Evolution of host repertoires and thediversification of butterflies
Mariana Pires Braga
Academic dissertation for the Degree of Doctor of Philosophy in Animal Ecology at Stockholm
University to be publicly defended on Friday 15 February 2019 at 10.00 in Vivi Täckholmsalen(Q-salen), NPQ-huset, Svante Arrhenius väg 20.
Abstract

All herbivorous insects are specialized to some extent to their host plants, but the level of specialization varies greatly.
Insect-plant coevolution is often invoked to explain the large diversity of herbivorous insects, but the role of specialization
during diversification is still controversial. Although well-studied, our understanding of the evolution of species interactions
is still improving, and recent theoretical developments have highlighted the role of generalization (via colonization of
new hosts) on diversification. In this thesis, various approaches are combined for a detailed study of the origins ofmacroevolutionary patterns of host use and butterfly diversity. Chapter I provides a mechanistic basis for such patterns
through simulations of lineages evolved in silico. By separating the effects of the number of hosts used by a parasite lineage
and the diversity of resources they encompass, we found that resource diversity, rather than host range per se, was themain driver of parasite species richness in both simulated and empirical systems. In Chapter II, we combined network andphylogenetic analyses to quantify support for the two main hypothesized drivers of diversification of herbivorous insects.
Based on analyses of two butterfly families, Nymphalidae and Pieridae, we found that variability in host use is essential
for diversification, while radiation following the colonization of a new host is rare but can produce high diversity. We thenreconciled the two alternative hypotheses into a unified process of host-associated diversification where continuous probingof new hosts and retention of the ability to use hosts colonized in the past are the main factors shaping butterfly-plant
networks. While network analysis is a powerful tool for investigating patterns of interaction, other methods are necessary
to directly test the mechanisms generating the observed patterns. Therefore, in Chapter III we describe a model of hostrepertoire evolution we developed for Bayesian inference of evolution of host-parasite interactions. The approach wasvalidated with both simulated and empirical data sets. Finally, in Chapter IV we used the method described in Chapter
III to explicitly test the predictions made in Chapter II about the evolution of butterfly-plant networks. We found direct
evidence for the role of expansion of fundamental host repertoire and phylogenetic conservatism as important drivers ofhost repertoire evolution. Thus, using three different approaches, we found overall support for the idea that variation inhost use accumulated over evolutionary time is essential for butterfly diversification.
Keywords: coevolution, host-parasite interaction, inference, networks.
Stockholm 2019http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-163179
ISBN 978-91-7797-568-7
ISBN 978-91-7797-569-4

Department of Zoology
Stockholm University, 106 91 Stockholm

EVOLUTION OF HOST REPERTOIRES AND THE DIVERSIFICATIONOF BUTTERFLIESMariana Pires Braga

Evolution of host repertoires
and the diversification of
butterflies
Mariana Pires Braga

©Mariana Pires Braga, Stockholm University 2019
ISBN print 978-91-7797-568-7
ISBN PDF 978-91-7797-569-4
Cover artwork by Rasmus Erlandsson
Photos by Niklas Janz
Printed in Sweden by Universitetsservice US-AB, Stockholm 2019

"If I see anything vitalaround me, it is preciselythat spirit of adventure,
which seems
indestructible and isakin to curiosity."- Marie Curie

CONTENTS

Introduction.................................................................................................................................................. 9
Modeling evolution in silico.........................................................................................................11
Predicting patterns of interaction ..........................................................................................14
Inference of host repertoire evolution ...............................................................................17
Conclusions ................................................................................................................................................22
Svensk sammanfattning ..................................................................................................................23
References...................................................................................................................................................24
Acknowledgements .............................................................................................................................26
List of chapters........................................................................................................................................27

INTRODUCTION
All organisms are ecologically specialized to some extent. Herbivorous insects have long served as models for the study of specialization (Futuyma andMoreno 1988) because of their impressive diversity and variation in plantresource use. This variation resulted in gradients of specialization among individuals, populations and species (Forister et al. 2015), and has been shown
to mediate several ecological and evolutionary processes, such as the coexistence of competitors (Büchi and Vuilleumier 2014), persistence in face ofenvironmental perturbation (Devictor et al. 2008), insect-plant network stability (Mougi and Kondoh 2012; Lever et al. 2014), and diversification (Janzet al. 2006; Hardy and Otto 2014).
Host specialization - leading to monophagy - only represents one end of
the gradient, ignoring a great part of the variation in host range and in actual
shifts between host plants (Janz et al. 2001; Agosta et al. 2010). Individualsof several herbivorous insects (e.g. most lepidopterans and hymenopterans)complete all of their larval development on an individual host, and are therefore monophagous individuals, or parasites (Thompson 1994). Other insects
can move between and feed on two or more host individuals and are calledgrazers (Thompson 1994). Although grazers can feed on individuals of asame species (for example, in monocultures), grazing and polyphagy are generally correlated. Among parasitic insects, a variety of ecological, chemical,
and genetic factors may cause host preferences to change locally, producing
specialists with restricted diets in local communities (Fox and Morrow 1981).
Variation in host use along spatial and temporal gradients can take place atthe population level, when individuals are monophagous but feed on different
plants, or at the species level, when monophagous populations are specialized
on different hosts.
Due to the prevalence of host specificity and taxonomic conservatism
among herbivorous insects, many studies have focused on the mechanisms
and consequences of ecological specialization (Ehrlich and Raven 1964; Futuyma and Moreno 1988; Forister et al. 2012). This led to the generalizationthat host use by insects is both specialized and conservative (Ehrlich and9Raven 1964; Janz and Nylin 1998; Novotny and Basset 2005), which creates
something of a paradox considering that insects have evidently colonized agood deal of the diversity of plants in a evolutionarily short time (Agosta etal. 2010). For instance, the ancestor of the butterflies probably colonized arelatively derived seed plant (Janz and Nylin 2008), hence the present patterns of host use are better explained by colonizations from ancestral plants
onto the already diverse group of seed plants.
Even though a large-scale conservatism can be seen in most insect groups,
rapid shifts and colonizations have been observed as a consequence of habitatchange (Singer et al. 1993) and invasive or introduced plant species (Carroll
and Boyd 1992; Fraser and Lawton 1994; Fox et al. 1997). In some cases, acomplete shift in preferred host plant has occurred over a handful of generations (Singer et al. 1993), suggesting that under some circumstances adding
new host plants might not be as difficult as one would expect. There is, therefore, strong evidence both at evolutionary and ecological time scales that hostrange expansions (i.e. increase in number of hosts used) are not rare.
There is less consensus, however, about the importance of host range expansions for diversification (Hamm and Fordyce 2015; Janz et al. 2016;
Hamm and Fordyce 2016). Novel statistical approaches to investigate traitdependent diversification have been developed recently (Maddison et al.
2007; FitzJohn 2012), but so far have produced divergent results when used
to study host-associated diversification in butterflies (Hardy and Otto 2014;
Hamm and Fordyce 2015; Hardy et al. 2016). Part of this problem may lie inthe classification of host range into two opposing states (monophagous vs.
polyphagous), which precedes most analytical methods. Strictly speaking,host range is not an independently evolving trait in its own right, but an emergent property of the underlying dynamics of gaining and losing host plants.
The ideal method to investigate the role of hosts in diversification processes should be able to deal with both the number of hosts used by eachtaxon and the identity of the plants in the repertoire. The trait that evolves
across the insect evolutionary history is indeed the host repertoire, that is, theassemblage of hosts used by the insect, not just the number of hosts used (hostrange). Computational limitations still constrain the explicit modelling of
host repertoire evolution in a statistical framework. Thus, in this thesis, I suggest different ways to tackle this problem, using a combination of methods toinvestigate the coevolutionary dynamics between parasites and their hosts,
with a special focus on butterfly-plant interactions.

10MODELING EVOLUTION IN SILICOIn Chapter I, we investigated the mechanistic basis for the origins of macroevolutionary patterns of parasite diversity and host use, by simulating parasite
lineages evolved in silico. We were particularly interested in how the colonization of new hosts affects the phenotypic distribution and the fitness landscape of parasites, and whether it intensifies diversification.
We described an individual-based model in which (i) parasites undergo
sexual reproduction limited by genetic proximity, (ii) hosts are uniformly distributed along a one-dimensional resource gradient, and (iii) host use is determined by the interaction between the phenotype of the parasite and a heterogeneous fitness landscape. The model comprises three hierarchical levels:
individual, species, and fitness landscape. A species is a group of individuals
connected by reproduction, which only occurs between individuals with agenetic distance smaller than the mate recognition threshold. The fitness
landscape is composed of fitness peaks, which represent hosts for the parasites (Fig 1). The position of a given host in the fitness landscape represents
the optimum phenotype to use that given host (the parasite phenotype thatyields maximum survival). Survival decreases with increasing difference between the host optimum and the parasite phenotype.

Resource space

Fitness (survival probability)

1.00

1.2

0.75

1.2

Fitness
1.00

0.50

0.75
0.50
0.25

0.25

0.00
4

8

12

16

PhenotypeFigure 1 - Hypothetical fitness landscape with three hosts (peaks) and the resulting 1-dimensional resource space, where white patches represent phenotypes with positive fitness for each host. The solid vertical line is a projectionof a given phenotype to show that the fitness of a parasite on a given hostdepends on where the line crosses the fitness curve (dots), and that this varies
between hosts (this individual has a positive fitness in two hosts).

11

At each time step, three events happen in the following order: reproduction, host probing, and selection (Fig 2). The parasite population in a given
host at a given time step is composed of the offspring of the parasites fromthe previous time step that did not attempt to colonize a new host and survived
the selective pressure, in addition to the parasites that successfully colonized
this given host. A complete description of model is given in Chapter I.
1.00

Fitness (survival probability)

Fitness (survival probability)

1.00

Colonization

4

8

Phenotype12

Speciation

0.75 1.2

1.2

0.50

0.25

0.00
16

0.75 1.2

1.2

0.50

0.25

0.00

4

8

Ph

t

12

16

4

8

Phenotype12

Figure 2 - Changes in the amplitude of the parasite phenotypic distribution(shaded area) after colonization of a new host (expansion to the fitness peak on
the right), and after speciation (population splits in two).

Using this model, we performed simulations crossing parameter values,
which resulted in 135 parameter combinations. Each combination was iterated for 1000 generations and replicated three times. At the end of each simulation, we recorded the total number of hosts used by all parasites (hostrange), the difference between maximum and minimum parasite phenotypes(phenotypic amplitude), and the number of isolated reproductive units (species richness). Then we estimated the effect of each model variable on parasite species richness using a Poisson regression, and on phenotypic amplitudeand host range using Gaussian regressions. Finally, to estimate the relationships between model outcomes, we used partial correlations between species
richness, phenotypic amplitude, and host range.
We found two main effects of host use on the evolution of parasite lineages. First, the colonization of a novel host allowed parasites to explore newareas of the resource space, increasing phenotypic and genotypic variation.
Second, hosts produced heterogeneity in the parasite fitness landscape, which
led to reproductive isolation and therefore, speciation. Parasite species diversity was maximized when hosts were at an intermediate distance in resource
space, balancing the probabilities of colonization and divergent selection.
Colonization happens quickly when hosts are very similar but divergent selection is stronger when hosts are distant.
In order to validate the results from our simulation study, we analyzed
empirical data from butterfly-plant interactions. We used a metric of ordinated diet breadth (ODB) as a measure of phenotypic amplitude. This method
uses information on how often different host plant taxa are utilized by the1216same butterfly taxon as a proxy for resource similarity. We calculated ODB
for the repertoire of host plant orders utilized by 43 butterfly tribes of Nymphalidae butterflies. We also calculated Faith’s phylogenetic diversity ofplant orders used by each tribe (phylogenetic host range) and the taxonomic
host range as the number of orders used by each tribe. We then used phylogenetic path analysis to assess the direct and indirect effects of these variables
on species richness (number of species in the tribe).
a) Simulated data sets

Recognitionb) Empirical data set

Birth rate

Peak interval

Mutation rate

Taxonomic HR

0.17

0.68

0.95

0.15

Host range

0.22

0.8

0.33

Phylogenetic HR

0.60

Adjusted ODB
0.58

Phenotypic amplitude

Species richness

Species richness

0.61

Figure 3 - Predictors of parasite species in simulated and empirical data sets. a)
Relationships between model parameters and model outcomes. Arrows represent
the effect of parameters on outcomes and associated numbers show the devianceexplained by each parameter. Grey arrows show positive effects and the red arrowshows a negative effect. Lines connecting model outcomes indicate partial correlations between the three variables. b) Summary of phylogenetic path analysis forbutterfly-plant interactions showing that the effect of taxonomic host range onparasite species diversity is mediated by host diversity (heterogeneity) in Nymphalidae tribes. Arrow thickness is scaled approximately with the standardized
path coefficients, which are shown for each path.

On theoretical and empirical grounds, this study highlights the importanceof the differentiation between host range and host diversity, with the latter
having the main direct effect on diversification (Fig 3). Host range expansions lead to diversification, as long as they increase heterogeneity in the resource space, and consequently, in the fitness landscape. We see this as animportant step forward in our understanding of diversity patterns generated
by host-associated diversification.
In Chapter I we show that host use dynamics can drive diversification evenin sympatry. In Chapter II we also investigate host-associated diversification,
but focusing on the patterns of interaction generated by different processes.

13

PREDICTING PATTERNS OF INTERACTION
Evolutionary ecologists have long been interested in the causes and consequences of host shifts (Ehrlich and Raven 1964; Janz 2011). The various
hypotheses of how colonization of new hosts leads to diversification can beplaced along two main axes: (i) the relative prevalence of complete host shifts
vs. expansion of the number of hosts, and (ii) the relative importance of key
innovations vs. existing abilities (standing genetic variation and phenotypic
plasticity) for colonization of new hosts. In Chapter II we compared two alternative extremes among these hypotheses, the adaptive radiation scenario
and the variability scenario, which are also the two most prominent explanations for how changes in host use affect net diversification rates.
The adaptive radiation scenario hypothesizes that herbivorous insects
quickly radiate into many species following a shift from an old to a novel
plant taxon, by overcoming their defenses against herbivory. Therefore, hostshifting (i.e., complete change in host use) is considered the main driver ofdiversification (Ehrlich and Raven 1964; Fordyce 2010). In contrast, the variability scenario predicts that diversification is maximized in insect taxa withthe ability to use a wide range of potential hosts. The existence of such potential hosts – remnants of past host colonizations – makes host repertoires
unstable over evolutionary time, since insects can mix and match between
them relatively easily. The resulting oscillations in host range increase thechance of population fragmentation and thereby speciation, via both adaptive
and neutral processes (Janz and Nylin 2008).
We first investigated whether signs of the above-mentioned scenarios ofdiversification can be seen in extant networks of interaction, by translating
their predictions into network properties (Fig. 4). Radiations of herbivorous
insects on new host plants should result in a modular network. Modularity
emerges when the network contains recognizable subsets of taxa that interact
more with each other than with other taxa in the network. Each radiation in a
new host plant taxon should create a new module composed of closely related
plants and insects, which descend from the ancestor that made the host shift.
Alternatively, variability in host use should produce a nested network. Nestedness emerges if there is a specialist-generalist gradient in both trophic levels and the interacting assemblage of a taxon is a subset of the interacting
assemblages of taxa with more interactions. According to the variability scenario, temporal variation in host range produces a specialist-generalist

14

gradient at any point in time, with ancestral hosts being used by both specialist and generalist insects, while novel/uncommon hosts are only used by generalists.

V8

V1

Host 3V7

AncestralV2

V9

V3

V2

V4

Host 2V1

V5

V3

V6

V6

V7

Host 1V4

Recent V8V5

V9Specialist

Clade 1 Clade 2 Clade 3

Generalist

Figure 4 - Network structure predicted by the adaptive radiation (left) and thevariability (right) scenarios. Both networks have insects in columns and hosts inrows. Black cells indicate insect-host interaction.

To validate our verbal arguments, we simulated insect diversification as
taking place according to either the radiation or the variability scenario andthen measured modularity and nestedness in the resulting networks. As expected, radiations produced modular networks while variability in host use
produced nested networks.
The next step was to quantify the contribution of each scenario to empirical systems. We chose two butterfly families that were used as exemplars ofeach scenario: Pieridae, which is associated with adaptive radiations, andNymphalidae, which is associated with the variability scenario. The Nymphalidae-plant network included 566 interactions between 295 Nymphalidae
genera and 43 host-plant orders, and the Pieridae-plant network included 126
interactions between 67 Pieridae genera and 34 host-plant families. Despite
the general acceptance that coevolution with host plants resulted in diversification in Nymphalidae and Pieridae in different ways, we found that the network structures of the two families are very similar. Both networks are nested
and modular (Fig. 5), which indicates that the diversification of both families
was influenced by both scenarios. By comparing the structure of the empirical networks to that of simulated networks, we found that the high levels ofnestedness found in Pieridae and Nymphalidae suggest that the variability
scenario played an important role on the diversification of both butterfly families. The modularity levels, however, could have emerged from radiations or15

simply from phylogenetic conservatism in host repertoire, especially in Pieridae, where modularity is low.

c)

M1

d)

M5

M3

M5

M8

M8

M
M9

PierisMeleteHypsochila
Tatochila
Ascia
Mylothris
AporiaPereutePontia
Leptophobia
AppiasElphinstonia
Hesperochar
Nepheronia
EuremaAphrissa
Colias
Leptosia
DeliasArchonias
Catasticta
Belenois
PhuliaEuchloe
ColotisNathalis
Pyrisitia
Gandaca
Pseudoponti
Elodina
Charonias
Neophasia
Prioneris
CeporaDixeiaTalbotia
Pieriballia
Itaballia
Perrhybris
GanyraPierphulia
Theochila
Anthocharis
ZegrisMathania
Cunizza
Eroessa
Hebomoia
EroniaIxias
Teracolus
Pinacopteryx
Pareronia
Kricogonia
Teriocolias
Leucidia
DercasGonepteryx
PhoebisAnteosZereneCatopsilia
Pseudopieris
Enantia
Dismorphia
Lieinix
Leptidea

Charaxes
AcraeaNeptisActinote
PolyuraVanessa
AdelphaEuphydryas
Junonia
Hypolimnas
Boloria
Euptoieta
DanausTaenaris
MorphoMelitaea
AthymaLimenitis
FaunisCaligoPolygonia
Euryphura
Bebearia
Phalanta
EuploeaElymnias
Palla
Archaeoprepona
Chlosyne
PrecisDophlaEuphaedra
Issoria
Lycorea
Opsiphanes
Brassolis
CissiaMemphisSevenia
Phyciodes
Doleschallia
Nymphalis
Lasippa
Pantoporia
Tanaecia
Aterica
Euriphene
Pseudacraea
Argynnis
Brenthis
Libythea
Xanthotaenia
AemonaStichophthalma
Discophora
CaeroisDasyophthalma
Euptychia
Mycalesis
Bicyclus
EuxantheAgrias
Prepona
Mesoxantha
Diaethria
EunicaMimathyma
Anthanassa
EresiaYoma
Hypanartia
Antanartia
Lebadea
Pseudoneptis
ModuzaCymothoe
Cupha
Vagrans
Cethosia
IdeopsisParantica
AmaurisTellervo
Libytheana
Pierella
Gnophodes
Melanitis
Manataria
Aeropetes
Tarsocera
Torynesis
Dira
Paralethe
NeorinaPenthema
ZetheraThauria
Thaumantis
Amathusia
Zeuxidia
Antirrhea
Bia
Dynastor
Catoblepia
Opoptera
NaropeProterebia
ChazaraPseudochazara
Satyrus
Brintesia
Arethusana
Neominois
OeneisBerberia
Hipparchia
ErebiaCercyonis
Hyponephele
ManiolaPyronia
Aphantopus
Melanargia
StygionymphaCassionympha
Pseudonympha
Neocoenyra
YpthimaYpthimomorpha
Cyllopsis
Hermeuptychia
Chloreuptychia
Amphidecta
Taygetis
Forsterinaria
Magneuptychia
Neonympha
Pareuptychia
Taydebis
Palaeonympha
MegistoYphthimoides
Kirinia
Lopinga
Pararge
Lasiommata
EnodiaSatyrodes
Lethe
Neope
Ragadia
EritesOrsotriaena
Argyronympha
Dodonidia
Percnodaimon
Argyrophenga
Heteronympha
Oreixenica
Argynnina
Geitoneura
TisiphoneNesoxenica
Hypocysta
Lamprolenis
Oressinoma
Coenonympha
Prothoe
Hypna
Siderone
ZaretisConsulFountainea
Anaea
Calinaga
Pseudergolis
Stibochiona
Dichorragia
Amnosia
BiblisAriadneArchimestra
MestraEurytela
BybliaNeptidopsis
Dynamine
Lucinia
Haematera
Callicore
Epiphile
Nica
TemenisPyrrhogyra
Catonephele
Nessaea
Myscelia
Hamadryas
EctimaPanacea
Batesia
Timelaea
Asterocampa
Chitoria
Eulaceura
Doxocopa
Euripus
HestinaApaturaSephisa
Poladryas
Antillea
Ortilia
Castilia
TegosaAtlantea
Catacroptera
KallimaKallimoides
SalamisProtogoniomorpha
Siproeta
Anartia
Rhinopalpa
AglaisKaniska
Symbrenthia
Mynes
Araschnia
SmyrnaColobura
Tigridia
Historis
PycinaMarpesia
Cyrestis
Chersonesia
Parthenos
Lexias
EupteraHamanumida
Harma
Pardopsis
YrameaTerinos
Lachnoptera
Cirrochroa
VindulaEueides
Heliconius
Agraulis
Dione
Philaethria
Dryas
Dryadula
Idea
Tirumala
AnetiaThyridia
Scada
Forbestra
Mechanitis
Aeria
Elzunia
Tithorea
Methona
Episcada
Ceratinia
Pteronymia
Hyalenna
Dircenna
Callithomia
Godyris
Greta
Pseudoscada
Heterosais
Brevioleria
Mcclungia
Hypoleria
Ithomia
PagyrisPlacidina
Hyalyris
Hypothyris
Napeogenes
Epityches
Megoleria
OleriaHyposcadaPatricia
AthesisAthyrtis
Melinaea
Paititia
Olyras

16

M
M7

M
M9

M
M6

M
M7

M
M4

M
M6

M
M2

M
M4

M
M1

M
M3

Talbotia
Pieris
Ascia
Pontia
Leptophobia
Elphinstonia
PhuliaEuchloe
Pierphulia
Theochila
Anthocharis
ZegrisMeleteMylothris
PereuteHesperocharis
DeliasArchonias
Catasticta
Pseudopontia
Charonias
Mathania
Cunizza
EuremaAphrissa
Pyrisitia
Teriocolias
Leucidia
DercasPhoebisAnteosZereneCatopsilia
Pseudopieris
Enantia
Dismorphia
Lieinix
Leptidea
Neophasia
Kricogonia
AppiasNepheronia
Belenois
ColotisElodina
Prioneris
CeporaDixeiaPieriballia
Itaballia
Perrhybris
GanyraHebomoia
EroniaIxias
Teracolus
Pinacopteryx
Pareronia
Hypsochila
Tatochila
Nathalis
Eroessa
Leptosia
Gandaca
Gonepteryx
AporiaColiasGnophodes
Melanitis
Manataria
Aeropetes
Tarsocera
Torynesis
Dira
Paralethe
NeorinaPenthema
ZetheraDynastor
Opoptera
NaropeProterebia
ChazaraPseudochazara
Satyrus
Brintesia
Arethusana
Neominois
OeneisBerberia
Hipparchia
ErebiaCercyonis
Hyponephele
ManiolaPyronia
Aphantopus
Melanargia
StygionymphaCassionympha
Pseudonympha
Neocoenyra
YpthimaYpthimomorpha
Cyllopsis
Hermeuptychia
Chloreuptychia
Amphidecta
Taygetis
Forsterinaria
Magneuptychia
Neonympha
Pareuptychia
Taydebis
Palaeonympha
MegistoYphthimoides
Kirinia
Lopinga
Pararge
Lasiommata
EnodiaSatyrodes
Lethe
Neope
EritesOrsotriaena
Argyronympha
Dodonidia
Percnodaimon
Argyrophenga
Heteronympha
Oreixenica
Argynnina
Geitoneura
TisiphoneNesoxenica
Hypocysta
Lamprolenis
Oressinoma
Coenonympha
Charaxes
Euryphura
Palla
Archaeoprepona
Euphaedra
Memphis
Doleschallia
Pantoporia
Libythea
EuxanthePreponaDiaethria
EunicaCupha
ProthoeDichorragia
Haematera
Callicore
Epiphile
Nica
TemenisPyrrhogyra
Euptychia
Ragadia
AcraeaActinote
Vanessa
Euphydryas
J unonia
Hypolimnas
Boloria
Euptoieta
DanausMorphoMelitaea
Limenitis
Chlosyne
PrecisIssoria
Sevenia
Phyciodes
Yoma
Poladryas
Antillea
Ortilia
Castilia
TegosaAtlantea
Catacroptera
KallimaKallimoides
Protogoniomorpha
Siproeta
Anartia
Megoleria
HyposcadaAthymaPhalanta
Euploea
Aterica
Hypanartia
LebadeaModuzaCethosia
IdeopsisParantica
AmaurisTellervo
ConsulLucinia
Idea
Tirumala
AnetiaAeria
Elzunia
Tithorea
NeptisPolyuraAdelphaPolygonia
DophlaNymphalis
Tanaecia
Euriphene
Pseudacraea
AgriasMesoxantha
Mimathyma
Sephisa
EupteraHamanumida
Anthanassa
Cymothoe
Vagrans
Hypna
Siderone
ZaretisFountainea
Anaea
BiblisAriadneArchimestra
MestraEurytela
BybliaNeptidopsis
Dynamine
Catonephele
Nessaea
Myscelia
Hamadryas
EctimaPanacea
Batesia
ApaturaParthenos
LexiasHarma
Pardopsis
YrameaTerinos
Lachnoptera
Cirrochroa
VindulaEueides
Heliconius
Agraulis
Dione
Philaethria
Dryas
Dryadula
Lycorea
Lasippa
Argynnis
Brenthis
EresiaAntanartia
Pseudoneptis
Libytheana
Calinaga
Pseudergolis
Stibochiona
Amnosia
Timelaea
Asterocampa
Chitoria
Eulaceura
Doxocopa
Euripus
Hestina
SalamisRhinopalpa
AglaisSymbrenthia
Mynes
Araschnia
SmyrnaColobura
Tigridia
Historis
PycinaMarpesia
Cyrestis
Chersonesia
Taenaris
FaunisCaligoBebearia
Elymnias
Opsiphanes
Brassolis
CissiaXanthotaenia
AemonaStichophthalma
Discophora
CaeroisDasyophthalma
Mycalesis
Bicyclus
Pierella
Thauria
Thaumantis
Amathusia
Zeuxidia
Antirrhea
Bia
Catoblepia
KaniskaThyridia
Scada
Forbestra
Mechanitis
Methona
Episcada
Ceratinia
Pteronymia
Hyalenna
Dircenna
Callithomia
Godyris
Greta
Pseudoscada
Heterosais
Brevioleria
Mcclungia
Hypoleria
Ithomia
PagyrisPlacidina
Hyalyris
Hypothyris
Napeogenes
Epityches
OleriaPatricia
AthesisAthyrtis
Melinaea
Paititia
OlyrasM0

M
M2

M
Module

Modu
M

M

M
N

Poales
Malpighiales
Rosales
Solanales
LamialesGentianales
Sapindales
ArecalesFabales
EricalesMalvalesZingiberales
Asterales
MyrtalesFagales
Caryophyllales
Piperales
Ranunculales
Dipsacales
Saxifragales
LauralesBoraginales
Magnoliales
LilialesBrassicales
Asparagales
Pandanales
Oxalidales
Celastrales
Vitales
Selaginellales
Cycadales
Cucurbitales
Commelinales
Santalales
Proteales
Icacinales
HypnalesGeraniales
Dioscoreales
Dilleniales
Aquifoliales
Apiales

M
M0

Modu
M

M
N

PoalesSapindales
LauralesMagnoliales
Santalales
Proteales
Geraniales
Apiales
Selaginellales
HypnalesLamialesFabalesMalvalesAsterales
Caryophyllales
Ranunculales
Dipsacales
Saxifragales
Boraginales
Vitales
Cucurbitales
Commelinales
Dioscoreales
Gentianales
Piperales
Celastrales
Aquifoliales
EricalesMyrtalesFagales
Oxalidales
Icacinales
Malpighiales
Dilleniales
Rosales
Brassicales
ArecalesZingiberales
LilialesAsparagales
Pandanales
Cycadales
Solanales

Capparaceae
Fabaceae
Brassicaceae
Loranthaceae
Santalaceae
Tropaeolaceae
Cleomaceae
Resedaceae
Asteraceae
Rhamnaceae
Euphorbiaceae
Sapindaceae
Olacaceae
Ericaceae
Bignoniaceae
Annonaceae
Zygophyllaceae
Verbenaceae
Solanaceae
Simaroubaceae
Salicaceae
Rosaceae
Rhizophoraceae
Polygonaceae
Pinaceae
Hypericaceae
Elaegnaceae
Celastraceae
Caryophyllaceae
Boraginaceae
Berberidaceae
Bataceae
Amaranthaceae
Akaniaceaeb)M
ModuleAkaniaceae
Brassicaceae
Tropaeolaceae
Cleomaceae
Resedaceae
Bataceae
Amaranthaceae
Loranthaceae
Santalaceae
Olacaceae
Verbenaceae
Polygonaceae
Boraginaceae
Fabaceae
Sapindaceae
Bignoniaceae
Simaroubaceae
Hypericaceae
Pinaceae
Zygophyllaceae
Capparaceae
Euphorbiaceae
Rhizophoraceae
Celastraceae
Asteraceae
Solanaceae
Caryophyllaceae
Rhamnaceae
Annonaceae
Ericaceae
Salicaceae
Rosaceae
Elaegnaceae
Berberidaceaea)Figure 5 - Structure of butterfly-plant networks. (a-b) Nymphalidae genera incolumns and host plant orders in rows. (c-d) Pieridae genera in columns and hostplant families in rows. Colored cells are butterfly-plant interactions. Each color
shows interactions within a module, and grey cells are interactions between modules. (a,c) Rows and columns sorted to emphasize modular affinity (order ofmodules is arbitrary). (b,d) Rows and columns sorted to emphasize nestedness,ordered from the upper right corner according to descending number of interactions.

We then performed additional analyzes in order to assess the contributionsof each scenario. First, we estimated the effect of each host plant on network
structure and found that, as predicted, ancestral hosts produce network nestedness while novel hosts produce modularity. Finally, we measured the phylogenetic diversity within each module. In both networks, most modules werecomposed of closely related butterflies and distantly related plants.
Combining all results from Chapter II, we found that the modules in thestudied networks are formed by grouping closely related butterflies that use
a main host taxon (module hub). Several modules also include a number ofother distantly related hosts that are used by a subset of the butterflies in themodule, producing nestedness within modules. Hosts with a long coevolutionary history with the butterflies connect the various modules, producing
overall network nestedness. These results led us to argue that the variability
and radiation scenarios can be reconciled into a unified view of evolution of
butterfly-plant interactions in which the continuous probing of new hosts allows both ongoing diversification through variability in host use and episodic
radiations on new hosts.

INFERENCE OF HOST REPERTOIRE EVOLUTION
While Chapters I and II provide new insights into host-associated diversification using different approaches, the explicit modeling of host repertoire
evolution has been hindered by methodological and computational constraints. Recent developments in phylogenetic Bayesian inference of evolution of discrete traits (Landis et al. 2013) considerably reduced these constraints, allowing the development of models of host repertoire evolution thatcan deal with the inherent complexity of the trait.
Inspired by the approach proposed by (Landis et al. 2013) for biogeographic inference, in Chapter III we describe a model of host repertoire evolution where parasite lineages can gain and lose hosts over time. In addition
to allowing parasites to use a large number of host taxa simultaneously, thismethod also allows the modeling of host colonization as a two-step process,where first the parasite gains the ability to use a host and then starts to use it
in nature. We implemented this feature by adding an intermediate state to themodel, such that each host could assume one of three states for each parasite:
0 (non-host), 1 (potential host) or 2 (actual host). Thus, we could reconstruct
not only realized host repertoires (composed by only actual hosts), but also
fundamental host repertoires (composed of both potential and actual hosts).
Another important feature of the model is the estimation of the effect of host
phylogenetic relatedness on the likelihood of colonizing new hosts.
The model is implemented in RevBayes (Höhna et al. 2016), allowing us
to perform simulation as well as Bayesian Markov chain Monte Carlo(MCMC) inference under the model. In Chapter III, we explored the statistical behavior of our model by simulating evolution of host-parasite interactions under a range of parameters. Overall, we were able to infer the true
parameter values regardless of the level of phylogenetic conservatism in bothparasites and hosts. We also compared the true coevolutionary history of eachsimulation to the corresponding posterior distribution of the sampled character histories. Estimation error was calculated as the sum of squared differences between estimated and true coevolutionary histories. Accuracy in theestimation of coevolutionary history was highest when the degree of phylogenetic conservatism on both butterflies and plants was high. Overall, error
was higher on the estimation of actual hosts (state 2) than potential hosts(state 1), but both were within acceptable levels.

17

We then demonstrated the empirical application of our approach with a
Bayesian inference of the coevolutionary history between 34 Nymphalini
butterflies and 25 angiosperm families (Fig 6). We estimated the rate of hostrepertoire evolution along the Nymphalini tree as being between 0.53 and1.22 events per million years. Bayes factors favored the independence model,
where the probability of gaining a given hosts is not affected by the phylogenetic distance between hosts.
Polygonia gracilis
Polygonia_gracilis
Polygonia zephyrus
Polygonia_zephyrus
Polygonia progne
Polygonia_progne
Polygonia oreas
Polygonia_oreas
Polygonia comma
Polygonia_comma
Polygonia
interrogationis
Polygonia_interrogationis
Polygonia satyrus
Polygonia_satyrus
Polygonia c album
Polygonia_c_album
Polygonia faunus
Polygonia_faunus
Polygonia egea
Polygonia_egea
Polygonia c aureum
Polygonia_c_aureum
Nymphalis antiopa
Nymphalis_antiopa
Nymphalis xanthomelas
Nymphalis_xanthomelas
Nymphalis californica
Nymphalis_californica
Nymphalis polychloros
Nymphalis_polychloros
Nymphalis l album
Nymphalis_l_album
Kaniska_canace
Kaniska canace
Aglais urticae
Aglais_urticae
Aglais milberti
Aglais_milberti
Aglais io
Aglais_io
Antanartia_delius
Antanartia delius
Vanessa_indica
Vanessa indica
Vanessa_tameamea
Vanessa tameamea
Vanessa_atalanta
Vanessa atalanta
Vanessa abyssinica
Vanessa_abyssinica
Vanessa virginiensis
Vanessa_virginiensis
Vanessa_cardui
Vanessa cardui
Vanessa_kershawi
Vanessa kershawi
Vanessa dimorphica
Vanessa_dimorphica
Vanessa hippomene
Vanessa_hippomene
Vanessa_annabella
Vanessa annabella
Vanessa_itea
Vanessa itea
Hypanartia_bella
Hypanartia bella
Hypanartia_lethe
Hypanartia lethe
Ericaceae

Asteraceae

Boraginaceae

Malvaceae

Grossulariaceae

Urticaceae

Salicaceae

Betulaceae

Cannabaceae

Fabaceae

Ulmaceae

Rosaceae

Ceratophyllaceae

AraceaeAcoraceae

Typhaceae

Aponogetonaceae

Chloranthaceae

Lauraceae

Piperaceae

Winteraceae

Trimeniaceae

Myristicaceae

Hydatellaceae

Amborellaceae

Boraginaceae

Asteraceae

Ericaceae

Grossulariaceae

Malvaceae

Salicaceae

Betulaceae

Urticaceae

Cannabaceae

Ulmaceae

Rosaceae

Fabaceae

Ceratophyllaceae

Acoraceae

Aponogetonaceae

AraceaeTyphaceae

Chloranthaceae

Winteraceae

Piperaceae

Lauraceae

Myristicaceae

Trimeniaceae

Hydatellaceae

Amborellaceae

Figure 6 - Data set of interactions between Nymphalini butterflies (rows) and angiosperm host plants (columns). Black cells show actual hosts (interaction recorded in nature) and grey cells show potential hosts (larvae were able to feed onhost during establishment tests).

Gain and loss rate estimates were not symmetric, and that also varied between states. The gain of the ability to use a host was estimated to be veryrare (0.25% to 1% of overall rate), whereas loss was common (34% to 78%
of overall rate). Transition rates between states 1 and 2 were more symmetricand gain was more common than loss (1®2 between 17% and 54%; 2®1
between 3% and 15% of overall rate). These results support the idea that recolonization of hosts that were used by ancestors of extant butterflies might
be more common than independent colonizations of the same host taxon byphylogenetically widespread butterflies (Janz et al. 2001).

18

As the approach described in Chapter III was able to accurately infer coevolutionary histories in data sets simulated under a range of parameter combinations, we used it to cross-validate the network approach used in Chapter
II and test the main conclusions from that study. For that, we reconstructed
the historical interactions between Pieridae butterflies and their host plants(same data set as used in Chapter II). We modeled host repertoire evolution
along a phylogenetic tree containing 66 genera of Pieridae. For the host tree,
we pruned the phylogenetic tree for angiosperm families by keeping the 33
families known to be hosts of pierid butterflies and then collapsing the remaining branches to more ancestral nodes until only 50 terminal branches
were left. Host repertoires were, therefore, composed of 50 hosts. By doing
this pruning, we ensured that all angiosperm lineages were represented in theanalysis, at the same time as keeping the number of hosts within the limits ofcomputational tractability.
One of the main ideas we tested was that the evolution of butterfly-plant
networks is mainly driven by the probing of potential hosts combined withphylogenetic conservatism in host-use abilities. We estimated the rate of repertoire evolution along the Pieridae tree as being between 0.055 and 0.11
events per million years, which is ten times slower than the estimated rate for
Nymphalini butterflies. The asymmetry between transition rates, however,was very similar to Nymphalini. The gain 0®1 was estimated to be very rare
(0.6% of overall rate), whereas the loss 1®0 was common (66%). Conversely, transition between states 1 and 2 were similar (14% and 19% of overall rate), which means that recolonization of a host that was used in the pastis easier than the colonization of a completely new host. Together, these results provide the basis for the observed phylogenetic conservatism in the hostrepertoires of pierid butterflies.
We then reconstructed ancestral networks at two time slices during thediversification of Pieridae (Fig. 7). We selected seven internal nodes in the
Pieridae tree between 60 and 50 Mya to reconstruct the first ancestral network. For the second ancestral network, we selected 16 nodes between 40
and 20 Mya. The two ancestral networks were composed of interactions between each selected node and the hosts that were in states 1 or 2 in at least
95% of the sampled histories. This means that only interactions with morethan 0.95 posterior probability were included. Figure 8 shows the posterior
probability of every possible interaction in the ancestral network at 40-20
Mya.

19

ModuleCapparaceae

Fabaceae

Loranthaceae

Brassicaceae

Actual hostPotential host60-50 Mya40-20 Mya

Present

Figure 7 - Two ancestral networks and extant network of interactions between
Pieridae and Angiosperms. Butterflies are represented by circles and plants bysquares. Colors show network modules.

Figure 8 - Posterior probabilities for interactions between ancestors of extant pierid butterflies (internal nodes) and angiosperm families at 40-20 Mya. Squares arecolored according to the module that the host belongs to in the extant network (seeFigure 5c). Black dots mark the interactions with actual hosts (state 2). Opacity
indicates interaction probability. Note that almost all interactions have been sampled at least once.

In Chapter II we suggest that the evolution of butterfly-plant interactions
goes through three phases of change in network structure. The reconstructed
evolutionary history of interactions between Pieridae and their host plants
presented in Chapter IV exemplifies each one of them. The first ancestral
network (60-50 Mya) shows the origin of a new module (green) when early

20

Pierinae switched to Capparaceae. At least until 50 Mya, those butterflies
were specialized on the new host group, resulting in a network composed of
two independent modules of closely related butterflies. Thus, this network
exemplifies the first phase in the evolution of butterfly-plant interactions.
In the second ancestral network (40-20 Mya), Pierinae butterflies started
to expand their host repertoires, adding new potential and actual hosts. This
expansion promoted the formation of new modules (pink and purple), but
the retention of ancestral hosts (in this case, Capparaceae) kept these modules connected. Thus, this network exemplifies the second phase, whenprobing of new hosts increases network connectance. Interestingly, thisnetwork represents the origin of the four main modules in the extant network, which are formed by butterflies associated to the module hubs identified in Chapter II (Fig. 9).

Figure 9 - Relative contribution of each taxon to the modular structure, definedby measures of connectivity with taxa within the same module (y-axis) and withtaxa assigned to other modules (x-axis) of the network. Names of modules andnetwork hubs are shown. Colors of modules are consistent with Figs. 7 and 8.

Finally, the third phase, which is characterized by network nestedness, is
exemplified by the network at present time, where the number of both butterflies and hosts increased in each module, as well as the number of connections between modules. The colonization of new hosts combined withthe recolonization of ancestral hosts (mainly Fabaceae and Capparaceae)
resulted in the observed nestedness in the extant network.

21

CONCLUSIONS
Understanding how ecological interactions change is crucial to explain
phenomena at various timescales, such as the emergence of infectious diseases, community assembly, and parasite diversification (Hoberg and Brooks2015). By analyzing the same data set with completely independent approaches (Chapters II and IV), we validated the use of network analysis totest hypotheses about the evolution of ecological interactions and provided
direct support for the unification proposed in Chapter II to explain the diversification of herbivorous insects.
The approach we describe in Chapter III was designed to reconstruct andquantify changes in host-parasite associations by modeling the process ofgaining and losing hosts. Thus, it allowed us to explicitly model features thatare intrinsic to the system, such as the existence of potential hosts and theeffect of host phylogenetic relatedness on the colonization of new hosts. In
this thesis, this approach was used to investigate host-associated diversification in butterflies, but it has potential to address various questions in evolutionary ecology. The implementation of the model in RevBayes facilitates
future development to incorporate more of the complexity in species interactions.
As a whole, this thesis contains strong evidence for the role of host range
expansions on the evolution of butterfly-plant interactions and on butterfly
diversification. Even though closely related butterflies tend to use similar repertoires of host plants, that does not prevent the probing of new hosts. Part ofthe observed phylogenetic conservatism in host repertoires comes from theretention of the ability to use hosts used in the past, which facilitates recolonization. Such ancestral abilities favors changes in host repertoires, which
increase the diversity of hosts used by a clade. The resulting host diversity,
in turn, promotes butterfly diversification.

22

SVENSK SAMMANFATTNING
Alla växtätande insekter är i någon mån specialiserade på sina värdväxter, men specialiseringens omfattning är olika beroende på art. Samevolution mellan insekter (parasiter) och växter (värdorganismer) åberopas ofta som förklaringen till den stora artmångfald som uppvisas bland insekter, men det råder delade meningar om hur passstor inverkan specialisering faktiskt har på artbildning. Trots att ämnet har utforskats
under lång tid förbättras fortfarande vår förståelse av evolutionen bakom samspelet
mellan värdväxter och parasiter, och nyare teoribildning understryker vikten av breddning (att kunna tillgodogöra sig nya värdväxter) för artbildning hos parasiter. I den
här avhandlingen använder vi oss av olika perspektiv för att i detalj undersöka ursprunget till makroevolutionära mönster mellan värdväxtutnyttjande och artbildning
hos dagfjärilar.
Kapitel I avhandlar, genom datorsimuleringar, mekanistiskt de processer som leder fram till de makroevolutionära mönstren. Genom att åtskilja effekten av antal ätbara värdväxter hos en grupp fjärilar från hur olika värdväxterna är sinsemellan kundevi visa att olikhet, snarare än antal i sig, ligger bakom artmångfald hos parasiter, bådei simulerade och verkliga system.
I kapitel II kombinerade vi nätverks- och släktskapsanalyser för att jämföra stödet
för de två olika drivkrafter som föreslagits som huvudorsaker till artbildning inom
växtätande insekter. Utifrån två dagfjärilsfamiljer, Nymphalidae och Pieridae, drog vi
slutsatsen att bredd i växtutnyttjande är avgörande för artbildning. Men det visade sigockså att när insekter klarar att tillgodogöra sig en ny värdväxt så kan det ge upphov
till snabb artbildning, även om det är förhållandevis ovanligt att det inträffar. Vi kundepå så vis förena de två olika föreslagna drivkrafterna och visa att artbildning kopplad
till växtutnyttjande gynnas både av koloniseringar av nya värdväxter och av ett kontinuerligt utforskande av de växter som använts historiskt, och som de alltså inte helt
förlorat förmågan att äta.
Trots att nätverksanalys är en utmärkt metod för att kartlägga mönster i samspel
mellan arter så behövs det andra metoder för att uttryckligen testa vilka mekanismer
som ger upphov till mönstren ifråga. I kapitel III utvecklade vi därför en evolutionär
modell för värdväxtutnyttjande som vi kunde analysera med hjälp av bayesianska metoder. Tillvägagångssättet utvärderades med både simulerade och empiriska data.
I kapitel IV använde vi oss slutligen av metoden ifrån kapitel III för att uttryckligen testa förutsägelserna om evolution av fjäril-växt-nätverken i kapitel II. Vi hittade
stöd för att den viktigaste drivkraften är ett kontinuerligt utforskande av den repertoar
av potentiella värdväxter som de ärvt från sina förfäder. Därmed kunde vi, med hjälp
av tre olika metoder, sluta oss till att skillnader i värdväxtutnyttjande som uppstått
under evolutionära tidsrymder, är avgörande för artbildning bland fjärilar.

23

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25

ACKNOWLEDGEMENTS
It has been more than eight years since I stepped into Walter Boeger’s lab atFederal University of Paraná in Brazil and discovered the world of host-parasite interactions. In the course of these years I was lucky enough to workwith amazing people, from whom I learned about everything I know today.
Some of the ideas in this thesis actually come from spontaneous meetings
with Walter, when I would just go to him with my head full of questions andwe would talk (and draw!) until we found some resolution. I really don’tknow what I would be doing today if I hadn’t worked with you, Walter.
Thank you so much for everything!
Even though I had a great supervisor, I wanted to go abroad for my PhD.
Choosing to move to Stockholm to work with Niklas and Sören was the easiest and best decision I made. From the first emails, I knew I was in good
hands. And over these four years, my admiration for you two just increased.
Thank you for the freedom you gave me to try new things, the trust you always had on me, and the constant support during these intense years. I
couldn’t ask for better supervisors.
I was also very lucky to have Fredrik as a co-supervisor. I learned so muchfrom you! Thank you for finding the time for me, and for your enthusiasm
with every progress we made. It was also great to meet all the board-gamelovers at Naturhistoriska riksmuseet and be part of the “Fredrikers”.
There are many current and former members of Zootis that I wish to thank.
Kalle and Chris, thank you for being a great follow-up group, being my reality-check and making sure that I kept on track. Siw, Minna, Anette and Ulf,
you guys are the best. Thank you so much for making life that much easierfor us. As for the Zootis Party People (specially the original ones), you havebeen my extended family over these four years. This department is truly special, and although I could name the people that have been my closest friends,
I do believe that everyone contributes to the special friendly atmosphere at
Zootis.
One person, however, has been extra special. Thank you, Rasmus, for all
the random conversations along these years about laundry, recycling or public housing programs in Sweden. Thank you for being by my side during thesebusy months. It really made a big difference!
None of this would have been possible without the support of my family.
My parents always encouraged me to go after what I wanted, even if it meant
to be apart from them. It was not easy to move so far away, but Sweden received me really well (even though it was a rainy November). It has been apleasure to live and work in this country. I hope to come back soon!

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LIST OF CHAPTERS
I. Braga, MP, SBL Araujo, S Agosta, D Brooks, E Hoberg, S Nylin, N
Janz and WA Boeger. (2018) Host use dynamics in a heterogeneous fitness landscape generates oscillations in host range and diversification.
Evolution, vol. 72(9), pp. 1773–1783.
II. Braga, MP, PR Guimarães Jr., CW Wheat, S Nylin and N Janz. (2018)
Unifying host-associated diversification processes using butterfly-plant
networks. Nature Communications, 10.1038/s41467–018–07677–x.
III. Braga, MP, M Landis, S Nylin, N Janz, F Ronquist. Bayesian analysis
of host repertoire evolution. - Manuscript.
IV. Braga, MP, M Landis, S Nylin, N Janz, F Ronquist. Evolution of butterfly-plant networks revealed by Bayesian inference of host repertoire. Manuscript.

Candidate contributions to thesis articles*
I

II

III

IV

Conceived the study

Substantial

Substantial

Significant

Substantial

Designed the study

Significant

Substantial

Substantial

Substantial

Collected the data

Substantial

Minor

Substantial

Minor

Analysed the data

Substantial

Substantial

Substantial

Substantial

Manuscript preparation

Substantial

Substantial

Substantial

Substantial* Contribution Explanation
Minor: contributed in some way, but contribution was limited.
Significant: provided a significant contribution to the work.
Substantial: took the lead role and performed the majority of the work.

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