# A phylogenetic study to assess the link between biome specialization and diversification in swallowtail butterflies

Sara Gamboa, Fabien L Condamine, Juan L Cantalapiedra, Sara Varela, Jonathan S Pelegrín, Iris Menéndez, Fernando Blanco, Manuel Hernández Fernández

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## To cite this version:

Sara Gamboa, Fabien L Condamine, Juan L Cantalapiedra, Sara Varela, Jonathan S Pelegrín, et al.. A phylogenetic study to assess the link between biome specialization and diversification in swallowtail butterflies. Global Change Biology, 2022, 28 (20), pp.5901 - 5913. 10.1111/gcb.16344. hal-03869949

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Submitted on 24 Nov 2022

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RESEARCH ARTICLE

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WILEY

# A phylogenetic study to assess the link between biome specialization and diversification in swallowtail butterflies

Sara Gamboa<fnref n="1" />,<fnref n="2" />,<fnref n="3" />,<fnref n="4" /> ID | Fabien L. Condamine<fnref n="4" /> ID | Juan L. Cantalapiedra<fnref n="5" /> ID |

Sara Varela<fnref n="1" /> ID | Jonathan S. Pelegrín<fnref n="6" />,<fnref n="7" /> ID | Iris Menéndez<fnref n="2" />,<fnref n="3" /> ID | Fernando Blanco<fnref n="8" /> ID |

Manuel Hernández Fernández<fnref n="2" />,<fnref n="3" /> ID

Correspondence

Sara Gamboa, Centro de Investigación Mariña (CIM), Universidade de Vigo, Grupo de Ecoloxía e Bioloxía Animal (GEA), MAPAS Lab (L. 24) Edificio CITEXVI, Rua Fonte das Abelleiras, s/n, 36310 Vigo, Pontevedra, Spain.

Email: sara.gamboa@uvigo.es

Abstract

The resource-use hypothesis, proposed by E.S. Vrba, states that habitat fragmentation caused by climatic oscillations would affect particularly biome specialists (species inhabiting only one biome), which might show higher speciation and extinction rates than biome generalists. If true, lineages would accumulate biome-specialist species. This effect would be particularly exacerbated for biomes located at the periphery of the global climatic conditions, namely, biomes that have high/low precipitation and high/low temperature such as rainforest (warm-humid), desert (warm-dry), steppe (cold-dry) and tundra (cold-humid). Here, we test these hypotheses in swallowtail butterflies, a clade with more than 570 species, covering all the continents but Antarctica, and all climatic conditions. Swallowtail butterflies are among the most studied insects, and they are a model group for evolutionary biology and ecology studies. Continental macroecological rules are normally tested using vertebrates, this means that there are fewer examples exploring terrestrial invertebrate patterns at global scale. Here, we compiled a large Geographic Information System database on swallowtail butterflies' distribution maps and used the most complete time-calibrated phylogeny to quantify diversification rates (DRs). In this paper, we aim to answer the following questions: (1) Are there more biome-specialist swallowtail butterflies than biome generalists? (2) Is DR related to biome specialization? (3) If so, do swallowtail butterflies inhabiting extreme biomes show higher DRs? (4) What is the effect of species distribution area? Our results showed that swallowtail family presents a great number of biome specialists which showed substantially higher DRs compared to generalists. We also found that biome specialists are unevenly distributed across biomes. Overall, our results are consistent with the resource-use hypothesis, species climatic niche and biome fragmentation as key factors promoting isolation.

KEYWORDS

bioclimatology, ecological specialization, macroecology, macroevolution, Papilionidae, resource-use, speciation

This is an open access article under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2022 The Authors. *Global Change Biology* published by John Wiley & Sons Ltd.

<footnote n="1">¹Centro de Investigación Mariña (CIM), Universidade de Vigo, Grupo de Ecoloxía Animal (GEA), MAPAS Lab, Vigo, Pontevedra, Spain</footnote>

<footnote n="2">²Departamento de Geodinámica, Estratigrafía y Paleontología, Facultad de Ciencias Geológicas, Universidad Complutense de Madrid, Madrid, Spain</footnote>

<footnote n="3">³Departamento de Cambio Medioambiental, Instituto de Geociencias (UCM, CSIC), Madrid, Spain</footnote>

<footnote n="4">⁴CNRS, UMR 5554 Institut des Sciences de l'Evolution de Montpellier (Université de Montpellier), Montpellier, France</footnote>

<footnote n="5">⁵Departamento de Ciencias de la Vida, Edificio de Ciencias Campus Científico-Tecnológico, Universidad de Alcalá, Alcalá de Henares, Spain</footnote>

<footnote n="6">⁶Área de Biología y Ciencias Ambientales Facultades de Ciencias Básicas y Educación, Universidad Santiago de Cali, Santiago de Cali, Valle del Cauca, Colombia</footnote>

<footnote n="7">⁷Departamento de Biología, Facultad de Ciencias Naturales y Exactas, Universidad del Valle, Campus Meléndez, Santiago de Cali, Valle del Cauca, Colombia</footnote>

<footnote n="8">⁸Museum für Naturkunde, Leibniz-Institut für Evolutions und Biodiversitätsforschung, Berlin, Germany</footnote>
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## Funding information

Agence Nationale de la Recherche, Grant/
Award Number: ANR-10-LABX-25-01;
Alexander von Humboldt-Stiftung, Grant/
Award Number: AOBJ: 637491; Centre
National de la Recherche Scientifique;
Consejería de Educación, Juventud y
Deporte, Comunidad de Madrid, Grant/
Award Number: 2017-P1/AMB-5298;
Deutsche Forschungsgemeinschaft,
Grant/Award Number: LO 2368/1-1;
H2020 European Research Council, Grant/
Award Number: 947921 and 851188;
Marie Curie International Outgoing
Fellow, Grant/Award Number: 627684;
Ministerio de Ciencia e Innovación, Grant/
Award Number: PGC2018-094955-A-100;
Ministerio de Universidades, España,
Grant/Award Number: CT31/21; Next
Generation European Union Programme;
Universidad Complutense de Madrid,
Grant/Award Number: CT27/16-CT28/16;
Universidade de Vigo; Consorcio
Interuniversitario do Sistema Universitario
de Galicia (CISUG)

# 1 | INTRODUCTION

Geographical patterns of species distribution are determined by current climatic conditions and by long-term historical and macroevolutionary processes (Barnosky, 2001; Lomolino et al., 2016; Wiens & Donoghue, 2004). Climate shifts influence the expansion and contraction of biomes and the subsequent establishment or demise of ecological and geographic barriers, conditioning the evolution of life forms worldwide (Scheffers et al., 2016; Tian et al., 2018). Some theories point to these large-scale processes as major forces triggering faunal turnover, in contrast to biotic interactions (Benton, 2009). In this regard, the resource-use hypothesis (Vrba, 1980, 1987) highlights the role of biomes, and their fragmentation-expansion dynamics responding to global climatic changes, as arenas for macroevolutionary processes (i.e. speciation and extinction; see Hernández Fernández & Vrba, 2005). According to this hypothesis, large-scale environmental changes result in biome fragmentation and promote diversification, particularly among biome-specialist species (Maguire & Stigall, 2008). On the contrary, biome-generalist species might be less impacted by global changes.

Furthermore, the resource-use hypothesis predicts that biomes at the extremes of the climatic gradient should be more affected by global climatic changes and the associated fragmentation events (Hernández Fernández & Vrba, 2005; Vrba, 1992). If this is true, there should be an overrepresentation of biome-specialist species as a result of vicariance and speciation processes (Hernández Fernández & Vrba, 2005). In this context, extreme biomes are the ones located at the extreme of the variation of the two major characterizing climatic variables, temperature and aridity. These include the ones that are traditionally considered as 'harsh' such as subtropical desert (warm-arid), steppe (cold-arid) and tundra (cold-humid) but also the evergreen equatorial rainforest, which is

a warm biome located at the extreme of the precipitation variable (Hernández Fernández & Vrba, 2005). The rainforest, although it is usually considered as a very stable climate across time (Pennington et al., 2015; Whitmore, 1998), represents the warm-humid extreme of the Earth's climates and is greatly affected by climatic fluctuations and global aridity phases, which can lead to fragmentation of these forests (Brée et al., 2020; Onstein et al., 2018; Whitmore, 1998; Wüster et al., 2005).

When considering a possible relationship between species biome specialization and diversification rates (DRs), it is important to consider the potential effect of species distribution areas. A relationship between the number of biomes occupied by a species and its distribution area is expected. The broader the range of a species, the more likely its range includes different biomes, while species with a narrower distribution are more likely to evolve as endemic of a single biome. Furthermore, several authors proposed a direct correlation between species age and area, where species would have increasing sizes of their distribution ranges the longer they persisted (Willis, 1922). In general terms, this idea has been rejected, as there are numerous examples of widely distributed young species and old species with very restricted distributions (Gaston, 2003), and most authors consider that range sizes expand relatively rapidly after speciation to, perhaps more gradually, decline as species age (Webb & Gaston, 2000). Nevertheless, other works have proposed that there is a positive correlation between species area and age, and therefore DR, at least among closely related species or genera (Miller, 1997; Taylor & Gotelli, 1994).

The resource-use hypothesis has been previously tested in different vertebrate groups showing a high prevalence of biome specialists (Cantalapiedra et al., 2011; Gómez Cano et al., 2013; Hernández Fernández et al., 2022; Hernández Fernández & Vrba, 2005; Moreno Bofarull et al., 2008; Vrba, 1987) and a relationship between
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bioclimatic specialization and higher speciation rates (Cantalapiedra et al., 2011; Menéndez et al., 2021). However, the large-scale biogeographic patterns of non-vertebrate groups are poorly explored and barely used as models to test macroecological rules.

The globally distributed swallowtail butterflies (family Papilionidae) constitute a species-rich group including 32 genera and more than 570 described species (Scriber et al., 1995; Tyler et al., 1994). Although most species are found in tropical regions, the ecological diversity of Papilionidae also includes some lineages adapted to temperate and even cold environments (Condamine et al., 2012; Condamine, Nabholz, et al., 2018). Moreover, as one of the best known and broadly studied insect groups, swallowtails are recognized as model organisms in evolutionary biology, ecol- ogy, genetics and conservation biology (e.g. Collins & Morris, 1985; Condamine et al., 2012; Scriber et al., 1995). As statistical models and phylogenetic data become more available for studying evolutionary processes (Allio et al., 2021), they can be used to test the relation- ship between DRs and climatic variables across their geographical range (e.g. Gómez-Rodríguez et al., 2015; Kozak & Wiens, 2010).

Here, we tested several predictions of the resource-use hypoth-
esis in swallowtail butterflies using a phylogenetic approach with the
aim to test the universality of the global patterns observed in ver-
tebrates. Swallowtail butterflies, as ectothermic and strictly herbiv-
orous organisms, are expected to directly respond to global climate
changes (Bale et al., 2002; Clusella-Trullas et al., 2011; Kingsolver
et al., 2013). Thus, following Vrba's hypothesis, we expected to ob-
serve a high proportion of biome specialists. This proportion should
reflect underlying higher mean DRs in biome specialists than in
biome generalists. We also expected a higher proportion of biome-
specialist species in the biomes representing the extremes of the
climatic gradient (rainforest, desert, steppe and tundra) explained
by a hypothesized higher degree of contraction and fragmentation
processes during the climatic fluctuations of the late Cenozoic.
Specialist lineages associated with these biomes should also present
higher mean DRs than specialists from other biomes. Finally, we will
also test the relationship between species distribution area and DR
to consider the effect of area as a possible alternative to the within-
biome specialization expected under the resource-use hypothesis.

2 | MATERIALS AND METHODS

## 2.1 | Biome, area and phylogenetic data

We worked at a global scale and focused on the butterfly family Papilionidae (Lepidoptera: Papilionoidea), a well-known family with a worldwide distribution and occurring in most terrestrial environments (Condamine et al., 2012, 2013). We used the most complete phylogeny of Papilionidae to date (Allio et al., 2020, 2021), and considered the species included in all Papilionidae genera and subgenera following several studies (see Appendix S1 in Supporting Information). We focused our study on the current geographical distribution of species. Species distribution ranges, gathered from an extensive review of specialized

literature from multiple sources containing distribution maps and infor-
mation at several scales (see Appendix S2 in Supporting Information),
were digitized, georeferenced and summarized using the Geographic
Information System software ArcGIS (ESRI, 2018) and QGIS (QGIS
Development Team, 2018). In those cases with several sources contain-
ing information for the same species, we took in consideration the most
updated one. Species distribution areas were quantified using the R soft-
ware version 4.1.0 (R Core Team, 2021) and the packages raster (v3.4-13;
Hijmans, 2021); rgdal (v1.5-25; Bivand et al., 2021) and maptools (v1.1-1;
Bivand & Lewin-Koh, 2021) under the equal-area Eckert IV projection.

2.2 | Bioclimatic characterization of the species

The biomes inhabited by a species were determined by the overlap be-
tween the reported geographical distribution of each species and the
biome map (Hernández Fernández, 2001). Here, we consider a biome
as inhabited by a species if it constitutes 15% or more of its geographi-
cal range. For the cases where the species overlapped isolated, small
and distinct biome patches, we also recorded the presence of a spe-
cies in a biome if the species is present in 50% or more of that biome
patch (Hernández Fernández, 2001). Furthermore, for those species
with presence in mountain environments, following Moreno Bofarull
et al. (2008) and Cantalapiedra et al. (2011), we considered the altitu-
dinal vegetation belts (ETOPO2v2, NOAA National Geophysical Data
Center, 2006), which were not included in Walter's map (Figure 1). The
overlap between species distribution ranges and biomes was calcu-
lated using ArcGIS software. These criteria allow to represent the ad-
aptation capacity of species while maintaining their climatic specificity
and, at the same time, allow us to compare our results with previous
works using the same methodology (Cantalapiedra et al., 2011; Gómez
Cano et al., 2013; Hernández Fernández et al., 2022; Hernández
Fernández & Vrba, 2005; Menéndez et al., 2021; Moreno Bofarull
et al., 2008). Then, we computed the biomic specialization index (BSI)
defined by Hernández Fernández and Vrba (2005) as the number of in-
habited biomes by a species. Biome-specialist species were defined as
those occupying only one biome, with a BSI = 1 (Hernández Fernández
& Vrba, 2005). In turn, species with BSI >1 were considered as biome
generalists, differentiating between 'semi-generalists' (1 < BSI < 5) and
'extreme generalists' (BSI ≥5) (Hernández Fernández & Vrba, 2005).

2.3 | Biome specialization

We tested the resource-use hypothesis, which predicts the uneven
distribution of biome specialists and biome generalists across bi-
omes, against null models where biome specialization was randomly
distributed. In addition to the analysis of the whole family, we parti-
tioned the data according to the recognized subfamilies (Condamine
et al., 2012; Nazari et al., 2007). The monotypic subfamily Baroniinae
was not considered because it is composed of the single Mexican
species *Baronia brevicornis* (inhabitant of tropical deciduous wood-
lands), which was not enough for statistical purposes.
<figure><img src="image_6.png" /><figcaption>FIGURE 1 World biomes distribution considered in this work (modified from Walter, 1970).</figcaption></figure>

We compared the observed proportion of biome-specialist and
biome-generalist species with null models generated by 10,000
Monte Carlo randomizations of the observed data (Gotelli, 2000).
These data were coded as a binary matrix representing the presence
or absence of every species in each biome. Since specific ecological
features of each biome affect species richness (Jetz & Fine, 2012),
the randomization we conducted placed species presences in bi-
omes randomly while constraining the overall observed species rich-
ness of each biome, which generates changes in the degree of biomic
specialization of those species. Finally, simulated samples of BSI in-
cidence were obtained and the significance (p-value) of observed
BSI distribution was calculated comparing observed and simulated
proportions (Hernández Fernández & Vrba, 2005). All analyses were
performed using the R environment (R Core Team, 2021).

2.4 | Species-specific DRs and biome specialists

To assess the relationship between diversification and the level of
biome occupancy, we estimated species-specific DRs, or ‘tip rates’.
Tip rates are widely used to study DR variation in relation to geogra-
phy, ecology and phenotypes (Title & Rabosky, 2019). We calculated
tip rates from a swallowtail phylogeny (Allio et al., 2021), which in-
cludes 391 swallowtail species (~70%), representing all genera and
subgenera including the only Baroniinae species *B. brevicornis*, 75
out of 76 Parnassiinae species, and 315 out of ~515 Papilioninae
species.

To estimate species-specific DRs, we used species-level lineage
DRs (Jetz et al., 2012) using the *evol.distinct* function in the R pack-
age *picante* (v1.8.1; Kembel et al., 2010). DR is a summary statistic that
infers speciation rates for all tips in the phylogeny without requiring
a formal parametric inference model and is based on the mean equal
splits measure of evolutionary isolation (Redding & Mooers, 2006). DR
values were estimated according to the number of splitting events and
the internode distances of those branches going from each tip to the
tree's root, giving greater weight to branches and splitting events closer
to the present (Jetz et al., 2012). When applied to extant species phy-
logenies (ultrametric phylogenies), DR analyses represent net diversifi-
cation (i.e. speciation minus extinction; Cantalapiedra et al., 2017).

We assessed the significance of the relationship between species DRs and the number of biomes occupied using phylogenetic generalized least squares (PGLS) to estimate the expected covariance in cross-species data while controlling for potential phylogenetic signal (Mundry, 2014). PGLS were performed using the R package *caper* (v1.0.1; Orme et al., 2018) and estimating the lambda parameter using the maximum likelihood function. We used phylogenetic analysis of variance (ANOVA) to test the existence of significant differences in biome-specialist DRs among the different biomes. Phylogenetic ANOVA test was performed using the R packages *phytools* (v0.7-80; Revell, 2012) and *geiger* (v2.0.7; Pennell et al., 2014).

Moreover, we also explored the relationship between biomic spe-
cialization and diversification using the Hidden State Speciation and
Extinction (HiSSE) model in the R package *hisse* (v1.9.19; Beaulieu &
O'Meara, 2016). In addition to the trait of interest, which differentiated
between biome specialists and biome generalists (coded as 1 and 0, respectively), the HiSSE model allowed us to include unobserved factors (hidden states) that could affect diversification. We allowed unlinked rates of speciation (λ0, λ1), extinction (μ0, μ1) and transitions (q01, q10) associated with the two trait states. For the HiSSE model, we set two hidden states (A, B) contained within each observed trait state (i.e. states 0A, 0B, 1A, 1B) so that speciation and extinction rates can vary independently across all four states. Transition rates between all observed and hidden states were also free to vary except for dual transitions (e.g. q0A to q1B, q1A to q0B). We optimized the fit of all models by maximum likelihood and evaluated model performance based on the corrected Akaike information criterion (AICc).

Finally, we explored the impact of species ranges on our results to assess whether a correlation between biome specialization and diversification in swallowtails could be a consequence of the size of the species distribution area. To this effect, we performed a variance partitioning to split the DR variance explained by species distribution area and/or by the number of occupied biomes, using the R package vegan (v2.5-7; Oksanen et al., 2020). Lastly, we tested the significance of the relationships between swallowtail DRs and species distribution areas using a PGLS to consider the potential phylogenetic signal (Mundry, 2014). For the PGLS, the lambda parameter was estimated using the maximum likelihood function.

## 3 | RESULTS

### 3.1 | Distribution of the BSI

We collected the distribution ranges of a total of 593 swallowtail species from around the world. The group was represented in the 10 terrestrial biomes considered, and in all continents except Antarctica, where there is no evidence of their presence. No swallowtail species inhabits all the 10 biomes, the maximum being the eight biomes occupied by *Papilio polyxenes*.

Our results showed that the frequency distribution of BSI was intensely right skewed, with a low mean BSI (BSI = 1.67) (Figure 2a). Overall, 323 species (54.5%) of Papilionidae species inhabit only one biome, and 186 species (31.5%) inhabit two biomes. Moreover, just 1.6% of Papilionidae (nine species) can be considered as extreme biome generalists, inhabiting five or more different biomes (Table S3.1). The American swallowtail (*P. polyxenes*) is the most biome-generalist species of the group, inhabiting eight different biomes, being only absent in taiga and tundra environments.

Our results showed a significantly high proportion of biome-specialist species (BSI = 1) (Figure 2a; Table S3.1) while the frequencies of species inhabiting two (BSI = 2) and three (BSI = 3) biomes were significantly lower. The frequencies of species with BSI = 4–5 were not significantly different from those expected under random processes. Nonetheless, the frequencies of species inhabiting six to eight different biomes (BSI = 6–8) were significantly higher than expected.

Both studied subfamilies showed similar distributions of frequencies and proportion of biome specialization to the family species (Figure S3.1; Table S3.2). However, Parnassiinae showed a substantially lower mean BSI (1.36) and a higher proportion of biome-specialist species (77.6%) than Papilioninae (1.71% and 51.0%, respectively). The frequency of species inhabiting two (BSI = 2) and three (BSI = 3) biomes was significantly low in both subfamilies. Finally, extreme biome-generalist species were scarce in both subfamilies, with less than 1.6% of Papilioninae and less than 1.4% of Parnassiinae inhabiting five or more different biomes, although the observed values in several of these BSI categories were significantly higher than expected by chance.

### 3.2 | Proportion of biome specialists among biomes

Six biomes showed a higher proportion of biome-specialist species than expected (Figure 2b; Table S3.3). Within the family, 331 papilionid species occur in the equatorial rainforests, of which

<figure><img src="image_7.png" /></figure>

**FIGURE 2** Biotic specialization among Papilionidae: (a) Observed (bars) and simulated (dots) frequency distribution of the biotic specialization index (BSI) in Papilionidae. (b) Observed (bars) and simulated (dots) distribution of biome-specialist (BSI = 1) Papilionidae species across different biomes. While dots in both figures indicate the expected values by chance, symbols above or below the dots indicate whether observed results (bars) are significantly higher (above) or lower (bellow) than expected by chance with: \*\*\*p < .001; \*\*p > .001; \*p > .01; n.s. = not significant. *Papilio demoleus* picture by Fabien L. Condamine.
43% (143 species) are restricted to it, against 25% expected. In all, 101 biome specialists live exclusively in the tropical deciduous woodland, which constitutes around 30% of all the species present in this biome (25% expected). The sclerophyllous woodland hosts 34.8% of biome specialists, in contrast to 11.1% expected. The steppe is the biome with the highest degree of specialization, harbouring 69% of biome specialists, against 12% expected. Boreal coniferous forest houses six biome-specialist species, which constitutes around 26% of the total number of species inhabiting this biome, in contrast to 7% expected. On the other hand, two biomes showed a lower proportion of biome-specialist species than expected: broad-leaf deciduous forest and savannah, which also constitutes the single biome that hosts no biome-specialist species.

Papilioninae and Parnassiinae showed substantial differences in their frequencies of biome specialists (Figure S3.2; Table S3.4). Most papilionine biome specialists (about 93% of them) inhabit equatorial rainforest or tropical deciduous woodland, while the vast majority of parnassiine biome specialists are found in the steppe, where biome-specialist species constitute 88.9% of all the inhabiting parnassians. To a much lesser extent, tropical deciduous woodland and sclerophyllous woodland, with around 50% of biome-specialist species, also showed a high proportion of biome-specialist Parnassiinae species. Three biomes host no Papilioninae biome specialists: savannah, steppe and tundra. At the same time, three biomes host no Parnassiinae of any kind: equatorial rainforest, savannah and tropical desert. Moreover, both subfamilies showed a similar percentage of biome specialists in the taiga biome, which was significantly higher than expected.

## 3.3 | DRs and biome specialists

The highest diversification values were those from the Papilioninae species *Troides rhadamanthus* and *T. riedeli*, while the lowest value corresponded to *B. brevicornis*, from the monotypic subfamily Baroniinae (Figure 3). Among clades, tribes Leptocircini and Teinopalpini showed the lowest DR values among all Papilioninae, while genus *Parnassius* showed the highest DR values among all Parnassiinae. Regarding the species-rich genus *Papilio* (tribe Papilionini), the *Pterourus* clade as well as the *memnon* and *aegeus* species groups within the subgenus *Menelaides* also showed high DRs. When separated by subfamilies, Parnassiinae showed higher DR mean values than Papilioninae for every represented BSI.

Lineage-specific DR mean values through the different BSI groups (Figure 4; Table S3.5) varied from 0.16 (BSI = 1) to 0.08 (BSI = 6). However, there was a high variability of statistical dispersion among categories, with BSI = 1 and BSI = 2 groups showing generally the smaller standard deviation values than groups of more generalist species.

The high correlation ($R^2$ = .86) and the negative slope from PGLS analysis (Table 1) reflected the relationship between occupying a smaller number of biomes and higher rates of diversification among Papilionidae ($p$ = .023; Table 1). Biome-specialist species of different

biomes showed significant differences in their DRs (phylANOVA $p$ ≤ .001; Table S3.6 in Supporting Information), with tundra specialist species showing the highest DR values, followed by steppe specialists, while sclerophyllous woodland specialists showed the lowest DR values (Figure 4b).

The HiSSE analyses revealed that the model including a hidden effect on diversification of state 1 (biome specialists) was supported against the other SSE models (AICc = 3043.76 vs. AICc = 3058.76 for the second-best fitting model, ΔAIC = 15.0). Our result showed that biome-specialist species diversified twice as fast as biome generalists (0.187 vs. 0.099 events/lineage/Myr; Table S3.7 in Supporting Information) and indicated that the diversification of biome specialists was likely influenced by other unmeasured traits.

Based on the results of variance partitioning (Figure 5), the effects of BSI (number of biomes occupied by a species) had the most significant unique effect on swallowtail DRs (13.3%), whereas species area explained 6.6% of the total variability. The combined effects of BSI and area variables represented 2.2% of the total variability. The total variance explained by the two variables was 22.1%.

The results of the PGLS showed a negative linear relationship between species area and species DR, meaning that species occupying less area tend to have higher DRs. However, the relationship between species diversification and area, while significant ($p$ = .002), had much lower explanatory power than the one showed between species diversification and the number of occupied biomes (BSI; Table 1).

# 4 | DISCUSSION

## 4.1 | Are there more biome-specialist swallowtail butterflies than biome generalists?

Globally, our results agree with the first prediction of the resource-use hypothesis (Vrba, 1980, 1987), showing a high proportion of biome-specialist species (BSI = 1) (Figure 2a). Apart from biome specialist, we also showed that extreme biome-generalist species inhabiting more than five biomes were observed infrequently but still more than expected. These results are in line with the results previously obtained by Hernández Fernández and Vrba (2005) for large African mammals, Moreno Bofarull et al. (2008) for South American mammals, Cantalapiedra et al. (2011) for ruminants worldwide, Menéndez et al. (2021) for squirrels of the world and Hernández Fernández et al. (2022) for world's mammals, which suggests that the resource-use hypothesis provides a common ground for the understanding of evolutionary processes triggered by climatic changes, irrespective of the studied taxa. Moreover, swallowtail butterflies showed a lower mean BSI (they inhabit fewer biomes) than most previously studied mammal groups. Papilionidae butterflies are herbivorous insects with highly specialized host-plant preferences (Allio et al., 2021; Condamine et al., 2012), and, therefore, their trophic niche could have made them particularly prone to biome specialization (Moreno Bofarull et al., 2008; Vrba, 1980).
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>FIGURE 3 Diversification rate (DR) estimations for Papilionidae as inferred using the DR metric (Jetz et al., 2012). Dots indicate the presence (full) or absence (void) of each species in the considered biomes. Butterfly pictures by Fabien L. Condamine.</figcaption></figure>

## 4.2 | Are biome-specialist swallowtail species evenly distributed across biomes?

Not all biomes showed the same percentage of endemic species. Our results show that some biomes located at the extremes of the climatic conditions, namely tundra, steppe and rainforest have high percentages of biome-specialist species (more than 33% of their species are endemic; Figure 2b). The rainforest biome stands out by its high number of biome-specialist species (n = 143). The rainforest is an old biome, with more than 60 million

years of evolutionary history (Morley, 2011), and with a general pattern of hosting many species for most clades (Condamine et al., 2012; Jaramillo & Cárdenas, 2013; Novotny et al., 2006; Potts & Behrensmeyer, 1992). The steppe and the tundra are relatively modern biome, with less than 15 million years (Abbott & Brochmann, 2003; Barbolini et al., 2020; Friesen et al., 2016) that expanded in the late Neogene-Quaternary associated with global cooling and orogenic pulses (Horton et al., 2010; Hurka et al., 2019; Strömberg, 2011). The adaptation of the genus *Parnassius* to these biomes, where they are in the majority, should
<figure><img src="image_10.png" /><figcaption>FIGURE 4 Biome specialists have higher diversification rates (DRs). (a) DRs estimated using the DR metric (Jetz et al., 2012), and grouped by species' biomic specialization index (BSI) for all Papilionidae present in the phylogeny; (b) diversification rate estimated for biome-specialist species (BSI = 1) present in the phylogeny, divided as a function of the biome they inhabit; Savannah and subtropical desert are not included as there are not specialist species in these biomes. Coloured dots indicate the mean values. Bars correspond to 95% CI. Significance levels (p) are provided for phylogenetic generalized least squares and phylogenetic ANOVA analyses of DRs for BSI and biome, respectively. ANOVA, analysis of variance; CI, confidence interval.</figcaption></figure>

<table><caption>TABLE 1 Results of the phylogenetic generalized least squares regressions of species diversification rates and species biome specialization index (BSI) or range area</caption><thead><tr><th rowspan="2">Variable</th><th rowspan="2">Intercept</th><th rowspan="2">Slope</th><th rowspan="2">Significance</th><th colspan="2">Adjusted</th></tr><tr><th>R<sup>2</sup></th><th>λ</th></tr></thead><tbody><tr><td>BSI</td><td>0.158 ± 0.012</td><td>-1.425 ± 0.006</td><td>p = .023</td><td>.810</td><td>0.835</td></tr><tr><td>Area</td><td>0.280 ± 0.036</td><td>-0.044 ± 0.001</td><td>p = .002</td><td>.487</td><td>0.852</td></tr></tbody></table>

<figure><img src="image_11.png" /><figcaption>FIGURE 5 Venn diagram showing the variance partition of conditional and shared effects of species distribution area (left), and species biomic specialization index (right) as fractions of swallowtail diversification rates. Values are percentages of the total variation explained by the full model.</figcaption></figure>

have happened during the late Neogene, meaning that tundra- and steppe-adapted endemic species should be relatively young (Allio et al., 2021; Figure S3.2).

However, our results do not support that the desert biome host many biome-specialist species (one single endemic species out of seven species inhabits desert environments). This biome, characterized by wide inter-annual fluctuations in precipitation and by hampering plant-water relationships (Schowalter et al., 1999), constitutes an unfavourable biome for phytophagous insects, like butterflies (Larsen, 1995). In agreement with that idea, a single swallowtail species, *P. saharae*, has specialized in this biome.

Moreover, some non-extreme biomes also showed high percentages of biome-specialist species. The prevalence of biome-specialist species in the tropical deciduous woodlands was pointed out in previous works on mammals (Cantalapiedra et al., 2011; Hernández Fernández et al., 2022; Hernández Fernández & Vrba, 2005; Moreno Bofarull et al., 2008). Even though this biome cannot be considered a climatic extreme, it is a markedly heterogeneous environment whose historical dynamic is closely associated with rainforest fluctuations (Dexter et al., 2018; Haffer, 2008; Hoorn et al., 2010). These dynamics could have provided patches and refugia that promote speciation (Vrba, 1992). In this regard, there are several lines of evidence among Papilionidae characterized by the presence of closely related specialists in the equatorial forests and the tropical deciduous woodlands (Figure 3). Sclerophyllous woodlands and shrublands showed a high proportion of biome specialists related to the high
number of species from the Parnassiinae subfamily in this biome (Figure S3.2; Table S3.4), which could be related to the role of the Mediterranean peninsulas as Pleistocene refuges for isolated populations (Dapporto, 2010; Zinetti et al., 2013). Finally, the taiga also showed more specialists than expected (Figure 2b). In this case, taiga specialist swallowtails are linked to mountain systems, which are usually recognized as biodiversity hotspots (Menéndez et al., 2021; Rahbek & Graves, 2001). With these results, we do not find a consistent pattern supporting that, only lineages adapted to extreme climatic conditions show a pattern of increasing endemism because additional geographic factors appear to be acting as determinants of ancillary biome specialization.

## 4.3 | Is DR related to biome specialization?

Biome specialists showed the highest mean DRs, while biome generalists presented decreasing DR as the number of inhabited biomes increases. Finally, extreme biome-generalists (those occupying five or more biomes) showed a large variation among species, related to the relatively small number of species in this category. Our results on DR are consistent with those retrieved from the HiSSE analyses that showed that biome specialists diversified faster than biome generalists (Table S3.7). In addition, HiSSE results showed the effect of a hidden variable on diversification. Thus, significant effects of other variables, abiotic (e.g. tectonic; Badgley et al., 2017) of biotic (e.g. hostplants; Allio et al., 2021; Muto-Fujita et al., 2017), are expected.

It is important to note that estimating DRs from extant species phylogenies is challenging (Burin et al., 2019; Louca & Pennell, 2020). Even more because of the relative incompleteness of species sampling in some tropical regions. Additionally, estimation becomes particularly complex when the group is ancient because there is an increasing probability that any possible diversification episode may have been veiled due to extinction wiping out entire lineages (Marshall, 2017).

Vrba hypothesized that biomes located at the extremes of the climatic conditions would be more impacted by global climatic changes, increasing their fragmentation rates, and thus, favouring extinction and speciation events, with a net increase in diversification. Results from swallowtail butterflies show that steppe, tundra and tropical deciduous woodland biomes have the highest DRs among biome-specialist swallowtails (Figure 4b; Table S3.6 in Supporting Information). Steppe and tundra biomes are mainly occupied by the genus *Parnassius*, which showed some of the highest DRs among swallowtail butterflies (Figure 3). Nevertheless, our estimation for the DR of rainforest specialists was not as high as in other extreme biomes (Figure 4b). This could be related to a biotic process, or to the fact that the sampling for rainforest species for the phylogeny of the family covers ~55% of all rainforest specialists (Figure 3). The sclerophyllous woodland and shrubland showed the lowest DR values among all biome-specialist swallowtails. Most of these biome specialists occupy the Mediterranean area. This region, because of

its geography consisting of several islands and peninsulas restricted northwards by high mountain ranges, could have acted as refugia for temperate species during the glacial maxima of the Pleistocene, favouring the development of endemism (Bilton et al., 1998; Todisco et al., 2012; Zinetti et al., 2013). Their low DR values could mean that some sclerophyllous woodlands specialists may be considered as reliefs, survivors of once more speciose clades that suffered extinction events during the glacial-interglacial alternation (Brown, 1995; Condamine, Rolland, et al., 2018).

## 4.4 | The effect of species distribution areas on diversification

Both Variance Partitioning and PGLS results showed a stronger relationship between swallowtail DRs and their degree of biome specialization in comparison to species area. Biome specialization is about twice as explanatory as distribution area when studying swallowtail DRs in both analyses ($R^2$ adjusted = .133 vs. $R^2$ adjusted = .066; Figure 5) and PGLS ($R^2$ = .810 vs. $R^2$ = .487). These results confirm that species with large areas can be young, and species with small areas can be old (Gaston, 2003) and can reflect that, although swallowtail species diversification might be somehow related to dispersal capabilities, it is much more related to niche adaptation. The differences in explanatory power observed between variance partition and PGLS approaches are most probably related to the phylogenetic nature of the PGLS, suggesting that the correlation between variables is stronger within clades, getting diluted when swallowtails are studied altogether. These results agree with previous works that found these age-area relationships among closely related species (Gaston, 2003).

## 5 | CONCLUSIONS

The swallowtails lineage presents a greater number of biome specialists and a lower number of biome generalists than expected. We show that this pattern stems from differential diversification of lineages: substantially higher DRs were detected among biome specialists compared to biome generalists. We also found that biome specialists are unevenly distributed across biomes, suggesting that past fragmentation events shaped the degree of biomes specialization Overall, our results are consistent with the resource-use hypothesis, which states that global climatic changes, and the hypothetically subsequent biome fragmentation, promote divergence and speciation events in biome-specialist lineages. Distinguishing the role of biomes as a main barrier for species expansion, and thus a main constraint conditioning the evolutionary pathways of lineages, is an open question yet to be answered. New analyses and simulations need to be conducted to measure the role of dispersal, species climatic niche and biome fragmentation as key factors promoting isolation. Our results point out the relevance of future conservation policies to maintain the ecological and evolutionary
diversity within this family. Special effort should be placed into the identification and preservation of areas including specialist species with significantly high DRs (extreme biomes, mountain ranges) because fragmentation of such areas under the current and future situation of global climatic change will continue to foster the diversification of the group.

## ACKNOWLEDGEMENTS

This is a contribution by the Palaeoclimatology, Macroecology and Macroevolution of Vertebrates (PMMV) research team from the Complutense University of Madrid as a part of the Research Group UCM 910607 on Evolution of Cenozoic Mammals and Continental Palaeoenvironments as well as by the MAPAS project that has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. 947921). S.G. and I.M. were founded by a predoctoral grant from the Universidad Complutense de Madrid (CT27/16-CT28/16). S.G. received a pre-doctoral stay grant from the Universidad Complutense de Madrid that enables the development of this study. S.G. was funded by the Ministry of Universities and the Next Generation European Union programme through a Margarita Salas Grant from the Universidad Complutense de Madrid (CT31/21). This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (project GAIA, agreement no. 851188), the Marie Curie International Outgoing Fellow under the European Union's Seventh Framework Programme (project BIOMME, agreement no. 627684), a PICS grant from the CNRS (project PASTA) and an 'Investissement d'Avenir' grant from the Agence Nationale de la Recherche (project CASMA, CEBA, ref. ANR-10-LABX-25-01) to F.L.C. J.L.C. was funded by the Humboldt Foundation, the German Research Foundation (DFG, AOBJ: 637491) and the Talent Attraction Program of the Madrid Government (2017-P1/AMB-5298). F.B. was funded by a research grant from Deutsche Forschungsgemeinschaft (LO 2368/1-1). This work was partially supported by the Spanish Ministry of Science, Innovation and Universities (project PGC2018-094955-A-100). Funding for open access charge: Universidade de Vigo and Consorcio Interuniversitario do Sistema Universitario de Galicia (GISUG). We thank the anonymous reviewers whose comments and suggestions helped improve and clarify this manuscript.

## CONFLICT OF INTEREST

All authors declare that they have no conflicts of interest to disclose.

## DATA AVAILABILITY STATEMENT

The R script used to analyse the data is available in GitHub (https://github.com/paleobicha/Papilionidae_Biome_Diversification_Analyses). The database containing detailed information of biome occupancy for each species is available in the Dryad Digital Depository (https://doi.org/10.5061/dryad.sbcc2fr5b).

## ORCID

Sara Gamboa ID https://orcid.org/0000-0002-0829-3747

Fabien L. Condamine ID https://orcid.org/0000-0003-1673-9910

Juan L. Cantalapiedra ID https://orcid.org/0000-0003-0913-7735

Sara Varela ID https://orcid.org/0000-0002-5756-5737

Jonathan S. Pelegrín ID https://orcid.org/0000-0001-5954-5476

Iris Menéndez ID https://orcid.org/0000-0003-1866-8351

Fernando Blanco ID https://orcid.org/0000-0001-6339-3460

Manuel Hernández Fernández ID https://orcid.org/0000-0001-5640-9647

## REFERENCES

Abbott, R. J., & Brochmann, C. (2003). History and evolution of the arctic flora: In the footsteps of Eric Hultén. *Molecular Ecology*, *12*(2), 299–313. https://doi.org/10.1046/j.1365-294X.2003.01731.x

Allio, R., Nabholz, B., Wanke, S., Chomicki, G., Pérez-Escobar, O. A., Cotton, A. M., Clamens, A., Kergoat, G. J., Sperling, F. A. H., & Condamine, F. L. (2021). Genome-wide macroevolutionary signatures of key innovations in butterflies colonizing new host plants. *Nature Communications*, *12*, 354. https://doi.org/10.1038/s41467-020-20507-3

Allio, R., Scornavacca, C., Nabholz, B., Clamens, A. L., Sperling, F. A., & Condamine, F. L. (2020). Whole genome shotgun phylogenomics resolves the pattern and timing of swallowtail butterfly evolution. *Systematic Biology*, *69*(1), 38–60. https://doi.org/10.1093/sysbio/syz030

Badgley, C., Smiley, T. M., Terry, R., Davis, E. B., DeSantis, L. R. G., Fox, D. L., Hopkins, S. S. B., Jezkova, T., Matocq, M. D., & Matzke, N. (2017). Biodiversity and topographic complexity: Modern and geo-historical perspectives. *Trends in Ecology and Evolution*, *32*, 211–226. https://doi.org/10.1016/j.tree.2016.12.010

Bale, J. S., Masters, G. J., Hodkinson, I. D., Awmack, C., Bezemer, T. M., Brown, V. K., Butterfielsd, J., Buse, A., Coulson, J. C., Farrar, J., Good, J. E. G., Harrington, R., Hartley, S., Jones, T. H., Lindroth, R. L., Press, M. C., Symrnioudis, I., Watt, A. D., & Whittaker, J. B. (2002). Herbivory in global climate change research: Direct effects of rising temperature on insect herbivores. *Global Change Biology*, *8*(1), 1–16. https://doi.org/10.1046/j.1365-2486.2002.00451.x

Barbolini, N., Woutersen, A., Dupont-Nivet, G., Silvestro, D., Tardif, D., Coster, P. M. C., Meijer, N., Chang, C., Zhang, H. X., Licht, A., Rydin, C., Koutsodendris, A., Han, F., Rohrmann, A., Liu, X. J., Zhang, Y., Donnadieu, Y., Fluteau, F., Ladant, J. B., ... Hoorn, C. (2020). Cenozoic evolution of the steppe-desert biome in Central Asia. *Science Advances*, *6*(41), eabb8227. https://doi.org/10.1126/sciadv.abb8227

Barnosky, A. D. (2001). Distinguishing the effects of the red queen and court jester on Miocene mammal evolution in the northern Rocky Mountains. *Journal of Vertebrate Paleontology*, *21*(1), 172–185. https://doi.org/10.1671/0272-4634(2001)021[0172:DTEOTR]2.0.CO;2

Beaulieu, J. M., & O'Meara, B. C. (2016). Detecting hidden diversification shifts in models of trait-dependent speciation and extinction. *Systematic Biology*, *65*(4), 583–601. https://doi.org/10.1093/sysbio/syw022

Benton, M. (2009). The red queen and the court jester: Species diversity and the role of biotic and abiotic factors through time. *Science*, *323*(5915), 728–732. https://doi.org/10.1126/science.1157719

Bilton, D. T., Mirol, P. M., Mascheretti, S., Fredga, K., Zima, J., & Searle, J. B. (1998). Mediterranean Europe as an area of endemism for small mammals rather than a source for northwards postglacial colonization. *Proceedings of the Royal Society of
<figure><img src="image_12.png" /></figure>

London B: Biological Sciences, 265(1402), 1219-1226. https://doi.org/10.1098/rspb.1998.0423

Bivand, R., Keitt, T., & Rowlingson, B. (2021). Rgdal: Bindings for the 'Geospatial' data. Abstraction library. R package version 1.5-25. https://CRAN.R-project.org/package=rgdal

Bivand, R., & Lewin-Koh, N. (2021). Maptools: Tools for handling spatial objects. R package version 1.1-1. https://CRAN.R-project.org/package=maptools

Brée, B., Helmstetter, A. J., Bethune, K., Ghogue, J. P., Sonké, B., & Couvreur, T. L. (2020). Diversification of African rainforest restricted clades: Piptostigmatae and Annickieae (Annonaceae). Diversity, 12(6), 227. https://doi.org/10.3390/d12060227

Brown, J. H. (1995). Macroecology. The University of Chicago Press.

Burin, G., Alencar, L. R., Chang, J., Alfaro, M. E., & Quental, T. B. (2019). How well can we estimate diversity dynamics for clades in diversity decline? Systematic Biology, 68(1), 47-62. https://doi.org/10.1093/sysbio/syy037

Cantalapiedra, J. L., Hernández, F. M., & Morales, J. (2011). Biotic specialisation and speciation rates in ruminants (Cetartiodactyla, Mammalia): A test of the resource use hypothesis at the global scale. PLoS One, 6, e28749. https://doi.org/10.1371/journal.pone.0028749

Cantalapiedra, J. L., Prado, J. L., Hernández Fernández, M., & Alberdi, M. T. (2017). Decoupled ecomorphological evolution and diversification in Neogene-quaternary horses. Science, 355(6325), 627-630.

Clusella-Trullas, S., Blackburn, T. M., & Chown, S. L. (2011). Climatic predictors of temperature performance curve parameters in ectotherms imply complex responses to climate change. The American Naturalist, 177(6), 738-751. https://doi.org/10.5061/dryad.8665

Collins, N. M., & Morris, M. G. (1985). Threatened swallowtail butterflies of the world: The IUCN red data book. International Union for Conservation of Nature and Natural Resources.

Condamine, F. L., Nabholz, B., Clamens, A. L., Dupuis, J. R., & Sperling, F. A. (2018). Mitochondrial phylogenomics, the origin of swallowtail butterflies, and the impact of the number of clocks in Bayesian molecular dating. Systematic Entomology, 43(3), 460-480. https://doi.org/10.1111/syen.12284

Condamine, F. L., Rolland, J., Höhna, S., Sperling, F. A., & Sanmartín, I. (2018). Testing the role of the red queen and court jester as drivers of the macroevolution of Apollo butterflies. Systematic Biology, 67(6), 940-964. https://doi.org/10.1093/sysbio/syy009

Condamine, F. L., Sperling, F. A., & Kergoat, G. J. (2013). Global biogeographical pattern of swallowtail diversification demonstrates alternative colonization routes in the northern and southern hemispheres. Journal of Biogeography, 40(1), 9-23. https://doi.org/10.1111/j.1365-2699.2012.02787.x

Condamine, F. L., Sperling, F. A., Wahlberg, N., Rasplus, J. Y., & Kergoat, G. J. (2012). What causes latitudinal gradients in species diversity? Evolutionary processes and ecological constraints on swallowtail biodiversity. Ecology Letters, 15(3), 267-277. https://doi.org/10.1111/j.1461-0248.2011.01737.x

Dapporto, L. (2010). Speciation in Mediterranean refugia and post-glacial expansion of Zerynthia polyxena (Lepidoptera, Papilionidae). Journal of Zoological Systematics and Evolutionary Research, 48(3), 229-237. https://doi.org/10.1111/j.1439-0469.2009.00550.x

Dexter, K. G., Pennington, R. T., Oliveira-Filho, A. T., Bueno, M. L., Silva de Miranda, P. L., & Neves, D. M. (2018). Inserting tropical dry forests into the discussion on biome transitions in the tropics. Frontiers in Ecology and Evolution, 6, 104. https://doi.org/10.3389/fevo.2018.00104

ESRI. (2018). ArcGIS [GIS software] version 10.6. Environmental Systems Research Institute.

Friesen, N., German, D. A., Hurka, H., Herden, T., Oyuntsetseg, B., & Neuffer, B. (2016). Dated phylogenies and historical biogeography

of Dontostemon and Clausia (Brassicaceae) mirror the palaeogeographical history of the Eurasian steppe. Journal of Biogeography, 43(4), 738-749. https://doi.org/10.1111/jbi.12658

Gaston, K. J. (2003). The structure and dynamics of geographic ranges. Oxford University Press.

Gómez Cano, A. R., Cantalapiedra, J. L., Mesa, A., Bofarull, A. M., & Hernández Fernández, M. (2013). Global climate changes drive ecological specialisation of mammal faunas: Trends in rodent assemblages from the Iberian Plio-Pleistocene BMC Evolutionary Biology, 13(1), 94. https://doi.org/10.1186/1471-2148-13-94

Gómez-Rodríguez, C., Baselga, A., & Wiens, J. J. (2015). Is diversification rate related to climatic niche width? Global Ecology and Biogeography, 24(4), 383-395. https://doi.org/10.1111/geb.12229

Gotelli, N. J. (2000). Null model analysis of species co-occurrence patterns. Ecology, 81(9), 2606-2621. https://doi.org/10.1890/0012-9658(2000)081[2606:NMAOSC]2.0.CO;2

Haffer, J. (2008). Hypotheses to explain the origin of species in Amazonia. Brazilian Journal of Biology, 68, 917-947. https://doi.org/10.1590/S1519-69842008000500003

Hernández Fernández, M. (2001). Bioclimatic discriminant capacity of terrestrial mammal faunas. Global Ecology and Biogeography, 10, 189-204. https://doi.org/10.1046/j.1466-822x.2001.00218.x

Hernández Fernández, M., Pelegrín, J. S., Gómez Cano, A. R., García Yelo, B. A., Moreno-Bofarull, A., Sánchez-Fontela, N., Rodríguez-Ruiz, C., Ramiro Camacho, A., Domingo, L., Menéndez, I., Martín-Perea, D. M., Bazán, C. M., Alcalde, G. M., Domingo, M. S., Luna, B., Peinado Cortés, M. M., Arias, A., González Couturier, G., Márquez Villena, A., ... Cantalapiedra, J. L. (2022). Macroevolution and climate changes: A global multi-family test supports the resource-use hypothesis in terrestrial mammals. Historical Biology, 34, 1471-1479. https://doi.org/10.1080/08912963.2022.2042807

Hernández Fernández, M., & Vrba, E. S. (2005). Macroevolutionary processes and biomic specialisation: Testing the resource-use hypothesis. Evolutionary Ecology, 19, 199-219. https://doi.org/10.1007/s10682-004-8152-7

Hijmans, R. J. (2021). Raster: Geographic data analysis and modeling. R package version 3.4-13. https://CRAN.R-project.org/package=raster

Hoorn, C., Wesselingh, F. P., Ter Steege, H., Bermudez, M. A., Mora, A., Sevink, J., Sanmartín, I., Sánchez-Meseguer, A., Anderson, C. L., Figueiredo, J. P., Jaramillo, C., Riff, D., Negri, F. R., Hoochiemstra, H., Lundberg, J., Stadler, T., Särkinen, T., & Antonelli, A. (2010). Amazonia through time: Andean uplift, climate change, landscape evolution, and biodiversity. Science, 330(6006), 927-931. https://doi.org/10.1126/science.1194585

Horton, D. E., Poulsen, C. J., & Pollard, D. (2010). Influence of high-latitude vegetation feedbacks on late Palaeozoic glacial cycles. Nature Geoscience, 3(8), 572-577. https://doi.org/10.1038/ngeo922

Hurka, H., Friese, N., Bernhardt, K.-G., Neuffer, B., Smirnov, S., Schmako, A., & Blattner, F. (2019). The Eurasian steppe belt: Status quo, origin and evolutionary history. Turczaninowia, 22, 5-71. https://doi.org/10.14258/turczaninowia.22.3.1

Jaramillo, C., & Cárdenas, A. (2013). Global warming and neotropical rainforest: A historical perspective. Annual Review of Earth and Planetary Sciences, 41, 741-766. https://doi.org/10.1146/annurev-earth-042711-105403

Jetz, W., & Fine, P. V. (2012). Global gradients in vertebrate diversity predicted by historical area-productivity dynamics and contemporary environment. PLoS Biology, 10(3), e1001292. https://doi.org/10.1371/journal.pbio.1001292

Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K., & Mooers, A. O. (2012). The global diversity of birds in space and time. Nature, 491(7424), 444-448. https://doi.org/10.1038/nature11631

Kembel, S. W., Cowan, P. D., Helmus, M. R., Cornwell, W. K., Morlon, H., Ackerly, D. D., Blomberg, S. P., & Webb, C. O. (2010). Picante: R tools for integrating phylogenies and ecology. Bioinformatics, 26(11), 1463-1464.
<figure><img src="image_13.png" /></figure>

Kingsolver, J. G., Diamond, S. E., & Buckley, L. B. (2013). Heat stress and the fitness consequences of climate change for terrestrial ectotherms. *Functional Ecology*, 27(6), 1415-1423. https://doi.org/10.1111/1365-2435.12145

Kozak, K. H., & Wiens, J. J. (2010). Accelerated rates of climatic-niche evolution underlie rapid species diversification. *Ecology Letters*, 13(11), 1378-1389. https://doi.org/10.1111/j.1461-0248.2010.01530.x

Larsen, T. B. (1995). Butterfly biodiversity and conservation in the Afrotropical region. In A. Pullin (Ed.), *Ecology and conservation of butterflies* (pp. 290-303). Springer Netherlands.

Lomolino, M. V., Riddle, B. R., & Whittaker, R. J. (2016). *Biogeography*. Oxford University Press.

Louca, S., & Pennell, M. W. (2020). Extant timetrees are consistent with a myriad of diversification histories. *Nature*, 580(7804), 502-505. https://doi.org/10.1038/s41586-020-2176-1

Maguire, K. C., & Stigall, A. L. (2008). Paleobiogeography of Miocene Equinae of North America: A phylogenetic biogeographic analysis of the relative roles of climate, vicariance, and dispersal. *Palaeogeography, Palaeoclimatology, Palaeoecology*, 267(3), 175-184. https://doi.org/10.1016/j.palaeo.2008.06.014

Marshall, C. R. (2017). Five palaeobiological laws needed to understand the evolution of the living biota. *Nature Ecology and Evolution*, 1, 0165. https://doi.org/10.1038/s41559-017-0165

Menéndez, I., Gómez Cano, A. R., Cantalapiedra, J. L., Peláez-Campomanes, P., Álvarez-Sierra, M. Á., & Hernández Fernández, M. (2021). A multi-layered approach to the diversification of squirrels. *Mammal Review*, 51(1), 66-81. https://doi.org/10.1111/mam.12215

Miller, A. I. (1997). A new look at age and area: The geographic and environmental expansion of genera during the Ordovician radiation. *Paleobiology*, 23(4), 410-419. https://doi.org/10.1017/S0094837300019813

Moreno Bofarull, A., Royo, A. A., Hernández Fernández, M., Ortiz-Jaureguizar, E., & Morales, J. (2008). Influence of continental history on the ecological specialisation and macroevolutionary processes in the mammalian assemblage of South America: Differences between small and large mammals. *BMC Evolutionary Biology*, 8(1), 97. https://doi.org/10.1186/1471-2148-8-97

Morley, R. J. (2011). Cretaceous and tertiary climate change and the past distribution of megathermal rainforests. In M. Bush, J. Flenley, & W. Gosling (Eds.), *Tropical rainforest responses to climatic change* (pp. 1-34). Springer.

Mundry, R. (2014). Statistical issues and assumptions of phylogenetic generalized least squares. In L. Z. Garamszegi (Ed.), *Modern phylogenetic comparative methods and their application in evolutionary biology* (pp. 131-153). Springer.

Muto-Fujita, A., Takemoto, K., Kanaya, S., Nakazato, T., Tokimatsu, T., Matsumoto, N., Kono, M., Chubachi, Y., Ozaki, K., & Kotera, M. (2017). Data integration aids understanding of butterfly-host plant networks. *Scientific Reports*, 7, 43368. https://doi.org/10.1038/srep43368

Nazari, V., Zakharov, E. V., & Sperling, F. A. (2007). Phylogeny, historical biogeography, and taxonomic ranking of Parnassiinae (Lepidoptera, Papilionidae) based on morphology and seven genes. *Molecular Phylogenetics and Evolution*, 42(1), 131-156. https://doi.org/10.1016/j.ympev.2006.06.022

NOAA National Geophysical Data Center. (2006). 2-minute gridded global relief data (ETOPO2) v2. NOAA National Centers for Environmental Information.

Novotny, V., Drozd, P., Miller, S. E., Kulfan, M., Janda, M., Basset, Y., & Weiblen, G. D. (2006). Why are there so many species of herbivorous insects in tropical rainforests? *Science*, 313(5790), 1115-1118. https://doi.org/10.1126/science.1129237

Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R., O'Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Szoecs, E., & Wagner, H. (2020). *Vegan: Community ecology*

package. R package version 2.5-7. https://CRAN.R-project.org/package=vegan

Onstein, R. E., Baker, W. J., Couvreur, T. L., Faurby, S., Herrera-Alsina, L., Svenning, J. C., & Kissling, W. D. (2018). To adapt or go extinct? The fate of megafaunal palm fruits under past global change. *Proceedings of the Royal Society B*, 285(1880), 20180882. https://doi.org/10.1098/rspb.2018.0882

Orme, D., Freckleton, R., Thomas, G., Petzoldt, T., Fritz, S., Isaac, N., & Pearse, W. (2018). *Caper: Comparative analyses of phylogenetics and evolution* in R. R package version 1.0.1. https://CRAN.R-project.org/package=caper

Pennell, M. W., Eastman, J. M., Slater, G. J., Brown, J. W., Uyeda, J. C., FitzJohn, R. G., Alfaro, M. E., & Harmon, L. J. (2014). Geiger v2.0: An expanded suite of methods for fitting macroevolutionary models to phylogenetic trees. *Bioinformatics*, 30(15), 2216-2218.

Pennington, R. T., Hughes, M., & Moonlight, P. W. (2015). The origins of tropical rainforest hyperdiversity. *Trends in Plant Science*, 20(11), 693-695. https://doi.org/10.1016/j.tplants.2015.10.005

Potts, R., & Behrensmeyer, A. K. (1992). Late Cenozoic terrestrial ecosystems. In A. K. Behrensmeyer, J. D. Damuth, W. A. DiMichele, R. Potts, H. Sues, & S. L. Wing (Eds.), *Terrestrial ecosystems through time: Evolutionary paleoecology of terrestrial plants and animals* (pp. 419-451). The University of Chicago Press.

QGIS Development Team. (2018). *QGIS geographic information system*. Open Source Geospatial Foundation Projects. http://qgis.osgeo.org

R Core Team. (2021). *R: A language and environment for statistical computing*. R Foundation for Statistical Computing. http://www.R-project.org

Rahbek, C., & Graves, G. R. (2001). Multiscale assessment of patterns of avian species richness. *Proceedings of the National Academy of Sciences*, 98(8), 4534-4539. https://doi.org/10.1073/pnas.071034898

Redding, D. W., & Mooers, A. Ø. (2006). Incorporating evolutionary measures into conservation prioritization. *Conservation Biology*, 20(6), 1670-1678. https://doi.org/10.1111/j.1523-1739.2006.00555.x

Revell, L. J. (2012). *Bioools: An R package for phylogenetic comparative biology (and other things). Methods in Ecology and Evolution*, 3(2), 217-223.

Scheffers, B. R., De Meester, L., Bridge, T. C., Hoffmann, A. A., Pandolfi, J. M., Corlett, R. T., Butchart, S. H., Pearce-Kelly, P., Kovacs, K. M., Dudgeon, D., Pacifici, M., Rondinini, C., Foden, W. B., Martin, T. G., Mora, C., Bickford, D., & Watson, J. E. (2016). The broad footprint of climate change from genes to biomes to people. *Science*, 354(6313), aaf7671. https://doi.org/10.1126/science.aaf7671

Schowalter, T. D., Lightfoot, D. C., & Whitford, W. G. (1999). Diversity of arthropod responses to host-plant water stress in a desert ecosystem in southern New Mexico. *The American Midland Naturalist*, 142(2), 281-290. https://doi.org/10.1674/0003-0031(1999)142[0281:DOARTH]2.0:CO;2

Scriber, J. M., Tsubaki, Y., & Lederhouse, R. C. (1995). *Swallowtail butterflies: Their ecology and evolutionary biology*. Scientific Publishers.

Strömberg, C. A. (2011). Evolution of grasses and grassland ecosystems. *Annual Review of Earth and Planetary Sciences*, 39, 517-544. https://doi.org/10.1146/annurev-earth-040809-152402

Taylor, C. M., & Gotelli, N. J. (1994). The macroecology of *Cyprinella*: Correlates of phylogeny, body size, and geographical range. *The American Naturalist*, 144(4), 549-569.

Tian, F., Cao, X., Dallmeyer, A., Lohmann, G., Zhang, X., Ni, J., Andreev, A., Anderson, P. M., Lozhkin, A. V., Bezrukova, E., Rudaya, N., Xu, Q., & Herzschuh, U. (2018). Biome changes and their inferred climatic drivers in northern and eastern continental Asia at selected times since 40 ka cal ka BP. *Vegetation History and Archaeobotany*, 27(2), 365-379. https://doi.org/10.1007/s00334-017-0653-8

Title, P. O., & Rabosky, D. L. (2019). Tip rates, phylogenies and diversification: What are we estimating, and how good are the estimates?
<figure><img src="image_14.png" /></figure>

Methods in Ecology and Evolution, 10(6), 821-834. https://doi.org/10.1111/2041-210X.13153

Todisco, V., Gratton, P., Zakharov, E. V., Wheat, C. W., Sbordoni, V., & Sperling, F. A. (2012). Mitochondrial phylogeography of the Holarctic *Parnassius phoebus* complex supports a recent refugial model for alpine butterflies. *Journal of Biogeography*, *39*(6), 1058-1072. https://doi.org/10.1111/j.1365-2699.2011.02675.x

Tyler, H. A., Brown, K. S., & Wilson, K. H. (1994). *Swallowtail butterflies of the Americas*. Scientific Publishers.

Vrba, E. S. (1980). Evolution, species and fossils-how does life evolve. *South African Journal of Science*, 76(2), 61-84.

Vrba, E. S. (1987). Ecology in relation to speciation rates: Some case histories of Miocene-recent mammal clades. *Evolutionary Ecology*, 1(4), 283-300. https://doi.org/10.1007/BF02071554

Vrba, E. S. (1992). Mammals as a key to evolutionary theory. *Journal of Mammalogy*, 73(1), 1-28. https://doi.org/10.2307/1381862

Walter, H. (1970). *Vegetationszonen und Klima*. Verlag Eugen Ulmer.

Webb, T. J., & Gaston, K. J. (2000). Geographic range size and evolutionary age in birds. *Proceedings of the Royal Society of London. Series B: Biological Sciences*, 267(1455), 1843-1850. https://doi.org/10.1098/rspb.2000.1219

Whitmore, T. C. (1998). An introduction to tropical rain forests (2nd ed.). Oxford University Press.

Wiens, J. J., & Donoghue, M. J. (2004). Historical biogeography, ecology and species richness. *Trends in Ecology & Evolution*, 19(12), 639-644. https://doi.org/10.1016/j.tree.2004.09.011

Willis, J. C. (1922). *Age and area: A study in geographical distribution and origin of species*. Cambridge University Press.

Wüster, W., Ferguson, J. E., Qijada-Mascareñas, J. A., Pook, C. E., Da Graca Salomao, M., & Thorpe, R. S. (2005). Tracing an invasion: Landbridges, refugia, and the phylogeography of the neotropical rattlesnake (Serpentes: Viperidae: *Crotalus durissus*). *Molecular Ecology*, 14(4), 1095-1108. https://doi.org/10.1111/j.1365-294X.2005.02471.x

Zinetti, F., Dapporto, L., Vovlas, A., Chelazzi, G., Bonelli, S., Balletto, E., & Ciofi, C. (2013). When the rule becomes the exception. No evidence of gene flow between two *Zerynthia* cryptic butterflies suggests the emergence of a new model group. *PLoS One*, 8(6), e65746. https://doi.org/10.1371/journal.pone.0065746

## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Gamboa, S., Condamine, F. L., Cantalapiedra, J. L., Varela, S., Pelegrín, J. S., Menéndez, I., Blanco, F., & Hernández Fernández, M. (2022). A phylogenetic study to assess the link between biome specialization and diversification in swallowtail butterflies. *Global Change Biology*, 28, 5901-5913. https://doi.org/10.1111/gcb.16344