasting environment comparative approach (CECA) applied to 20 T. undulata populations suggested that processes associated with environmental change differently influence functional and neutral diversity. Long-winged phenotypes were more common in disturbed than in stable habitats, indicative of recent establishment. Color morph diversity was higher in disturbed environments consistent with the notion that polymorphism promotes establishment success. Conversely, neutral diversity (AFLP) was lower in disturbed habitats, pointing to a stronger eroding effect of genetic drift in disturbed compared to stable habitats.

I compared genetic and morphological variation between sympatric populations of the two species. Populations of the generally dispersive T. subulata were genetically less differentiated compared with the more sedentary T. undulata, suggesting that the latter species has been less influenced by the homogenizing effects of gene flow. Non-parallel body size differences pointed to species-specific drivers of morphological change.

Finally, comparisons of reproductive output of T. subulata females that had been experimentally mated with males from the same or from a different population suggested that responses to interbreeding and genetic admixture can differ in direction and magnitude even between populations within a species, and thus influence whether dispersal translates into gene flow.

My thesis emphasizes the complexity of microevolution and illustrates how the effects of different ecological and evolutionary processes can vary according to disturbance regimes and geographic areas, and differ between closely related sympatric species.

Microevolution in pygmy grasshoppers Doctoral dissertation, Department of Biology and Environmental Science, Linnaeus University, Kalmar, 2017 Cover photo: Tetrix subulata, by Jon Tinnert ISBN: 978-91-88357-87-8 Published by: Linnaeus University Press, 351 95 Växjö Printed by: DanagårdLiTHO AB, 2017

List of papers

The thesis is based on the following original papers. Published papers and papers accepted for publication were printed with the permission of the publishers. Papers are referred to in the text by their Roman numerals. I. Tinnert, J., Hellgren, O., Lindberg, J., Koch-Schmidt, P. & Forsman, A. (2016) Population genetic structure, differentiation, and diversity in Tetrix subulata pygmy grasshoppers: roles of population size and immigration. Ecology and Evolution 6:7831-7846. II. Tinnert, J. & Forsman, A. Contrasting patterns of neutral and functional genetic diversity in stable and disturbed environments. Manuscript III. Tinnert, J. & Forsman, A. (2017) The role of dispersal for genetic and phenotypic variation: insights from comparisons of sympatric pygmy grasshoppers. Biological Journal of the Linnean Society, DOI: 10.1093/biolinnean/blx055 IV. Tinnert, J., Berggren, H. & Forsman, A. (2016) Population-specific effects of interbreeding and admixture on reproductive decisions and offspring quality. Annales Zoologici Fennici 53: 55-68.

List of papers

The thesis is based on the following original papers. Published papers and papers accepted for publication were printed with the permission of the publishers. Papers are referred to in the text by their Roman numerals. I. Tinnert, J., Hellgren, O., Lindberg, J., Koch-Schmidt, P. & Forsman, A. (2016) Population genetic structure, differentiation, and diversity in Tetrix subulata pygmy grasshoppers: roles of population size and immigration. Ecology and Evolution 6:7831-7846. II. Tinnert, J. & Forsman, A. Contrasting patterns of neutral and functional genetic diversity in stable and disturbed environments. Manuscript III. Tinnert, J. & Forsman, A. (2017) The role of dispersal for genetic and phenotypic variation: insights from comparisons of sympatric pygmy grasshoppers. Biological Journal of the Linnean Society, DOI: 10.1093/biolinnean/blx055 IV. Tinnert, J., Berggren, H. & Forsman, A. (2016) Population-specific effects of interbreeding and admixture on reproductive decisions and offspring quality. Annales Zoologici Fennici 53: 55-68.

Min avhandling illustrerar komplexiteten i mikroevolution och visar hur effekterna av olika ekologiska och evolutionära processer kan variera beroende på störningsregim och geografiskt område, och dessutom skilja mellan nära besläktade arter som lever tillsammans i samma miljö.

Mikroevolution hos torngräshoppor Sammanfattning Kunskap om hur spatiotemporal miljövariation påverkar ekologiska och evolutionära processer och därigenom bidrar till genetisk och fenotypisk diversitet i naturliga populationer är nyckeln till att förstå och bevara biologisk mångfald. I denna avhandling har jag använt mig av torngräshoppor för att studera hur miljöförhållanden, populationsdynamik, spridningsförmåga och admixture påverkar genetisk struktur och diversitet. Jag har också utvärderat hur variation som är funktionellt viktig kan påverka populationers förmåga att överleva i nyskapade eller föränderliga habitat.

Jag analyserade genetisk variation med hjälp av AFLP (Amplified Fragment Lenght Polymorphism) från individer av Tetrix subulata insamlade på 20 lokaler i Sverige. Resultaten visade att dessa populationer hade en signifikant genetisk struktur och att genflödet mellan populationer var begränsat. Den genetiska diversiteten inom populationerna ökade dessutom med ökad populationsstorlek och med andelen långvingade individer i populationer på Öland, men något sådant samband fanns inte på fastlandet.

Jag använde mig av en Contrasting Environment Comparative Approach (CECA) på 20 populationer av T. undulata för att undersöka om processer associerade med miljöförändringar påverkar funktionell och neutral diversitet på olika sätt. I störda miljöer var långvingade fenotyper vanligare än i stabila miljöer vilket tyder på dessa nyligen blivit koloniserade. Det var högre diversitet av färgmorfer i störda miljöer än i stabila vilket stämmer med vad som förväntas givet att polymorfi kan leda till ökad kolonisationsframgång. Däremot var neutral diversitet (AFLP) lägre i störda habitat vilket tyder på att genetisk drift har en starkare eroderande effekt i störda miljöer jämfört med stabila.

Spridningens roll för genetisk och morfologisk variation utvärderades därefter genom att jämföra populationer av de två arterna från områden där båda arterna lever tillsammans i samma habitat. Graden av genetisk differentiering mellan populationer var generellt lägre hos arten T. subulata med högre spridningsförmåga, vilket tyder på denna art har påverkats mer av homogeniserande genflöde jämfört med T. undulata som är mer stationär. Skillnaden i kroppstolek hos gräshoppor från olika områden samvarierade inte mellan arterna, vilket tyder på att drivkrafterna som påverkar morfologiska förändringar är artspecifika.

Slutligen gjordes en studie av reproduktiv förmåga hos honor som blivit experimentellt parade med hanar från samma eller en annan population. Resultatet tydde på att effekterna av korsningar och genetisk blandning kan skilja i både riktning och storhet mellan populationer även inom arter och därigenom påverka huruvida spridning ger upphov till genflöde.

Min avhandling illustrerar komplexiteten i mikroevolution och visar hur effekterna av olika ekologiska och evolutionära processer kan variera beroende på störningsregim och geografiskt område, och dessutom skilja mellan nära besläktade arter som lever tillsammans i samma miljö.

Mikroevolution hos torngräshoppor Sammanfattning Kunskap om hur spatiotemporal miljövariation påverkar ekologiska och evolutionära processer och därigenom bidrar till genetisk och fenotypisk diversitet i naturliga populationer är nyckeln till att förstå och bevara biologisk mångfald. I denna avhandling har jag använt mig av torngräshoppor för att studera hur miljöförhållanden, populationsdynamik, spridningsförmåga och admixture påverkar genetisk struktur och diversitet. Jag har också utvärderat hur variation som är funktionellt viktig kan påverka populationers förmåga att överleva i nyskapade eller föränderliga habitat.

Jag analyserade genetisk variation med hjälp av AFLP (Amplified Fragment Lenght Polymorphism) från individer av Tetrix subulata insamlade på 20 lokaler i Sverige. Resultaten visade att dessa populationer hade en signifikant genetisk struktur och att genflödet mellan populationer var begränsat. Den genetiska diversiteten inom populationerna ökade dessutom med ökad populationsstorlek och med andelen långvingade individer i populationer på Öland, men något sådant samband fanns inte på fastlandet.

Jag använde mig av en Contrasting Environment Comparative Approach (CECA) på 20 populationer av T. undulata för att undersöka om processer associerade med miljöförändringar påverkar funktionell och neutral diversitet på olika sätt. I störda miljöer var långvingade fenotyper vanligare än i stabila miljöer vilket tyder på dessa nyligen blivit koloniserade. Det var högre diversitet av färgmorfer i störda miljöer än i stabila vilket stämmer med vad som förväntas givet att polymorfi kan leda till ökad kolonisationsframgång. Däremot var neutral diversitet (AFLP) lägre i störda habitat vilket tyder på att genetisk drift har en starkare eroderande effekt i störda miljöer jämfört med stabila.

Spridningens roll för genetisk och morfologisk variation utvärderades därefter genom att jämföra populationer av de två arterna från områden där båda arterna lever tillsammans i samma habitat. Graden av genetisk differentiering mellan populationer var generellt lägre hos arten T. subulata med högre spridningsförmåga, vilket tyder på denna art har påverkats mer av homogeniserande genflöde jämfört med T. undulata som är mer stationär. Skillnaden i kroppstolek hos gräshoppor från olika områden samvarierade inte mellan arterna, vilket tyder på att drivkrafterna som påverkar morfologiska förändringar är artspecifika.

Slutligen gjordes en studie av reproduktiv förmåga hos honor som blivit experimentellt parade med hanar från samma eller en annan population. Resultatet tydde på att effekterna av korsningar och genetisk blandning kan skilja i både riktning och storhet mellan populationer även inom arter och därigenom påverka huruvida spridning ger upphov till genflöde.

5

Introduction

In their struggle for existence, some individuals are able to survive or reproduce to a greater extent than their conspecifics, thereby leaving more descendants in future generations. Heritable traits that affect the success of individuals change in frequency over time as populations evolve in response to their environmental conditions (Darwin 1859). Typically, environmental conditions are different in spatially separated locations and populations therefore follow different evolutionary paths, sometimes towards local adaptation (Kawecki and Ebert 2004, Edelaar et al. 2008). Furthermore, environmental conditions in any given location are not stable; they fluctuate and change over time (Levins 1968, Bell 2010). Temporal environmental changes may alter how successful traits are, shift allele frequencies of populations and drive evolution of different or divergent local adaptations (Kawecki and Ebert 2004, Edelaar et al. 2008, Bell 2010, Lind and Grahn 2011). To infer adaptation and evolution from genetic diversity and differentiation is however not trivial and might be considered one of the most controversially debated topics in population genetics (Holderegger et al. 2006). Spatiotemporal environmental variation is predicted to have important implications for patterns of genetic and phenotypic diversity within and among populations (Bell 2010). Analyzes of such patterns can improve scientific understanding of the underlying evolutionary and ecological processes that generate and maintain biodiversity.

My thesis concerns how dispersal and gene flow affect processes that give rise to the patterns of phenotypic and genotypic diversity in natural populations. First, I investigate how population size, geographic separation and dispersal barriers influence the genetic structure among populations. Next I suggest that a Contrasting Environment Comparative Approach (CECA) can be used to infer drivers of neutral and functional diversity within populations. I specifically investigate how changing environmental conditions and disturbance events differently influence neutral and functional genetic diversity. Thereafter, I study effects of dispersal on genetic structure among and diversity within populations by comparing two sympatric closely related

TABLE OF CONTENTS List of papers ........................................................................................................ 1 Sammanfattning ................................................................................................... 2 Introduction .......................................................................................................... 5

Environmental variation.................................................................................. 6 Drivers of phenotypic and genetic diversity ................................................... 6 Dispersal ......................................................................................................... 8 Gene flow ........................................................................................................ 8 Unresolved questions ...................................................................................... 9

Aims of thesis ..................................................................................................... 10 Two species of polymorphic grasshoppers – in the service of science .............. 11

Study area and sampling .......................................................................... 13 Estimates of population size .................................................................... 15 Estimates of immigration rate.................................................................. 16 Estimates of functional genetic diversity ................................................. 17 DNA extraction and molecular genetic analyses ..................................... 17 Evaluation and conversion of fragment analysis data ............................. 18 Estimates of neutral genetic diversity within populations ....................... 18 Analyses of neutral genetic structure among populations ....................... 19 Estimates of standardized ‘functional diversity differentials’ within populations .............................................................................................. 19 Comparing patterns of genetic differentiation between species .............. 20 Investigating interbreeding and admixture effects in T. subulata............ 21

Results and discussion ........................................................................................ 22 Dispersal barriers, geographic distance, immigration and population size influence patterns of population genetic diversity and differentiation .......... 22 Changing environments differently influence neutral and functional genetic diversity ........................................................................................................ 25 Dispersal affects population structure and diversity ..................................... 28 Effects of interbreeding and admixture ......................................................... 33 Conclusions and future directions ................................................................. 36 Acknowledgements ....................................................................................... 39 References ..................................................................................................... 40

5

Introduction

In their struggle for existence, some individuals are able to survive or reproduce to a greater extent than their conspecifics, thereby leaving more descendants in future generations. Heritable traits that affect the success of individuals change in frequency over time as populations evolve in response to their environmental conditions (Darwin 1859). Typically, environmental conditions are different in spatially separated locations and populations therefore follow different evolutionary paths, sometimes towards local adaptation (Kawecki and Ebert 2004, Edelaar et al. 2008). Furthermore, environmental conditions in any given location are not stable; they fluctuate and change over time (Levins 1968, Bell 2010). Temporal environmental changes may alter how successful traits are, shift allele frequencies of populations and drive evolution of different or divergent local adaptations (Kawecki and Ebert 2004, Edelaar et al. 2008, Bell 2010, Lind and Grahn 2011). To infer adaptation and evolution from genetic diversity and differentiation is however not trivial and might be considered one of the most controversially debated topics in population genetics (Holderegger et al. 2006). Spatiotemporal environmental variation is predicted to have important implications for patterns of genetic and phenotypic diversity within and among populations (Bell 2010). Analyzes of such patterns can improve scientific understanding of the underlying evolutionary and ecological processes that generate and maintain biodiversity.

My thesis concerns how dispersal and gene flow affect processes that give rise to the patterns of phenotypic and genotypic diversity in natural populations. First, I investigate how population size, geographic separation and dispersal barriers influence the genetic structure among populations. Next I suggest that a Contrasting Environment Comparative Approach (CECA) can be used to infer drivers of neutral and functional diversity within populations. I specifically investigate how changing environmental conditions and disturbance events differently influence neutral and functional genetic diversity. Thereafter, I study effects of dispersal on genetic structure among and diversity within populations by comparing two sympatric closely related

TABLE OF CONTENTS List of papers ........................................................................................................ 1 Sammanfattning ................................................................................................... 2 Introduction .......................................................................................................... 5

Environmental variation.................................................................................. 6 Drivers of phenotypic and genetic diversity ................................................... 6 Dispersal ......................................................................................................... 8 Gene flow ........................................................................................................ 8 Unresolved questions ...................................................................................... 9

Aims of thesis ..................................................................................................... 10 Two species of polymorphic grasshoppers – in the service of science .............. 11

Study area and sampling .......................................................................... 13 Estimates of population size .................................................................... 15 Estimates of immigration rate.................................................................. 16 Estimates of functional genetic diversity ................................................. 17 DNA extraction and molecular genetic analyses ..................................... 17 Evaluation and conversion of fragment analysis data ............................. 18 Estimates of neutral genetic diversity within populations ....................... 18 Analyses of neutral genetic structure among populations ....................... 19 Estimates of standardized ‘functional diversity differentials’ within populations .............................................................................................. 19 Comparing patterns of genetic differentiation between species .............. 20 Investigating interbreeding and admixture effects in T. subulata............ 21

Results and discussion ........................................................................................ 22 Dispersal barriers, geographic distance, immigration and population size influence patterns of population genetic diversity and differentiation .......... 22 Changing environments differently influence neutral and functional genetic diversity ........................................................................................................ 25 Dispersal affects population structure and diversity ..................................... 28 Effects of interbreeding and admixture ......................................................... 33 Conclusions and future directions ................................................................. 36 Acknowledgements ....................................................................................... 39 References ..................................................................................................... 40

7

Functional population structure and differentiation results from the same suit of random processes as neutral diversity, but is also affected by deterministic processes such as natural selection (Frankham 1996). Genetic diversity acted on by natural selection is referred to as adaptive or selective genetic diversity (Holderegger et al. 2006). Adaptive and functional genetic differences are commonly found among individuals and provide the raw material for evolutionary change (Bolnick et al. 2011, Whitlock et al. 2013). Traits that have positive effects on an individual’s fitness increase in frequency as populations evolve towards local adaptive peaks and each population is expected to evolve in response to its environmental selection pressures (Kawecki and Ebert 2004, Bolnick and Nosil 2007, Kingsolver and Pfennig 2007, Edelaar et al. 2008, Laurila et al. 2008, Lind and Grahn 2011). The process of local adaptation is predicted to remove genetic variants from within populations and contribute to increased divergence among populations (Bohonak 1999). However, selection in temporally changing environments could also help maintain genetic diversity, depending on strength and dynamics of selection in relation to generation time and whether or not generations are overlapping (Levins 1968, Frank and Slatkin 1990, Hedrick 2006, Bell 2010).

Functional genetic diversity of populations can be estimated using quantitative genetics studies and breeding experiments and is often more difficult to measure as compared to neutral genetic diversity which can be estimated using molecular genetic markers in the laboratory (Holderegger et al. 2006, Leinonen et al. 2008). Because neutral genetic diversity is easier to estimate it has often been used as a surrogate for functional genetic diversity to infer evolutionary potential and viability of populations (Reed and Frankham 2001, Holderegger et al. 2006). Such methods have received criticism and authors generally discourage against using neutral genetic diversity as a proxy for functional genetic diversity (Reed and Frankham 2001, Holderegger et al. 2006).

Functional traits are expected to influence the performance of individuals and thereby influence the growth, dynamics and distribution of populations. Through its effects on fitness of individuals and populations, functional genetic diversity is expected to show different patterns of genetic structure as compared to neutral diversity. By comparing patterns of neutral and functional genetic diversity among populations in different environments it is possible to make inferences about the relative contribution of different processes that operate in natural populations. This information may be of importance for conservation of threatened or endangered species resulting from ongoing climate change and habitat fragmentation.

Environments normally change over time and natural populations fluctuate in size in response to variation in habitat quality and resource availability. When locations differ or change in quality individuals are confronted with the option to stay or disperse to try and find a better location.

6

species with different dispersal ability. Finally, to evaluate whether dispersal translates into gene flow I experimentally cross two populations in a common garden and investigate effects of interbreeding on reproductive allocation and effects of genetic admixture on performance in first and second generation offspring.

Environmental variation The environment encompasses everything that affects the organism and environmental conditions set the stage on which individuals, populations and species perform. Naturally occurring chemical, physical, geological and hydrological processes shape our planet and generate both large scale and small scale differences in abiotic environmental conditions (Hooper et al. 2005). Furthermore, in response to spatially different abiotic environmental conditions biotic differences in species and community composition have developed that adds to the environmental differences among spatially separated locations (Hooper et al. 2005).

Spatial distribution of environments makes up a mosaic of patches that meet or do not meet the requirement of individuals, populations and species. The distribution, size, quality and persistence of such habitat patches are central for population dynamics and the ecology of species (Hanski 1998). In addition, spatially different environmental conditions are not stable; they fluctuate and change on both large and small temporal scales, thereby changing the distribution, size and quality of habitat patches (Bell 2010). The fact that environments vary and change over time is a fundamental driver in ecology and evolution, and increased understanding of how environmental variation influence population structure, diversity and fitness is important for successful conservation of threatened and endangered populations and species. The ability to affect the distribution, dynamics and interaction of populations also allows for environmental variation to influence evolutionary processes and act as a driver of phenotypic and genetic diversity.

Drivers of phenotypic and genetic diversity Patterns of population structure and differentiation in selectively neutral genetic diversity are mainly the outcome of random processes such as genetic drift and mutations (Kimura 1989, 1991). Genetic drift results from the inability of populations to transmit identical genetic proportions from one generation to the next and it is predicted to reduce neutral genetic diversity within populations and increase divergence among populations (Slatkin 1987). The rate at which genetic drift reduces diversity and generates structure among populations depends on effective population size and the spatiotemporal dynamics of populations (Frankham 1996).

7

Functional population structure and differentiation results from the same suit of random processes as neutral diversity, but is also affected by deterministic processes such as natural selection (Frankham 1996). Genetic diversity acted on by natural selection is referred to as adaptive or selective genetic diversity (Holderegger et al. 2006). Adaptive and functional genetic differences are commonly found among individuals and provide the raw material for evolutionary change (Bolnick et al. 2011, Whitlock et al. 2013). Traits that have positive effects on an individual’s fitness increase in frequency as populations evolve towards local adaptive peaks and each population is expected to evolve in response to its environmental selection pressures (Kawecki and Ebert 2004, Bolnick and Nosil 2007, Kingsolver and Pfennig 2007, Edelaar et al. 2008, Laurila et al. 2008, Lind and Grahn 2011). The process of local adaptation is predicted to remove genetic variants from within populations and contribute to increased divergence among populations (Bohonak 1999). However, selection in temporally changing environments could also help maintain genetic diversity, depending on strength and dynamics of selection in relation to generation time and whether or not generations are overlapping (Levins 1968, Frank and Slatkin 1990, Hedrick 2006, Bell 2010).

Functional genetic diversity of populations can be estimated using quantitative genetics studies and breeding experiments and is often more difficult to measure as compared to neutral genetic diversity which can be estimated using molecular genetic markers in the laboratory (Holderegger et al. 2006, Leinonen et al. 2008). Because neutral genetic diversity is easier to estimate it has often been used as a surrogate for functional genetic diversity to infer evolutionary potential and viability of populations (Reed and Frankham 2001, Holderegger et al. 2006). Such methods have received criticism and authors generally discourage against using neutral genetic diversity as a proxy for functional genetic diversity (Reed and Frankham 2001, Holderegger et al. 2006).

Functional traits are expected to influence the performance of individuals and thereby influence the growth, dynamics and distribution of populations. Through its effects on fitness of individuals and populations, functional genetic diversity is expected to show different patterns of genetic structure as compared to neutral diversity. By comparing patterns of neutral and functional genetic diversity among populations in different environments it is possible to make inferences about the relative contribution of different processes that operate in natural populations. This information may be of importance for conservation of threatened or endangered species resulting from ongoing climate change and habitat fragmentation.

Environments normally change over time and natural populations fluctuate in size in response to variation in habitat quality and resource availability. When locations differ or change in quality individuals are confronted with the option to stay or disperse to try and find a better location.

6

species with different dispersal ability. Finally, to evaluate whether dispersal translates into gene flow I experimentally cross two populations in a common garden and investigate effects of interbreeding on reproductive allocation and effects of genetic admixture on performance in first and second generation offspring.

Environmental variation The environment encompasses everything that affects the organism and environmental conditions set the stage on which individuals, populations and species perform. Naturally occurring chemical, physical, geological and hydrological processes shape our planet and generate both large scale and small scale differences in abiotic environmental conditions (Hooper et al. 2005). Furthermore, in response to spatially different abiotic environmental conditions biotic differences in species and community composition have developed that adds to the environmental differences among spatially separated locations (Hooper et al. 2005).

Spatial distribution of environments makes up a mosaic of patches that meet or do not meet the requirement of individuals, populations and species. The distribution, size, quality and persistence of such habitat patches are central for population dynamics and the ecology of species (Hanski 1998). In addition, spatially different environmental conditions are not stable; they fluctuate and change on both large and small temporal scales, thereby changing the distribution, size and quality of habitat patches (Bell 2010). The fact that environments vary and change over time is a fundamental driver in ecology and evolution, and increased understanding of how environmental variation influence population structure, diversity and fitness is important for successful conservation of threatened and endangered populations and species. The ability to affect the distribution, dynamics and interaction of populations also allows for environmental variation to influence evolutionary processes and act as a driver of phenotypic and genetic diversity.

Drivers of phenotypic and genetic diversity Patterns of population structure and differentiation in selectively neutral genetic diversity are mainly the outcome of random processes such as genetic drift and mutations (Kimura 1989, 1991). Genetic drift results from the inability of populations to transmit identical genetic proportions from one generation to the next and it is predicted to reduce neutral genetic diversity within populations and increase divergence among populations (Slatkin 1987). The rate at which genetic drift reduces diversity and generates structure among populations depends on effective population size and the spatiotemporal dynamics of populations (Frankham 1996).

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Apart from having consequences for genetic structure among populations gene flow could also affect the composition of genes within populations. One consequence of gene flow is that new genetic varieties are introduced and incorporated in the population gene pool. This is expected to increase standing genetic diversity within populations, conceal deleterious recessive alleles, create novel genotypes by segregation and recombination, and ultimately influence the performance of individuals and populations (Lynch 1991, Lande and Shannon 1996, Verhoeven et al. 2011). However, gene flow might also reduce population fitness by genetic swamping and disruption of local adaptations that push populations away from local adaptive peaks (Rhymer and Simberloff 1996, Fenster and Galloway 2000, Verhoeven et al. 2011). Moreover, negative fitness effects caused by genetic incompatibilities may not be evident until second or third generation of offspring when co-adapted gene complexes breakup during recombination (Nieminen et al. 2001, Tallmon et al. 2004, Whitlock et al. 2013). Through its influence on population fitness gene flow may influence dynamics of population size, distribution and interaction with neighboring populations and shape patterns of phenotypic and genetic structure of populations.

Unresolved questions There has been great advances in modeling methods and development of genetic tools to increase our knowledge of how fluctuating environmental conditions, recurrent local extinction and (re-)colonization events, fluctuating population sizes, genetic drift, dispersal, and gene flow will over time form patterns of genetic and phenotypic diversity within, and divergence among populations (Charlesworth and Charlesworth 2017). However, despite extensive research there is no consensus regarding how dispersal, admixture and gene flow in combination with random and deterministic processes generate patterns of genetic and phenotypic diversity within and divergence among populations in spatiotemporally fluctuating environments. To expand knowledge about such complex questions and identify generalizations regarding how evolution operates requires that these processes are investigated in many different study systems.

8

Dispersal Dispersal may occur as a natural consequence of competition for resources in habitats that fluctuate or differ in quality. When fitness might be higher somewhere else or become low in their current location then populations are expected to evolve capabilities to disperse (Bonte et al. 2012). However, because dispersal is associated with costs, populations are also expected to evolve abilities to stay where they are (Bonte et al. 2012, Richardson et al. 2014). The relative success of dispersers versus stayers drives the evolutionary equilibrium between the two opposing strategies. Ecological properties such as habitat size, quality, rarity and interconnectedness are important aspects that can influence the balance between costs and benefits of dispersal (Ewers and Didham 2006, Bonte et al. 2012). Evidence for the dual nature of dispersal can be observed as polymorphism in dispersive traits such as wing length dimorphism in some insects (Harrison 1980). The ability to disperse can generate movement of individuals among habitat patches and it is commonly assumed that dispersal reduces differentiation among populations and increases genetic diversity within populations (Bohonak 1999). However, non-random dispersal for example in the form of matching habitat choice or spatial sorting could have the opposite effect and increase population structure and reduce genetic diversity (Edelaar et al. 2008, Shine et al. 2011). Furthermore, influences of dispersal on patterns of phenotypic and genetic diversity can vary depending on the relative success of dispersers and on whether dispersal actually translates into gene flow.

Gene flow Individuals that have dispersed and settled in a new population might confront different environmental conditions that favor other traits compared to their population of origin (Whitlock et al. 2013). Population differentiation in morphology, behavior or genetic composition could negatively affect the ability of immigrants to mate and contribute offspring to the next generation (Nosil et al. 2003, Frankham et al. 2011, Richardson et al. 2014). When immigrants have lower fitness compared to resident individuals, either because of inferior survival or reproductive success, then dispersal is expected to generate a low degree of gene flow between populations. However, resident individuals might increase their mating effort or allocation of resources to reproduction in favor of immigrants, and thereby produce more or higher quality offspring. Admixed offspring could also experience higher fitness from heterotic effects or novel combinations of genetic traits (Verhoeven et al. 2011). When immigrants have higher fitness compared to resident individuals, either because of different reproductive success or increased offspring fitness, then dispersal is expected to generate a higher degree of gene flow among populations.

9

Apart from having consequences for genetic structure among populations gene flow could also affect the composition of genes within populations. One consequence of gene flow is that new genetic varieties are introduced and incorporated in the population gene pool. This is expected to increase standing genetic diversity within populations, conceal deleterious recessive alleles, create novel genotypes by segregation and recombination, and ultimately influence the performance of individuals and populations (Lynch 1991, Lande and Shannon 1996, Verhoeven et al. 2011). However, gene flow might also reduce population fitness by genetic swamping and disruption of local adaptations that push populations away from local adaptive peaks (Rhymer and Simberloff 1996, Fenster and Galloway 2000, Verhoeven et al. 2011). Moreover, negative fitness effects caused by genetic incompatibilities may not be evident until second or third generation of offspring when co-adapted gene complexes breakup during recombination (Nieminen et al. 2001, Tallmon et al. 2004, Whitlock et al. 2013). Through its influence on population fitness gene flow may influence dynamics of population size, distribution and interaction with neighboring populations and shape patterns of phenotypic and genetic structure of populations.

Unresolved questions There has been great advances in modeling methods and development of genetic tools to increase our knowledge of how fluctuating environmental conditions, recurrent local extinction and (re-)colonization events, fluctuating population sizes, genetic drift, dispersal, and gene flow will over time form patterns of genetic and phenotypic diversity within, and divergence among populations (Charlesworth and Charlesworth 2017). However, despite extensive research there is no consensus regarding how dispersal, admixture and gene flow in combination with random and deterministic processes generate patterns of genetic and phenotypic diversity within and divergence among populations in spatiotemporally fluctuating environments. To expand knowledge about such complex questions and identify generalizations regarding how evolution operates requires that these processes are investigated in many different study systems.

8

Dispersal Dispersal may occur as a natural consequence of competition for resources in habitats that fluctuate or differ in quality. When fitness might be higher somewhere else or become low in their current location then populations are expected to evolve capabilities to disperse (Bonte et al. 2012). However, because dispersal is associated with costs, populations are also expected to evolve abilities to stay where they are (Bonte et al. 2012, Richardson et al. 2014). The relative success of dispersers versus stayers drives the evolutionary equilibrium between the two opposing strategies. Ecological properties such as habitat size, quality, rarity and interconnectedness are important aspects that can influence the balance between costs and benefits of dispersal (Ewers and Didham 2006, Bonte et al. 2012). Evidence for the dual nature of dispersal can be observed as polymorphism in dispersive traits such as wing length dimorphism in some insects (Harrison 1980). The ability to disperse can generate movement of individuals among habitat patches and it is commonly assumed that dispersal reduces differentiation among populations and increases genetic diversity within populations (Bohonak 1999). However, non-random dispersal for example in the form of matching habitat choice or spatial sorting could have the opposite effect and increase population structure and reduce genetic diversity (Edelaar et al. 2008, Shine et al. 2011). Furthermore, influences of dispersal on patterns of phenotypic and genetic diversity can vary depending on the relative success of dispersers and on whether dispersal actually translates into gene flow.

Gene flow Individuals that have dispersed and settled in a new population might confront different environmental conditions that favor other traits compared to their population of origin (Whitlock et al. 2013). Population differentiation in morphology, behavior or genetic composition could negatively affect the ability of immigrants to mate and contribute offspring to the next generation (Nosil et al. 2003, Frankham et al. 2011, Richardson et al. 2014). When immigrants have lower fitness compared to resident individuals, either because of inferior survival or reproductive success, then dispersal is expected to generate a low degree of gene flow between populations. However, resident individuals might increase their mating effort or allocation of resources to reproduction in favor of immigrants, and thereby produce more or higher quality offspring. Admixed offspring could also experience higher fitness from heterotic effects or novel combinations of genetic traits (Verhoeven et al. 2011). When immigrants have higher fitness compared to resident individuals, either because of different reproductive success or increased offspring fitness, then dispersal is expected to generate a higher degree of gene flow among populations.

11

Two species of polymorphic grasshoppers – in the service of science

Pygmy grasshoppers (Tetrix subulata and Tetrix undulata) are small, diurnal, univoltine, iteroparous, ground dwelling insects that usually occupy microhabitats in relatively open areas where they live on the soil surface and feed on microalgae growing on moist soils, mosses and detritus (Holst 1986, Ahnesjö and Forsman 2006, Karpestam and Forsman 2011). Both species are widely distributed across Europe and commonly found in a range of habitats such as clear cuttings, shore meadows, agricultural areas and pastures. Adults and late instars nymphs hibernate during winter and emerge in April-May when reproduction ensues. Females mate repeatedly with several different males and produce multiple pods of eggs. Each pod may contain up to 35 eggs and nymphs develop through five (males) or six (females) instars before eclosing as adults.

Pygmy grasshoppers provide a classic example of color polymorphism (Figure 1). Previous studies suggest that the different color variants differ in heat balance, thermal physiology, body size, reproductive life history, predator avoidance behaviors, microhabitat utilization and diet, such that they occupy different niches and can be considered eco-morphs (Forsman et al. 2002, Ahnesjö and Forsman 2003, 2006, Forsman et al. 2007, Karpestam and Forsman 2011). Common garden and split-brood rearing experiments of pygmy grasshoppers have shown that color morphs are genetically influenced and not affected to any important degree by developmental plasticity (Ahnesjö and Forsman 2003, Karlsson et al. 2009, Karlsson and Forsman 2010, Forsman et al. 2011, Johansson et al. 2013). Estimates of color morph diversity therefore provide a reliable proxy of functionally important genetic and phenotypic diversity within populations. Furthermore, in line with expectations from theory, experimental evidence from pygmy grasshoppers have revealed that higher levels of color morph diversity protects individuals and populations against predation (Karpestam et al. 2016), reduces the effects

10

Aims of thesis

This thesis contributes to the knowledge and understanding of evolutionary and ecological processes that generate patterns of phenotypic and genetic diversity within and among natural populations. It explores how dispersal affects population genetic structure, investigates causes and consequences of functional genetic diversity, and examines drivers of phenotypic variation. It also addresses how behavior, reproductive allocation and genetic incompatibility may influence fitness of dispersers and affect the translation of dispersal into gene flow. More specifically, the aims of this thesis are to:

I. study how dispersal barriers, geographic distance, immigration and population size influence patterns of genetic diversity within and differentiation among populations (Papers I &II).

II. investigate weather temporally changing environments differently

affect patterns of functional and neutral diversity (Papers II). III. explore whether and how differences in dispersal ability between

species influence patterns of genetic structure and diversity in natural populations (Papers I, II & III).

IV. experimentally examine how population interbreeding and admixture

may influence how dispersal translates into gene flow (Paper IV).

11

Two species of polymorphic grasshoppers – in the service of science

Pygmy grasshoppers (Tetrix subulata and Tetrix undulata) are small, diurnal, univoltine, iteroparous, ground dwelling insects that usually occupy microhabitats in relatively open areas where they live on the soil surface and feed on microalgae growing on moist soils, mosses and detritus (Holst 1986, Ahnesjö and Forsman 2006, Karpestam and Forsman 2011). Both species are widely distributed across Europe and commonly found in a range of habitats such as clear cuttings, shore meadows, agricultural areas and pastures. Adults and late instars nymphs hibernate during winter and emerge in April-May when reproduction ensues. Females mate repeatedly with several different males and produce multiple pods of eggs. Each pod may contain up to 35 eggs and nymphs develop through five (males) or six (females) instars before eclosing as adults.

Pygmy grasshoppers provide a classic example of color polymorphism (Figure 1). Previous studies suggest that the different color variants differ in heat balance, thermal physiology, body size, reproductive life history, predator avoidance behaviors, microhabitat utilization and diet, such that they occupy different niches and can be considered eco-morphs (Forsman et al. 2002, Ahnesjö and Forsman 2003, 2006, Forsman et al. 2007, Karpestam and Forsman 2011). Common garden and split-brood rearing experiments of pygmy grasshoppers have shown that color morphs are genetically influenced and not affected to any important degree by developmental plasticity (Ahnesjö and Forsman 2003, Karlsson et al. 2009, Karlsson and Forsman 2010, Forsman et al. 2011, Johansson et al. 2013). Estimates of color morph diversity therefore provide a reliable proxy of functionally important genetic and phenotypic diversity within populations. Furthermore, in line with expectations from theory, experimental evidence from pygmy grasshoppers have revealed that higher levels of color morph diversity protects individuals and populations against predation (Karpestam et al. 2016), reduces the effects

10

Aims of thesis

This thesis contributes to the knowledge and understanding of evolutionary and ecological processes that generate patterns of phenotypic and genetic diversity within and among natural populations. It explores how dispersal affects population genetic structure, investigates causes and consequences of functional genetic diversity, and examines drivers of phenotypic variation. It also addresses how behavior, reproductive allocation and genetic incompatibility may influence fitness of dispersers and affect the translation of dispersal into gene flow. More specifically, the aims of this thesis are to:

I. study how dispersal barriers, geographic distance, immigration and population size influence patterns of genetic diversity within and differentiation among populations (Papers I &II).

II. investigate weather temporally changing environments differently

affect patterns of functional and neutral diversity (Papers II). III. explore whether and how differences in dispersal ability between

species influence patterns of genetic structure and diversity in natural populations (Papers I, II & III).

IV. experimentally examine how population interbreeding and admixture

may influence how dispersal translates into gene flow (Paper IV).

13

Figure 2. Tetrix undulata pygmy grasshopper females belonging to the macropterous morph with long and functional wings (left) and micropterous short-winged flightless morph (right). Photo: J. Tinnert.

In response to natural selection populations are expected to evolve traits that provide an advantage and increase local adaptation in response to local environmental conditions (Kawecki and Ebert 2004). An example of such local adaptation is found in pygmy grasshoppers that may rapidly evolve in response to natural selection acting on color morphs in recently disturbed areas ravaged by forest fires (Forsman et al. 2011). The adaptive value of the black phenotype on burnt background was further supported by a detection experiment performed on human test subjects that acted as visual predators looking for images of differently colored grasshoppers on a computer screen showing photographs of natural grasshopper microhabitat (Karpestam et al. 2012, Karpestam et al. 2013). The experiment performed by Karpestam et al. (2013) also showed that detection rate of grasshopper color morph changed depending on the visual background, suggesting that differential predation might be an important driver of local adaptation of color patterns and induce shifts in color morph frequencies. In addition, fitness differences among color morphs are not only influenced by how cryptic they are, but also by selection acting on characters associated with color morph, such as temperature preference, thermal physiology, reproductive life history, body size, microhabitat utilization and diet (Caesar et al. 2010, Forsman et al. 2011, Karpestam and Forsman 2011). Pygmy grasshopper color morphs represent integrated phenotypes and selection acting on these traits may also increase local adaptation of populations. Indeed, my personal observations from collecting grasshoppers in the field corroborate the previously reported findings (e.g., Forsman et al. 2011) that color morph frequencies generally differ among populations.

Study area and sampling During 2004-2012 we collected pygmy grasshoppers (T. subulata and T. undulata) from 20 natural populations of each species in southern Sweden (Figure 3). Populations of T. subulata were collected at sites that represented stable habitats of similar type located on the Swedish mainland and on the

12

of intra-specific competition (Caesar et al. 2010), and promotes establishment success of founder groups (Forsman et al. 2012, Wennersten et al. 2012).

Figure 1. Tetrix undulata representing reddish brown, striped, striated brown, grey, and black colour morphs. Photo: A. Forsman.

Like many other insects, pygmy grasshoppers display dispersal polymorphism; a short winged flight incapable morph that lacks flight muscles coexists with a long winged flight capable morph (Figure 2) (Harrison 1980, Holst 1986, Berggren et al. 2012). Evidence from common garden breeding experiments indicate that wing morph in pygmy grasshoppers is not influenced to any important degree by phenotypic plasticity (Berggren et al. 2012). In the studied region, the incidence of the long-winged morph is generally higher in T. subulata than in T. undulata and the long-winged flight capable phenotype can be used as a proxy for immigration (Tinnert et al. 2016a, Tinnert et al. 2016b). Furthermore, the discrepancy in dispersal capability among wing morphs generates a dynamic process, and a high initial incidence of long winged individuals in recently founded populations is followed by a decline in frequency over time as flight capable individuals are able to emigrate (Berggren et al. 2012).

Previous findings by Berggren et al. (2012) and by Forsman and Appelqvist (1999) indicate that pygmy grasshoppers are sedentary and normally move only a few meters per day. A dispersal rate of that magnitude would suggest a limited gene flow that would result in a high level of genetic differentiation among populations. However, Berggren et al. (2012) also reported observations of longed winged individuals actively flying up to 75 meters when placed in inhospitable environments. The ability of long winged individuals to fly suggests that pygmy grasshoppers have the capacity to disperse over longer distances, possibly leading to gene flow.

13

Figure 2. Tetrix undulata pygmy grasshopper females belonging to the macropterous morph with long and functional wings (left) and micropterous short-winged flightless morph (right). Photo: J. Tinnert.

In response to natural selection populations are expected to evolve traits that provide an advantage and increase local adaptation in response to local environmental conditions (Kawecki and Ebert 2004). An example of such local adaptation is found in pygmy grasshoppers that may rapidly evolve in response to natural selection acting on color morphs in recently disturbed areas ravaged by forest fires (Forsman et al. 2011). The adaptive value of the black phenotype on burnt background was further supported by a detection experiment performed on human test subjects that acted as visual predators looking for images of differently colored grasshoppers on a computer screen showing photographs of natural grasshopper microhabitat (Karpestam et al. 2012, Karpestam et al. 2013). The experiment performed by Karpestam et al. (2013) also showed that detection rate of grasshopper color morph changed depending on the visual background, suggesting that differential predation might be an important driver of local adaptation of color patterns and induce shifts in color morph frequencies. In addition, fitness differences among color morphs are not only influenced by how cryptic they are, but also by selection acting on characters associated with color morph, such as temperature preference, thermal physiology, reproductive life history, body size, microhabitat utilization and diet (Caesar et al. 2010, Forsman et al. 2011, Karpestam and Forsman 2011). Pygmy grasshopper color morphs represent integrated phenotypes and selection acting on these traits may also increase local adaptation of populations. Indeed, my personal observations from collecting grasshoppers in the field corroborate the previously reported findings (e.g., Forsman et al. 2011) that color morph frequencies generally differ among populations.

Study area and sampling During 2004-2012 we collected pygmy grasshoppers (T. subulata and T. undulata) from 20 natural populations of each species in southern Sweden (Figure 3). Populations of T. subulata were collected at sites that represented stable habitats of similar type located on the Swedish mainland and on the

12

of intra-specific competition (Caesar et al. 2010), and promotes establishment success of founder groups (Forsman et al. 2012, Wennersten et al. 2012).

Figure 1. Tetrix undulata representing reddish brown, striped, striated brown, grey, and black colour morphs. Photo: A. Forsman.

Like many other insects, pygmy grasshoppers display dispersal polymorphism; a short winged flight incapable morph that lacks flight muscles coexists with a long winged flight capable morph (Figure 2) (Harrison 1980, Holst 1986, Berggren et al. 2012). Evidence from common garden breeding experiments indicate that wing morph in pygmy grasshoppers is not influenced to any important degree by phenotypic plasticity (Berggren et al. 2012). In the studied region, the incidence of the long-winged morph is generally higher in T. subulata than in T. undulata and the long-winged flight capable phenotype can be used as a proxy for immigration (Tinnert et al. 2016a, Tinnert et al. 2016b). Furthermore, the discrepancy in dispersal capability among wing morphs generates a dynamic process, and a high initial incidence of long winged individuals in recently founded populations is followed by a decline in frequency over time as flight capable individuals are able to emigrate (Berggren et al. 2012).

Previous findings by Berggren et al. (2012) and by Forsman and Appelqvist (1999) indicate that pygmy grasshoppers are sedentary and normally move only a few meters per day. A dispersal rate of that magnitude would suggest a limited gene flow that would result in a high level of genetic differentiation among populations. However, Berggren et al. (2012) also reported observations of longed winged individuals actively flying up to 75 meters when placed in inhospitable environments. The ability of long winged individuals to fly suggests that pygmy grasshoppers have the capacity to disperse over longer distances, possibly leading to gene flow.

15

Figure 3. Map of study area in the south of Sweden, showing location and Sample ID of 13 Tetrix undulata (white dots) and 13 Tetrix subulata (grey dots) pygmy grasshopper populations. The seven black dots represent locations where both species co-occur in sympatry.

Estimates of population size Population size of T. subulata was estimated for each locality based on information on number of collected individuals. The number of collected individuals at each site underestimates actual population size, and we investigated factors that could potentially influence total catch, such as differences in the time invested in sampling and the number of people involved in each sampling event. For subsets of the 20 sampling locations of T. subulata we recorded the number of people involved (n = 19 locations) and search time (n = 17 locations) in addition to the total number of grasshoppers collected. Total number of grasshoppers collected increased with increasing number of people involved in the search but was not significantly associated with search time (Paper I). It also was not necessary to adjust for search time because the questions addressed concerned population size rather than population density. There was a strong correlation between total number of grasshoppers collected and number of grasshoppers collected per person involved in the search, indicating that total number of grasshoppers collected is a reliable surrogate for population size (Paper I). These analyses indicate

14

island of Öland in the Baltic Sea. These locations were selected such that the data set contained populations with varying degrees of inter population distances and connectedness. Sampled populations of T. undulata were also selected such that the data set contained populations with varying degrees of inter population distance. Moreover, T. undulata populations were selected such that they varied also with regard to habitat stability. At seven sampling locations both species co-occur in sympatry and these sites represent relatively stable habitats on the Swedish mainland, thus allowing for between species comparisons (Figure 3).

Pygmy grasshoppers predominantly move around on the ground surface, making it difficult to capture them with a net. Instead they were captured while slowly walking through the area, visually searching for them, and then catching them by hand (Forsman & Appelqvist 1999; Forsman et al. 2012). Collection took place during spring and summer days when weather conditions were suitable for grasshopper activity (clear or overcast days with a temperature of at least 15°C). The number of grasshopper individuals collected at each locality during one visit was recorded and used as a proxy for population size. Pygmy grasshoppers do not have a uniform or random spatial distribution (Ahnesjö & Forsman 2006; Forsman & Appelqvist 1999). We therefore initially searched the entire areas and then concentrated our search and capture effort to those parts, microhabitats, and substrate types (i.e., humid bare soil, areas covered by mosses) that are preferred by pygmy grasshoppers (Ahnesjö & Forsman 2006; Berggren et al. 2012; Forsman et al. 2011; Forsman et al. 2012). Captured individuals were identified to species according to Holst (Holst 1986), classified according to sex, wing morph (Berggren et al. 2012), and color morph (Ahnesjö and Forsman 2003, Forsman et al. 2007), and preserved in 90% ethanol until DNA extraction. For some populations tissue samples were not from the whole animal but from one of the hind legs.

15

Figure 3. Map of study area in the south of Sweden, showing location and Sample ID of 13 Tetrix undulata (white dots) and 13 Tetrix subulata (grey dots) pygmy grasshopper populations. The seven black dots represent locations where both species co-occur in sympatry.

Estimates of population size Population size of T. subulata was estimated for each locality based on information on number of collected individuals. The number of collected individuals at each site underestimates actual population size, and we investigated factors that could potentially influence total catch, such as differences in the time invested in sampling and the number of people involved in each sampling event. For subsets of the 20 sampling locations of T. subulata we recorded the number of people involved (n = 19 locations) and search time (n = 17 locations) in addition to the total number of grasshoppers collected. Total number of grasshoppers collected increased with increasing number of people involved in the search but was not significantly associated with search time (Paper I). It also was not necessary to adjust for search time because the questions addressed concerned population size rather than population density. There was a strong correlation between total number of grasshoppers collected and number of grasshoppers collected per person involved in the search, indicating that total number of grasshoppers collected is a reliable surrogate for population size (Paper I). These analyses indicate

14

island of Öland in the Baltic Sea. These locations were selected such that the data set contained populations with varying degrees of inter population distances and connectedness. Sampled populations of T. undulata were also selected such that the data set contained populations with varying degrees of inter population distance. Moreover, T. undulata populations were selected such that they varied also with regard to habitat stability. At seven sampling locations both species co-occur in sympatry and these sites represent relatively stable habitats on the Swedish mainland, thus allowing for between species comparisons (Figure 3).

Pygmy grasshoppers predominantly move around on the ground surface, making it difficult to capture them with a net. Instead they were captured while slowly walking through the area, visually searching for them, and then catching them by hand (Forsman & Appelqvist 1999; Forsman et al. 2012). Collection took place during spring and summer days when weather conditions were suitable for grasshopper activity (clear or overcast days with a temperature of at least 15°C). The number of grasshopper individuals collected at each locality during one visit was recorded and used as a proxy for population size. Pygmy grasshoppers do not have a uniform or random spatial distribution (Ahnesjö & Forsman 2006; Forsman & Appelqvist 1999). We therefore initially searched the entire areas and then concentrated our search and capture effort to those parts, microhabitats, and substrate types (i.e., humid bare soil, areas covered by mosses) that are preferred by pygmy grasshoppers (Ahnesjö & Forsman 2006; Berggren et al. 2012; Forsman et al. 2011; Forsman et al. 2012). Captured individuals were identified to species according to Holst (Holst 1986), classified according to sex, wing morph (Berggren et al. 2012), and color morph (Ahnesjö and Forsman 2003, Forsman et al. 2007), and preserved in 90% ethanol until DNA extraction. For some populations tissue samples were not from the whole animal but from one of the hind legs.

17

been much influenced by recent immigration (and that might show signatures associated with admixture).

Estimates of functional genetic diversity To compare the degree of functionally important phenotypic and genetic variation within populations of T. undulata, we used information on color morphs. Pygmy grasshoppers exhibit a high degree of color polymorphism. Color morphs range from light grey via different shades of brown to black, some being uniform and others mottled or patterned with longitudinal stripes, vertical bars or speckles (Forsman et al. 2002, Ahnesjö and Forsman 2003, 2006, Forsman et al. 2007, Karpestam and Forsman 2011) (Figure 1). Individuals vary also with regard to texture of the integument, the surface being either smooth, granular or consisting of longitudinal ridges and grooves. Color morphs in pygmy grasshoppers are genetically influenced and not affected to any important degree by developmental plasticity (see Nabours 1929, Karlsson et al. 2009, Karlsson and Forsman 2010, Forsman 2011, Forsman et al. 2011 and references therein). To quantify color morph diversity in populations we used the coefficient of unalikeability (Perry and Kader 2005, Kader and Perry 2007). Measures of population variability for categorical characters are different from those used for quantitative or continuous traits where variation about the mean is the norm. The coefficient of unalikeability (u2) is a measure of the proportion of possible comparisons which are unalike, and was calculated based on the equation: u2 = 1 - ∑i=1pi

2, where p represents the proportion of the population in each of the ith categories of the categorical variable (i.e., color morph) (Perry and Kader 2005, Kader and Perry 2007). Unalikeability can take any value from 0.0 to 1.0, with higher values representing higher diversity. Estimates of color morph unalikeability for T. undulata study populations were independent of sample size (r = 0.35, n = 20, p = 0.14).

DNA extraction and molecular genetic analyses DNA was extracted from the femur of each individual using the Phenol-Chloroform method according to Sambrook (Sambrook et al. 2002). Analysis of amplified fragment length polymorphism (AFLP) was carried out as described by Vos (Vos et al. 1995, Bensch and Akesson 2005), using: the restriction enzymes EcoRI and TrueI, the adapters EcoRI and MseI, the pre amplification primers MC X ET and four combinations of selective primers (pair 1-ETAG X MCGA, pair 2-ETAG X MCAG, pair 3-ETCG X MCAC, pair 4-ETAG X MCAC). Three negative controls and nine positive controls were included on each plate. PCR reactions were diluted 1:8 of which 2µl were sent to Uppsala Genome Center for fragment analysis using capillary electrophoresis on an ABI3730XL DNA Analyzer (Applies Biosystems). All DNA extractions and methods associated with AFLP were performed in the same laboratory (Lund University, Lund) and results and conclusions are therefore not influenced by

16

that humans that search for pygmy grasshoppers tend to behave in accordance with optimal foraging theory. The cumulative number of grasshoppers collected increases asymptotically with search time. When several people search at the same area, the asymptote is reached faster but the cumulative number of grasshoppers collected does not increase. This is likely because the number and density of grasshoppers remaining in an area decreases in an exponential decaying manner over time, and individuals tend to stop searching when detection rate drops below a critical level. Thus, giving up density is almost invariable but giving up time decreases when more people are searching, such that total search effort is comparable across sampling sites and/or occasions.

Census population size of T. subulata and T. undulata was estimated for each locality based on information on number of collected individuals and capture probability. A previous capture-mark-recapture study in which 442 marked pygmy grasshoppers were recaptured on five occasions, showed a recapture probability of 40% despite varying weather conditions (Forsman and Appelqvist 1999). In a follow up study one year later, 196 grasshoppers were marked and released in the same location and the recaptured rate in that study was 37% (Forsman and Appelqvist 1999). In a more recent study Berggren et al. (2012) released 73 pygmy grasshoppers in a cattle-grazed pasture and 38% of these were recaptured 4 days later. To estimate census population size values of number of collected individuals were therefore adjusted for a capture probability set to 0.4.

Estimates of immigration rate The proportion of long-winged individuals at each site was used as a proxy for immigration rate or recent colonization events. Earlier work (Berggren et al. 2012) has shown that the proportion males and females that belong to the long-winged morph is highly correlated across samples from different populations and years, and that the incidence of the long-winged morph does not differ consistently between males and females in this part of the distribution range. Furthermore, results from previous capture-mark recapture studies show that capture probability is independent of both sex (Forsman and Appelqvist 1999) and wing morph (Berggren et al. 2012). It is therefore unlikely that the estimates of the proportion of long-winged phenotypes reported in the present study were influenced to any important degree by any sampling bias according to sex or wing morph or by any differences in sex ratio among samples from different collection sites. Furthermore, it is only the long winged phenotype that is able to fly (Berggren et al. 2012). Taken together, this suggests that a high incidence of the long-winged flight capable morph may be used as a proxy to identify populations that have either been recently established (and hence might be hypothesized to show signatures associated with founder events), or that represent older populations that have

17

been much influenced by recent immigration (and that might show signatures associated with admixture).

Estimates of functional genetic diversity To compare the degree of functionally important phenotypic and genetic variation within populations of T. undulata, we used information on color morphs. Pygmy grasshoppers exhibit a high degree of color polymorphism. Color morphs range from light grey via different shades of brown to black, some being uniform and others mottled or patterned with longitudinal stripes, vertical bars or speckles (Forsman et al. 2002, Ahnesjö and Forsman 2003, 2006, Forsman et al. 2007, Karpestam and Forsman 2011) (Figure 1). Individuals vary also with regard to texture of the integument, the surface being either smooth, granular or consisting of longitudinal ridges and grooves. Color morphs in pygmy grasshoppers are genetically influenced and not affected to any important degree by developmental plasticity (see Nabours 1929, Karlsson et al. 2009, Karlsson and Forsman 2010, Forsman 2011, Forsman et al. 2011 and references therein). To quantify color morph diversity in populations we used the coefficient of unalikeability (Perry and Kader 2005, Kader and Perry 2007). Measures of population variability for categorical characters are different from those used for quantitative or continuous traits where variation about the mean is the norm. The coefficient of unalikeability (u2) is a measure of the proportion of possible comparisons which are unalike, and was calculated based on the equation: u2 = 1 - ∑i=1pi

2, where p represents the proportion of the population in each of the ith categories of the categorical variable (i.e., color morph) (Perry and Kader 2005, Kader and Perry 2007). Unalikeability can take any value from 0.0 to 1.0, with higher values representing higher diversity. Estimates of color morph unalikeability for T. undulata study populations were independent of sample size (r = 0.35, n = 20, p = 0.14).

DNA extraction and molecular genetic analyses DNA was extracted from the femur of each individual using the Phenol-Chloroform method according to Sambrook (Sambrook et al. 2002). Analysis of amplified fragment length polymorphism (AFLP) was carried out as described by Vos (Vos et al. 1995, Bensch and Akesson 2005), using: the restriction enzymes EcoRI and TrueI, the adapters EcoRI and MseI, the pre amplification primers MC X ET and four combinations of selective primers (pair 1-ETAG X MCGA, pair 2-ETAG X MCAG, pair 3-ETCG X MCAC, pair 4-ETAG X MCAC). Three negative controls and nine positive controls were included on each plate. PCR reactions were diluted 1:8 of which 2µl were sent to Uppsala Genome Center for fragment analysis using capillary electrophoresis on an ABI3730XL DNA Analyzer (Applies Biosystems). All DNA extractions and methods associated with AFLP were performed in the same laboratory (Lund University, Lund) and results and conclusions are therefore not influenced by

16

that humans that search for pygmy grasshoppers tend to behave in accordance with optimal foraging theory. The cumulative number of grasshoppers collected increases asymptotically with search time. When several people search at the same area, the asymptote is reached faster but the cumulative number of grasshoppers collected does not increase. This is likely because the number and density of grasshoppers remaining in an area decreases in an exponential decaying manner over time, and individuals tend to stop searching when detection rate drops below a critical level. Thus, giving up density is almost invariable but giving up time decreases when more people are searching, such that total search effort is comparable across sampling sites and/or occasions.

Census population size of T. subulata and T. undulata was estimated for each locality based on information on number of collected individuals and capture probability. A previous capture-mark-recapture study in which 442 marked pygmy grasshoppers were recaptured on five occasions, showed a recapture probability of 40% despite varying weather conditions (Forsman and Appelqvist 1999). In a follow up study one year later, 196 grasshoppers were marked and released in the same location and the recaptured rate in that study was 37% (Forsman and Appelqvist 1999). In a more recent study Berggren et al. (2012) released 73 pygmy grasshoppers in a cattle-grazed pasture and 38% of these were recaptured 4 days later. To estimate census population size values of number of collected individuals were therefore adjusted for a capture probability set to 0.4.

Estimates of immigration rate The proportion of long-winged individuals at each site was used as a proxy for immigration rate or recent colonization events. Earlier work (Berggren et al. 2012) has shown that the proportion males and females that belong to the long-winged morph is highly correlated across samples from different populations and years, and that the incidence of the long-winged morph does not differ consistently between males and females in this part of the distribution range. Furthermore, results from previous capture-mark recapture studies show that capture probability is independent of both sex (Forsman and Appelqvist 1999) and wing morph (Berggren et al. 2012). It is therefore unlikely that the estimates of the proportion of long-winged phenotypes reported in the present study were influenced to any important degree by any sampling bias according to sex or wing morph or by any differences in sex ratio among samples from different collection sites. Furthermore, it is only the long winged phenotype that is able to fly (Berggren et al. 2012). Taken together, this suggests that a high incidence of the long-winged flight capable morph may be used as a proxy to identify populations that have either been recently established (and hence might be hypothesized to show signatures associated with founder events), or that represent older populations that have

19

Analyses of neutral genetic structure among populations Genetic differentiation between sampling localities (pairwise FST) was estimated using 5000 permutations implemented in AFLPsurv (Vekemans et al. 2002). Gene flow between pairs of populations was calculated from these values of FST, under the assumption of an infinite-island model of population structure (Wright 1951), based on the equation Nm = 0.25(1/ FST - 1).

To assess the impact of potential barriers and evaluate genetic structure in T. subulata we performed a nested analysis of molecular variance, AMOVA (Excoffier and Lischer 2010). Individuals were grouped by sampling site and then nested within two geographic regions (Öland and mainland) (Figure 3). Genetic variation was estimated for the 1564 AFLP markers and AMOVA was used to partition the proportion of total genetic variation explained: between geographic regions, among sample locations within regions, and among individuals within sample locations. The overall FST statistic, AMOVA and Pairwise FST values were calculated as implemented in Arlequin version 3.5 (Excoffier and Lischer 2010). The statistical significance of the AMOVA and the FST analyses was assessed by 1000 permutations of individuals among populations.

The isolation by distance hypothesis (IBD) was evaluated among populations of T. subulata by performing a Mantel test with 10,000 randomizations implemented in ARLEQUIN version 3.5 (Excoffier and Lischer 2010). Because the AMOVA results indicated a significant effect of geographic region the IBD was evaluated both for the complete data set and for subsets of the data that comprised sample locations either from the Swedish mainland or from Öland. Pairwise FST values may increase monotonically with increasing inter-population geographic distances, or increase up to a certain threshold distance beyond which the effects of gene flow can be so small compared with the effects of genetic drift and mutations that the expected FST–distance correlation does not manifest anymore (Rousset 1997, Hutchison and Templeton 1999, van Strien et al. 2015). To further assess the presence and intensity of any isolation by distance in our data we evaluated the FST – geographic distance correlation from subsets of mainland population pairs that differed with regard to threshold inter-population distance (van Strien et al. 2015). For this analysis we used five distance intervals (0-30km, 0-50 km, 0-100 km, 0-250 km, 0-550 km).

Estimates of standardized ‘functional diversity differentials’ within populations To quantify the difference between relative functional color morph diversity (unalikeability) and relative neutral genetic diversity (Hj, as estimated from AFLP data) within populations in T. undulata, we calculated standardized ‘functional diversity differentials’. Measures of relative unalikeability and relative Hj for each population was standardized across populations to a mean

18

any difficulties associated with transferring AFLP information across laboratories.

Evaluation and conversion of fragment analysis data Chromatograms from all PCR-plates were visually evaluated using GeneMarker 2.6.4 (SoftGenetics) and cleaned by removing chromatogram files with chromatograms of poor quality. Chromatograms were visualized and evaluated using GeneMapper 5.0 (Applied Biosystems) and peak heights from a total of 1970 polymorphic sites were extracted for further analysis. Peak heights were normalized and converted to a binary presence-absence matrix of genotypes using AFLPscore (Whitlock et al. 2008). This resulted in binary matrixes consisting of ones and ceros. The results on genetic diversity and structure reported for T. subulata are based on a matrix of 1564 AFLP loci and results for T. undulata are based on a matrix of 1419 AFLP loci. For the comparative study of two species of pygmy grasshoppers, the binary matrix for each species was reduced to include only loci present in both species. This reduced the bias due to unequal sample size and generated one binary matrix of 1404 AFLP loci per species.

To assess the role of any genotyping errors, genotyping repeatability was estimated on the binary matrix of 1564 AFLP loci from T. subulata. Data for nine positive control individuals from different collection sites were replicated across all plates and was used to measure genotyping repeatability, which was calculated for each allele using mismatch error rate (Bonin et al. 2004). Alleles with the highest mismatch error rate were iteratively removed until a smaller but higher quality data set with a low desired average error rate (mean: 4.8%, range 1.1% - 14 %) remained that consisted of only 638 loci. Results based on this reduced (638 loci) binary matrix were qualitatively similar to results based on the larger and more informative (1564 loci) binary matrix reported below. The estimates and exact parameter values associated with the statistical tests changed somewhat but overall conclusions were robust to choice of data.

Estimates of neutral genetic diversity within populations AFLP markers are dominant and the estimation of allele frequencies was estimated from the proportion of recessive genotypes in the sample and performed using a Bayesian method with non-uniform prior distribution of allele frequencies, assuming Hardy-Weinberg proportions of genotypes and a predefined FIS value of 0 (Zhivotovsky 1999). Statistics of genetic diversity; number of polymorphic loci (PL), proportion of polymorphic loci (PPL) at the 5% level, and expected heterozygosity or Nei’s genetic diversity Hj (analogous to He) (Nei 1973), were estimated following the treatment of Lynch and Milligan (1994) using AFLPsurv (Vekemans et al. 2002).

19

Analyses of neutral genetic structure among populations Genetic differentiation between sampling localities (pairwise FST) was estimated using 5000 permutations implemented in AFLPsurv (Vekemans et al. 2002). Gene flow between pairs of populations was calculated from these values of FST, under the assumption of an infinite-island model of population structure (Wright 1951), based on the equation Nm = 0.25(1/ FST - 1).

To assess the impact of potential barriers and evaluate genetic structure in T. subulata we performed a nested analysis of molecular variance, AMOVA (Excoffier and Lischer 2010). Individuals were grouped by sampling site and then nested within two geographic regions (Öland and mainland) (Figure 3). Genetic variation was estimated for the 1564 AFLP markers and AMOVA was used to partition the proportion of total genetic variation explained: between geographic regions, among sample locations within regions, and among individuals within sample locations. The overall FST statistic, AMOVA and Pairwise FST values were calculated as implemented in Arlequin version 3.5 (Excoffier and Lischer 2010). The statistical significance of the AMOVA and the FST analyses was assessed by 1000 permutations of individuals among populations.

The isolation by distance hypothesis (IBD) was evaluated among populations of T. subulata by performing a Mantel test with 10,000 randomizations implemented in ARLEQUIN version 3.5 (Excoffier and Lischer 2010). Because the AMOVA results indicated a significant effect of geographic region the IBD was evaluated both for the complete data set and for subsets of the data that comprised sample locations either from the Swedish mainland or from Öland. Pairwise FST values may increase monotonically with increasing inter-population geographic distances, or increase up to a certain threshold distance beyond which the effects of gene flow can be so small compared with the effects of genetic drift and mutations that the expected FST–distance correlation does not manifest anymore (Rousset 1997, Hutchison and Templeton 1999, van Strien et al. 2015). To further assess the presence and intensity of any isolation by distance in our data we evaluated the FST – geographic distance correlation from subsets of mainland population pairs that differed with regard to threshold inter-population distance (van Strien et al. 2015). For this analysis we used five distance intervals (0-30km, 0-50 km, 0-100 km, 0-250 km, 0-550 km).

Estimates of standardized ‘functional diversity differentials’ within populations To quantify the difference between relative functional color morph diversity (unalikeability) and relative neutral genetic diversity (Hj, as estimated from AFLP data) within populations in T. undulata, we calculated standardized ‘functional diversity differentials’. Measures of relative unalikeability and relative Hj for each population was standardized across populations to a mean

18

any difficulties associated with transferring AFLP information across laboratories.

Evaluation and conversion of fragment analysis data Chromatograms from all PCR-plates were visually evaluated using GeneMarker 2.6.4 (SoftGenetics) and cleaned by removing chromatogram files with chromatograms of poor quality. Chromatograms were visualized and evaluated using GeneMapper 5.0 (Applied Biosystems) and peak heights from a total of 1970 polymorphic sites were extracted for further analysis. Peak heights were normalized and converted to a binary presence-absence matrix of genotypes using AFLPscore (Whitlock et al. 2008). This resulted in binary matrixes consisting of ones and ceros. The results on genetic diversity and structure reported for T. subulata are based on a matrix of 1564 AFLP loci and results for T. undulata are based on a matrix of 1419 AFLP loci. For the comparative study of two species of pygmy grasshoppers, the binary matrix for each species was reduced to include only loci present in both species. This reduced the bias due to unequal sample size and generated one binary matrix of 1404 AFLP loci per species.

To assess the role of any genotyping errors, genotyping repeatability was estimated on the binary matrix of 1564 AFLP loci from T. subulata. Data for nine positive control individuals from different collection sites were replicated across all plates and was used to measure genotyping repeatability, which was calculated for each allele using mismatch error rate (Bonin et al. 2004). Alleles with the highest mismatch error rate were iteratively removed until a smaller but higher quality data set with a low desired average error rate (mean: 4.8%, range 1.1% - 14 %) remained that consisted of only 638 loci. Results based on this reduced (638 loci) binary matrix were qualitatively similar to results based on the larger and more informative (1564 loci) binary matrix reported below. The estimates and exact parameter values associated with the statistical tests changed somewhat but overall conclusions were robust to choice of data.

Estimates of neutral genetic diversity within populations AFLP markers are dominant and the estimation of allele frequencies was estimated from the proportion of recessive genotypes in the sample and performed using a Bayesian method with non-uniform prior distribution of allele frequencies, assuming Hardy-Weinberg proportions of genotypes and a predefined FIS value of 0 (Zhivotovsky 1999). Statistics of genetic diversity; number of polymorphic loci (PL), proportion of polymorphic loci (PPL) at the 5% level, and expected heterozygosity or Nei’s genetic diversity Hj (analogous to He) (Nei 1973), were estimated following the treatment of Lynch and Milligan (1994) using AFLPsurv (Vekemans et al. 2002).

21

Investigating interbreeding and admixture effects in T. subulata A laboratory mating experiment was designed to address effects of interbreeding and admixture in T. subulata. Pygmy grasshoppers were collected from two natural populations and transferred to the laboratory at Linnaeus University, Kalmar, measured for femur length, classified for wing morph, and sexed. Half of the females from each population were randomly assigned to one of two mating treatments (Figure 4).

Figure 4. Experimental mating design, crossing T. subulata grasshoppers collected from populations in Vanserum (island of Öland) and Sävsjö (Swedish mainland). Females in the ‘pure’ treatments were mated with a male that originated from their own source population. Females in the ‘mixed’ treatment were mated with a male from the foreign source population.

Females in the ‘pure’ treatment were paired up with a male from the same source population, whereas females in the ‘mixed’ treatment were mated with a male from the foreign population. Pairs of males and females were housed together in separate cages and their cages were searched for egg clutches regularly. Effects of interbreeding on reproductive allocation were investigated by comparing the number of egg clutches laid by females in the ‘mixed’ treatment compared to egg clutches laid by females in the ‘pure’ treatment. A similar comparison between treatment groups was also made for the number of eggs that females included in each clutch. Genetic compatibility and effects of admixture were investigated by comparing hatching success of eggs and viability of offspring produced by females in ‘pure’ and ‘mixed’ treatment groups. Offspring that resulted from the experiment were housed together in sibling groups of up to 15 individuals. This allowed for investigation of how admixture effects production of viable second generation (F2) offspring within families originating from ‘pure’ and ‘mixed’ treatment groups.

20

= 0 and std = 1. A functional diversity differential was calculated for each population as the difference between relative functional color morph diversity and relative ‘neutral’ diversity. Under this approach, positive values indicate that relative intra-population functional diversity is greater than neutral diversity, whereas negative values indicate that relative functional diversity is smaller than neutral diversity. By standardizing to a mean of zero, the estimates of relative functional and neutral diversities as well as the functional diversity differentials are comparable across populations, environments and studies. This is comparable to standardized selection gradients developed and used to characterize and compare the strength and direction of selection operating on quantitative phenotypic characters in natural populations (Lande and Arnold 1983, Arnold and Wade 1984).

Comparing patterns of genetic differentiation between species The difference in dispersal ability between the species is predicted to generate a pattern of higher genetic population differentiation in T undulata compared to T. subulata. This prediction was investigated by calculating Wright’s fixation index (FST) for each species, based on binary matrixes of 1404 AFLP loci, implementing AFLPsurv. AFLPsurv measures the genetic correlation between pairs of genetic loci sampled within populations relative to pairs sampled among all populations (Vekemans et al. 2002). Genetic differentiation between sampling localities (pairwise FST) was estimated for each species separately with 10000 permutations implemented in AFLPsurv (Vekemans et al. 2002). The statistical significance of the pairwise FST values was assessed by 1000 permutations of individuals among populations based on overall FST statistic and Pairwise FST values calculated as implemented in ARLEQUIN version 3.5 (Excoffier and Lischer 2010).

Estimates of pairwise FST were used to compare and evaluate whether the magnitude of population differentiation was similar or different in T. subulata and T. undulata. Identical genetic divergence in both species is expected to show a 1:1 relationship in a bi-plot if the FST -values for the different pairs of localities for T. subulata are plotted on the vertical y-axis against the corresponding FST -values for T. undulata for the same pairs of localities on the horizontal x-axis (Paper III). Values above the isometric line would represent a higher differentiation in T. subulata populations and values below the line would represent a higher population differentiation in T. undulata. To test if the distribution of observations was generally below the isometric line we calculated the perpendicular distance from the isometric line for each observation and then used a t-test to evaluate whether the mean distance was significantly smaller than cero. To further evaluate whether pair-wise genetic differentiation was correlated in the two species, and to examine whether such a correlation remained significant after controlling for geographical distance we performed a Mantel test and a partial Mantel test with 5,000 permutations, implemented in vegan package in Rstudio V. 0.98.50 (R Core Team 2014).

21

Investigating interbreeding and admixture effects in T. subulata A laboratory mating experiment was designed to address effects of interbreeding and admixture in T. subulata. Pygmy grasshoppers were collected from two natural populations and transferred to the laboratory at Linnaeus University, Kalmar, measured for femur length, classified for wing morph, and sexed. Half of the females from each population were randomly assigned to one of two mating treatments (Figure 4).

Figure 4. Experimental mating design, crossing T. subulata grasshoppers collected from populations in Vanserum (island of Öland) and Sävsjö (Swedish mainland). Females in the ‘pure’ treatments were mated with a male that originated from their own source population. Females in the ‘mixed’ treatment were mated with a male from the foreign source population.

Females in the ‘pure’ treatment were paired up with a male from the same source population, whereas females in the ‘mixed’ treatment were mated with a male from the foreign population. Pairs of males and females were housed together in separate cages and their cages were searched for egg clutches regularly. Effects of interbreeding on reproductive allocation were investigated by comparing the number of egg clutches laid by females in the ‘mixed’ treatment compared to egg clutches laid by females in the ‘pure’ treatment. A similar comparison between treatment groups was also made for the number of eggs that females included in each clutch. Genetic compatibility and effects of admixture were investigated by comparing hatching success of eggs and viability of offspring produced by females in ‘pure’ and ‘mixed’ treatment groups. Offspring that resulted from the experiment were housed together in sibling groups of up to 15 individuals. This allowed for investigation of how admixture effects production of viable second generation (F2) offspring within families originating from ‘pure’ and ‘mixed’ treatment groups.

20

= 0 and std = 1. A functional diversity differential was calculated for each population as the difference between relative functional color morph diversity and relative ‘neutral’ diversity. Under this approach, positive values indicate that relative intra-population functional diversity is greater than neutral diversity, whereas negative values indicate that relative functional diversity is smaller than neutral diversity. By standardizing to a mean of zero, the estimates of relative functional and neutral diversities as well as the functional diversity differentials are comparable across populations, environments and studies. This is comparable to standardized selection gradients developed and used to characterize and compare the strength and direction of selection operating on quantitative phenotypic characters in natural populations (Lande and Arnold 1983, Arnold and Wade 1984).

Comparing patterns of genetic differentiation between species The difference in dispersal ability between the species is predicted to generate a pattern of higher genetic population differentiation in T undulata compared to T. subulata. This prediction was investigated by calculating Wright’s fixation index (FST) for each species, based on binary matrixes of 1404 AFLP loci, implementing AFLPsurv. AFLPsurv measures the genetic correlation between pairs of genetic loci sampled within populations relative to pairs sampled among all populations (Vekemans et al. 2002). Genetic differentiation between sampling localities (pairwise FST) was estimated for each species separately with 10000 permutations implemented in AFLPsurv (Vekemans et al. 2002). The statistical significance of the pairwise FST values was assessed by 1000 permutations of individuals among populations based on overall FST statistic and Pairwise FST values calculated as implemented in ARLEQUIN version 3.5 (Excoffier and Lischer 2010).

Estimates of pairwise FST were used to compare and evaluate whether the magnitude of population differentiation was similar or different in T. subulata and T. undulata. Identical genetic divergence in both species is expected to show a 1:1 relationship in a bi-plot if the FST -values for the different pairs of localities for T. subulata are plotted on the vertical y-axis against the corresponding FST -values for T. undulata for the same pairs of localities on the horizontal x-axis (Paper III). Values above the isometric line would represent a higher differentiation in T. subulata populations and values below the line would represent a higher population differentiation in T. undulata. To test if the distribution of observations was generally below the isometric line we calculated the perpendicular distance from the isometric line for each observation and then used a t-test to evaluate whether the mean distance was significantly smaller than cero. To further evaluate whether pair-wise genetic differentiation was correlated in the two species, and to examine whether such a correlation remained significant after controlling for geographical distance we performed a Mantel test and a partial Mantel test with 5,000 permutations, implemented in vegan package in Rstudio V. 0.98.50 (R Core Team 2014).

23

subsets of mainland population pairs that differed with regard to threshold inter-population distance (van Strien et al. 2015). This showed that the FST-distance correlation was absent when calculated over all mainland sites, whereas correlations calculated over more closely located mainland sites (< 50 km) were clearly positive (Figure 5).

Figure 5. Pairwise genetic differences (pairwise FST) among 20 populations of pygmy grasshoppers (Tetrix subulata). Open circles indicate comparisons between locations on the Swedish mainland and black dots represent comparisons between locations on the island of Öland. The five regression lines in the plot represent different inter-population intervals (0-30km, 0-50 km, 0-100 km, 0-250 km, 0-550 km) and the length of each line corresponds to different inter-population distances.

That the degree of genetic differentiation among subpopulations was only weakly related to geographic isolation, and only over smaller spatial scales, in the present study may be attributed in part to the ecological characteristics and population dynamics of our study species. Pygmy grasshoppers are environmental trackers that can establish and flourish for a few years when and where conditions are favorable, typically in disturbed environments (Forsman et al. 2011, Berggren et al. 2012). Such abundance fluctuations and extinction (re)colonization dynamics, combined with that they occur at low density over a broad range of mainland habitats, could have a homogenizing effect on neutral population genetic structure (Lande 1988, Frankham 1996).

Apart from large scale patterns of genetic differentiation I also studied genetic diversity within populations and its association with immigration and population size. A positive association between population size and neutral genetic diversity resulting from the reduced effect of genetic drift in large populations is well supported from both genetic studies and mathematical models (Frankham 1996, Amos and Harwood 1998). In my study the results showed that estimates of within-population genetic diversity (Hj) increased overall with increasing population size (Figure 6a). This suggests that rare

22

Results and discussion

Dispersal barriers, geographic distance, immigration and population size influence patterns of population genetic diversity and differentiation To investigate the patterns of genetic differentiation in T. subulata I performed a genetic study of 20 sample sites with varying degrees of inter-population distance and connectedness. The study design included a possible dispersal barrier consisting of 5 km of open water between sampling sites on the island of Öland and sites on the Swedish mainland. The genetic analyses revealed that the investigated sample sites represent separate populations with low to moderate levels of genetic differentiation, as indicated by pairwise FST values (Table 2 in Paper I). The signature of divergence was particularly strong between populations from the mainland and those on the island of Öland (Table 3 in Paper I), indicating that the open water acts as a dispersal barrier, reducing gene flow and contributing to large-scale genetic differentiation. The population structure found in pygmy grasshoppers indicates that they do not disperse and interbreed freely among localities. This conclusion conforms well with the finding in previous studies of local adaptive differentiation and of rapid evolutionary shifts in functionally important traits in response to divergent and fluctuating selection (Forsman et al. 2011, Forsman et al. 2012, Wennersten et al. 2012, Tinnert et al. 2016a).

I also evaluated the isolation by distance hypothesis and compared degree of genetic differentiation (pairwise FST) to the pairwise geographic distance that separated populations. However, I found no evidence for isolation by distance among the populations despite that I performed separate analyses for mainland and insular populations. Lack of isolation by distance could be interpreted as a complete absence of gene flow, or indicate extensive gene flow among subpopulations due to the lack of any distance related or physical barriers to dispersal (van Strien et al. 2015). To discriminate between these competing explanations and further assess the presence and intensity of any isolation by distance I evaluated the FST–geographic distance correlation from

23

subsets of mainland population pairs that differed with regard to threshold inter-population distance (van Strien et al. 2015). This showed that the FST-distance correlation was absent when calculated over all mainland sites, whereas correlations calculated over more closely located mainland sites (< 50 km) were clearly positive (Figure 5).

Figure 5. Pairwise genetic differences (pairwise FST) among 20 populations of pygmy grasshoppers (Tetrix subulata). Open circles indicate comparisons between locations on the Swedish mainland and black dots represent comparisons between locations on the island of Öland. The five regression lines in the plot represent different inter-population intervals (0-30km, 0-50 km, 0-100 km, 0-250 km, 0-550 km) and the length of each line corresponds to different inter-population distances.

That the degree of genetic differentiation among subpopulations was only weakly related to geographic isolation, and only over smaller spatial scales, in the present study may be attributed in part to the ecological characteristics and population dynamics of our study species. Pygmy grasshoppers are environmental trackers that can establish and flourish for a few years when and where conditions are favorable, typically in disturbed environments (Forsman et al. 2011, Berggren et al. 2012). Such abundance fluctuations and extinction (re)colonization dynamics, combined with that they occur at low density over a broad range of mainland habitats, could have a homogenizing effect on neutral population genetic structure (Lande 1988, Frankham 1996).

Apart from large scale patterns of genetic differentiation I also studied genetic diversity within populations and its association with immigration and population size. A positive association between population size and neutral genetic diversity resulting from the reduced effect of genetic drift in large populations is well supported from both genetic studies and mathematical models (Frankham 1996, Amos and Harwood 1998). In my study the results showed that estimates of within-population genetic diversity (Hj) increased overall with increasing population size (Figure 6a). This suggests that rare

22

Results and discussion

Dispersal barriers, geographic distance, immigration and population size influence patterns of population genetic diversity and differentiation To investigate the patterns of genetic differentiation in T. subulata I performed a genetic study of 20 sample sites with varying degrees of inter-population distance and connectedness. The study design included a possible dispersal barrier consisting of 5 km of open water between sampling sites on the island of Öland and sites on the Swedish mainland. The genetic analyses revealed that the investigated sample sites represent separate populations with low to moderate levels of genetic differentiation, as indicated by pairwise FST values (Table 2 in Paper I). The signature of divergence was particularly strong between populations from the mainland and those on the island of Öland (Table 3 in Paper I), indicating that the open water acts as a dispersal barrier, reducing gene flow and contributing to large-scale genetic differentiation. The population structure found in pygmy grasshoppers indicates that they do not disperse and interbreed freely among localities. This conclusion conforms well with the finding in previous studies of local adaptive differentiation and of rapid evolutionary shifts in functionally important traits in response to divergent and fluctuating selection (Forsman et al. 2011, Forsman et al. 2012, Wennersten et al. 2012, Tinnert et al. 2016a).

I also evaluated the isolation by distance hypothesis and compared degree of genetic differentiation (pairwise FST) to the pairwise geographic distance that separated populations. However, I found no evidence for isolation by distance among the populations despite that I performed separate analyses for mainland and insular populations. Lack of isolation by distance could be interpreted as a complete absence of gene flow, or indicate extensive gene flow among subpopulations due to the lack of any distance related or physical barriers to dispersal (van Strien et al. 2015). To discriminate between these competing explanations and further assess the presence and intensity of any isolation by distance I evaluated the FST–geographic distance correlation from

25

Changing environments differently influence neutral and functional genetic diversity To illuminate the roles of stochastic and deterministic ecological and evolutionary processes as drivers of neutral and functional diversity I compared neutral and functional diversity among stable and disturbed populations of T. undulata pygmy grasshopper in Sweden (Paper II). I hypothesized that disturbed environments inhabited by recently established populations should be characterized by: a high incidence of dispersal phenotypes (due to increased dispersal capability), high diversity in functional traits (because of the enhancing effect of diversity on establishment) and low neutral genetic diversity (owing to the eroding effect of drift associated with small founder groups and fluctuating population sizes). Results from comparisons between contrasting environments uncovered that populations in disturbed habitats had a higher incidence of long-winged phenotypes, as expected if these populations were recently established by flight-capable colonizers (Figure 7a). Intra-population functional genetic and phenotypic diversity, as estimated using color morph unalikeability, was higher in disturbed than in stable environments (Figure 7b), as expected given that color polymorphism increases establishment success (Forsman et al. 2012, Wennersten et al. 2012).

Neutral genetic diversity on the other hand also varied among populations (PPL = 60.5 to 82.5; Hj = 0.23 to 0.35), but was instead lower overall in disturbed than in stable habitats, indicating stronger eroding effects on neutral diversity of genetic drift associated with recent founding events or population bottlenecks in disturbed compared to stable habitats (Figure 7c). Furthermore, in support of the above interpretations, comparisons of standardized functional diversity differentials showed that functional diversity was higher than neutral diversity in disturbed habitats and smaller than neutral diversity in stable habitats (Figure 8). The overall results from the comparison of contrasting environments conform to the hypothesis that random and deterministic processes differently influence neutral and functional diversity within populations.

24

alleles may have been lost from smaller populations due to the eroding effects of random genetic drift, in concordance with predictions from neutral genetic theory (Wright 1931, Crow and Kimura 1970), and the result conforms to the pattern reported in comparisons across species in other organisms (Frankham 1996, 2012).

The inability of short winged individuals to fly suggests that it is mainly long winged individuals that contribute to migration and generate gene flow. As predicted from theory, there was a positive association between immigration and genetic diversity among populations on Öland (Figure 6b). However, this pattern of increased genetic diversity with proportion of flight capable phenotypes was not found among populations on the Swedish mainland. This might perhaps indicate that ecological conditions or landscape structures differently influence evolutionary processes among populations on the Swedish mainland as compared to populations on Öland.

Figure 6. Relationships of genetic diversity (Hj) in populations of Tetrix subulata pygmy grasshoppers with a) population size (as estimated by number of collected individuals) size and b) immigration rate (as estimated by proportion long-winged flight capable phenotypes). Open circles represent the Swedish mainland and filled circles represent populations on Öland.

The positive associations of population size and immigration with neutral genetic diversity found among pygmy grasshoppers in this study cannot be directly applied to functional genetic diversity and adaptive potential. This study focused on the spatial aspects of environmental variation and population dynamics but did not consider the temporal scale. I therefore performed another study to investigate how changing environments may affect patterns of neutral and functional diversity and differentiation.

Population size(collected individuals)

0 50 100 150 200 250 300

Gen

etic

div

ersi

ty (H

j)

0.15

0.20

0.25

0.30

0.35

Immigration rate(proportion long-winged)

0.0 0.2 0.4 0.6 0.8 1.00.15

0.20

0.25

0.30

0.35

= Öland = Mainland

a) b)

25

Changing environments differently influence neutral and functional genetic diversity To illuminate the roles of stochastic and deterministic ecological and evolutionary processes as drivers of neutral and functional diversity I compared neutral and functional diversity among stable and disturbed populations of T. undulata pygmy grasshopper in Sweden (Paper II). I hypothesized that disturbed environments inhabited by recently established populations should be characterized by: a high incidence of dispersal phenotypes (due to increased dispersal capability), high diversity in functional traits (because of the enhancing effect of diversity on establishment) and low neutral genetic diversity (owing to the eroding effect of drift associated with small founder groups and fluctuating population sizes). Results from comparisons between contrasting environments uncovered that populations in disturbed habitats had a higher incidence of long-winged phenotypes, as expected if these populations were recently established by flight-capable colonizers (Figure 7a). Intra-population functional genetic and phenotypic diversity, as estimated using color morph unalikeability, was higher in disturbed than in stable environments (Figure 7b), as expected given that color polymorphism increases establishment success (Forsman et al. 2012, Wennersten et al. 2012).

Neutral genetic diversity on the other hand also varied among populations (PPL = 60.5 to 82.5; Hj = 0.23 to 0.35), but was instead lower overall in disturbed than in stable habitats, indicating stronger eroding effects on neutral diversity of genetic drift associated with recent founding events or population bottlenecks in disturbed compared to stable habitats (Figure 7c). Furthermore, in support of the above interpretations, comparisons of standardized functional diversity differentials showed that functional diversity was higher than neutral diversity in disturbed habitats and smaller than neutral diversity in stable habitats (Figure 8). The overall results from the comparison of contrasting environments conform to the hypothesis that random and deterministic processes differently influence neutral and functional diversity within populations.

24

alleles may have been lost from smaller populations due to the eroding effects of random genetic drift, in concordance with predictions from neutral genetic theory (Wright 1931, Crow and Kimura 1970), and the result conforms to the pattern reported in comparisons across species in other organisms (Frankham 1996, 2012).

The inability of short winged individuals to fly suggests that it is mainly long winged individuals that contribute to migration and generate gene flow. As predicted from theory, there was a positive association between immigration and genetic diversity among populations on Öland (Figure 6b). However, this pattern of increased genetic diversity with proportion of flight capable phenotypes was not found among populations on the Swedish mainland. This might perhaps indicate that ecological conditions or landscape structures differently influence evolutionary processes among populations on the Swedish mainland as compared to populations on Öland.

Figure 6. Relationships of genetic diversity (Hj) in populations of Tetrix subulata pygmy grasshoppers with a) population size (as estimated by number of collected individuals) size and b) immigration rate (as estimated by proportion long-winged flight capable phenotypes). Open circles represent the Swedish mainland and filled circles represent populations on Öland.

The positive associations of population size and immigration with neutral genetic diversity found among pygmy grasshoppers in this study cannot be directly applied to functional genetic diversity and adaptive potential. This study focused on the spatial aspects of environmental variation and population dynamics but did not consider the temporal scale. I therefore performed another study to investigate how changing environments may affect patterns of neutral and functional diversity and differentiation.

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27

Figure 8. Comparison of standardized functional diversity differentials of Tetrix undulata pygmy grasshopper populations in disturbed and stable environments. (For details see Paper II)

Results from the analysis showed a negative correlation between color morph diversity and neutral genetic diversity across all populations (Paper II) (Figure 9a). This pattern is in agreement with predictions from theory and adds to the body of evidence (Reed and Frankham 2001, Holderegger et al. 2006, Leinonen et al. 2008), that estimates of neutral genetic diversity may be a poor surrogate for adaptive or functional genetic diversity to infer evolutionary potential of populations.

The data also showed a positive correlation between color morph diversity and immigration rate (as estimated using proportion of long-winged phenotypes) across all populations and an overall increase in color morph diversity with increasing census population size across all populations (Paper II) (Figure 9b). The positive association between immigration rate and functional phenotypic color morph diversity is predicted by theory and might reflect an introduction of novel genes by immigrating individuals. However, as suggested by this study, population dynamics in combination with changing environments and spatial sorting might generate a similar pattern, driven not by immigration but rather by higher establishment success of more diverse founder groups that manage to disperse and establish new populations.

1 2

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Figure 7. Comparisons of disturbed and stable populations of Tetrix undulata pygmy grasshoppers, showing a) immigration rate (proportion of flight capable phenotypes), b) functional genetic diversity (color morph diversity), and c) neutral genetic diversity (heterozygosity estimated using AFLP). N-values indicate number of populations in each type of environment.

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27

Figure 8. Comparison of standardized functional diversity differentials of Tetrix undulata pygmy grasshopper populations in disturbed and stable environments. (For details see Paper II)

Results from the analysis showed a negative correlation between color morph diversity and neutral genetic diversity across all populations (Paper II) (Figure 9a). This pattern is in agreement with predictions from theory and adds to the body of evidence (Reed and Frankham 2001, Holderegger et al. 2006, Leinonen et al. 2008), that estimates of neutral genetic diversity may be a poor surrogate for adaptive or functional genetic diversity to infer evolutionary potential of populations.

The data also showed a positive correlation between color morph diversity and immigration rate (as estimated using proportion of long-winged phenotypes) across all populations and an overall increase in color morph diversity with increasing census population size across all populations (Paper II) (Figure 9b). The positive association between immigration rate and functional phenotypic color morph diversity is predicted by theory and might reflect an introduction of novel genes by immigrating individuals. However, as suggested by this study, population dynamics in combination with changing environments and spatial sorting might generate a similar pattern, driven not by immigration but rather by higher establishment success of more diverse founder groups that manage to disperse and establish new populations.

1 2

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n = 12 n = 8

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Figure 7. Comparisons of disturbed and stable populations of Tetrix undulata pygmy grasshoppers, showing a) immigration rate (proportion of flight capable phenotypes), b) functional genetic diversity (color morph diversity), and c) neutral genetic diversity (heterozygosity estimated using AFLP). N-values indicate number of populations in each type of environment.

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29

dispersal and gene flow from population size and dynamics I compared two closely related and ecologically similar species of pygmy grasshoppers that differ in their dispersal capability but occur in sympatry and occupy the same habitats. Dispersal capacity for each species was inferred from frequencies of long-winged flight capable phenotypes which were consistently higher in populations of T. subulata than in T. undulata (Figure 11a), suggesting that the genetic signatures of dispersal should be lower on average in the flight-capable T. subulata compared with T. undulata. To study consequences of dispersal for functional and neutral genetic diversity and differentiation I estimated for each species, pairwise fixation index (FST) among populations and genetic diversity (Hj) within populations using 1404 polymorphic AFLP markers.

Comparisons of pairwise FST-values indicated that differentiation was generally lower in the flight-capable species T. subulata (Paper III) (Figure 10). This result was also supported by the Mantel test and the partial Mantel test that control for geographic distance between populations, suggesting that the two species did not display similar patterns of gene flow and genetic differentiation (Paper III). This finding is in agreement with expectations from theory (Wright 1978, Slatkin 1993, Charlesworth and Charlesworth 2017) and in line with results from studies of other systems (as reviewed by Bohonak 1999). However, dispersal is not the only process that affects population differentiation (Whitlock and McCauley 1999, Charlesworth and Charlesworth 2017) and in this study we were not able to evaluate the potential roles of species specific interbreeding, admixture, and population dynamics.

Figure 10. Comparison of pairwise FST values (estimated from AFLP data implementing AFLPsurv) between T. subulata and T. undulata pygmy grasshoppers from seven localities where both species co-occur in sympatry. The dash reference line represents the null-hypothesis of no difference in pairwise FST between species. Points above the line indicate a

28

Figure 9. Associations of functional diversity (estimated using coefficient of unalikeability) to a) neutral genetic diversity (Hj) and b) immigration rate (proportion flight capable phenotypes) in 20 populations of Tetrix undulata pygmy grasshoppers inhabiting stable (open circles) or disturbed (filled dots) environments.

This study points to the importance of functional genetic and phenotypic diversity for population establishment and persistence and supports the growing awareness that measures of neutral genetic diversity might be a poor substitute for functional diversity. It also emphasizes the importance of understanding processes that affect both neutral and adaptive diversity and processes that only operate on functional traits.

The influence of dispersal on genetic diversity within populations might also contribute to patterns of population differentiation among populations. To investigate the role of dispersal for population structure I therefore compared two species of pygmy grasshoppers that differ in dispersal capability.

Dispersal affects population structure and diversity Dispersal and gene flow may contribute to increased genetic diversity within populations and mitigate the eroding effects of selection and drift (Slatkin 1993). In addition to influencing diversity within populations, gene flow is predicted to reduce genetic differentiation among populations (Slatkin 1993). One approach to investigate how gene flow affects genetic and phenotypic variation is to make comparisons among populations of the same species that differ with regard to immigration rate. This approach was used in Paper I and it showed that there was a positive association between immigration and genetic diversity among populations on the island of Öland (Figure 6b). However, populations inhabiting different areas are exposed to dissimilar environmental conditions and comparisons of genetic diversity between populations may be compromised further by environmental effects mediated via population size and dynamics (Frankham 1996, Willi et al. 2006, Tinnert et al. 2016b). In an attempt to disentangle and separate the structuring role of

'Neutral' genetic diversity (Hj)

0.22 0.24 0.26 0.28 0.30 0.32 0.34 0.36'Fun

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29

dispersal and gene flow from population size and dynamics I compared two closely related and ecologically similar species of pygmy grasshoppers that differ in their dispersal capability but occur in sympatry and occupy the same habitats. Dispersal capacity for each species was inferred from frequencies of long-winged flight capable phenotypes which were consistently higher in populations of T. subulata than in T. undulata (Figure 11a), suggesting that the genetic signatures of dispersal should be lower on average in the flight-capable T. subulata compared with T. undulata. To study consequences of dispersal for functional and neutral genetic diversity and differentiation I estimated for each species, pairwise fixation index (FST) among populations and genetic diversity (Hj) within populations using 1404 polymorphic AFLP markers.

Comparisons of pairwise FST-values indicated that differentiation was generally lower in the flight-capable species T. subulata (Paper III) (Figure 10). This result was also supported by the Mantel test and the partial Mantel test that control for geographic distance between populations, suggesting that the two species did not display similar patterns of gene flow and genetic differentiation (Paper III). This finding is in agreement with expectations from theory (Wright 1978, Slatkin 1993, Charlesworth and Charlesworth 2017) and in line with results from studies of other systems (as reviewed by Bohonak 1999). However, dispersal is not the only process that affects population differentiation (Whitlock and McCauley 1999, Charlesworth and Charlesworth 2017) and in this study we were not able to evaluate the potential roles of species specific interbreeding, admixture, and population dynamics.

Figure 10. Comparison of pairwise FST values (estimated from AFLP data implementing AFLPsurv) between T. subulata and T. undulata pygmy grasshoppers from seven localities where both species co-occur in sympatry. The dash reference line represents the null-hypothesis of no difference in pairwise FST between species. Points above the line indicate a

28

Figure 9. Associations of functional diversity (estimated using coefficient of unalikeability) to a) neutral genetic diversity (Hj) and b) immigration rate (proportion flight capable phenotypes) in 20 populations of Tetrix undulata pygmy grasshoppers inhabiting stable (open circles) or disturbed (filled dots) environments.

This study points to the importance of functional genetic and phenotypic diversity for population establishment and persistence and supports the growing awareness that measures of neutral genetic diversity might be a poor substitute for functional diversity. It also emphasizes the importance of understanding processes that affect both neutral and adaptive diversity and processes that only operate on functional traits.

The influence of dispersal on genetic diversity within populations might also contribute to patterns of population differentiation among populations. To investigate the role of dispersal for population structure I therefore compared two species of pygmy grasshoppers that differ in dispersal capability.

Dispersal affects population structure and diversity Dispersal and gene flow may contribute to increased genetic diversity within populations and mitigate the eroding effects of selection and drift (Slatkin 1993). In addition to influencing diversity within populations, gene flow is predicted to reduce genetic differentiation among populations (Slatkin 1993). One approach to investigate how gene flow affects genetic and phenotypic variation is to make comparisons among populations of the same species that differ with regard to immigration rate. This approach was used in Paper I and it showed that there was a positive association between immigration and genetic diversity among populations on the island of Öland (Figure 6b). However, populations inhabiting different areas are exposed to dissimilar environmental conditions and comparisons of genetic diversity between populations may be compromised further by environmental effects mediated via population size and dynamics (Frankham 1996, Willi et al. 2006, Tinnert et al. 2016b). In an attempt to disentangle and separate the structuring role of

'Neutral' genetic diversity (Hj)

0.22 0.24 0.26 0.28 0.30 0.32 0.34 0.36'Fun

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31

Figure 11. Comparison of a) the proportion of dispersal phenotypes (as estimated using proportion of long-winged phenotypes), b) neutral genetic diversity (Hj, as estimated from AFLP data), and c) least-squares means of body size (estimated from femur length) between two species of pygmy grasshoppers (T. subulata and T. undulata) that were collected from seven localities where both species co-occur in sympatry. Points connected by lines represent the same sampling locality (see Figure 3) and each color corresponds to the same location in all panels.

In addition to studying patterns of neutral genetic diversity and differentiation in pygmy grasshoppers I also investigated patterns of differentiation among populations in a functionally important phenotypic trait. Measurements of femur length were used as a proxy for body size. It was hypothesized that because both species were collected from the same set of localities, any differences in body size among localities should be correlated, either as a result of convergent evolution in response to common selective regimes (Arendt and Reznick 2008) or owing to shared phenotypic plasticity responses of growth and body size to environmental sources of variation (Pigliucci and Preston 2004, Arnett and Kinnison 2016).

Analysis revealed that body size differed among populations from different localities in both T. subulata and in T. undulata (Paper III) (Figure 12). Comparisons between species showed that there were no difference in body size between T. subulata and T. undulata sampled from the same set of locations. However, the variation in body size among populations seen in T. subulata was independent of the variation seen in T. undulata (Paper III) (Figure 11c, Figure 12), indicating independent evolution or unique species-specific plastic responses to environmental sources of variation. While these findings were not in agreement with predictions from hypothesis they conform to patterns found among other grasshoppers (Ortego et al. 2015). Although we sampled populations in sympatry, it is possible that there were differences in microhabitat utilization between species within sampling localities. If so, this may have exposed them to different selective regimes and induced different plastic responses. Indeed, my overall impression emerging from several years of studying these species in Sweden is that T. subulata is closely associated with moist areas whereas T. undulata occurs also in drier microhabitats (Holst

30

relatively higher differentiation between populations of T. subulata and points below the line indicate a relatively higher differentiation between populations of T. undulata.

The result from the pairwise comparison showed that neutral genetic diversity (Hj) within populations did not differ consistently between T. subulata (range 0.263 – 0.327) and T. undulata (range 0.265 – 0.346) pygmy grasshoppers from the same sampling locations (Paper III) (Figure 11b). However, a statistically significant difference in genetic diversity between species was revealed when the effects of census population size was accounted for, with genetic diversity being higher in T. subulata than in T. undulata (Paper III). Although statistically significant, this result provided only weak support for the prediction that neutral genetic diversity within populations (Hj) should be higher in the dispersive T. subulata than in the sedentary T. undulata. The lower than expected difference in Hj between species may be explained by any number of ecological factors.

It is possible that despite being sampled at sites where both species occurred in sympatry, they may have differed with regards to average population size and this may have obscured any effect of immigration and gene flow on genetic diversity (Frankham 1996). Our comparisons of genetic diversity between species may also have been compromised by differences in past population dynamics and founder events. For instance, a high incidence of the long-winged phenotype in populations of T. subulata might indicate recent immigration into old populations, but as shown in paper II, it could also represent populations that were recently established by a small number of founder individuals, having low neutral genetic diversity and a high incidence of long-winged phenotypes. An additional explanation for the seemingly weak effect of immigration on genetic diversity may be that dispersal and immigration does not necessarily translate into gene flow. Incompatibility between resident and immigrant individuals might hinder interbreeding and genetic admixture, and studies have shown that the outcome is often context dependent, ranging from positive to negative even among populations within species (Lynch 1991, Verhoeven et al. 2011, Rius and Darling 2014). If T. subulata and T. undulata are differently affected by such incompatibilities it is possible that quality and success of dispersal events could compensate for effects of lower quantity and thereby obscure effects of greater dispersal ability in T. subulata.

31

Figure 11. Comparison of a) the proportion of dispersal phenotypes (as estimated using proportion of long-winged phenotypes), b) neutral genetic diversity (Hj, as estimated from AFLP data), and c) least-squares means of body size (estimated from femur length) between two species of pygmy grasshoppers (T. subulata and T. undulata) that were collected from seven localities where both species co-occur in sympatry. Points connected by lines represent the same sampling locality (see Figure 3) and each color corresponds to the same location in all panels.

In addition to studying patterns of neutral genetic diversity and differentiation in pygmy grasshoppers I also investigated patterns of differentiation among populations in a functionally important phenotypic trait. Measurements of femur length were used as a proxy for body size. It was hypothesized that because both species were collected from the same set of localities, any differences in body size among localities should be correlated, either as a result of convergent evolution in response to common selective regimes (Arendt and Reznick 2008) or owing to shared phenotypic plasticity responses of growth and body size to environmental sources of variation (Pigliucci and Preston 2004, Arnett and Kinnison 2016).

Analysis revealed that body size differed among populations from different localities in both T. subulata and in T. undulata (Paper III) (Figure 12). Comparisons between species showed that there were no difference in body size between T. subulata and T. undulata sampled from the same set of locations. However, the variation in body size among populations seen in T. subulata was independent of the variation seen in T. undulata (Paper III) (Figure 11c, Figure 12), indicating independent evolution or unique species-specific plastic responses to environmental sources of variation. While these findings were not in agreement with predictions from hypothesis they conform to patterns found among other grasshoppers (Ortego et al. 2015). Although we sampled populations in sympatry, it is possible that there were differences in microhabitat utilization between species within sampling localities. If so, this may have exposed them to different selective regimes and induced different plastic responses. Indeed, my overall impression emerging from several years of studying these species in Sweden is that T. subulata is closely associated with moist areas whereas T. undulata occurs also in drier microhabitats (Holst

30

relatively higher differentiation between populations of T. subulata and points below the line indicate a relatively higher differentiation between populations of T. undulata.

The result from the pairwise comparison showed that neutral genetic diversity (Hj) within populations did not differ consistently between T. subulata (range 0.263 – 0.327) and T. undulata (range 0.265 – 0.346) pygmy grasshoppers from the same sampling locations (Paper III) (Figure 11b). However, a statistically significant difference in genetic diversity between species was revealed when the effects of census population size was accounted for, with genetic diversity being higher in T. subulata than in T. undulata (Paper III). Although statistically significant, this result provided only weak support for the prediction that neutral genetic diversity within populations (Hj) should be higher in the dispersive T. subulata than in the sedentary T. undulata. The lower than expected difference in Hj between species may be explained by any number of ecological factors.

It is possible that despite being sampled at sites where both species occurred in sympatry, they may have differed with regards to average population size and this may have obscured any effect of immigration and gene flow on genetic diversity (Frankham 1996). Our comparisons of genetic diversity between species may also have been compromised by differences in past population dynamics and founder events. For instance, a high incidence of the long-winged phenotype in populations of T. subulata might indicate recent immigration into old populations, but as shown in paper II, it could also represent populations that were recently established by a small number of founder individuals, having low neutral genetic diversity and a high incidence of long-winged phenotypes. An additional explanation for the seemingly weak effect of immigration on genetic diversity may be that dispersal and immigration does not necessarily translate into gene flow. Incompatibility between resident and immigrant individuals might hinder interbreeding and genetic admixture, and studies have shown that the outcome is often context dependent, ranging from positive to negative even among populations within species (Lynch 1991, Verhoeven et al. 2011, Rius and Darling 2014). If T. subulata and T. undulata are differently affected by such incompatibilities it is possible that quality and success of dispersal events could compensate for effects of lower quantity and thereby obscure effects of greater dispersal ability in T. subulata.

33

Effects of interbreeding and admixture Interbreeding and admixture are predicted to impact on colonization success and range expansion, mold the evolution of dispersal strategies, and ultimately shape patterns of population divergence (Slatkin 1987, Ebert et al. 2002, Forsman et al. 2011, Rius and Darling 2014). Yet, the effects of interbreeding and admixture are poorly known and were therefore addressed in a laboratory mating experiment in which crossings were made between two populations of T. subulata.

The mating experiment showed that females originating from the population in Vanserum increased their allocation to offspring by producing a generally higher number of egg-clutches if they were paired up with a male from Sävsjö (Figure 13). This effect was not evident among females from Sävsjö that were paired up with males from Vanserum.

Figure 13. Distribution of egg clutches produced by Tetrix subulata pygmy grasshoppers originating from two different populations (Sävsjö and Vanserum). Females in the ‘pure’ treatments were paired with a male from their own source population and females in the ‘mixed’ treatment were paired with a male from the other source population.

The mating experiment also showed that females from Sävsjö, that were paired up with a male from a different source population, changed their allocation of resources to offspring by including fewer eggs in their first egg clutch (Paper IV) (Figure 14). Females originating from Vanserum did not differ in clutch size depending on treatment group. Grasshoppers were generally larger in Sävsjö, suggesting that increased allocation to offspring by females from Vanserum might be triggered by male body size, a trait which is frequently suggested to be associated with mate quality. To evaluate the possibility that the difference in body size between populations could

32

1986, Forsman and Appelqvist 1999, Ahnesjö and Forsman 2006). Accordingly, a plethora of biotic factors (such as quantity, species composition and, nutritional quality of food resources, competitors and predators) and abiotic environmental conditions (e.g., humidity, soil surface temperatures) may have contributed to the observed patterns of body size.

Figure 12. Comparison of least square means (± s.e.) of body size (as estimated from femur length) among populations of two species of pygmy grasshoppers (T. subulata and T. undulata) that were collected from seven localities where both species co-occur. The dash reference line represents the isometric relationship expected under parallel (convergent) evolution or shared plasticity responses in the two species.

It is also possible that evolutionary shifts and environmentally induced plasticity responses differed between species despite that they were exposed to similar environments. This may be the case if there are differences between species in overall genetic architecture that produce dissimilar correlations between complex phenotypic traits such as: color pattern, thermal biology, life-history traits, behavior, diet, and microhabitat utilization (Forsman 1999, Forsman et al. 2002, Ahnesjö and Forsman 2003, 2006). Selection on traits that are developmentally or genetically associated with body size may therefore translate into different evolutionary modifications despite being exposed to similar selective regimes (Arendt and Reznick 2008).

The findings of this study are in agreement with the notion that dispersal may influence emerging patterns of genetic structure and differentiation between populations, and neutral genetic diversity within populations. That populations of pygmy grasshoppers are differentiated indicate that genetic compatibility could interfere with the translation of migration into gene flow. Compatibility of populations was investigated by crossing two differentiated population of T. subulata.

33

Effects of interbreeding and admixture Interbreeding and admixture are predicted to impact on colonization success and range expansion, mold the evolution of dispersal strategies, and ultimately shape patterns of population divergence (Slatkin 1987, Ebert et al. 2002, Forsman et al. 2011, Rius and Darling 2014). Yet, the effects of interbreeding and admixture are poorly known and were therefore addressed in a laboratory mating experiment in which crossings were made between two populations of T. subulata.

The mating experiment showed that females originating from the population in Vanserum increased their allocation to offspring by producing a generally higher number of egg-clutches if they were paired up with a male from Sävsjö (Figure 13). This effect was not evident among females from Sävsjö that were paired up with males from Vanserum.

Figure 13. Distribution of egg clutches produced by Tetrix subulata pygmy grasshoppers originating from two different populations (Sävsjö and Vanserum). Females in the ‘pure’ treatments were paired with a male from their own source population and females in the ‘mixed’ treatment were paired with a male from the other source population.

The mating experiment also showed that females from Sävsjö, that were paired up with a male from a different source population, changed their allocation of resources to offspring by including fewer eggs in their first egg clutch (Paper IV) (Figure 14). Females originating from Vanserum did not differ in clutch size depending on treatment group. Grasshoppers were generally larger in Sävsjö, suggesting that increased allocation to offspring by females from Vanserum might be triggered by male body size, a trait which is frequently suggested to be associated with mate quality. To evaluate the possibility that the difference in body size between populations could

32

1986, Forsman and Appelqvist 1999, Ahnesjö and Forsman 2006). Accordingly, a plethora of biotic factors (such as quantity, species composition and, nutritional quality of food resources, competitors and predators) and abiotic environmental conditions (e.g., humidity, soil surface temperatures) may have contributed to the observed patterns of body size.

Figure 12. Comparison of least square means (± s.e.) of body size (as estimated from femur length) among populations of two species of pygmy grasshoppers (T. subulata and T. undulata) that were collected from seven localities where both species co-occur. The dash reference line represents the isometric relationship expected under parallel (convergent) evolution or shared plasticity responses in the two species.

It is also possible that evolutionary shifts and environmentally induced plasticity responses differed between species despite that they were exposed to similar environments. This may be the case if there are differences between species in overall genetic architecture that produce dissimilar correlations between complex phenotypic traits such as: color pattern, thermal biology, life-history traits, behavior, diet, and microhabitat utilization (Forsman 1999, Forsman et al. 2002, Ahnesjö and Forsman 2003, 2006). Selection on traits that are developmentally or genetically associated with body size may therefore translate into different evolutionary modifications despite being exposed to similar selective regimes (Arendt and Reznick 2008).

The findings of this study are in agreement with the notion that dispersal may influence emerging patterns of genetic structure and differentiation between populations, and neutral genetic diversity within populations. That populations of pygmy grasshoppers are differentiated indicate that genetic compatibility could interfere with the translation of migration into gene flow. Compatibility of populations was investigated by crossing two differentiated population of T. subulata.

35

It is predicted from theory that some effects of genetic mixing do not influence fitness of first generation offspring, instead they emerge in second generation of offspring or later when locally adapted genetically linked traits are broken up during recombination. Comparisons of second generation offspring, produced by sibling groups from the original mating experiment, showed that mating treatment affected survival in first clutches. The effect of admixture differed depending on population with higher survival in Sävsjö and lower survival in Vanserum (Paper IV) (Figure 15). There was no difference in survival depending on mating treatment in second clutches.

Figure 15. Number of second generation offspring (F2), produced by females originating from two different populations (Sävsjö and Vanserum). Females in the ‘pure’ treatments were paired with a male from their own source population and females in the ‘mixed’ treatment were paired with a male from the other source population. Points represent sibling groups produced by individual females and the mean value and SD are shown for each treatment group.

The mating experiment showed that pygmy grasshoppers are able to mate and produce offspring with individuals originating from a different population. Effects of mating treatment on mating behavior and resource allocation to offspring differed between the two populations. This could lead to asymmetric and different levels of gene flow among populations despite equal immigration rates. However, experiments in laboratory environments may not incorporate the full complexity of natural conditions. This study may therefore

34

contribute to the differences in reproductive output between mating treatments we tested for associations within populations between female reproductive output and male body size. Results did not support that females adjusted the number of clutches or clutch size depending on male body size (Paper IV). It therefore seems unlikely that the observed effects of mating treatment reflected responses to the differences in body size between the two populations.

There were no effects of admixture on hatching success or nymph survival in our experiment which seems to suggest that viability of offspring is unaffected by genetic mixing (Paper IV) (Figure 14).

Figure 14. Number of eggs, hatchability of eggs, and nymph survival for female Tetrix subulata from two different populations (Sävsjö and Vanserum). Females in the ‘pure’ treatments were paired with a male from their own source population and females in the ‘mixed’ treatment were paired with a male from the other source population. Points represent individual females and the mean value and SD are shown for each treatment group.

35

It is predicted from theory that some effects of genetic mixing do not influence fitness of first generation offspring, instead they emerge in second generation of offspring or later when locally adapted genetically linked traits are broken up during recombination. Comparisons of second generation offspring, produced by sibling groups from the original mating experiment, showed that mating treatment affected survival in first clutches. The effect of admixture differed depending on population with higher survival in Sävsjö and lower survival in Vanserum (Paper IV) (Figure 15). There was no difference in survival depending on mating treatment in second clutches.

Figure 15. Number of second generation offspring (F2), produced by females originating from two different populations (Sävsjö and Vanserum). Females in the ‘pure’ treatments were paired with a male from their own source population and females in the ‘mixed’ treatment were paired with a male from the other source population. Points represent sibling groups produced by individual females and the mean value and SD are shown for each treatment group.

The mating experiment showed that pygmy grasshoppers are able to mate and produce offspring with individuals originating from a different population. Effects of mating treatment on mating behavior and resource allocation to offspring differed between the two populations. This could lead to asymmetric and different levels of gene flow among populations despite equal immigration rates. However, experiments in laboratory environments may not incorporate the full complexity of natural conditions. This study may therefore

34

contribute to the differences in reproductive output between mating treatments we tested for associations within populations between female reproductive output and male body size. Results did not support that females adjusted the number of clutches or clutch size depending on male body size (Paper IV). It therefore seems unlikely that the observed effects of mating treatment reflected responses to the differences in body size between the two populations.

There were no effects of admixture on hatching success or nymph survival in our experiment which seems to suggest that viability of offspring is unaffected by genetic mixing (Paper IV) (Figure 14).

Figure 14. Number of eggs, hatchability of eggs, and nymph survival for female Tetrix subulata from two different populations (Sävsjö and Vanserum). Females in the ‘pure’ treatments were paired with a male from their own source population and females in the ‘mixed’ treatment were paired with a male from the other source population. Points represent individual females and the mean value and SD are shown for each treatment group.

37

finding is that estimates of neutral genetic diversity should not be used as a substitute to infer about viability and evolutionary potential of natural populations. Development of management and protection strategies for endangered populations and species should build on information on ecologically and functionally important aspects of genetic and phenotypic variation in order to be successful.

In several cases my analyses and comparisons generated mixed outcomes. This is expected and likely illustrates how the effects of different micro-evolutionary drivers can vary according to geographic areas (Paper I), disturbance regimes (Paper II), and differ even between closely related sympatric species (Paper III). One example of this context dependence is the finding that genetic diversity increased with population size and with increasing proportion of long-winged phenotypes across T. subulata populations on the island of Öland, thus implicating immigration as an important determinant of within-population diversity, but not on the mainland (Paper I). Another aspect of the complexity is that isolation by distance in T. subulata was evident for short inter-population distances on the mainland, but gradually disappeared as populations separated by longer distances were included (Paper I). This underscores the importance of landscape structure and spatial sampling scheme for conclusions regarding the role of gene flow and isolation by distance.

Finally, the mating experiment illustrate that pre-zygotic behavioral responses and effects of interbreeding and admixture can differ considerably even among populations within species (Paper IV). Clearly, we need a better understanding of why admixture effects are heterogeneous. To identify population characteristics that can be used to forecast the outcome of interbreeding and admixture is an important task for future research. This will require more long-term studies of populations in the wild that are exposed to natural ecological conditions and selective pressures. Within a conservation context, the consequences of mixing individuals from different populations should be carefully investigated under controlled conditions. This should be done using the particular source and donor populations that will be the targets of future conservation actions, before any large scale translocations or supplementations are implemented in the wild.

In closing, the complex nature of nature outlined by my work, albeit somewhat frustrating, contributes to the captivation of evolutionary ecology and biodiversity research. The strong context dependence also justifies further investigations into unresolved issues and calls for revisiting old topics with different approaches and in different species, and perhaps into new avenues. My studies of natural populations relied on observational data, firm conclusions regarding causes and consequences of variation require experimental manipulation approaches. Future studies can of course also benefit from modern techniques. For instance, recent advances in RAD-sequencing have opened cost-effective opportunities for quantification of both

36

have underestimated the importance of interbreeding and how it will affect fitness and the translation of dispersal into gene flow in natural populations of pygmy grasshoppers.

Conclusions and future directions In this thesis I aimed to explore how dispersal affects population genetic structure, investigate causes and consequences of functional genetic diversity, and examine drivers of phenotypic variation. As study system I used two species of pygmy grasshoppers. By virtue of their dispersal polymorphism, color polymorphism, life-history, and transient, habitat-tracking, meta-population like dynamics, these insects are well suited for investigating microevolution. I collected and compared genetic, morphological and ecological data from natural populations that represented different disturbance regimes, geographic regions, and species. I also conducted a between-population mating experiment to address how behavior, reproductive allocation decisions, and genetic incompatibility may influence fitness of dispersers and affect the translation of dispersal into gene flow.

Overall, my thesis emphasizes that microevolution is influenced by a complex interplay of stochastic and deterministic processes. It also illustrates that combining observational and experimental approaches and integrating ecological and molecular data is key to identifying drivers of population genetic structure in natural populations.

Some of my findings were in agreement with predictions from theory and with empirical findings in previous studies of other species. For instance, within-population neutral genetic diversity was found to increase overall with estimates of census population size in T. subulata (Paper I), as expected if rare alleles are lost from smaller populations due to drift. The results based on standardized functional diversity differentials within the CECA approach, demonstrating that the effects and relative importance of ecological and evolutionary processes are different for neutral and functional aspects of diversity (Paper II), also conformed to expectations.

Not all findings were in good agreement with expectations. Perhaps the most striking exception was the comparison between species in sympatry (Paper III). The stronger differentiation among populations in the generally dispersive T. subulata was anticipated. However, the prediction that neutral genetic diversity within populations should be higher in the generally dispersive T. subulata than in the more sedentary T. undulata received only weak support, at best.

Some results were also in direct conflict with what is often assumed and believed. I have in mind here especially the negative association between neutral (AFLP) diversity and functional color morph diversity across populations of T. subulata (Paper II). An important lesson emerging from this

37

finding is that estimates of neutral genetic diversity should not be used as a substitute to infer about viability and evolutionary potential of natural populations. Development of management and protection strategies for endangered populations and species should build on information on ecologically and functionally important aspects of genetic and phenotypic variation in order to be successful.

In several cases my analyses and comparisons generated mixed outcomes. This is expected and likely illustrates how the effects of different micro-evolutionary drivers can vary according to geographic areas (Paper I), disturbance regimes (Paper II), and differ even between closely related sympatric species (Paper III). One example of this context dependence is the finding that genetic diversity increased with population size and with increasing proportion of long-winged phenotypes across T. subulata populations on the island of Öland, thus implicating immigration as an important determinant of within-population diversity, but not on the mainland (Paper I). Another aspect of the complexity is that isolation by distance in T. subulata was evident for short inter-population distances on the mainland, but gradually disappeared as populations separated by longer distances were included (Paper I). This underscores the importance of landscape structure and spatial sampling scheme for conclusions regarding the role of gene flow and isolation by distance.

Finally, the mating experiment illustrate that pre-zygotic behavioral responses and effects of interbreeding and admixture can differ considerably even among populations within species (Paper IV). Clearly, we need a better understanding of why admixture effects are heterogeneous. To identify population characteristics that can be used to forecast the outcome of interbreeding and admixture is an important task for future research. This will require more long-term studies of populations in the wild that are exposed to natural ecological conditions and selective pressures. Within a conservation context, the consequences of mixing individuals from different populations should be carefully investigated under controlled conditions. This should be done using the particular source and donor populations that will be the targets of future conservation actions, before any large scale translocations or supplementations are implemented in the wild.

In closing, the complex nature of nature outlined by my work, albeit somewhat frustrating, contributes to the captivation of evolutionary ecology and biodiversity research. The strong context dependence also justifies further investigations into unresolved issues and calls for revisiting old topics with different approaches and in different species, and perhaps into new avenues. My studies of natural populations relied on observational data, firm conclusions regarding causes and consequences of variation require experimental manipulation approaches. Future studies can of course also benefit from modern techniques. For instance, recent advances in RAD-sequencing have opened cost-effective opportunities for quantification of both

36

have underestimated the importance of interbreeding and how it will affect fitness and the translation of dispersal into gene flow in natural populations of pygmy grasshoppers.

Conclusions and future directions In this thesis I aimed to explore how dispersal affects population genetic structure, investigate causes and consequences of functional genetic diversity, and examine drivers of phenotypic variation. As study system I used two species of pygmy grasshoppers. By virtue of their dispersal polymorphism, color polymorphism, life-history, and transient, habitat-tracking, meta-population like dynamics, these insects are well suited for investigating microevolution. I collected and compared genetic, morphological and ecological data from natural populations that represented different disturbance regimes, geographic regions, and species. I also conducted a between-population mating experiment to address how behavior, reproductive allocation decisions, and genetic incompatibility may influence fitness of dispersers and affect the translation of dispersal into gene flow.

Overall, my thesis emphasizes that microevolution is influenced by a complex interplay of stochastic and deterministic processes. It also illustrates that combining observational and experimental approaches and integrating ecological and molecular data is key to identifying drivers of population genetic structure in natural populations.

Some of my findings were in agreement with predictions from theory and with empirical findings in previous studies of other species. For instance, within-population neutral genetic diversity was found to increase overall with estimates of census population size in T. subulata (Paper I), as expected if rare alleles are lost from smaller populations due to drift. The results based on standardized functional diversity differentials within the CECA approach, demonstrating that the effects and relative importance of ecological and evolutionary processes are different for neutral and functional aspects of diversity (Paper II), also conformed to expectations.

Not all findings were in good agreement with expectations. Perhaps the most striking exception was the comparison between species in sympatry (Paper III). The stronger differentiation among populations in the generally dispersive T. subulata was anticipated. However, the prediction that neutral genetic diversity within populations should be higher in the generally dispersive T. subulata than in the more sedentary T. undulata received only weak support, at best.

Some results were also in direct conflict with what is often assumed and believed. I have in mind here especially the negative association between neutral (AFLP) diversity and functional color morph diversity across populations of T. subulata (Paper II). An important lesson emerging from this

39

Acknowledgements First of all I would like to thank my supervisor, Professor Anders Forsman at Linnaeus University. Thanks for excellent scientific guidance, encouragement, patience and support.

My co-supervisor, Professor Erik Svensson at Lund University, for interesting discussions and advice.

I would like to thank my colleagues (both past and current) in the Evolutionary Ecology Group and in the Fish Ecology Group. Petter Tibblin, Johanna Sunde, Hanna Berggren, Daniela Polic, Lena Wennersten, Per Larsson, Per Koch-Schmidt, Einat Karpestam, Jenny Lindberg, Sofia Ceasar, Olof Hellgren, Magnus Karlsson, and Jonas Ahnesjö. I have you to thank for so much during my time at the University and there is no way to account for everything I have learnt and experienced together with you. All I can say is that it has been a pleasure to spend this time working with such fine future and present scientists.

Thanks to colleagues at the department; PhD students, senior scientists and staff. Thanks for all help with practical issues and for stimulating conversations both about private and work related topics.

Thanks to EEMiS and everyone involved in this collaboration between research fields. There are so many PhD students, post-docs and senior scientists that have contributed to the success of this project and I am very grateful for everything you have done for me.

Thank you Josefine Larsson for your help with analysis of AFLP and for help with organizing my visit at Södertörn University College.

Thanks to all colleagues and fellow PhD students whom I have met during conferences, symposiums, courses and meetings. I am grateful and glad to have met so many interesting and intelligent people.

I have most likely forgotten to thank someone which I am truly sorry for. If you feel that you do not belong to any of the above mentioned categories I would like to thank you as well for everything you have done to help me.

38

neutral and functional genetic diversity, also in non-model organisms. If these types of molecular data are used for analyses within the CECA approach (Paper II) functional and neutral genetic diversity can be directly compared at the same level of biological organization. Technological advancements will continue to open new opportunities and spur future research breakthroughs. Yet the most important drivers of scientific progress will always be curiosity and persistence.

39

Acknowledgements First of all I would like to thank my supervisor, Professor Anders Forsman at Linnaeus University. Thanks for excellent scientific guidance, encouragement, patience and support.

My co-supervisor, Professor Erik Svensson at Lund University, for interesting discussions and advice.

I would like to thank my colleagues (both past and current) in the Evolutionary Ecology Group and in the Fish Ecology Group. Petter Tibblin, Johanna Sunde, Hanna Berggren, Daniela Polic, Lena Wennersten, Per Larsson, Per Koch-Schmidt, Einat Karpestam, Jenny Lindberg, Sofia Ceasar, Olof Hellgren, Magnus Karlsson, and Jonas Ahnesjö. I have you to thank for so much during my time at the University and there is no way to account for everything I have learnt and experienced together with you. All I can say is that it has been a pleasure to spend this time working with such fine future and present scientists.

Thanks to colleagues at the department; PhD students, senior scientists and staff. Thanks for all help with practical issues and for stimulating conversations both about private and work related topics.

Thanks to EEMiS and everyone involved in this collaboration between research fields. There are so many PhD students, post-docs and senior scientists that have contributed to the success of this project and I am very grateful for everything you have done for me.

Thank you Josefine Larsson for your help with analysis of AFLP and for help with organizing my visit at Södertörn University College.

Thanks to all colleagues and fellow PhD students whom I have met during conferences, symposiums, courses and meetings. I am grateful and glad to have met so many interesting and intelligent people.

I have most likely forgotten to thank someone which I am truly sorry for. If you feel that you do not belong to any of the above mentioned categories I would like to thank you as well for everything you have done to help me.

38

neutral and functional genetic diversity, also in non-model organisms. If these types of molecular data are used for analyses within the CECA approach (Paper II) functional and neutral genetic diversity can be directly compared at the same level of biological organization. Technological advancements will continue to open new opportunities and spur future research breakthroughs. Yet the most important drivers of scientific progress will always be curiosity and persistence.

41

C. F. Palmer, C. Turlure, and J. M. J. Travis. 2012. Costs of dispersal. Biological Reviews 87:290-312.

Caesar, S., M. Karlsson, and A. Forsman. 2010. Diversity and relatedness enhance survival in colour polymorphic grasshoppers. Plos One 5.

Charlesworth, B. and D. Charlesworth. 2017. Population genetics from 1966 to 2016. Heredity 118:2-9.

Crow, J. F. and M. Kimura. 1970. An introduction to population genetics theory. Harper and Row, New York.

Darwin, C. R. 1859. The Origin of Species. John Murray, London. Ebert, D., C. Haag, M. Kirkpatrick, M. Riek, J. W. Hottinger, and V. I. Pajunen.

2002. A selective advantage to immigrant genes in a Daphnia metapopulation. Science 295:485-488.

Edelaar, P., A. M. Siepielski, and J. Clobert. 2008. Matching habitat choice causes directed gene flow: a neglected dimension in evolution and ecology. Evolution 62:2462-2472.

Ewers, R. M. and R. K. Didham. 2006. Confounding factors in the detection of species responses to habitat fragmentation. Biological Reviews 81:117-142.

Excoffier, L. and H. E. L. Lischer. 2010. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources 10:564-567.

Fenster, C. B. and L. F. Galloway. 2000. Inbreeding and outbreeding depression in natural populations of Chamaecrista fasciculata (Fabaceae). Conservation Biology 14:1406-1412.

Forsman, A. 1999. Reproductive life history variation among colour morphs of the pygmy grasshopper Tetrix subulata. Biological Journal of the Linnean Society 67:247-261.

Forsman, A. 2011. Rethinking the thermal melanism hypothesis: rearing temperature and coloration in pygmy grasshoppers. Evolutionary Ecology 25:1247-1257.

Forsman, A., J. Ahnesjo, and S. Caesar. 2007. Fitness benefits of diverse offspring in pygmy grasshoppers. Evolutionary Ecology Research 9:1305-1318.

Forsman, A. and S. Appelqvist. 1999. Experimental manipulation reveals differential effects of colour pattern on survival in male and female pygmy grasshoppers. Journal of Evolutionary Biology 12:391-401.

Forsman, A., M. Karlsson, L. Wennersten, J. Johansson, and E. Karpestam. 2011. Rapid evolution of fire melanism in replicated populations of Pygmy Grasshoppers. Evolution 65:2530-2540.

Forsman, A., K. Ringblom, E. Civantos, and J. Ahnesjo. 2002. Coevolution of color pattern and thermoregulatory behavior in polymorphic pygmy grasshoppers Tetrix undulata. Evolution 56:349-360.

Forsman, A., L. Wennersten, M. Karlsson, and S. Caesar. 2012. Variation in founder groups promotes establishment success in the wild. Proceedings of the Royal Society B-Biological Sciences 279:2800-2806.

40

References Ahnesjö, J. and A. Forsman. 2003. Correlated evolution of colour pattern and

body size in polymorphic pygmy grasshoppers, Tetrix undulata. Journal of Evolutionary Biology 16:1308-1318.

Ahnesjö, J. and A. Forsman. 2006. Differential habitat selection by pygmy grasshopper color morphs; interactive effects of temperature and predator avoidance. Evolutionary Ecology 20:235-257.

Amos, W. and J. Harwood. 1998. Factors affecting levels of genetic diversity in natural populations. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 353:177-186.

Arendt, J. and D. Reznick. 2008. Convergence and parallelism reconsidered: what have we learned about the genetics of adaptation? Trends in Ecology & Evolution 23:26-32.

Arnett, H. and M. Kinnison. 2016. Predator-induced phenotypic plasticity of shape and behavior: parallel and unique patterns across sexes and species. Current Zoology DOI: 10.1093/cz/zow072.

Arnold, S. J. and M. J. Wade. 1984. On the measurement of natual and sexual selection - theory. Evolution 38:709-719.

Bell, G. 2010. Fluctuating selection: the perpetual renewal of adaptation in variable environments. Philosophical Transactions of the Royal Society B-Biological Sciences 365:87-97.

Bensch, S. and M. Akesson. 2005. Ten years of AFLP in ecology and evolution: why so few animals? Molecular Ecology 14:2899-2914.

Berggren, H., J. Tinnert, and A. Forsman. 2012. Spatial sorting may explain evolutionary dynamics of wing polymorphism in pygmy grasshoppers. Journal of Evolutionary Biology 25:2126-2138.

Bohonak, A. J. 1999. Dispersal, gene flow, and population structure. Quarterly Review of Biology 74:21-45.

Bolnick, D. I., P. Amarasekare, M. S. Araujo, R. Burger, J. M. Levine, M. Novak, V. H. W. Rudolf, S. J. Schreiber, M. C. Urban, and D. A. Vasseur. 2011. Why intraspecific trait variation matters in community ecology. Trends in Ecology & Evolution 26:183-192.

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Bonte, D., H. Van Dyck, J. M. Bullock, A. Coulon, M. Delgado, M. Gibbs, V. Lehouck, E. Matthysen, K. Mustin, M. Saastamoinen, N. Schtickzelle, V. M. Stevens, S. Vandewoestijne, M. Baguette, K. Barton, T. G. Benton, A. Chaput-Bardy, J. Clobert, C. Dytham, T. Hovestadt, C. M. Meier, S.

41

C. F. Palmer, C. Turlure, and J. M. J. Travis. 2012. Costs of dispersal. Biological Reviews 87:290-312.

Caesar, S., M. Karlsson, and A. Forsman. 2010. Diversity and relatedness enhance survival in colour polymorphic grasshoppers. Plos One 5.

Charlesworth, B. and D. Charlesworth. 2017. Population genetics from 1966 to 2016. Heredity 118:2-9.

Crow, J. F. and M. Kimura. 1970. An introduction to population genetics theory. Harper and Row, New York.

Darwin, C. R. 1859. The Origin of Species. John Murray, London. Ebert, D., C. Haag, M. Kirkpatrick, M. Riek, J. W. Hottinger, and V. I. Pajunen.

2002. A selective advantage to immigrant genes in a Daphnia metapopulation. Science 295:485-488.

Edelaar, P., A. M. Siepielski, and J. Clobert. 2008. Matching habitat choice causes directed gene flow: a neglected dimension in evolution and ecology. Evolution 62:2462-2472.

Ewers, R. M. and R. K. Didham. 2006. Confounding factors in the detection of species responses to habitat fragmentation. Biological Reviews 81:117-142.

Excoffier, L. and H. E. L. Lischer. 2010. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources 10:564-567.

Fenster, C. B. and L. F. Galloway. 2000. Inbreeding and outbreeding depression in natural populations of Chamaecrista fasciculata (Fabaceae). Conservation Biology 14:1406-1412.

Forsman, A. 1999. Reproductive life history variation among colour morphs of the pygmy grasshopper Tetrix subulata. Biological Journal of the Linnean Society 67:247-261.

Forsman, A. 2011. Rethinking the thermal melanism hypothesis: rearing temperature and coloration in pygmy grasshoppers. Evolutionary Ecology 25:1247-1257.

Forsman, A., J. Ahnesjo, and S. Caesar. 2007. Fitness benefits of diverse offspring in pygmy grasshoppers. Evolutionary Ecology Research 9:1305-1318.

Forsman, A. and S. Appelqvist. 1999. Experimental manipulation reveals differential effects of colour pattern on survival in male and female pygmy grasshoppers. Journal of Evolutionary Biology 12:391-401.

Forsman, A., M. Karlsson, L. Wennersten, J. Johansson, and E. Karpestam. 2011. Rapid evolution of fire melanism in replicated populations of Pygmy Grasshoppers. Evolution 65:2530-2540.

Forsman, A., K. Ringblom, E. Civantos, and J. Ahnesjo. 2002. Coevolution of color pattern and thermoregulatory behavior in polymorphic pygmy grasshoppers Tetrix undulata. Evolution 56:349-360.

Forsman, A., L. Wennersten, M. Karlsson, and S. Caesar. 2012. Variation in founder groups promotes establishment success in the wild. Proceedings of the Royal Society B-Biological Sciences 279:2800-2806.

40

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Ahnesjö, J. and A. Forsman. 2006. Differential habitat selection by pygmy grasshopper color morphs; interactive effects of temperature and predator avoidance. Evolutionary Ecology 20:235-257.

Amos, W. and J. Harwood. 1998. Factors affecting levels of genetic diversity in natural populations. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 353:177-186.

Arendt, J. and D. Reznick. 2008. Convergence and parallelism reconsidered: what have we learned about the genetics of adaptation? Trends in Ecology & Evolution 23:26-32.

Arnett, H. and M. Kinnison. 2016. Predator-induced phenotypic plasticity of shape and behavior: parallel and unique patterns across sexes and species. Current Zoology DOI: 10.1093/cz/zow072.

Arnold, S. J. and M. J. Wade. 1984. On the measurement of natual and sexual selection - theory. Evolution 38:709-719.

Bell, G. 2010. Fluctuating selection: the perpetual renewal of adaptation in variable environments. Philosophical Transactions of the Royal Society B-Biological Sciences 365:87-97.

Bensch, S. and M. Akesson. 2005. Ten years of AFLP in ecology and evolution: why so few animals? Molecular Ecology 14:2899-2914.

Berggren, H., J. Tinnert, and A. Forsman. 2012. Spatial sorting may explain evolutionary dynamics of wing polymorphism in pygmy grasshoppers. Journal of Evolutionary Biology 25:2126-2138.

Bohonak, A. J. 1999. Dispersal, gene flow, and population structure. Quarterly Review of Biology 74:21-45.

Bolnick, D. I., P. Amarasekare, M. S. Araujo, R. Burger, J. M. Levine, M. Novak, V. H. W. Rudolf, S. J. Schreiber, M. C. Urban, and D. A. Vasseur. 2011. Why intraspecific trait variation matters in community ecology. Trends in Ecology & Evolution 26:183-192.

Bolnick, D. I. and P. Nosil. 2007. Natural selection in populations subject to a migration load. Evolution 61:2229-2243.

Bonin, A., E. Bellemain, P. B. Eidesen, F. Pompanon, C. Brochmann, and P. Taberlet. 2004. How to track and assess genotyping errors in population genetics studies. Molecular Ecology 13:3261-3273.

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44

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Slatkin, M. 1993. Isolation by distance in equilibrium and nonequilibrium populations. Evolution 47:264-279.

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Tinnert, J., H. Berggren, and A. Forsman. 2016a. Population-specific effects of interbreeding and admixture on reproductive decisions and offspring quality. Annales Zoologici Fennici 53:55-68.

45

Tinnert, J., O. Hellgren, J. Lindberg, P. Koch-Schmidt, and A. Forsman. 2016b. Population genetic structure, differentiation, and diversity in Tetrix subulata pygmy grasshoppers: roles of population size and immigration. Ecology and Evolution 6:7831-7846.

van Strien, M. J., R. Holderegger, and H. J. Van Heck. 2015. Isolation-by-distance in landscapes: considerations for landscape genetics. Heredity 114:27-37.

Vekemans, X., T. Beauwens, M. Lemaire, and I. Roldan-Ruiz. 2002. Data from amplified fragment length polymorphism (AFLP) markers show indication of size homoplasy and of a relationship between degree of homoplasy and fragment size. Molecular Ecology 11:139-151.

Wennersten, L., J. Johansson, E. Karpestam, and A. Forsman. 2012. Higher establishment success in more diverse groups of pygmy grasshoppers under seminatural conditions. Ecology 93:2519-2525.

Verhoeven, K. J. F., M. Macel, L. M. Wolfe, and A. Biere. 2011. Population admixture, biological invasions and the balance between local adaptation and inbreeding depression. Proceedings of the Royal Society B-Biological Sciences 278:2-8.

Whitlock, M. C. and D. E. McCauley. 1999. Indirect measures of gene flow and migration: F-ST not equal 1/(4Nm+1). Heredity 82:117-125.

Whitlock, R., H. Hipperson, M. Mannarelli, R. K. Butlin, and T. Burke. 2008. An objective, rapid and reproducible method for scoring AFLP peak-height data that minimizes genotyping error. Molecular Ecology Resources 8:725-735.

Whitlock, R., G. Stewart, S. Goodman, S. Piertney, R. Butlin, A. Pullin, and T. Burke. 2013. A systematic review of phenotypic responses to between-population outbreeding. Environmental Evidence 2:13.

Willi, Y., J. Van Buskirk, and A. A. Hoffmann. 2006. Limits to the adaptive potential of small populations. Pages 433-458 Annual Review of Ecology Evolution and Systematics. Annual Reviews, Palo Alto.

Vos, P., R. Hogers, M. Bleeker, M. Reijans, T. Vandelee, M. Hornes, A. Frijters, J. Pot, J. Peleman, M. Kuiper, and M. Zabeau. 1995. AFLP - a new technique for DNA-fingerprinting. Nucleic Acids Research 23:4407-4414.

Wright, S. 1931. Evolution in Mendelian populations. Genetics 16:0097-0159. Wright, S. 1951. The genetical structure of populations. Annals of Eugenics

15:323-354. Wright, S. 1978. Evolution and the genetics of population, variability within and

among natural populations. The University of Chicago Press, Chicago Zhivotovsky, L. A. 1999. Estimating population structure in diploids with

multilocus dominant DNA markers. Molecular Ecology 8:907-913.

44

extinction risk in butterfly populations. American Naturalist 157:237-244.

Nosil, P., B. J. Crespi, and C. P. Sandoval. 2003. Reproductive isolation driven by the combined effects of ecological adaptation and reinforcement. Proceedings of the Royal Society B-Biological Sciences 270:1911-1918.

Ortego, J., V. Garcia-Navas, V. Noguerales, and P. J. Cordero. 2015. Discordant patterns of genetic and phenotypic differentiation in five grasshopper species codistributed across a microreserve network. Molecular Ecology 24:5796-5812.

Perry, M. and G. Kader. 2005. Variation as Unalikeability. Teaching Statistics 27:58-60.

Pigliucci, M. and K. Preston. 2004. Phenotypic integration: studying the ecology and evolution of complex phenotypes. Oxford University Press, Oxford.

R Core Team. 2014. R: A Language and Environment for Statistical Computing. Vienna, Austria.

Reed, D. H. and R. Frankham. 2001. How closely correlated are molecular and quantitative measures of genetic variation? A meta-analysis. Evolution 55:1095-1103.

Rhymer, J. M. and D. Simberloff. 1996. Extinction by hybridization and introgression. Annual Review of Ecology and Systematics 27:83-109.

Richardson, J. L., M. C. Urban, D. I. Bolnick, and D. K. Skelly. 2014. Microgeographic adaptation and the spatial scale of evolution. Trends in Ecology & Evolution 29:165-176.

Rius, M. and J. A. Darling. 2014. How important is intraspecific genetic admixture to the success of colonising populations? Trends in Ecology & Evolution 29:233-242.

Rousset, F. 1997. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145:1219-1228.

Sambrook, J., F. Fritch, and T. Maniatis. 2002. Molecular clooning, a laboratory manual. Spring Harbor Laboratory Press: Cold Spring Harbor, NY.

Shine, R., G. P. Brown, and B. L. Phillips. 2011. An evolutionary process that assembles phenotypes through space rather than through time. Proceedings of the National Academy of Sciences of the United States of America 108:5708-5711.

Slatkin, M. 1987. Gene flow and the geographic structure of natural-populations. Science 236:787-792.

Slatkin, M. 1993. Isolation by distance in equilibrium and nonequilibrium populations. Evolution 47:264-279.

Tallmon, D. A., G. Luikart, and R. S. Waples. 2004. The alluring simplicity and complex reality of genetic rescue. Trends in Ecology & Evolution 19:489-496.

Tinnert, J., H. Berggren, and A. Forsman. 2016a. Population-specific effects of interbreeding and admixture on reproductive decisions and offspring quality. Annales Zoologici Fennici 53:55-68.