Butterfly seasonality vs. life history traits in Manas National Park, India: Effects of vegetation, nectar, megafauna, invasive shrubs, and fire

Seasonal humidity patterns in the tropics shape vegetation and insect communities, with life history traits offering insight into how seasonality influences species richness, abundance, and composition. To explore these effects, we surveyed adult butterflies across 18 one-hectare plots in Manas National Park, Assam, northeastern India (12 within the park, 6 in adjoining rural habitats), characterized by environmental variables including vegetation structure, nectar, invasive shrubs, and mammalian megafauna visitation. We recorded 121/4053 butterfly species/individuals (average species per plot 32.1±14.07 SD) in post-monsoon and 104/2727 species/individuals (24.8±11.93 SD) in pre-monsoon, indicating a seasonal turnover. As elsewhere in the tropics, forests were richer in species than grasslands, and this habitat effect was consistent across both seasons. Species richness peaked at intermediate tree and shrub cover, following gentle hump-shaped patterns. Abundance was also higher in post-monsoon and highest in rural habitats. In contrast, diversity remained unaffected by season. Megafauna visitation to the plots reduced butterfly richness, while megafauna activity signs at vines increased it, regardless of season. In multivariate analyses, season alone explained only around 2% of the variation in butterfly species composition, yet this effect was statistically highly significant and aligned with species’ life history traits. Species typical for post-monsoon tended to develop on forbs or tall grasses and inhabit large global ranges, pre-monsoon species tended to have larger wingspan and longer flight periods. The consistency of butterfly–environment relationships across seasons implies utility of short-term surveys for understanding such patterns, although long-term seasonally informed studies remain superior to them. Article type – Biodiversity in Asia Butterfly seasonality vs. life history traits in Manas National Park, India: Effects of vegetation, nectar, megafauna, invasive shrubs, and fire Gaurab Nandi Das 1,2, Martin Konvicka 1,2, Pavel Vrba 2 1 Faculty of Sciences, University of South Bohemia, 370 05 Ceske Budejovice, Czech Republic 2 Institute of Entomology, Biological Centre CAS, 370 05 Ceske Budejovice, Czech Republic Correspondence: Gaurab Nandi Das, Institute of Entomology, Biological Centre CAS, 370 05 Ceske Budejovice, Czech Republic & Faculty of Sciences, University of South Bohemia, 370 05 Ceske Budejovice, Czech Republic. Email: [email protected]

Seasonal humidity patterns in the tropics shape vegetation and insect communities, with life history traits offering insight into how seasonality influences species richness, abundance, and composition. To explore these effects, we surveyed adult butterflies across 18 one-hectare plots in Manas National Park, Assam, northeastern India (12 within the park, 6 in adjoining rural habitats), characterized by environmental variables including vegetation structure, nectar, invasive shrubs, and mammalian megafauna visitation. We recorded 121/4053 butterfly species/individuals (average species per plot 32.1±14.07 SD) in post-monsoon and 104/2727 species/individuals (24.8±11.93 SD) in pre-monsoon, indicating a seasonal turnover. As elsewhere in the tropics, forests were richer in species than grasslands, and this habitat effect was consistent across both seasons. Species richness peaked at intermediate tree and shrub cover, following gentle hump-shaped patterns. Abundance was also higher in post-monsoon and highest in rural habitats. In contrast, diversity remained unaffected by season. Megafauna visitation to the plots reduced butterfly richness, while megafauna activity signs at vines increased it, regardless of season. In multivariate analyses, season alone explained only around 2% of the variation in butterfly species composition, yet this effect was statistically highly significant and aligned with species’ life history traits. Species typical for post-monsoon tended to develop on forbs or tall grasses and inhabit large global ranges, pre-monsoon species tended to have larger wingspan and longer flight periods. The consistency of butterfly–environment relationships across seasons implies utility of short-term surveys for understanding such patterns, although long-term seasonally informed studies remain superior to them.

Indomalaya region, insect conservation, monsoon, ordination, seasonal tropical forests

Predictable fluctuations in humidity serve as a key driver of vegetation patterns and insect seasonality in the seasonal tropics (Wolda, 1988; Kunte, 1997; Sinu et al., 2013; Schmitt et al., 2021). Rains or floods stimulate the growth of fresh plant tissues, which feed the leaf chewing larvae and adults, who feed the carnivores. The termination of rains brings more sunshine, and hence, flowering of plants and ripening of fruits. In Lepidoptera, who have spent the rainy season as voracious larvae, this is the time of abundant nectar and rotting fruits, supporting the adults, who mutualistically pollinate the plants. The following dry season is pupal/egg time for most insects. It is followed by pre-monsoon, a period of gradually increasing humidity and larval eclosion/adult emergence. This dynamic manifests as two peaks in adult Lepidoptera abundance/diversity, the post-monsoon peak (species that emerged during the rains to utilise the post-monsoon cornucopia of resources and to breed/lay eggs) and pre-monsoon peak (partly emergence from pupae, partly adults that spent dry season on limited resources). Many studies have found two such peaks in adult butterfly presence in seasonal tropics (Denlinger, 1980; Singh et al., 2015; Miya et al., 2025). The quantitative analysis of life history traits, originally developed as a communication tool in community ecology (Pavoine et al., 2014; Wong et al., 2019; Zakharova et al., 2019; Halali et al., 2021a; Attiwilli et al., 2022; Jambhekar & Driscoll, 2023), may substantially contribute to understanding the mechanistic background of insect seasonal patterns. Life histories are not distributed randomly among species but reflect co-adapted responses to the environment. For instance, large wingspan in Lepidoptera requires prolonged larval feeding (Johnson et al., 2014) but allows consumption of nutrient-poor plant tissues (Cizek, 2013; Seifert et al., 2023), whereas small size may allow fast development, but requires nutrient-rich diet (cf. Teder, 2020). In the last decades, a boom of butterfly studies across Indomalaya Realm encompasses community descriptions (Vu & Quang, 2011; Houlihan et al., 2013; Wilson et al., 2015; Das et al., 2022; Leksono et al., 2025), life histories investigations (Karmakar et al., 2018; Nitin et al., 2018; Hsu et al., 2019; https://wingscales.com/; https://butterflycircle.blogspot.com/) and adult seasonality surveys (Spitzer et al., 1993; Hamer et al., 2005; Con & Lien, 2015; Halali et al., 2021b; Miya et al., 2025; cf. Franzen et al., 2017). In the Republic of India, with its vast seasonally dry areas, researchers have been fascinated with seasonal patterns in adult occurrence (Padhye et al., 2006; Verma, 2009; Saikia et al., 2010; Hussain et al., 2011; Samraj & Agnihotri, 2021; Sharma & Sharma, 2021; Naik et al., 2022; Gupta & Kumar, 2024; Karmakar et al., 2024; Neha & Kumar, 2025; Revathy & Sukanya, 2025). Understanding butterfly seasonality is crucial for the proper timing of biodiversity surveys that use butterflies to assess the conservation value of habitats and biodiversity changes. Here, we analyse the seasonality of adult butterflies in a national park in Assam, NE India. We build on an earlier study (Das & Konvicka, in review) targeting the relationships between butterflies and large mammalian herbivores, for which the national parks of Assam are well known (Cedric et al., 2016; Lahkar et al., 2020; Islam et al., 2022). During the post-monsoon, the peak butterfly activity in the region (Singh et al., 2015), we recorded butterflies with respect to habitat types, vegetation structure, and megafauna activity in a selection of three national parks. We found that forests hosted more species than grasslands and adjoining rural habitats, whereas the numbers of individuals peaked in rural habitats. The butterfly species composition at grasslands affected by wild megafauna was similar to rural habitats harbouring domestic livestock, and butterfly species prevailing at those habitat types displayed wider geographic distributions than woodland butterflies. To further explore the patterns, we replicated the survey in one of the three parks, Manas National Park, in the pre-monsoon season, and present the comparison of the two seasons here. Besides recording butterflies from pre-assigned study plots, we recorded information on vegetation structure, nectar supply, signs of megafauna presence, invasive shrubs, and fire (used by park managers during the dry period to facilitate grasslands’ regeneration). We then compared post-monsoon and pre-monsoon patterns in butterfly assemblages and related this to seasonal changes in their environment. We expanded the analyses by considering life history traits of the recorded species (cf. McGill et al., 2006; Das et al., 2025), relating seasonal butterfly community change to the life histories of the species prevailing during post-monsoon and pre-monsoon seasons. Specifically, we asked 1) which of the two seasons harbours higher species richness, more individuals, and higher species diversity; 2) whether basic quantitative community indices (species richness, abundance, diversity) respond to environment differently during post-monsoon vs. pre-monsoon; 3) which butterfly species’ life history traits incline towards post-monsoon vs. pre-monsoon; and 4) whether the butterfly species composition responds to interactions between season and environmental conditions.

Study area Manas NP (protected since 1928, currently 850 km 2 ) is situated within the Brahmaputra Valley semi-evergreen forests ecoregion, within the gently rolling piedmonts of Eastern Himalayas dissected by valleys of several rivers that rapidly descend from the mountains. In the North, it adjoins steep Himalayan slopes protected as the Royal Manas NP (established 1966, area 1,057 km 2 ) in neighbouring Bhutan, while in the South, it is flanked by low-elevated farmland. The region is characterized by humid subtropical dry winter climate (Olson et al., 2001). Most of the plants’ growth and flowering occurs during the monsoon (June–September). Post-monsoon (October–early December) is time of ripening of fruits. A third of woody plants shed leaves during dry season (December –early March). Pre-monsoon (March–early June) is characterised by gradual return of short rains and appearance of the first flowers (Devi & Garkoti, 2013). The main land covers in the park are forests (≈45% of the area, comprised of sub-Himalayan alluvial semi evergreen forests, east Himalayan mixed moist and dry deciduous forests) and grasslands (≈40%, it includes savannah grassland, alluvial grassland) (Sarma et al., 2008; Das et al., 2020). The grasslands are managed by burning during dry season to support rare vertebrates (cf. Takahata et al., 2010). Zoologically, the park is notable for charismatic megafauna including Indian elephant (≈600 individuals), one-horned rhinoceros (≈50), Indian water buffalo (≈250), gaur (≈600), four species of deer including the endangered swamp deer ( Rucervus duvaucelli ranjitsinhi ), wild boar, plus a high predators’ diversity (Lahkar et al., 2024). Perhaps the most remarkable vertebrates are the critically endangered pygmy hog ( Porcula salvania ) and Bengal florican ( Houbaropsis bengalensis ), both associated with the grasslands (Ghosh et al., 2014; Thakur et al., 2024). The fieldwork During the post-monsoon survey (18 October–3 December, 2024), we established 12 study plots within the park + six additional plots in the flanking rural landscape. The plots were one-hectare circles centered on georeferenced points, located along car-accessible roads across all four conservation ranges (Figure 1). The pre-monsoon survey targeted the same plots in 17 March–5 April, 2025. During each survey, we recorded butterflies during four visits, each lasting 20–30 min, during which we surveyed the plot equipped with entomological net, recorded all species including their abundances, and photographed captured individuals to aid identification. Further, we also exposed a single canopy trap (30x90 cm, diameter x height, baited with rotten banana and apple) hanged to 2–5 m centrally at each plot for 1–3 days (mean days±SD, post-monsoon 2.2±0.55, pre-monsoon 1.5±0.71). The butterfly nomenclature follows Das et al. (2023). In subsequent analyses, the number of visits and trap exposure (in days) were treated as nuisance (co)variables. Each plot was characterized by: (i) habitat, recognizing closed forest (N = 4, average tree cover 75%, abundant vines), open forest (N = 4, average canopy 35%, abundant shrubs), grasslands (N = 4, mean grass cover 90%, sparse trees or shrubs), and rural habitats (N = 6, mean shrubs 45 %, mean grass 70%); (ii) Ground % projections of vegetation strata (E4 – emergent trees, E3 – trees over and climbers, E1 – herbs, E0 – barren ground); (iii) nectar (ordinal 1–3 scale, almost none to abundant) split by the above vegetation strata; (iv) covers of three invasive shrubs: Lantana camara, Mikania micrantha and Chromolaena odorata (cf. Nath et al., 2019). Megafauna activity signs (v) were recorded at 1–3 scale (absent to heavily impacted), split by the vegetation strata, plus as animal trails’ density (1–5 scale, none to a patch densely crisscrossed by the trails), and herbivore dung (1–5 scale, none to abundant and concentrated, i.e. latrines). To record actual megafauna visitation (vi), we visited each plot twice during each season at times of megafauna activity peaks, sunrise, and sunset, and counted all species and individuals, wild, feral, or free ranging domestic, within or closely around (45 kg. Based on sums of the morning and evening observations per plot and season, we define (vii) megafauna diversity and (viii) megafauna abundance as the numbers of megafauna species/individuals. Effects of fire (ix), used by park administration as a management tool to support the grasslands and open woodlands, were again estimated visually, split by vegetation strata. Life history traits Because data on the life histories of Indian butterflies are not yet available in the detail routinely analysed for some temperate faunas (e.g., Essens et al., 2017; Bartonova et al., 2024), we used readily available data on wingspan (indicative of resource requirements and dispersal, cf. Shirey et al., 2022), larval host plant form (distinguishing trees, shrubs, forbs, climbers, grasses, tall grass and carnivorous larvae: Das et al., 2025), larval host plant scope (sum of positive records of the above), and adult flight period length (in months, from Shirey et al., 2022). We also included two biogeographic attributes, not life history traits sensu stricto but related to species’ resource breadth, the global distribution, and Indian distribution (from Das et al., 2023). Regressions of basic community parameters Prior to analyses, we pooled butterfly records from the four repeated visits to a plot per season, obtaining 18 post- and 18 pre-monsoon samples of butterfly assemblages. We also pooled the megafauna records from the morning and evening counts per season. To simplify the predictors’ structures for megafauna visitation (vi), we computed the principal component analysis (PCAs, Canoco v.5.1x) (ter Braak & Smilauer, 2018), obtaining four vectors, subsequently used as composite predictors visitation 1–4, with eigenvalues 0.220, 0.136. 0.122 and 0.117 (Figure S1). For nectar (iii) and fire (ix), we summed all values >1 from all vegetation strata, thus obtaining simple numeric predictors. To compare quantitative community parameters, i.e., species richness, total abundance, and the Shannon’s diversity index, emphasizing unique species (Magurran, 2013), and to relate them to environmental predictors, we applied generalized linear mixed models using template model builder (R libarary glmmTMB ) (Brooks et al., 2017). Plot identity was the random factor, the two repeated visits were nested within the plot. To model species richness and abundance, negative binomial distribution (with log link function) was used to handle the overdispersion. Shannon’s diversity was modelled with identity link in gaussian distribution. In model selection we followed information theory approach, i.e., Akaike information criteria ( AIC ). The focal predictor for the three response variables was Season (categorical, post– vs. pre– monsoon). After constructing the model y ~Season + (1|plot), we sequentially added to it the environmental variables (or groups of variables) i–ix, in three forms. The model y ~Season +x +(1|plot), where x = variables i–ix, describes additive effect of the variable, i.e., its identical effect in both seasons. The model y ~Season *x +(1|plot), describes multiplicative relationships, when both the single effects of Season and x, plus their interaction, affect the response variable. In contrast, the models y ~Season :x +(1|plot) includes only the interaction terms, i.e., situation in which x affects the butterfly community differently in post- and pre-monsoon. The “DHARMa” package was used to evaluate the models with respect to normality of residuals and homogeneity of variance (Hartig, 2024). Ordinations of community composition and interpretation by traits To investigate species’ composition of the samples, we used the linear redundancy analysis (RDA) with 999 hierarchical Monte-Carlo permutations (two seasonal aspects forming whole plots permuted as time series, 18 split plots permuted in random) for significance testing; 17 singleton species were excluded from the analyses. We followed identical logic as in regressions. We first tested for the potential nuisance covariables number of visits and trap exposure and selected the latter as a default covariate. We then tested for covariate-controlled effect of Season, and then we constructed three models for each of the predictors x, i–ix. The first contained additive effect of season (~ Season + x | trap exposure ), the second was full multiplication (~ Season + x + Season * x | trap exposure ), and the third contained interaction only (~ Season :x | trap exposure + Season + x ). For vegetation strata (ii), megafauna signs (v), megafauna visitation (vi) and invasive shrubs (iv), forward selections of explanatory variables were carried out during the modelling. For interpreting the RDAs derived patterns by life history traits, the fourth-corner approach (Legendre et al., 1997; Dray & Legendre, 2008) was used. It relates the results of ordination of two tables (here, species composition vs. environmental predictors) to the table of traits. This is technically done via two-steps ordinations, explaining species ordination scores from the previous analysis (RDA1) by the traits, using forward-selection of traits in a RDA2, again with 999 Monte-Carlo permutations.

The recorded material consisted of 121/4053 species/individuals during post-monsoon, and 104/2727 species/individuals during pre-monsoon. The average numbers of species per plot were 32.1±14.07 (median 32.0, range 9–57) in post-monsoon, and 24.8±11.93 (median 22.5, range 10–51) in pre-monsoon. The numbers of individuals were 225.2±140.02 (median 198, range 44–593) in post-monsoon, and 151.5±74.88 (median 148, range 48–320) in pre-monsoon. The post-monsoon and pre-monsoon species numbers per plot closely positively correlated (species: Pearsons’ r = 0.73, t = 4.28, p<0.001), while individuals’ numbers did not ( r = 0.13, t = 0.53, p=0.60). Unique for post-monsoon were 31 species, recorded in 229 individuals. In descending order (with pooled numbers of records), these were Parnara ganga (76), P. guttatus (46), Anthene emolus (14), Telicota bambusae (12), Notocrypta curvifascia (11), Graphium agamemnon (10), Acytolepis puspa (9), Tarucus balkanica (5), Cheritra freja (4), Gandaca harina, Rapala varuna, Papilio chaon, Parnara bada, Telicota colon, Vanessa indica (3 each), Cephrenes acalle, Delias acalis, Eurema blanda, Hebomoia glaucippe, Ixias pyrene, Koruthaialos butleri, Leptotes plinius, Moduza procris (2 each), Badamia exclamationis, Cethosia biblis, Euploea algea, Herona marathus, Nacaduba kurava, Rapala manea, Spalgis epius, Tagiades gana (singletons). For pre-monsoon, there were 14/181 unique species/individuals, in descending order: Pachliopta aristolochiae (65), Pieris canidia (41), Lampides boeticus (36), Zizeeria karsandra (19), Papilio clytia (6), Euchrysops cnejus (3), Astictopterus jama (3), Phaedyma columella (2), and the singletons Colias cf. fieldii, Curetis acuta, Pantoporia paraka, Papilio protenor, Pieris brassicae, Spindasis lohita . Quantitative community patterns None of the potential nuisance covariates affected species richness, abundance, or Shannon’s diversity per plot. Richness and abundance were higher in post-monsoon than in pre-monsoon, whereas diversity values did not differ (ΔAIC < 2.0) (Table 1 & S1, Figure S2). Species richness differed among habitats, being highest in closed forests, followed by open forests, rural habitats, and grasslands. The additive relationship ~habitat +Season (Figure 2) implied that the pattern was identical in the two seasons. The relationship with vegetation strata exhibited additive hump-shaped responses to tree and shrub layers, with richness following gently upward slopes followed by slight declines. Increase richness with nectar was also additive with season. Richness also increased with the invasive C. odorata, independently of season, and with megafaunal signs on vines. For megafauna visitation, the first axis (running from forests to grasslands) decreased species richness, again in both seasons. Rural habitats hosted the highest butterfly abundances, followed by closed forests, open forests, and grasslands, again with additive relationship to season. Independently on season, abundance responded to shrub layer (a hump-shaped response), nectar, and the invasive shrub C. odorata . Damage on vines increased abundance in both seasons, more so in post-monsoon. For megafauna visitation, a model containing interaction with season performed better than alternative models. It revealed that in post-monsoon, butterfly abundance decreased from sites with Rusa unicolor, Elephas maximus and Bos indicus (i.e., forests and rural habitats) towards sites with Bubalus arnee or Bos gaurus (essentially grasslands), the pattern was flat in pre-monsoon, when the effect did not exist (Figure 3). Neither megafauna diversity nor abundance affected butterfly abundance. Shannon’s diversity per plot was not affected by season. It was highest in closed and open forest, followed by rural habitats and grasslands. Regarding vegetation strata, diversity responded to tree layer in a hump-shaped manner independently on season (Figure 3). It was independent on nectar abundance, but responded to covers of the invasive shrubs, with a notable seasonal pattern. The post-monsoon blooming of C. odorata increased butterfly diversity, but it decreased it in pre-monsoon. The long-blooming L. camara, in contrast, decreased butterfly diversity in post-monsoon, but increased it in pre-monsoon (Figure 3). Neither fire, relevant only in pre-monsoon, nor megafauna visitation, diversity, or abundance, affected butterfly diversity. Species composition and interpretation by life history traits Season alone accounted for slightly more than 2% of variation in butterfly species composition, but the signal was highly significant and interpretable by the species’ life histories (Table 2 & S2). Butterflies typical for post-monsoon tend to develop on forbs or tall grasses and inhabit large global ranges; those typical for pre-monsoon tend to have larger wing spans and long adult flight periods. Other predictors explained butterfly species composition more strongly than the season. Habitat explained ≈30 % of variation, followed by vegetation strata (≈15 %), megafauna activity signs and invasive shrubs (≈10 % each). Megafauna visitation, diversity, and abundance, as well as fire, had no separate significant effects (Figure 4). Adding season to models with those predictors generally increased the explained variation. In terms of variation explained, additive relationships with season performed better than multiplicative relationships in case of vegetation strata, megafauna diversity, and invasive plants; multiplicative relationships performed better in case of activity signs, nectar, and megafauna abundance. Interpreting the models containing season and an additional predictor by butterfly life history traits (Table 2 & S2) yielded the strongest models, in terms of explained variation, for vegetation strata, invasive shrubs and activity signs. Besides the traits related to season, the models pointed to association of butterflies developing on climbers or forbs with rural habitats; to association of butterflies developing on tall grasses with high E3 cover (trees); to association of forbs-developing species with nectar (Figure 5); to association of large and widely distributed butterflies with invasive shrubs and butterflies with wide host plant scopes with L. camara ; to association of forbs-developing butterflies with megafauna trails and to avoidance of such trails by tall-grass developing species; to association of butterflies widely distributed in India with megafauna diversity; to association of butterflies with wide global distribution and development on forbs with megafauna abundance; and finally, to association between development on forbs with unburn plots.

Comparing seasonal patterns of adult butterfly species richness, abundance, and diversity across a selection of sites in Manas National Park, northeastern India, revealed that species richness and abundance were higher during the post-monsoon season, whereas Shannon’s diversity did not differ between post-monsoon and pre-monsoon. Models relating species richness, abundance, and diversity to season and diverse environmental predictors describing the study plots were additive in most cases, implying that environmentally-driven patterns observed in one of the two seasonal aspects would hold during the other aspect. Seasonality also affected butterfly species composition, although the effect was rather weak, and the species composition changes were attributable to certain life history traits. The higher richness and abundance during post-monsoon agree with other authors working in monsoon climates of NE India (Kunte, 1997; Chandekar et al., 2014; Singh et al., 2015; Naik et al., 2022; also see Gupta et al., 2019; Neha & Kumar, 2025), and southern Asia in general (cf. Weerakoon & Ranawana, 2021; Tawakkal et al., 2022). After monsoon rains, the diversity of butterflies reaches its peak, and the same seems to hold for moths (Dar et al., 2021). Reports from more humid pats of India report butterfly and insects peak during the height of monsoon (Arun & Vijayan, 2004; Arya et al., 2022), but at that time, alluvial habitats of Assam are technically inaccessible. Still, the observation that the ranking of habitat types by butterfly richness and/or abundance, and the relationships of richness/abundance to vegetation strata, nectar, or megafauna presence followed identical patterns during pre- and post-monsoon seasons is encouraging for future ecological surveys, because it implies validity of these patterns across seasons. The evidence at hand applies only to Manas NP, and further studies should test the generality of the pattern. Three instances in which the relationships were not additive included the response of butterfly abundance to megafauna visitation. In the post-monsoon, the megafauna species displayed quite distinct relationships to habitats (e.g., elephants and the Rusa unicolor deer mostly under closed forest canopy, big bovines at open grasslands, cf. Das and Konvicka, in review). The pre-monsoon pattern of megafauna visitation was more dispersed across habitats, probably because forage became scarcer and the forest canopy was more open. The second such instance was the response of Shannon’s diversity to vegetation strata. The higher diversity peak under lower tree canopy cover is explicable by lower overall E3 cover during pre-monsoon, when part of the trees shed their leaves. Lastly, the interactive relationship with two invasive shrubs is straightforwardly attributable to the timing of their bloom. Chromolaena odorata blooms relatively shortly in post-monsoon, whereas L. camara flowering extends into pre-monsoon, when it may represent much needed nectar source (cf. Mukherjee et al., 2022). The seasonal changes in butterfly species composition were reflected in life history traits. Species associated with post-monsoon tend to develop on tall grasses (represented by the genera Bambusa, Saccharum and Imperata ) or forbs. The phenology of tall grasses is tied to monsoons in the region, as the plants grow in volume during rainy season, when their tissues are most palatable for herbivorous insects. During dry season they desiccate and may be consumed by megaherbivores or burnt. Examples of butterflies utilising this resource and displaying post-monsoon peaks include Mycalesis spp. (Nymphalidae: Satyrinae) or Telicota spp. (Hesperiidae: Hesperiinae) developing on bamboos, and Jamides elpis (Lycaenidae) developing on other large monocots. Similarly to tall grasses, many forbs bloom during monsoons and desiccate during dry season. Forbs-developing butterflies with post-monsoon peak included the nympahlids Hypolimnas bolina and Tirumala limniace, or the lycaenids Everes lacturnus and, Leptotes plinius . Larger wingspan was associated with pre-monsoon. This is partly interpretable by a high representation of small butterflies among the tall-grass and forbs feeders, but partly by abundant occurrence of the swallowtail Pachliopta aristolochiae (wingspan 92.5 mm) (Papilionidae), recorded only in pre-monsoon. Other large-winged species with increased pre-monsoon abundance included Papilio agenor (135 mm), P. helenus (111.5 mm) or Parantica melaneus (85 mm) (the latter Nymphalidae: Danainae). Incidentally, these large-winged species develop on Aristolochiacae or Apocyanacae vines (or shrubs), which display efficient water economy, bypass the seasonal pattern of rapid growth and desiccation, and probably remain palatable for butterfly larvae even during dry months. Butterfly wingspan appears positively related to mobility (Sekar, 2012; Bartonova et al., 2014) and being more mobile may be profitable under pre-monsoon scarcity of resources. The pre-monsoon assemblages also contained species with longer adult flight period. As the latter trait refers to flight period over entire range, it attains the highest values in migratory species covering vast geographic distances during their seasonal flights (cf. Reich et al., 2025). Indeed, three species recorded only during pre-monsoon, Pieris brassicae, Colias cf. fieldii and Lampides boeticus, display migratory habits (Lohman et al., 2008; Roy et al., 2021; Semwal et al., 2025), although the third of them fails the large wingspan criterion (wingspan 30 mm). Fang et al. (2023) reported increased flight activity of L. boeticus and C. fieldii during dry season in Yunnan, China. The other patterns revealed by life history traits analysis are also interpretable by the available knowledge of the recorded species’ ecology. Thus, the butterflies developing on climbers or forbs are likely profiting from presence of garden and crop plants utilised by both larvae and adults (e.g., the locally much grown Sesbania bispinosa is utilised by several butterflies, including abundant Zizina otis : https://www.ifoundbutterflies.org/sesbania-bispinosa). The association of development on tall grasses with high E3 cover was due to numerous forest interior satyrines developing at bamboos (e.g. Mycalesis spp. ). The link between large range size and invasive shrubs may arguably be due to generalist habits of species with wide distributions (Dapporto & Dennis, 2013; Bartonova et al., 2014), because in generalists, the utilisation of novel resources (such as L. camara flowers) is more expected than in specialists (cf. Solomon Raju et al., 2023). The positive association between development on forbs and megafauna trails may be mediated by disturbance of the ground (and opening of canopy), as both mechanisms suppress competitively dominant plants, such as tall grasses, favouring competitively inferior forbs (a temperate zone example: de Schaetzen et al., 2018). The associations between wide Indian or global distribution with megafauna diversity/abundance corroborates our earlier results (Das & Konvicka, in review) that Indian non-forested habitats, attracting many large herbivores, are evolutionarily younger than tropical forest, and hence host fewer butterfly species, typically of wide tropical distributions. This does not exclude presence of narrowly distributed endemics, such as the iconic Colias nilagiriensis (cf. Nitin et al., 2018), in non-forested habitats of certain regions.

Seasonality plays a significant role in shaping tropical butterfly communities, demonstrated by distinct post-monsoon peaks in species richness and abundance at Manas National Park, Assam, India. Notable shifts in species composition and abundance were closely linked to specific life history traits, highlighting the combined influence of environmental factors and species’ life histories on seasonal dynamics. Still, the relationships between butterfly species richness, abundance and diversity to environmental predictors were additive with respect to season, implying that even short-term surveys, despite not disclosing full butterfly fauna of the surveyed sites, may suffice for investigating patterns in butterfly–environment relationships. Long-term and seasonally informed monitoring, however, remains the best practice to understand butterfly ecology and guide effective conservation strategies in northeastern India and other tropical landscapes. ACKNOWLEDGMENTS We are grateful to the Principal Chief Conservator of Forests (Wildlife) and Chief Wildlife Warden Assam, for providing permits to work in protected areas of Assam. We are also obliged to the National Tiger Conservation Authority of both national (New Delhi) and regional (Guwahati) offices, for their approval for the work, especially to Dr. Vaibhav C. Mathur (Deputy Inspector General, NTCA) for his constant support. We are also thankful to the field director of Manas national park, Dr. C. Ramesh, and deputy director T. Sheshidhar Reddy; to the range officials Barin Boro (Bansbari range), Vivekananda Pathak (Bhuyanpara range), Bashiram Brahma (Kuklung range) and Doimalu Goyary (Panbari range), and to all forest guards for their support during the fieldwork. Our colleagues Hana Konvickova, Ranjit Lakra, Sachin Lakra, Oldrich Nedved, Swapna Devi Ray, as well as Subhom, Dilip, Sanjib, Sanjay, Arup and Marcus, helped in many ways. The authors appreciate the financial support from Grant Agency of the University of South Bohemia, Czech Republic (GA JU 100/2022/P). CONFLICT OF INTEREST STATEMENT The authors declare no conflicts of interest.

Arun, P. R., & Vijayan, V. S. (2004). Patterns in abundance and seasonality of insects in the Siruvani forest of western Ghats, Nilgiri Biosphere Reserve, southern India. The Scientific World Journal, 4, 381–392. https://doi.org/10.1100/tsw.2004.33 Arya, M. K., Chandra, H., Bisht, S., & Verma, A. (2022). Seasonal Abundance, Status, and Diversity of Hymenopterans (Insecta: Hymenoptera) in a Protected Landscape. In R. C. Sobti (Ed.), Biodiversity Threats and Conservation, (pp. 283–293). Boca Raton: CRC Press. Attiwilli, S., Karmakar, T., Isvaran, K., & Kunte, K. (2022). Habitat preference and functional traits influence responses of tropical butterflies to varied habitat disturbance. International Journal of Tropical Insect Science, 42, 855–864. https://doi.org/10.1007/s42690-021-00609-1 Bartonova, A. S., Skopek, P., Konvicka, M., Benes, J., Spitzer, L., Sbaraglia, C., Vrabec, V., Maresova, J. P., Konvickova, H., & Fric, Z. F. (2024). Czech Republic butterfly barcoding reveals that distribution of genetic lineages depends on species traits. Journal of Biogeography, 51, 1575–1586. https://doi.org/10.1111/jbi.14848 Bartonova, A., Benes, J., & Konvicka, M. (2014). Generalist-specialist continuum and life history traits of Central European butterflies (Lepidoptera)-are we missing a part of the picture? European Journal of Entomology, 111, 543–553. https://doi.org/10.14411/eje.2014.060 Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug, H. J., Machler, M., & Bolker, B. M. (2017). glmmTMB balances speed and f flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal, 9, 378–400. https://doi.org/10.32614/rj-2017-066 Cedric, G., Neha, P., Roshan, P., Uttam, S., & Rajendra, G. (2016). Assessing and managing the rising rhino population in Kaziranga (India). Ecological Indicators, 66, 55–64. https://doi.org/10.1016/j.ecolind.2016.01.023 Chandekar, S. K., Nimbalkar, R. K., & Kuvalekar, A. A. (2014). The seasonal patterns in the abundance of butterflies, their biotopes and nectar food plants from Maval Thahsil, Pune district, Maharashtra, India. International Journal of Plant, Animal and Environmental Sciences, 4, 50–64. Cizek, L. (2013). Diet composition and body size in insect herbivores: Why do small species prefer young leaves? European Journal of Entomology, 102, 675–681. Con, V. Q., & Lien, V. V. (2015). Seasonal dynamics of butterfly (Lepidoptera, Rhopalocera) abundance in the Tropical Rain Forest of Vietnam. Entomological Review, 95, 578–582. https://doi.org/10.1134/S0013873815050024 Dapporto, L., & Dennis, R. L. (2013). The generalist–specialist continuum: testing predictions for distribution and trends in British butterflies. Biological Conservation, 157, 229–236. https://doi.org/10.1016/j.biocon.2012.09.016 Dar, A. A., Jamal, K., Alhazmi, A., El-Sharnouby, M., Salah, M., & Sayed, S. (2021). Moth diversity, species composition, and distributional pattern in Aravalli Hill Range of Rajasthan, India. Saudi Journal of Biological Sciences, 28, 4884–4890. https://doi.org/10.1016/j.sjbs.2021.06.018 Das, G. N., & Konvicka, M. (in review) Megafauna activity and butterflies in national parks of Assam, India. Journal for Nature Conservation . Das, G. N., Faltynek Fric, Z., & Konvicka, M. (2025). Geography of life histories in a tropical fauna: The case of Indian butterflies. Ecology and Evolution, 15, e72217. https://doi.org/10.1002/ece3.72217 Das, G. N., Fric, Z. F., Panthee, S., Irungbam, J. S., & Konvicka, M. (2023). Geography of Indian Butterflies: Patterns Revealed by Checklists of Federal States. Insects, 14, 549. https://doi.org/10.3390/insects14060549 Das, J. P., Lahkar, B. P., Sahu, H. K., & Singha, H. (2020). Population Estimation of Asian Elephants in a Tropical Forest of Northeast India. Gajah, 52, 15–23. Das, K. S. A., Radhamany, D., & Molleman, F. (2022). Community Structure in an Isolated Tropical Forest Biome: One Year of Fruit-Feeding Butterfly Trapping in Four Habitats in the Western Ghats, India. Diversity, 15, 36. https://doi.org/10.3390/d15010036 de Schaetzen, F., van Langevelde, F., & WallisDeVries, M. F. (2018). The influence of wild boar ( Sus scrofa ) on microhabitat quality for the endangered butterfly Pyrgus malvae in the Netherlands. Journal of Insect Conservation, 22, 51–59. https://doi.org/10.1007/s10841-017-0037-5 Denlinger, D. L. (1980). Seasonal and annual variation of insect abundance in the Nairobi National Park, Kenya. Biotropica, 12, 100–106. https://doi.org/10.2307/2387725 Devi, A. F., & Garkoti, S. C. (2013). Variation in evergreen and deciduous species leaf phenology in Assam, India. Trees, 27, 985–997. https://doi.org/10.1007/s00468-013-0850-8 Dray, S., & Legendre, P. (2008). Testing the species traits-environment relationships: the fourth corner problem revisited. Ecology, 89, 3400–3412. https://doi.org/10.1890/08-0349.1 Essens, T., van Langevelde, F., Vos, R. A., Van Swaay, C. A. M., & WallisDeVries, M. F. (2017). Ecological determinants of butterfly vulnerability across the European continent. Journal of Insect Conservation, 21, 439–450. https://doi.org/10.1007/s10841-017-9972-4 Fang, S. Q., Li, Y. P., Pan, Y., Wang, C. Y., Peng, M. C., & Hu, S. J. (2023). Butterfly diversity in a rapidly developing urban area: A case study on a university campus. Diversity, 16, 4. https://doi.org/10.3390/d16010004 Franzen, M., Schrader, J., & Sjoberg, G. (2017). Butterfly diversity and seasonality of Ta Phin mountain area (N. Vietnam, Lao Cai province). Journal of Insect Conservation, 21, 465–475. https://doi.org/10.1007/s10841-017-9985-z Ghosh, S., Lahkar, B. P., Deuti, K., Nath, A., Banerji-Bhattacharya, G., Brahma, N., Das, J. P., Sinha, A., Lahkar, D., Barukial, B., Charles, L. P., & Adhikari, A. S. (2014). Survey for Endangered Grassland Species in Manas National Park. Field Directorate Tiger Project, Manas. Govt of Assam. Gupta, H., Tiwari, C., & Diwakar, S. (2019). Butterfly diversity and effect of temperature and humidity gradients on butterfly assemblages in a sub-tropical urban landscape. Tropical Ecology, 60, 150–158. https://doi.org/10.1007/s42965-019-00019-y Gupta, V., & Kumar, P. (2024). Butterfly Diversity and Seasonality at Hathnikund Barrage: Insights from a Natural Ecotone in Haryana, India. Proceedings of the Zoological Society, 77, 329–337. https://doi.org/10.1007/s12595-024-00537-1 Halali, S., Halali, D., Barlow, H. S., Molleman, F., Kodandaramaiah, U., Brakefield, P. M., & Brattström, O. (2021b). Predictability of temporal variation in climate and the evolution of seasonal polyphenism in tropical butterfly communities. Journal of Evolutionary Biology, 34, 1362–1375. https://doi.org/10.1111/jeb.13895 Halali, S., van Bergen, E., Breuker, C. J., Brakefield, P. M., & Brattström, O. (2021a). Seasonal environments drive convergent evolution of a faster pace‐of‐life in tropical butterflies. Ecology Letters, 24, 102–112. https://doi.org/10.1111/ele.13626 Hamer, K. C., Hill, J. K., Mustaffa, N., Benedick, S., Sherratt, T. N., Chey, V. K., & Maryati, M. (2005). Temporal variation in abundance and diversity of butterflies in Bornean rain forests: opposite impacts of logging recorded in different seasons. Journal of Tropical Ecology, 21, 417–425. https://doi.org/10.1017/S0266467405002361 Hartig, F. (2024). DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.4.7. https://CRAN.R-project.org/package=DHARMa Houlihan, P. R., Harrison, M. E., & Cheyne, S. M. (2013). Impacts of forest gaps on butterfly diversity in a Bornean peat-swamp forest. Journal of Asia-Pacific Entomology, 16, 67–73. https://doi.org/10.1016/j.aspen.2012.10.003 Hsu, Y.-F., Lu, C.-C., Huang, H.-C., Liang, J.-Y., & Huang, C.-L. (2019) Immature stages of Neptis reducta Fruhstorfer, 1908 (Lepidoptera, Nymphalidae, Neptini): morphology, ecology and food specialty. Zootaxa, 4688, 569–577. https://doi.org/10.11646/zootaxa.4688.4.8 Hussain, K. J., Ramesh, T., Satpathy, K. K., & Selvanayagam, M. (2011). Seasonal dynamics of butterfly population in DAE Campus, Kalpakkam, Tamil Nadu, India. Journal of Threatened Taxa, 3, 1401–1414. https://doi.org/10.11609/JoTT.o2603.1401-14 Islam, N., Ahmed, A., Barman, R., Deka, S., Choudhury, B., Saikia, P. K., & Deka, J. (2022). Revival of Eastern Swamp Deer Rucervus duvaucelii ranjitsinhi (Groves, 1982) in Manas National Park of Assam, India. Journal of Threatened Taxa, 14, 20488–20493. https://doi.org/10.11609/jott.7670.14.1.20488-20493 Jambhekar, R., & Driscoll, D. A. (2023). Species traits help explain butterfly habitat use in a tropical forest. Ecological Entomology, 48, 199–208. https://doi.org/10.1111/een.13215 Johnson, H., Solensky, M. J., Satterfield, D. A., & Davis, A. K. (2014). Does skipping a meal matter to a butterfly’s appearance? Effects of larval food stress on wing morphology and color in monarch butterflies. PLoS One, 9, e93492. https://doi.org/10.1371/journal.pone.0093492 Karmakar, P., Das, D., Mishra, A., Bharadwaj, G., & Borah, C. (2024). Seasonal diversity and abundance distribution of butterflies in Dihing Patkai national park, North East India. International Journal of Tropical Insect Science, 44, 1255–1269. https://doi.org/10.1007/s42690-024-01232-6 Karmakar, T., Nitin, R., Sarkar, V., Baidya, S., Mazumder, S., Chandrasekharan, V. K., Das, R., Girish Kuma, G. S., Lokhande, S., Veino, J., Veino, R., Mirza, Z., Sanap, R. V., Sarkar, B., & Kunte, K. (2018). Early stages and larval host plants of some northeastern Indian butterflies. Journal of Threatened Taxa, 10, 11780–11799. https://doi.org/10.11609/jott.3169.10.6.11780-11799 Kunte, K. J. (1997). Seasonal patterns in butterfly abundance and species diversity in four tropical habitats in northern Western Ghats. Journal of Biosciences, 22, 593–603. https://doi.org/10.1007/BF02703397 Lahkar, D., Ahmed, M. F., Begum, R. H., Das, S. K., & Harihar, A. (2020). Responses of a wild ungulate assemblage to anthropogenic influences in Manas National Park, India. Biological Conservation, 243, 108425. https://doi.org/10.1016/j.biocon.2020.108425 Lahkar, D., Ahmed, M. F., Begum, R. H., Das, S. K., Sarma, H. K., Swargowari, A., Jhala, Y. V., Samad, I., & Harihar, A. (2024). Post-conflict recovery of Tigers ( Panthera tigris ) in a transboundary landscape: the case of Manas National park, India. Biological Conservation, 300, 110837. https://doi.org/10.1016/j.biocon.2024.110837 Legendre, P., Galzin, R., & Harmelin-Vivien, M. L. (1997). Relating behavior to habitat: solutions to thefourth‐corner problem. Ecology, 78, 547–562. https://doi.org/10.1890/0012-9658(1997)078[0547:RBTHST]2.0.CO;2 Leksono, A. S., Yanuwiadi, B., Millah, N., & Abdullah, S. A. (2025). Butterfly diversity along an altitudinal gradient and land uses in East Java, Indonesia. Biodiversitas, 26, 551–563. https://doi.org/10.13057/biodiv/d260204 Lohman, D. J., Peggie, D., Pierce, N. E., & Meier, R. (2008). Phylogeography and genetic diversity of a widespread Old World butterfly, Lampides boeticus (Lepidoptera: Lycaenidae). BMC Evolutionary Biology, 8, 301. https://doi.org/10.1186/1471-2148-8-301 Magurran, A. E. (2013). Measuring Biological Diversity . UK: Wiley-Blackwell. Malhi, Y., Doughty, C. E., Galetti, M., Smith, F. A., Svenning, J. C., & Terborgh, J. W. (2016). Megafauna and ecosystem function from the Pleistocene to the Anthropocene. Proceedings of the National Academy of Sciences, 113, 838–846. https://doi.org/10.1073/pnas.1502540113 McGill, B. J., Enquist, B. J., Weiher, E., & Westoby, M. (2006). Rebuilding community ecology from functional traits. Trends in Ecology & Evolution, 21, 178–185. https://doi.org/10.1016/j.tree.2006.02.002 Miya, M. S., Shrestha, S., Dhakal, A., Shrestha, S., Karki, R., Thapa, K., Simkhada, N., Chataut, P., Tamang, P. M., & Chhetri, A. (2025). Seasonal Variation of Forest Butterfly Diversity in Tropical Lowland Nepal . Ecology and Evolution, 15, e71550. https://doi.org/10.1002/ece3.71550 Mukherjee, S. S., Ahmed, M. T., & Hossain, A. (2022). Role of a Global Invasive Species (GIS), Lantana camara in conservation and sustenance of local butterfly community. Acta Ecologica Sinica, 42, 600–604. https://doi.org/10.1016/j.chnaes.2021.02.008 Naik, D., Rao, R. S. P., Kunte, K., & Mustak, M. S. (2022). Seasonal dynamics and polyphenism of butterfly communities in the coastal plains of central Western Ghats, India. Journal of Biosciences, 47, 79. https://doi.org/10.1007/s12038-022-00316-0 Nath, A., Sinha, A., Lahkar, B. P., & Brahma, N. (2019). In search of Aliens: Factors influencing the distribution of Chromolaena odorata L. and Mikania micrantha Kunth in the Terai grasslands of Manas National Park, India. Ecological Engineering, 131, 16–26. https://doi.org/10.1016/j.ecoleng.2019.02.012 Neha & Kumar, P. (2025). Butterfly diversity in the urban ecosystem of Karnal, Haryana, India: seasonal patterns and conservation implications. The Journal of Basic and Applied Zoology, 86, 57. https://doi.org/10.1186/s41936-025-00478-x Nitin, R., Balakrishnan, V. C., Churi, P. V., Kalesh, S., Prakash, S., & Kunte, K. (2018). Larval host plants of the butterflies of the Western Ghats, India. Journal of Threatened Taxa, 10, 11495–11550. https://doi.org/10.11609/jott.3104.10.4.11495-11550 Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D’amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., & Kassem, K. R. (2001). Terrestrial ecoregions of the world: a new map of life on Earth: A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience, 51, 933–938. https://doi.org/10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2 Padhye, A. D., Dahanukar, N., Paingankar, M., Deshpande, M., & Deshpande, D. (2006). Season and landscape wise distribution of butterflies in Tamhini, northern Western Ghats, India. Zoos’print, 21, 2175–2181. Pavoine, S., Baguette, M., Stevens, V. M., Leibold, M. A., Turlure, C., & Bonsall, M. B. (2014). Life history traits, but not phylogeny, drive compositional patterns in a butterfly metacommunity. Ecology, 95, 3304–3313. https://doi.org/10.1890/13-2036.1 Reich, M. S., Shipilina, D., Talla, V., Bahleman, F., Kebe, K., Berger, J. L., Backstrom, N., Talavera, G., & Bataille, C. P. (2025). Isotope geolocation and population genomics in Vanessa cardui : Short-and long-distance migrants are genetically undifferentiated. PNAS nexus, 4, pgae586. https://doi.org/10.1093/pnasnexus/pgae586 Revathy, V. S., & Sukanya, V. S. (2025). Seasonal variations in diversity and abundance of butterfly population: Insights from Peechi Vazhani Wildlife Sanctuary, Kerala, India. International Journal of Tropical Insect Science, 45, 1703–1718. https://doi.org/10.1007/s42690-025-01553-0 Roy, R. C., Biswas, D., & Dey, R. (2021). Addition of Colias fieldii Ménétriés, 1855 (Lepidoptera: Pieridae: Coliadinae) to the checklist of Lepidoptera of Bangladesh. Revista Chilena de Entomología, 47, 433–436. https://doi.org/10.35249/rche.47.2.21.26 Saikia, M. K., Kalita, J., & Saikia, P. K. (2010). Seasonality of nymphalid butterflies in Rani-Garbhanga reserve forest, Assam, India. NeBIO, 1, 10–21. Samraj, J. M., & Agnihotri, M. (2021). Impact of land use pattern and seasonality on butterfly diversity in a sub-tropical Terai arc landscape (TAL), Pantnagar, Uttarakhand (India). Tropical Ecology, 62, 301–310. https://doi.org/10.1007/s42965-021-00150-9 Sarma, P. K., Lahkar, B. P., Ghosh, S., Rabha, A., Das, J. P., Nath, N. K., Dey, S., & Brahma, N. (2008). Land-use and land-cover change and future implication analysis in Manas National Park, India using multi-temporal satellite data. Current Science, 95, 223–227. Schmitt, T., Ulrich, W., Delic, A., Teucher, M., & Habel, J. C. (2021). Seasonality and landscape characteristics impact species community structure and temporal dynamics of East African butterflies. Scientific Reports, 11, 15103. https://doi.org/10.1038/s41598-021-94274-6 Seifert, C. L., Strutzenberger, P., & Fiedler, K. (2023). How do host plant use and seasonal life cycle relate to insect body size: A case study on European geometrid moths (Lepidoptera: Geometridae). Journal of Evolutionary Biology, 36, 743–752. https://doi.org/10.1111/jeb.14169 Sekar, S. (2012). A meta‐analysis of the traits affecting dispersal ability in butterflies: Can wingspan be used as a proxy? Journal of Animal Ecology, 81, 174–184. https://doi.org/10.1111/j.1365-2656.2011.01909.x Semwal, A., Sharma, P. L., Verma, S. C., & Devi, M. (2025). Migration of cabbage butterfly ( Pieris brassicae ): Influence of environmental factors on dispersal. Indian Horticulture, 70, 26–28. https://epubs.icar.org.in/index.php/IndHort/article/view/154055 Sharma, N., & Sharma, S. (2021). Assemblages and seasonal patterns in butterflies across different ecosystems in a sub-tropical zone of Jammu Shiwaliks, Jammu and Kashmir, India. Tropical Ecology, 62, 261–278. https://doi.org/10.1007/s42965-020-00139-w Shirey, V., Larsen, E., Doherty, A., Kim, C. A., Al-Sulaiman, F. T., Hinolan, J. D., Itliong, M. G. A., Naive, M. A. K., Ku, M., Belitz, M., Jeschke, G., Barve, V., Lamas, G., Kawahara, A. Y., Guralnick, R., Pierce, N. E., Lohman, D. L., & Ries, L. (2022). LepTraits 1.0 A globally comprehensive dataset of butterfly traits. Scientific Data, 9, 382. https://doi.org/10.1038/s41597-022-01473-5 Singh, A. P., Gogoi, L., & Sebastain, J. (2015). The seasonality of butterflies in a semi-evergreen forest: Gibbon Wildlife Sanctuary, Assam, northeastern India. Journal of Threatened Taxa, 7, 6774–6787. https://doi.org/10.11609/JoTT.o3742.6774-87 Sinu, P. A., Mandal, P., Banerjee, D., Mallick, S., Talukdar, T., & Pathak, S. K. (2013). Moth pests collected in light traps of tea plantations in North East India: species composition, seasonality and effect of habitat type. Current Science, 104, 646–651. Solomon Raju, A. J., Venkata Ramana, K, & Prasada Rao, C. H. (2023). The exotic perennial invasive weed, Lantana camara L. (Verbenaceae), a potential source of nectar for butterflies. Species, 24, e87s1601. https://doi.org/10.54905/disssi.v24i74.e87s1601 Spitzer, K., Novotny, V., Tonner, M., & Lepe, J. (1993). Habitat preferences, distribution and seasonality of the butterflies (Lepidoptera, Papilionoidea) in a montane tropical rain forest, Vietnam. Journal of Biogeography, 20, 109–121. https://doi.org/10.2307/2845744 Takahata, C., Amin, R., Sarma, P., Banerjee, G., Oliver, W., & Fa, J. E. (2010). Remotely-Sensed Active Fire Data for Protected Area Management: Eight-Year Patterns in the Manas National Park, India. Environmental Management, 45, 414–423. https://doi.org/10.1007/s00267-009-9411-8 Tawakkal, R. I., Novitasari, D., Muzaki, F. K., & Trisnawati, I. (2022). Seasonal butterfly (Lepidoptera: Rhopalocera) abundance in forest edges and hiking trail of Ngluyu forest, east Java, Indonesia. Serangga, 27, 77–92. Teder, T. (2020). Phenological responses to climate warming in temperate moths and butterflies: species traits predict future changes in voltinism. Oikos, 129, 1051–1060. https://doi.org/10.1111/oik.07119 ter Braak, C. J. F., & Smilauer, P. (2018). Canoco reference manual and user’s guide: software for ordination, version 5.1x . USA: Microcomputer Power. Thakur, M., Kalita, J., Brahma, N., Rajbongshi, K., Bhattacharyya, K., Sarmah, A. C., Sinha, A., Dutta, D. K., Das, D., & Lahkar, B. P. (2024). Current conservation status of Bengal Florican Houbaropsis bengalensis in Manas National Park, Assam, India. Journal of Threatened Taxa, 16, 25507–25515. https://doi.org/10.11609/jott.8247.16.7.25507-25515 Verma, S. K. (2009). Species composition and seasonal variation of butterflies in Dalma Wildlife Sanctuary, Jharkhand, India. Journal of Threatened Taxa, 1, 295–297. https://doi.org/10.11609/JoTT.o2126.295-7 Vu, L. V., & Quang Vu, C. (2011). Diversity pattern of butterfly communities (Lepidoptera, Papilionoidae) in different habitat types in a tropical rain forest of southern Vietnam. International Scholarly Research Notices, 2011, 818545. https://doi.org/10.5402/2011/818545 Weerakoon, B., & Ranawana. K. (2021). Seasonality and movement patterns of butterflies in an intermediate zone regenerating forest habitat in Sri Lanka. Arthropods, 10, 120–129. Wilson, J. J., Jisming-See, S. W., Brandon-Mong, G. J., Lim, A. H., Lim, V. C., Lee, P. S., & Sing, K. W. (2015). Citizen science: the first Peninsular Malaysia butterfly count. Biodiversity Data Journal, 3, e7159. https://doi.org/10.3897/BDJ.3.e7159 Wolda, H. (1988). Insect seasonality: why? Annual Review of Ecology and Systematics, 19, 1–18. https://doi.org/10.1146/annurev.es.19.110188.000245 Wong, M. K. L., Guenard, B., & Lewis, O. T. (2019). Trait‐based ecology of terrestrial arthropods. Biological Reviews, 94, 999–1022. https://doi.org/10.1111/brv.12488 Zakharova, L., Meyer, K. M., & Seifan, M. (2019). Trait-based modelling in ecology: A review of two decades of research. Ecological Modelling, 407, 108703. https://doi.org/10.1016/j.ecolmodel.2019.05.008 TABLE 1 Results of regression analyses (generalized linear mixed-effects models) for butterfly species richness, abundances (both negative binomial distributions), and Shannon diversity index (gaussian distribution), based on the model y ~Season +(1|plot) and best-supported additive or interactive models selected using the Akaike Information Criterion ( AIC ), relating all environmental predictors across 18 study plots in Manas National Park, Assam, India. Rf_var: variance of random effects, Rf_SD: standard deviation of random effect, Df: degrees of freedom, Dev.: deviance. Significance codes: # P <0.1, * P <0.05, ** P <0.01, *** P <0.001. | Model | Rf_var | Rf_SD | Coefficients | Df | Dev. | AIC | p -value | | Species richness | ||||||| | Null | 0.13 | 0.367 | 33 | 277.4 | 283.4 | || | Season | 0.15 | 0.393 | 3.13 +0.26 Post-monsoon ** | 32 | 269.6 | 277.6 | ** | | Habitat | 0.04 | 0.200 | 3.62 –0.11 Open forest –0.90 Grassland *** –0.33 Rural habitat # | 30 | 263.1 | 275.1 | ** | | Habitat +Season | 0.06 | 0.236 | 3.47 –0.11 Open forest –0.90 Grassland *** –0.34 Rural habitat # –0.26 Post-monsoon ** | 29 | 255.1 | 269.1 | *** | | Vegetation strata | <0.001 | <0.001 | 3.27 +1.03 Strata E3 *** –1.37 Strata E3 2*** +0.94 Strata E2 ** –0.53 Strata E2 2# | 29 | 238.0 | 252.0 | *** | | Vegetation strata +Season | <0.001 | <0.001 | 3.22 +1.13 Strata E3 *** –1.40 Strata E3 2*** +0.76 Strata E2 * –0.46 Strata E2 2 +0.10 Post-monsoon | 28 | 237.0 | 253.0 | *** | | Nectar | 0.11 | 0.332 | 3.27 +0.19 Nectar ** | 32 | 268.5 | 276.5 | ** | | Nectar +Season | 0.13 | 0.360 | 3.17 +0.13 Nectar * –0.18 Post-monsoon * | 31 | 264.7 | 274.7 | ** | | Invasive shrub | 0.15 | 0.390 | 3.26 +0.20 Chromolaena odorata *** | 32 | 264.6 | 272.6 | *** | | Invasive shrub +Season | 0.15 | 0.390 | 3.23 +0.17 Chromolaena odorata * – 0.06 Post-monsoon | 31 | 264.4 | 274.4 | ** | | Activity signs | 0.11 | 0.333 | 3.28 +0.17 Sign Ev * | 32 | 271.2 | 279.2 | * | | Activity signs +Season | 0.13 | 0.360 | 3.14 +0.13 Sign Ev * –0.24 Post-monsoon ** | 31 | 263.8 | 273.8 | ** | | Megafauna visitation | 0.11 | 0.330 | 3.28 –0.09 megafauna1 | 32 | 275.0 | 283.0 | NS | | Megafauna visitation +Season | 0.13 | 0.360 | 3.11 –0.11 megafauna1 * +0.31 Post-monsoon *** | 31 | 263.8 | 273.8 | ** | | Megafauna diversity | 0.12 | 0.351 | 3.29 –0.04 Megafauna diversity | 32 | 277.1 | 285.1 | NS | | Megafauna diversity +Season | 0.14 | 0.373 | 3.11 –0.09 Megafauna diversity # +0.31 Post-monsoon *** | 31 | 266.9 | 276.9 | ** | | Megafauna abundance | 0.11 | 0.335 | 3.28 –0.12 Megafauna abundance | 32 | 275.4 | 283.4 | NS | | Megafauna abundance +Season | 0.14 | 0.375 | 3.14 –0.06 Megafauna abundance +0.25 Post-monsoon ** | 31 | 269.0 | 279.0 | * | | Fire | 0.13 | 0.359 | 3.28 –0.10 Fire | 32 | 274.8 | 282.8 | NS | | Fire +Season | 0.15 | 0.393 | 3.14 +0.0004 Fire +0.26 Post-monsoon ** | 31 | 269.6 | 279.6 | * | | Abundance | ||||||| | Null | 0.04 | 0.210 | 33 | 432.5 | 438.5 | || | Season | 0.04 | 0.200 | 5.01 –0.38 Post-monsoon * | 32 | 428.3 | 436.3 | * | | Habitat | <0.001 | <0.001 | 5.28 –0.16 Open forest –0.85 Grassland *** +0.31 Rural habitat | 30 | 411.3 | 423.3 | *** | | Habitat +Season | <0.001 | <0.001 | 5.11 –0.15 Open forest –0.81 Grassland *** +0.29 Rural habitat –0.30 Post-monsoon * | 29 | 407.4 | 421.4 | *** | | Vegetation strata | <0.001 | <0.001 | 5.19 +0.87 Strata E2 # –1.80 Strata E2 2*** | 31 | 420.7 | 430.7 | ** | | Vegetation strata +Season | <0.001 | <0.001 | 5.09 +0.60 Strata E2 –1.70 Strata E2 2** –0.18 Post-monsoon | 30 | 419.9 | 431.9 | ** | | Nectar | <0.001 | <0.001 | 5.19 +0.32 Nectar *** | 32 | 420.8 | 428.8 | *** | | Nectar +Season | <0.001 | <0.001 | 5.09 +0.29 Necctar ** –0.17 Post-monsoon | 31 | 419.9 | 429.9 | ** | | Invasive shrub | 0.07 | 0.271 | 5.18 +0.23 Chromolaena odorata * | 32 | 426.5 | 434.5 | * | | Invasive shrub +Season | 0.06 | 0.243 | 5.10 +0.18 Chromolaena odorata –0.15 Post-monsoon | 31 | 426.1 | 436.1 | * | | Activity signs | 0.05 | 0.227 | 5.18 +0.26 Sign Ev ** | 32 | 425.4 | 433.4 | ** | | Activity signs +Season | 0.04 | 0.192 | 5.00 +0.25 Sign Ev ** –0.34 Post-monsoon * | 31 | 421.4 | 431.4 | ** | | Megafauna visitation | <0.001 | <0.001 | 5.23 –0.19 megafauna1 ** | 32 | 427.9 | 435.9 | * | | Megafauna visitation :Season | <0.001 | <0.001 | 5.40 +1.11 megafauna1 :Pre-monsoon ** –0.22 megafauna1 :Post-monsoon ** | 31 | 419.8 | 429.8 | ** | | Megafauna diversity | 0.04 | 0.200 | 5.22 +0.07 Megafauna diversity | 32 | 432.1 | 440.1 | NS | | Megafauna diversity +Season | 0.04 | 0.197 | 5.01 +0.01 Megafauna diversity –0.37 Post-monsoon * | 31 | 428.3 | 438.3 | NS | | Megafauna abundance | 0.07 | 0.260 | 5.21 –0.04 Megafauna abundance | 32 | 432.4 | 440.4 | NS | | Megafauna abundance +Season | <0.001 | <0.001 | 5.01 +0.06 Megafauna abundance –0.41 Post-monsoon * | 31 | 428.1 | 438.1 | NS | | Fire | <0.001 | <0.001 | 5.23 –0.16 Fire | 32 | 429.9 | 437.9 | NS | | Fire +Season | <0.001 | <0.001 | 5.06 –0.08 Fire –0.32 Post-monsoon | 31 | 427.8 | 437.8 | # | | Shannon index | ||||||| | Null | 0.32 | 0.569 | 33 | 63.6 | 69.6 | || | Season | 0.33 | 0.570 | 2.34 +0.60 Post-monsoon | 32 | 63.3 | 71.3 | NS | | Habitat | 0.16 | 0.393 | 2.83 +0.004 Open forest –0.88 Grassland ** –0.78 Rural habitat * | 30 | 53.6 | 65.6 | * | | Habitat +Season | 0.16 | 0.394 | 2.80 +0.004 Open forest –0.88 Grassland ** –0.78 Rural habitat * +0.06 Post-monsoon | 29 | 53.3 | 67.3 | * | | Vegetation strata | 0.08 | 0.283 | 2.37 +1.95 Strata E3 *** –1.54 Strata E3 2* | 31 | 52.4 | 62.4 | ** | | Vegetation strata :Season | 0.09 | 0.303 | 2.36 +1.26 Strata E3 :Pre-monsoon # –1.35 Strata E3 2 :Pre-monsoon +2.55 Strata E3 :Post-monsoon *** –1.90 Strata E3 2 :Post-monsoon ** | 29 | 49.7 | 63.7 | ** | | Nectar | 0.32 | 0.563 | 2.37 +0.06 Nectar | 32 | 63.1 | 71.1 | NS | | Nectar :Season | 0.34 | 0.581 | 2.36 –0.02 Nectar :Pre-monsoon +0.09 Nectar :Post-monsoon | 31 | 62.8 | 72.8 | NS | | Invasive shrub | 0.28 | 0.528 | 2.37 +0.21 Chromolaena odorata * –0.18 Lantana camara * | 31 | 56.6 | 66.6 | * | | Invasive shrub :Season | 0.32 | 0.565 | 2.27 –0.18 Chromolaena odorata :Pre-monsoon +0.31 Chromolaena odorata :Post-monsoon ** +0.08 Lantana camara :Pre-monsoon –0.17 Lantana camara :Post-monsoon * | 29 | 49.6 | 63.6 | ** | | Activity signs | 0.41 | 0.643 | 2.37 +0.12 Dung | 32 | 61.9 | 69.9 | NS | | Activity signs +Season | 0.41 | 0.643 | 2.33 +0.13 Dung –0.08 Post-monsoon | 31 | 61.5 | 71.5 | NS | | Megafauna visitation | 0.33 | 0.574 | 2.37 –0.09 megafauna4 | 32 | 62.1 | 70.1 | NS | | Megafauna visitation :Season | 0.35 | 0.594 | 2.36 –0.27 megafauna4 :Post-monsoon # –0.02 megafauna4 :Pre-monsoon | 31 | 60.6 | 70.6 | NS | | Megafauna diversity | 0.34 | 0.579 | 2.37 +0.02 Megafauna diversity | 32 | 63.5 | 71.5 | NS | | Megafauna diversity :Season | 0.36 | 0.603 | 2.40 +0.17 Megafauna diversity :Pre-monsoon –0.08 Megafauna diversity :Post-monsoon | 31 | 61.7 | 71.7 | NS | | Megafauna abundance | 0.31 | 0.555 | 2.37 –0.02 Megafauna abundance | 32 | 63.5 | 71.5 | NS | | Megafauna abundance :Season | 0.12 | 0.344 | 2.32 –0.87 Megafauna abundance :Post-monsoon * –0.14 Megafauna abundamce :Pre-monsoon | 31 | 59.4 | 69.4 | NS | | Fire | 0.32 | 0.570 | 2.37 –0.01 Fire | 32 | 63.5 | 71.5 | NS | | Fire + Season | 0.33 | 0.571 | 2.33 +0.02 Fire –0.08 Post-monsoon | 31 | 63.3 | 73.3 | NS | TABLE 2 Results of ordination RDA analyses relating the butterfly assemblages’ composition to environmental conditions across 18 study plots in Manas National Park, Assam, India, based on model ~Season |trap exposure and its interactive ~ Season : x | trap exposure forms, where x are the environmental predictors i-ix (see materials and methods). Eig1–Eig4: eigenvalues of the ordination axes, %Var: adjusted explained variation, F p s: pseudo-F values, “→ ←”: against/opposite direction, “→ →”: with/identical direction. Significance codes: # P <0.1, * P <0.05, ** P <0.01, *** P <0.001. | %Var | Eig1 | Eig2 | Eig3 | Eig4 | F P 1st | F P all | Selected traits | %Var | Eig1 | Eig2 | Eig3 | Eig4 | F P 1st | F P all | | | ~ Trap exposure | 0.1 | .030 | – | – | – | – | 1.0 # | |||||||| | ~ Season | 2.2 | .050 | – | – | – | – | 1.8 *** | – | – | – | – | – | – | – | – | | ~ Habitat | 30.1 | .280 | 0.058 | 0.018 | – | 4.1 * | 5.9 * | Wingspan → ← grasslands; Indian distribution → ← forests | 3.7 | 0.038 | 0.015 | – | – | 2.3 * | 3.2 ** | | ~ —- : Season | 0.1 | .031 | 0.014 | 0.011 | – | 0.5 * | 1.0 * | Climbers → → pre-monsoon rural habitats; wingspan → → pre-monsoon forests; tall grasses, Indian dist. → → post-monsoon | 10.1 | 0.093 | 0.035 | 0.004 | – | 2.9 *** | 4.3 *** | | ~ Vegetation strata (E3) | 15.3 | .173 | – | – | – | – | 7.1 # | Tall grasses, flight period → → forests; Indian dist., global dist. → ← forests | 13.1 | 0.161 | – | – | – | 5.4 *** | 5.4 *** | | ~ —- :Season | 0.0 | .018 | – | – | – | – | 0.8 NS | – | – | – | – | – | – | – | – | | ~ Nectar | 2.9 | .056 | – | – | – | – | 2.0 # | Forbs → → nectars | 5.0 | 0.059 | – | – | – | – | 7.2 ** | | ~ —- :Season | 1.8 | .043 | – | – | – | – | 1.6 # | Flight period → → pre-monsoon nectars; global dist., forbs → ← nectars | 22.8 | 0.248 | – | – | – | 12.5 *** | 12.5 *** | | ~ Invasive shrub | 10.3 | .091 | 0.060 | – | – | 1.7 # | 3.0 ** | Wingspan, hostplant scope, Indian dist. → → invasive shrubs; climbers → ← invasive shrubs | 18.6 | 0.212 | 0.001 | – | – | 7.6 *** | 7.7 *** | | ~ —- :Season | 0.0 | .028 | 0.020 | – | – | 0.5 NS | 0.9 NS | – | – | – | – | – | – | – | – | | ~ Activity signs (trails) | 9.7 | .120 | – | – | – | – | 4.7 ** | Forbs, global dist. → → trails; flight periods, tall grasses → ← trails | 21.5 | 0.242 | – | – | – | 9.0 *** | 9.0 *** | | ~ —- :Season | 1.4 | .036 | – | – | – | – | 1.5 # | Wingspan, forbs, global dist. → → post-monsoon trails | 12.3 | 0.143 | – | – | – | 6.3 ** | 6.3 ** | | ~ Megaf. visitation | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | | ~ Megaf. diversity | 1.5 | 0.042 | – | – | – | – | 1.5 NS | – | – | – | – | – | – | – | – | | ~ —- :Season | 0.0 | 0.015 | – | – | – | – | 0.5 NS | – | – | – | – | – | – | – | – | | ~ Megaf. abundance | 4.9 | 0.074 | – | – | – | – | 2.7 NS | – | – | – | – | – | – | – | – | | ~ —- :Season | 5.8 | 0.074 | – | – | – | – | 3.0 ** | Global dist. → → post-monsoon megafauna | 5.2 | 0.062 | – | – | – | – | 6.4 ** | | ~ Fire | 1.0 | 0.038 | – | – | – | – | 1.3 NS | – | – | – | – | – | – | – | – | | ~ —- :Season | 1.0 | 0.038 | – | – | – | – | 1.3 NS | – | – | – | – | – | – | – | – | FIGURE LEGENDS FIGURE 1 Map showing the position of Assam within the Republic of India, the Manas National Park targeted here, and positions of plots where seasonal effects of butterflies were studied. FIGURE 2 Model predicted partial effects of habitat (with seasonal additive effects) from the generalized linear mixed effects models comparing butterfly species richness, abundance, and diversity across 18 study plots in Manas National Park, Assam, India. See TABLE 1 & S1 for the models’ statistics. CF: closed forests; OF: open forests; GR: grasslands; RU: rural habitats. FIGURE 3 Examples of predicted values (partial effects ±95% confidence intervals) from seasonal interaction models using generalized linear mixed-effects models: top left–butterfly abundance vs. megafauna visitation (megafauna1), top right–diversity vs. vegetation strata (E3), bottom left–diversity vs. invasive shrub (C. odorata ), bottom right–diversity vs. invasive shrub ( L. camara ) across 18 study plots in Manas National Park, Assam, India. See TABLE 1 & S1 for the models’ statistics. FIGURE 4 Explained variation (%) of predictors in RDA ordination interpreting butterfly assemblage composition. For each environmental variable x at horizontal axis, the column refers to model containing only that variable (~ x |trap exposure ), the second to model with additive effect (~ Season +x |trap exposure ), the third to model with multiplication (~ Season +x +Season*x |trap exposure ), and the fourth to model containing interaction only (~ Season :x |trap exposure +Season +x ). FIGURE 5 Results of RDA ordination interpreting butterfly assemblage composition in relation to life history traits for interactive models ~ Season : x | trap exposure + Season + x, for four environmental predictors x ; (a) habitat, (b) nectar, (c) activity sign, and (d) megafauna abundance . See TABLE 2 for the models’ statistics. Supplementary Material File (figure_4.tif) - Download - 6.75 MB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 343views 223downloads Citations Download citation Gaurab Nandi Das, Martin Konvicka, Pavel Vrba. Butterfly seasonality vs. life history traits in Manas National Park, India: Effects of vegetation, nectar, megafauna, invasive shrubs, and fire. Authorea. 30 September 2025. DOI: https://doi.org/10.22541/au.175921401.17341204/v1 DOI: https://doi.org/10.22541/au.175921401.17341204/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.