Geography of life histories in a tropical fauna: The case of Indian butterflies

The butterfly fauna of the megadiverse Republic of India contains 1386 species. The species richness in its 36 federal states and union territories primarily reflects the peculiar geography of the Indian peninsula, which is isolated from the northerly situated Palearctic realm by arid deserts in the west and the Himalayas Mts. in the North, while just a narrow eastern conduit connects it with the rest of the Indo-Malayan Realm (Das et al., Insects 2023). Here, we further develop on these findings by focusing on species identities, relating the presences of individual species per federal states to physical geography, climate, land covers, and socioeconomic conditions of the states, interpreting the patterns by life history traits of individual species, and considering the phylogenetic signal in the environment x life history relationships. Physical geography was the strongest predictor of the states’ butterfly fauna compositions, followed by climate, land covers, and socioeconomics. The main faunal structures separate the humid Northeast from the rest of the country; distinguish humid Western Ghats states from the rest; and group together peninsular mountains. Analysing life histories showed that butterflies of the humid northeastern and southwestern states tend to be larger and develop on woody plants or large grasses; those of arid and high-altitude states tend to be smaller and develop on small forbs; whereas those utilising broad larval host plant scopes tend to be associated with shrubs and vines and inhabit large geographic ranges. More information on Indian butterflies’ life histories would likely yield more intricate insights.

The butterfly fauna of the megadiverse Republic of India contains 1386 species. The species richness in its 36 federal states and union territories primarily reflects the peculiar geography of the Indian peninsula, which is isolated from the northerly situated Palearctic realm by arid deserts in the west and the Himalayas Mts. in the North, while just a narrow eastern conduit connects it with the rest of the Indo-Malayan Realm (Das et al., Insects 2023). Here, we further develop on these findings by focusing on species identities, relating the presences of individual species per federal states to physical geography, climate, land covers, and socioeconomic conditions of the states, interpreting the patterns by life history traits of individual species, and considering the phylogenetic signal in the environment x life history relationships. Physical geography was the strongest predictor of the states’ butterfly fauna compositions, followed by climate, land covers, and socioeconomics. The main faunal structures separate the humid Northeast from the rest of the country; distinguish humid Western Ghats states from the rest; and group together peninsular mountains. Analysing life histories showed that butterflies of the humid northeastern and southwestern states tend to be larger and develop on woody plants or large grasses; those of arid and high-altitude states tend to be smaller and develop on small forbs; whereas those utilising broad larval host plant scopes tend to be associated with shrubs and vines and inhabit large geographic ranges. More information on Indian butterflies’ life histories would likely yield more intricate insights.

faunal structure, functional traits, Lepidoptera, Oriental realm, South Asia 1. Introduction Fauna of land masses reflect geological history, biotic evolution including speciation, dis- 1 Posted on 18 Jun 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175026207.79331488/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. persal and extinction, and life-supporting conditions, such as available energy/climate, which determine the biomes/landscape types and vegetation, and ultimately, resources for the animal components of ecosystems (Greenspoon et al. 2023; Rosenberg et al. 2023). The accelerating transformation of ecosystems and novel interactions peculiar for the current Anthropocene epoch (Malhi 2017) represent yet further level of factors, influencing the distribution of species. The monophyletic clade of butterflies (Lepidoptera: Papilionoidea) might represent a richer source of insights onto the diversity-distribution dynamics than birds, perhaps the most popular group in biogeography and macroecology (e.g., Pigot et al. 2018; Storch et al. 2023). Butterflies are similarly species-rich and easy to record. They are ecologically more homogeneous than birds, although their role in ecosystems is rarely verbalised. While their larvae convey to a general image of diet-specialised insect herbivores (Janz et al. 2001; Clarke 2022), their adults feed on high-sugar liquids, including nectar, honeydew, sap and rotting fruit. As rather inefficient pollinators (Wiklund et al. 1979; Bauder et al. 2015; but see Burgin et al. 2023), they exploit plants x pollinators or Hemiptera x ants’ interactions (Corke 1999; Gardner-Gee 2014). Fruits do not produce sugars for butterflies, but for seed-dispersing vertebrates (Giovannoni et al. 2017), and rotting decay is a by-product of high sugar content (Dudley 2000; Spengler 2019). As interactions exploiters, butterflies’ diversity should follow the complexity of interactions, which is highest in the humid tropics (Trøjelsgaard & Olesen 2013; Forister et al. 2014). The butterfly clade originated in tropics of Americas (Kawahara et al. 2023) and display the highest alpha-diversity in humid tropical forests (Brown Jr and Freitas 2000; Bonebrake et al. 2010). From them, these interaction-exploiters colonised less complex ecosystems. Regretfully, detailed knowledge of butterfly faunas and communities exists mainly for those regions of Earth, where lepidopterology as a hobby (Salmon et al. 2000), and studies of butterflies as an academic niche (Kudrna 1990), historically evolved: Europe, parts of North America (Scott 1986; Settele et al. 2009), less so Australia, Japan, or South Africa (Braby 2000; Mecenero et al. 2013; Japan Butterfly Conservation Association 2019). This selection includes both regions with long cultural continuity (Europe, Japan) and regions that underwent major ethnocultural transformations (Australia, North America). It is rather uniform, however, in terms of background ecological conditions, as they are mainly situated in temperate bioclimatic zones. From these regions, near-complete knowledge of life histories of entire butterfly faunas exists (e.g., Baguette and Stevens 2013; Middleton-Welling et al. 2020). The knowledge of life histories, formalised as life history traits’ analysis (McGill et al. 2006; de Bello et al. 2021) is instrumental for understanding mechanisms of assemblages’ composition, and underlying ecosystem processes (e.g., Diamond et al. 2011; Slancarova et al. 2016). The Republic of India is megadiverse prevailingly tropical country with venerable tradition of butterfly rese- arch (Evans 1932; Holloway 1974; Mani 1986), recent boom of butterfly distribution and ecology studies (e.g., Dar et al. 2022; Dewan et al. 2022; Jambhekar and Driscoll 2023; Naik et al. 2022) and over 1300 butterfly species recorded to date (Das et al. 2023). Belonging to Indo-Malayan zoogeography realm, the country dis- plays a complex (zoo)geography with the World’s highest mountains separating it from the Palearctic Realm in the North, southern mountain ranges acting as regional endemism centres, ocean separating it from other tropical regions, and only narrow conduits towards the rest of Indo-Malayan realm in the East, and to arid parts of the Palearctic realm in the West. Analysing the species richness of 36 federal states and territories, Das et al. (2023) showed that due to peculiarities of Indian geography, some of the well-established macroe- cology patterns, such as species-area relationship or latitudinal decrease of species richness, do not hold for this system, whereas topographic diversity and the precipitation/temperature ratio (energy availability) car- ried out. As found earlier (Holloway 1974; Kunte 2016; Dewan et al. 2022), humid and forested northeastern states were species richest, mountainous southwest harboured the highest endemism, and both core of the peninsula with more continental climate and the arid northwest were species-poor. Species richness, however, is a simplistic description of regional ecological conditions. Exploring the species’ composition of regional faunas should bring deeper insights into factors structuring the faunas, especially if studied through the prism of history traits (Dennis et al. 1998; Kleisner et al. 2012; Soininen et al. 2016). The latter are not yet available for Indian butterflies in the detail typically analysed from temperate 2 Posted on 18 Jun 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175026207.79331488/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. regions (Middleton-Welling et al. 2020), but some life history knowledge is available for all Indian species (cf. Kehimkar 2008; Shirey et al. 2022; Savela 2023), with more detailed knowledge existing for regional faunas (Dewan et al. 2022). Additionally, the patterns of distribution of majority of species are known at global level. Here, we capitalise on the existing knowledge of distribution and life histories of Indian butterflies and use multivariate techniques to disclose the patterns of species composition at the level of federal states and territories. We relate the patterns to predictors characterising the states, namely geography, climate, land covers, and current socioeconomic conditions. We then interpret the patterns found by available species traits. Because species ranges and life histories are closely tied to phylogeny, we use phylogeny-controlled analysis to differentiate between phylogeny-linked and unlinked patterns. We target the following hypotheses: (1) Unconstrained ordinations will reveal faunal structures (sensu Dennis et al. 1998) in species co-occurrence, defined as similarities or dissimilarities among states and regions. (2) These faunal structures should be best interpretable by physical geography, which reflects long-lasting Earth-forming processes, and by climate, reflecting energy and resources availability. (3) Measurements of land covers provide a robust but imperfect information on ecosystems composition, while socioeconomic conditions mainly reflect recent processes in human society; socioeconomic predictors should only weakly predict the species compositions. Regarding life history traits, (4) butterfly body size should increase with energy availability and climatic stability. (5) The growth forms of larval host plants should reflect the representation of plant growth forms in the respective biomes. (6) Species with wide global distribution should develop on wider scopes of host lant forms than species utilising narrow scopes of host plant forms. 2. Materials and Methods 2.1 Data preparation We used the matrix of all Indian butterfly species and their presences in 36 federal states and union territories (Das et al. 2023; Appendix 1 Figure S1) and updated it with more recent records, obtaining total of 1386 species by April 2024. Predictors describing individual states (Table 1) developed on those used by Das et al. (2023) in the study of species richness patterns. The six geography predictors were the cardinal coordinates of the states’ centroids, their average altitude, their areas, and the difference between highest and lowest altitude viewed as a measure of topographic and habitat heterogeneity. For climate , we used the 19 bioclimatic variables (WorldClim 2024), from CHELSA v.1.2 with 2.5 min spatial resolution (Karger et al. 2017), extracted and averaged for the areas within the federal states’ boundaries. For land covers , we used 24 statistics describing the conditions in Indian federal states, extracted using satellite imagery and provided by Bhuvan LULC 15-16 (Bhuvan 2023). The six socioeconomics variables were publicly available statistics describing conditions of individual states. To obtain information on butterfly life histories, we tabularised for all species the following information: (i) wing span (numeric, mm) as a simple measure of body size, potentially related to adult mobility (Sekar 2012), but also to development length and host plants defences (Bartonova et al. 2014); (ii) larval host plant growth form (categorical, distinguishing forbs, grasses, tall grasses, shrubs, climbers/vines, trees, and ants- dependent/carnivorous), which is straightforwardly linked to habitat use (Jain et al. 2017) and to host plants defences and butterfly voltinism (Altermatt 2010a); (iii) larval plants forms scope (numeric), obtained as a simple sum of larval host plant growth forms, assuming that broader dietary range links to more generalised resource use (Shreeve et al. 2001; Vanreusel and Van Dyck 2007); (iv) distribution range in India, defined as the number of states with positive occurrence (numeric, 1–36); (v) global distribution range (ordinal at 1–5 scale, where 1 – particular region, ideally smaller than the area of India, 2 – size of India, 3 – greater than India but smaller than Indo-Malayan realm, 4 – greater than Indo-Malayan realm or size of a continent, 5 – Cosmopolitan). For missing values (n = 464, i.e., 3.04% of the 15,246 cells of the traits’ matrix, all related to host plant forms), we used arithmetic means of relevant columns of the table. Note that (iv+v) do not represent life history traits in a strict sense but convey information on niche breadth and potential 3 Posted on 18 Jun 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175026207.79331488/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. conservation priorities (Dewan et al. 2022; Hausharter et al. 2023). Because species are not independent units but share phyletic histories, relations between life history traits and their distributions should be constrained by phylogeny in analyses (Harvey and Pagel 1991). We used the phylogeny tree and its list of all butterflies from Kawahara et al. (2023). From this, we manually selected all the genera distributed in India and removed the remaining genera, using ”keep.tip” function from the R package ape (Paradis and Schliep 2019). Species not represented in the tree were added to the roots of their respective genera, using the “add.species.to.genus” (R package phytools ) (Revell 2024). The genera not included to the phylogeny tree were added to the closest proximate node manually by following available individual phylogeny using function “bind.tip”. Lastly, we also removed any species not recorded in India from their genus root by utilizing the ”drop.tip” function. 3. Results The unconstrained PCA relating butterflies to federal states revealed prominent geography patterns (Figure 1). The first principal component (eig 0.389) separated humid monsoon northeastern states from the rest; the second component (eig 0.121) separated the southern peninsular states from those in the Himalayan North of the peninsula; the third (eig 0.088) distinguished Himalayan and southern mountainous (Western Ghats) states from both the northeast and interior of the peninsula; while the fourth (eig 0.040) accounted for only minor part of the variation. The partial pPCAs indicated that geography had overwhelming effects on the distribution of butterflies, explaining 62% of variation in the data. This was followed by climate (36 %), land covers (29%) and socioeconomics (18%) (Table 2). Visualizing the results showed that with geography as covariables, the relationships among states completely dissipated, whereas in the models controlled for climate , land covers andsocioeconomics , the basic patterns (northeast versus the rest, Himalayas versus the rest) remained preserved (Appendix 1 Figure S4). The constrained RDA ordinations (Table 2, Figure 2) corroborated that the strongest predictor of per- state butterfly species composition was geography , followed by climate , land covers andsocioeconomics . Consequently, controlling climate ,land covers and socioeconomics models by geography resulted in non- significant models. Climate , however, retained some independent effects if controlled by land covers andsocioeconomics , land covers retained a weak independent effect if controlled by climate and socioe- conomics , andsocioeconomics did not sustain controls by other sets for predictors. Interpreting the ordination results by life history traits (Table 3) revealed that in unconstrained analysis, large-bodied butterflies developing on trees, climbers or tall grasses inclined towards northeastern states (Figure 3a). Such butterflies also tend to have wide distributions across the Old-World tropics. Species de- veloping on shrubs, or having wide host plant scope, inclined towards southern mountainous states. Finally, small-bodied species developing on grasses and forbs inclined towards mountainous states. The patterns ob- tained by interpreting geography model were very similar (Figure 3b). The patterns for climate again showed the association of large-bodied butterflies with development on climbers, trees, tall grasses, but also shrubs, and the association of all these traits with monsoon climates with high precipitation and high temperatures. Such butterflies also display large global distribution ranges and inhabit many federal states. Small-bodied butterflies developing on forbs or short grasses displayed the opposite patterns, i.e., an association with low temperatures and precipitation (Figure 3c). For land covers , the large-bodied butterflies associated with tall grasses, trees and climbers inclined towards low land covers diversity and low representation of scatte- red trees, i.e., towards contiguous forests. They also displayed wide global distributions. In contrast, high representation of scattered trees was associated with development on shrubs, and occurrence in many Indian states. Small butterflies feeding on forbs were situated oppositely to any trees, either scattered or contiguous (Figure 3d). Interpreting the socioeconomics ordination was most puzzling, as it produced very strong first ordination axis (cf. Table 3), parallel with high rural population and high livestock. These conditions were associated with wide host plants scopes, feeding on shrubs, distribution in many Indian states, relatively small wing spans, and rather wide global ranges. The much weaker second ordination axis run in parallel 4 Posted on 18 Jun 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175026207.79331488/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. with low rural population and high livestock, conditions typical for sparsely populated arid areas. Here, a high representation of small-bodied butterflies develops on forbs or grasses. The large-bodied butterflies, typically feeding on tall grasses, were most represented in states with low rural population and low livestock, i.e. in the densely forested states (Figure 3e). The models considering butterfly phylogeny explained much lower amounts of variation than the models ignoring it (Table 3). The difference in explained variation between the models ignoring and considering phylogeny was highest for socioeconomics , implying that patterns of butterfly distribution explicable by socioeconomic conditions were the least phylogeny dependent. Interpreting the models by traits dissolved the associations between body size, feeding on trees and global distribution, while retaining the associations between feeding on shrubs and climbers with high number of host plant forms (Figure 4a). The link among global distribution, the number of inhabited Indian states and the number of host plant forms was retained in the analysis constrained by geography (Figure 4b), while body size lost the association with feeding on trees, implying a strong phylogenetic dependency of large bodies with development on tree foliage. The patterns were almost identical in analyses with climate ,land covers , and socioeconomics (Figures 4c, d, e). 4. Discussion Using multivariate analysis to explore the distributional patterns of butterfly species at the level of 36 federal states and territories forming the Republic of India, we found an overwhelming effect of geography, which explained over 60 percent of variation in the analysis constrained by geographic predictors. Climate, land covers and socioeconomic characteristics of the federal states had much weaker explanatory effects and were closely tied to geography. The patterns of species distribution were attributable to their life histories. Large-bodied species inclined towards northeastern states with warm and humid monsoon climate, dense forests, and low representation of rural population. The growth forms of larval host plants reflected a broad distribution of plant growth forms in the main biomes of India. Larval development on trees, climbers and tall grasses corresponded with occurrence in states situated in the humid forest biome, and development on forbs and grasses with states containing arid or alpine biomes. The scope of larval host plant forms used by individual species increased with feeding on shrubs and climbers and butterflies with broad host plant forms scopes inhabited high numbers of federal states and displayed wide global distributions. Despite the still limited information on life histories of Indian butterflies, analysing the traits links the species’ ecology and distribution. The overwhelming geography effect and existence of geographically linked faunal structures was already observed in the classic analysis by Holloway (1974), who used the best numeric method available at his time, a cluster analysis. He regretfully worked only with a selection of butterfly genera. Das et al. (2023) developed this approach while analysing species numbers recorded per Indian federal states, covering all butterfly species. The results presented here corroborate the earlier findings using species identities. The effect of geographic predictors is attributable to the peculiar geography of Indian peninsula, which affected phyletic history of Indian taxa. In the Cretaceous period, when basal radiation of Papilionoidea occurred in Gondwanan South America, Australia, and Eastern parts of the Northern continent (Kawahara et al. 2023), Indian peninsula was a large island, gradually drifting northwards after the breakup of Gondwana. Not too high diversity of indigenous lineages is expectable on islands (MacArthur and Wilson 1967) and the diversity was possibly affected by late-Cretaceous catastrophic volcanism of Deccan traps (cf. Wellman and McElhinny 1970; Schoene et al. 2015). Later, colonisation of the current peninsula by butterfly lineages occurred mainly from the archipelagos now forming eastern part of Indo-Malayan zoogeographic realm, which communicated with both Australian and East-Palearctic regions (cf. Kawahara et al. 2023). The peninsular India, however, had been and remains relatively isolated from eastern parts of Indo-Malayan region. Only a narrow conduit formed by Garo-Rajmahal gap in present-day Bangladesh connects it with more easterly tropical areas (Tiwari and Jassal 2001; Das et al. 2023), while the Himalayas block the communication with the Palearctic (Mani 1986). The colonisation from Africa, via transoceanic transfers or overland, had always been restricted (Kodandaramaiah and Wahlberg 2007; Irungbam et al. 2023). Consequently, the fauna of federal states east of Garo-Rajmahal gap is richer than the fauna of peninsular India (Holloway 1974; Kunte 5 Posted on 18 Jun 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175026207.79331488/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. et al. 2012). A notable exception are the Western Ghats states in SW India, which form a secondary speciation and endemism centre (Gaonkar 1996; Kunte 2016), but also display increased representation of afro-tropical species (Das et al. 2023). Climatic patterns are fundamentally governed by geography, which is evident from the effects of coasts and major mountain ranges on monsoons (Lu et al. 2020; Chauhan et al. 2023), or from differences in vegetation between windward and leeward mountain slopes (Pepin et al. 2017). Climate, however, directly affects the eco-physiological tolerance of insects (Sinclair et al. 2003; Vrba et al. 2017). Das et al. (2023) showed that per-state richness of Indian butterflies reflects the available energy, in agreement with various taxa from across the world (e.g., Wright 1983; Hawkins and Porter 2003; Carrara and V´ azquez 2010; Duncan et al. 2015). The two main climatic connections influencing the species composition of Indian states’ faunas are that connecting the mountainous and humid northeast with northwestern Himalayan and western Ghats states (shared species, e.g., Celastrina lavendularis ,Neptis nata ), and that connecting the trans-Himalayan Ladakh with sparsely forested arid states of NW peninsula, and central Deccan (e.g., Gegenes nostradamus , Belenois aurota ). In agreement with our original prediction, land covers and socioeconomic predictors executed weaker in- fluences on the composition of Indian states’ faunas, and even their influences reflected underlying effects of geography (e.g., low forest cover in the high mountainous Ladakh) or climate (e.g., the high livestock numbers in the arid Rajasthan, the warm and humid densely forested northeastern states). Consequently, the land covers produced ordinations very similar to climate and geography, whereas the analysis based on socioeconomic predictors produced completely different patterns, decoupled from the geography-based faunal structures. Given incomplete knowledge of life histories of many Indian species (cf. Shirey et al. 2022), we tabularised only a few readily available traits. Still, the distribution of the traits in federal states‘ faunas reflected highly significantly the predictors describing the states. The most easily available trait, the wing span (a body size proxy), increased towards northeastern states, and the climate ,land covers and socioeconomics models all agreed that forests of Indian northeast host the largest butterflies. Indeed, some of the largest species of Indian fauna are either restricted to northeastern states (e.g., Atrophaneura aidoneus , Papilio paradoxa , Stichophtalma sparta ), or occur there and in other humid forested regions (e.g., Troides aeacus , Byasa dasarada , Papilio arcturus ). By supplement, small-bodied butterflies were associated with mountainous and/or northwestern states, including the trans-Himalayan Ladakh. Some of the smallest Indian butterflies are restricted to the latter (e.g., Turanana chitrali , Agriades pheretiades , Pyrgus alpinus ), which does not exclude the presence of some small species in tropical peninsular India (e.g., Freyeria putli ,Zizula hylax , Spialia galba ). The debate on geographic distribution of butterfly (or Lepidoptera) body sizes has long been obscured by inconsistent results from different continents and clades, suggesting effects of phyletic history (Barlow 1994; Hawkins and Lawton 1995). A trade-off exists between body size and development speed, and hence the number of generations (Seifert et al. 2023). In high latitudes, lepidopteran body size – voltinism relationships are restricted by season length, available temperatures, and the ecosystems’ net primary productivity (Huston and Wolverton 2011; Zeuss et al. 2017). Similar constraints likely restrict butterfly body size in mountainous or arid regions, including those of India. In the highly productive rainforest states (cf. Das et al. 2023), presence of large-sized butterflies is fully expected. In ordinations of species traits, large wingspan corresponded with development on tall grasses (i.e., bamboos of the genera Bambusa ,Dendrocalamus , Saccharum ), climbers (e.g., Aristolochia , Cynanchum , Vinceto- xicum ) and trees, whereas small wingspan corresponded with development on small grasses and forbs. This appears linked to prevailing land covers in the respective states (humid forests in the noreastern, Himalayan slopes and Western Ghats states, grasslands in arid northwest and trans-Himalayan Ladakh). It does not explain, however, why the rainforest butterflies tend to grow large, rather than staying small and forming multiple generations. Young plant tissues are more palatable for young herbivorous insects larvae than old tissues (Cizek 2005). Then, development on woody plants requires that the larvae start feeding on fresh 6 Posted on 18 Jun 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175026207.79331488/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. foliage, which constraints the numbers of insect generations while favouring larger final size (Cizek et al. 2006; Altermatt 2010b; Seifert et al. 2023). Hatada and Matsumoto (2007) observed that larval development of the papilionid Luehdorfia japonica was longer on old than young leaves. Because availability of young plant tissues is seasonally less predictable in humid tropics than in seasonal regions, the herbivores, inclu- ding butterflies, may be selected for larger body size, and hence the ability to develop on older tissues. The relationships between development length, body size and voltinism is further linked to the plants’ antiherbi- vore defences (Smilanich et al. 2016), which remain little explored in the tropics (but see Endara et al. 2017; Segar et al. 2024). Host plant scope, expressed here as the number of host plants growth forms utilised per species, was related to larval development on shrubs, climbers and less so trees and tall grasses, but not small grasses and forbs. Notably, some genera of Indian flora contain trees, shrubs and climbers (e.g.,Morinda , Ficus ), but not forbs and small grasses. This implies that the basic distinction between species developing on small/unapparent and qualitatively protected plants, typically forbs, versus large/apparent and quantitatively protected plants, i.e. trees, shrubs and tall grasses (Feeny 1976) exists in Indian butterfly fauna. The host plant scope as used by us might also be related to taxonomic trophic range (i.e., the number of host species), and hence to niche breadth and commonness vs. rarity. Such relationships are unlikely straightforward, as common but strictly monophagous butterflies exist, e.g., in the Palearctic (cf. Bryan et al. 1997). The wide host scope was also colinear with the number of Indian states inhabited, suggesting that such butterflies inhabit many federal states, including species-poor central peninsular areas and small coastal territories. It was also collinear with wide global distribution. Butterflies displaying these traits combinations include such widespread pantropical species as Hypolimnas missipus , Lampides boeticus or Eurema blanda . In analyses controlled for phylogeny, the relationship between large body size and feeding on trees or tall grasses dissipated. This reflects the existence of species-rich clades, whose members are similar in size and conserved in associations with certain host plant forms. Examples at the large-bodied side include Graphium or Euthalia developing on trees (22 and 20 species in India, respectively); Byasa and Euploea developing on vines (7 and 12 species) or Lethe developing on tall grasses (42 species). At the small-bodied side,Heliophorus (12 species) and Jamides (8 species) all develop on small forbs. In contrast, the positive relationships between the number of host plant forms, numbers of Indian states and global distribution were independent on phylogeny, suggesting that broad larval trophic ranges covary with wide distributions independently of phylogeny of such successful taxa. To conclude, we demonstrated that analysing of factors affecting regional species compositions is possible for tropical faunas even at relatively crude level of political units, such as Indian federal states and territories. Geography crucially affects the composition of regional faunas, and interpretation of the faunal compositions by life history traits revealed intriguing patterns. The number of traits analysed, however, was severely restricted by still incomplete knowledge of life histories of many of the Indian species. Therefore, much work aiming to decipher detailed ecological requirements of individual species is needed.

We thank Prof. Petr Smilauer for his comments on formal analysis, Swapna Devi Ray for her assistance during preliminary data compilation and Matthew Sweney for language checking. The study was funded by the Grant Agency of the University of South Bohemia, grant number 100/2022/P. not-yet-known not-yet-known not-yet-known unknown Conflicts of Interest The authors declare no conflict of interest.

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Environmental drivers of voltinism and body size in insect assemblages across Europe.Global Ecology and Biogeography26: 154–165. https://doi.org/10.1111/geb.12525 TABLE 1. Overview of the predictors used to analyze the distribution of 1386 butterfly species recorded in 36 federal states and union territories of the Republic of India. The predictors marked as * and ** are compound predictors, obtained by PCA analyses of Climate andLand covers predictors, respectively (see Figures S1 and S2 for details). V ariables Description Unit V ariables Description Unit Geography Land covers x Centroid latitude DD Cr Crop land sq. km. y Centroid longitude DD Sh Current Shifting cultivation sq. km. zmin Minimum Altitude meter Fa Fallow sq. km. zmax Maximum Altitude meter Pl Plantation sq. km. z Mean Altitude meter Ba Barren rocky sq. km. Area Area of the state sq. km. Gu Gullied / Ravinous sq. km. AltDiff Altitude difference (z max – zmin) meter Ra Rann sq. km. Climate Sa Salt affected land sq. km. bio1 Annual mean Temperature SD Sandy area sq. km. bio2 Mean Diurnal Range (Mean of monthly (max temp - min temp)) ScL Scrub land sq. km. Mi Mining sq. km. bio3 Isothermality (bio2/bio7) ( ×100) Ru Rural sq. km. bio4 Temperature Seasonality (standard deviation ×100) °C Ur Urban sq. km. De Deciduous sq. km. bio5 Max Temperature of Warmest Month °C Ev Evergreen / Semi Evergreen sq. km. bio6 Min Temperature of Coldest Month °C FoP Forest Plantation sq. km. bio7 Temperature Annual Range (bio5-bio6) °C ScF Scrub Forest sq. km. bio8 Mean Temperature of Wettest Quarter °C Sw Swamp / Mangrove sq. km. bio9 Mean Temperature of Driest Quarter °C Gr Grass / Grazing land sq. km. 13 Posted on 18 Jun 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175026207.79331488/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. V ariables Description Unit V ariables Description Unit bio10 Mean Temperature of Warmest Quarter °C Sn Snow sq. km. bio11 Mean Temperature of Coldest Quarter °C InW Inland wetland sq. km. bio12 Annual Precipitation mm CoW Coastal wetland sq. km. bio13 Precipitation of Wettest Month mm Ri River / Stream / Canals sq. km. bio14 Precipitation of Driest Month mm WaB Waterbodies sq. km. bio15 Precipitation Seasonality (Coefficient of Variation) mm Land1** Land cover PCA axis 1 Land2** Land cover PCA axis 2 bio16 Precipitation of Wettest Quarter mm Land3** Land cover PCA axis 3 bio17 Precipitation of Driest Quarter mm Land4** Land cover PCA axis 4 bio18 Precipitation of Warmest Quarter mm Socioeconomics bio19 Precipitation of Coldest Quarter mm HumPopDens Population density sq. km. Climate1* Climate PCA axis 1 UrPop Urban population % Climate2* Climate PCA axis 2 RuPop Rural population % Climate3* Climate PCA axis 3 LitRate Literacy rate % Climate4* Climate PCA axis 4 GDPpercap Gross domestic product per Capita USD Livestock Total Livestock million TABLE 2. Results of unconstrained (PCA) and constrained (RDA) ordinations explaining the butterfly species composition at the level of 36 federal states and union territories of the Republic of India. The combinations of geography , climate , land covers andsocioeconomics predictors were obtained by forward selection procedures, the same combinations were used in partial ordinations as covariables (notified as “ | ” in the table). The models marked by “ + ” were used in subsequent third-order analyses with life history traits (Table 3). Model Model structure V ar adj Eig1 Eig2 Eig3 Eig4 FPaxis1 FPall axes Unconstrained (PCA)+ ˜ 0.389 0.121 0.088 0.040 ˜| Geography ˜ | x +y +x*y +x*z +y*z +y 2 +z2 +AltDiff 62.1 0.074 0.048 0.031 0.024 ˜| Climate ˜ | Climate1 +Climate2 +Climate3 36.3 0.158 0.073 0.064 0.036 ˜| Land covers ˜ | Land1 +Land3 29.6 0.187 0.106 0.064 0.036 ˜| Socioeconomics ˜ | Rural population +Livestock 18.4 0.258 0.108 0.079 0.038 Geography RDA+ x +y +x*y +x*z +y*z +y 2 +z2 +AltDiff 50.9 0.338 0.095 0.079 0.032 1.7 *** 5.5*** ˜| Climate y +x*z +y*z +z 2 +AltDiff | Climate 26.1 0.107 0.054 0.038 0.026 1.1 *** 3.3*** ˜| Land covers x +y +x*y +x*z +y*z +y 2 +z2 +AltDiff | Land covers 37.5 0.143 0.076 0.057 0.027 0.8 *** 3.5*** ˜| Socioeconomics x +y +x*y +x*z +y*z +y 2 +z2 +AltDiff | Socioeconomics 42.0 0.210 0.078 0.071 0.029 1.1 *** 4.0*** Climate RDA+ Climate1 +Climate2 +Climate3 30.3 0.281 0.063 0.018 – 4.2 *** 6.1*** ˜| Geography – | Geography – – – – – – – ˜| Land covers Climate1 +Climate2 +Climate3 | Land1 +Land3 18.3 0.111 0.047 0.023 – 1.9 *** 3.5*** ˜| Socioeconomics Climate1 +Climate2 | Socioeconomics 20.2 0.157 0.047 – – 3.7 *** 5.2*** Land covers RDA+ Land1 +Land3 25.4 0.230 0.066 – – 4.9 *** 6.9*** ˜| Geography Land3 | Geography 03.9 0.028 – – – 2.1 ** – ˜| Climate Land1 +Land3 | Climate 12.5 0.071 0.043 – – 1.9 ** 3.3*** ˜| Socioeconomics Land1 +Land3 | Socioeconomics 15.1 0.112 0.054 – – 2.5 *** 3.9*** Socioeconomics RDA + Rural population +Livestock 13.5 0.160 0.025 – – 3.1 ** 3.7** ˜| Geography – | Geography – – – – – – – ˜| Climate – | Climate – – – – – – – ˜| Land covers – | Land covers – – – – – – – 14 Posted on 18 Jun 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175026207.79331488/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. Monte-Carlo permutation tests significance values: **P<0.01, ***P<0.001 TABLE 3. Statistical properties of RDA models, using third-corner approach to relate the ordination axes from models relating the distribution of 1386 butterfly species in 36 Indian federal states and union territories to predictors describing the states (summarised in Table 2), to life history traits of the butterflies. The models are visualised at Figures 3 + 4. Model V ar adj Eig1 Eig2 Eig3 Eig4 FPaxis1 FPall axes Ignoring phylogeny Unconstrained (PCA) 18.0 0.101 0.062 0.013 0.008 15.5 *** 31.3*** Geography RDA 22.4 0.156 0.061 0.010 0.002 25.3 *** 40.9*** Climate RDA 17.3 0.128 0.036 0.009 0.006 20.2 *** 30.0*** Land covers RDA 31.4 0.225 0.095 – – 39.8 *** 64.4*** Socioeconomics 29.6 0.265 0.036 – – 49.6 *** 59.1*** Considering phylogeny Unconstrained (PCA) 11.1 0.056 0.012 0.005 0.003 12.0 *** 16.8*** Geography RDA 13.0 0.065 0.007 0.004 0.001 16.2 *** 19.8*** Climate RDA 10.5 0.047 0.011 0.004 0.002 11.3 *** 15.8*** Land covers RDA 11.2 0.048 0.009 0.002 0.001 12.8 *** 16.3*** Socioeconomics RDA 17.7 0.088 0.014 0.005 0.001 22.2 *** 28.3*** not-yet-known not-yet-known not-yet-known unknown FIGURE LEGENDS FIGURE 1. Visualisation of results of PCA analysis, relating occurrences of 1386 butterfly species recorded from the 36 federal states and union territories of the Republic of India. Intensity of colours is proportional to values of loadings at the 1st – 4th PCA axes. FIGURE 2. Visualisation of results of the (constrained) RDA analyses, relating occurrences of 1386 butterfly species recorded from the 36 federal states and union territories of the Republic of India togeography , climate , land covers andsocioeconomics . See Table 2 for terms of the ordination models and relevant statistics. Intensity of colours is proportional to values of loadings at the 1st and 2nd RDA axes of each model. FIGURE 3. Ordination diagrams obtained by the third-corner analyses (axes 1st and 2nd), using RDA to relate the ordination axes (blue darts) from unconstrained PCA (a) and constrained RDA analyses (con- strained by b) – geography , c) – climate , d) – land covers , e) – socioeconomics ), to life history traits (grey darts) of the butterfly species recorded in the 36 federal states and union territories of the Republic of India. See Tables 2 and 3 for the relevant statistics. Traits abbreviation: Ws – Wing span, Fr – (larval development on) forbs, Shr – shrubs, Cl – climbers/vines, Tr – trees, Gr – grasses, tGr – tall grasses, Ca/My – carnivorous/ants-dependent or Myrmecophily, Hp scope – larval host plants scope, Ind – Indian distribution range, Glo – Global range. FIGURE 4. Ordination diagrams obtained by the butterfly phylogeny constrained third-corner analyses (axes 1st and 2nd), using RDA to relate the ordination axes from unconstrained PCA (a) and constrained RDAs (constrained by b) – geography , c) – climate , d) –land covers , e) – socioeconomics ), to life history traits of the butterfly species recorded in the 36 federal states and union territories of the Republic of India. Only the relationships among traits (grey darts) are shown. See Tables 2 and 3 for the relevant statistics. 15 Posted on 18 Jun 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175026207.79331488/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. Traits abbreviation: Ws – Wing span, Fr – (larval development on) forbs, Shr – shrubs, Cl – climbers/vines, Tr – trees, Gr – grasses, tGr – tall grasses, Ca/My – carnivorous/ants-dependent or Myrmecophily, Hp scope – larval host plants scope, Ind – Indian distribution range, Glo – Global range. FIGURE 1 FIGURE 2 16 Posted on 18 Jun 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175026207.79331488/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. 17 Posted on 18 Jun 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175026207.79331488/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. not-yet-known not-yet-known not-yet-known unknown FIGURE 3 FIGURE 4 18 Posted on 18 Jun 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175026207.79331488/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. APPENDIX 1 FIGURE LEGENDS FIGURE S1. Key to Indian states with their butterfly species richness. State abbreviations: AN – Andaman and Nicobar, AP – Andhra Pradesh, AR – Arunachal Pradesh, AS – Assam, BR – Bihar, CG – Chhattisgarh, CH – Chandigarh, DD & DN – Daman and Diu and Dadra and Nagar Haveli, DL – Delhi, GA – Goa, GJ – Gujarat, HP – Himachal Pradesh, HR – Haryana, JH – Jharkhand, JK – Jammu and Kashmir, KA – Karnataka, KL – Kerala, LA – Ladakh, LD – Lakshadweep MH – Maharashtra, ML – Meghalaya, MN – Manipur, MP – Madhya Pradesh, MZ – Mizoram, NL – Nagaland, OD – Odisha, PB – Punjab, PY – Puducherry, RJ – Rajasthan, SK – Sikkim, TN – Tamil Nadu, TR – Tripura, TS – Telangana, UK – Uttarakhand, UP – Uttar Pradesh, WB – West Bengal. The colour scale is proportional to butterfly species richness. 19 Posted on 18 Jun 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175026207.79331488/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. FIGURE S2. Unconstrained ordination of bioclimatic variables PCA biplots showing the mutual positions of 19 bioclimatic variables at the first and the second ordination axes (a), positions of Indian federal states along first and second axes (b), positions of the variables at the third and fourth ordination axes (c), and positions of Indian federal states along the third and fourth axes (d). Key, bioclim variables (from WorldClim 2024): Bio1 – Annual Mean Temperature, Bio2 – Mean Diurnal Range (Mean of monthly max temp – min temp), Bio3 – Isothermality (Bio2/Bio7), Bio4 – Temperature Seasonality, Bio5 – Max Temperature of Warmest Month, Bio6 – Min Temperature of Coldest Month, Bio7 – Temperature Annual Range (BIO5–Bio6), Bio8 – Mean Temperature of Wettest Quarter, Bio9 – Mean Temperature of Driest Quarter, Bio10 – Mean Temperature of Warmest Quarter, Bio11 – Mean Temper- ature of Coldest Quarter, Bio12 – Annual Precipitation, Bio13 – Precipitation of Wettest Month, Bio14 – Precipitation of Driest Month, Bio15 – Precipitation Seasonality, Bio16 – Precipitation of Wettest Quarter, Bio17 – Precipitation of Driest Quarter, Bio18 – Precipitation of Warmest Quarter, Bio19 – Precipitation of Coldest Quarter. See Figure S1 for key to Indian states. FIGURE S3. Unconstrained ordination of land covers variables PCA biplots showing the mutual positions of 24 land covers variables at the first and the second ordination axes (a), positions of Indian federal states along the first and second axes (b), positions of the variables at the third and fourth ordination axes (c), and positions of Indian federal states along the third and fourth axes (d). Key, land covers variables (from Bhuvan 2023): Cr – Crop land, Sh – Current Shifting cultivation, Fa – Fallow, Pl – Plantation, Ba – Barren rocky, Gu – Gullied / Ravinous, Ra – Rann, Sa – Salt affected land, Sd – Sandy area, ScL – Scrub land, Mi – Mining, Ru – Rural, Ur – Urban, De – Deciduous, Ev – Evergreen / Semi Evergreen, FoP – Forest Plantation, ScF – Scrub Forest, Sw – Swamp / Mangrove, Gr – Grass / Grazing land, Sn – Snow, InW – Inland wetland, CoW – Coastal wetland, Ri – River / Stream / Canals, WaB – Waterbodies. See Figure S1 for key to Indian states. FIGURE S4. Visualisation of results of partial pPCA analyses, relating occurrences of 1386 butterflies recorded from the 36 federal states and union territories of the Republic of India, containing in covariables predictors describing geography (a, b), climate (c, d), land covers (e, f), and socioeconomics (g, h). Intensity of colours is proportional to values of loadings at the 1st to 2nd PCA axes of each predictor. See Table 2 for statistics of individual analyses. FIGURE S1. 20 Posted on 18 Jun 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175026207.79331488/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. not-yet-known not-yet-known not-yet-known unknown FIGURE S2. 21 Posted on 18 Jun 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175026207.79331488/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. FIGURE S3. 22 Posted on 18 Jun 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175026207.79331488/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. FIGURE S4 . 23 Posted on 18 Jun 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175026207.79331488/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. 24