Vegetation determines butterfly diversity and composition across the Arabuko-Sokoke coastal forest in Kenya, a tropical biodiversity hotspot

Vegetation determines butterfly diversity and composition across the Arabuko-Sokoke coastal forest in Kenya, a tropical biodiversity hotspot Maria Fungomeli1, Martin Wiemers2, Lucia Calderini3, Alessandro Chiarucci3 1 CoastalForestsConservationUnit,CentreforBiodiversity,NationalMuseumsofKenya,P.OBox596Kilifi,Kenya 2 Senckenberg German Entomological Research Institute, Eberswalder Str. 90, 15374 Müncheberg, Germany 3 Biogeography and Macroecology Group, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum – University of Bologna, via Irnerio 42, 40126 Bologna, Italy https://zoobank.org/7CF5CEB5-FD82-49CC-BD5A-3574223248E9 Corresponding author: Maria Fungomeli ([email protected]) Academic editor: Thomas Schmitt | Received 6 April 2025 | Accepted 31 October 2025 | Published 25 November 2025 Abstract Community structures, including butterfly diversity, are shaped by both biotic and abiotic factors, with forest type exerting a significant influence. The Arabuko Sokoke Forest (ASF), the largest remaining coastal forest fragment in Kenya and East Africa, is rich in biodiversity and endemic species. Given its varied forest types, ASF provides a unique opportunity to examine how these differences affect butterfly community structure. This study aims to investigate how vegetation diversity and structure influence butterfly community structures and species richness within ASF. We conducted butterfly and woody plant surveys during the dry season across four distinct forest types in ASF: Cynometra forest, Brachystegia woodland, mixed forest and the forest edge. Butterfly populations were sampled using transects measuring 10 m × 100 m and woody plant species were surveyed along overlapping transects. A total of 6,050 butterfly individuals were recorded, representing 86 species across 38 genera and five families. The woody vegetation comprised 178 species, belonging to 78 genera and 34 families. Significant differences in butterfly species abundance were observed across the forest types, though no significant differences were found in species richness. Beta diversity analyses revealed consistently high community dissimilarity across all forest types, driven predominantly by balanced variation in species abundances rather than nestedness. Brachystegia forest exhibited the highest total beta diversity, while forest edge exhibited the lowest. This indicates that species turnover, rather than richness differences, is the primary mechanism structuring butterfly communities at the landscape scale in Arabuko Sokoke Forest. Butterfly species diversity showed a strong correlation with plant species diversity. Additionally, butterfly wingspan size varied significantly amongst forest types. Our findings underscore the crucial role of natural plant forest diversity in supporting butterfly diversity and highlight the synergistic functions of the mixed forest and Brachystegia forest as key habitats. There is need for conservation strategies that account for multiple dimensions of biodiversity. While mixed forest serves as a reservoir of high species richness and abundance, Brachystegia forest offers critical value through their contribution to beta diversity at the landscape level. These results highlight the fundamental importance of conservation efforts directed to protect high plant diversity and structural heterogeneity to provide a broad spectrum of ecological niches and habitat connectivity for butterflies. Such strategies will enhance butterfly diversity and contribute to effective conservation in fragmented forests and especially in Arabuko Sokoke Forest. Key Words Arabuko Sokoke Forest, butterfly diversity, community structure, forest edge, habitat connectivity, habitat quality, plant species, species co-occurrence, tropical forests Contributions to Entomology 75 (2) 2025, 299–318|DOI 10.3897/contrib.entomol.75.e155016 Copyright Maria Fungomeli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Research Article Maria Fungomeli et al.: Butterfly and vegetation diversity interactions in Arabuko Sokoke Forest Kenya300 Introduction Community structures are shaped by the interplay of biotic and abiotic factors, with forest type exerting a significant influence on butterfly diversity. Different forest types can significantly impact butterfly diversity through changes in microclimate, resource availability, plant diversity and vegetation structure (Schweitzer and Dey 2011). Butterflies are widely regarded as effective bioindicators of habitat quality and ecosystem health due to their sensitivity to environmental changes and close associations with host plants and microhabitats (Bouyer et al. 2007; Dobson 2012). Their community composition and species richness often reflect underlying patterns in vegetation diversity and structure, making them valuable for monitoring biodiversity responses to habitat variation. Globally, an estimated 18,000 butterfly species have been documented, with approximately 3,600 species occurring in Africa and around 870 recorded in Kenya (Larsen 1991). The Arabuko-Sokoke Forest alone supports over 300 of Kenya’s butterfly species, highlighting its significance as a global biodiversity hotspot (Ayiemba 1995; ASF Management Team 2002). It is estimated that, globally, approximately 90% of butterflies are found in tropical areas, but their ecological role is less studied than in temperate regions, which also applies to vegetation studies (Bonebrake et al. 2010; see Fungomeli et al. (2020b)). Butterflies play a crucial role as a biogeographical and ecological indicator group for habitat fragmentation, anthropogenic disturbance and climate change effects (Larsen 1993; Heikkinen et al. 2009; Manzoor et al. 2013). Their life cycle highly depends on plants either for breeding (host plants) or food (nectar) and multiple other environmental factors (Collinge et al. 2003; Manzoor et al. 2013). They can serve as indicators for biodiversity in ecological studies due to their sensitivity to even minor changes in habitat conditions or disturbances (Lomov et al. 2006; Bouyer et al. 2007; Dobson 2012). In addition, butterflies play an essential ecological role as pollinators and herbivores (Courtney et al. 1982; Bonebrake et al. 2010; Rader et al. 2015). Herbivory has been valued as a mechanism that has promoted plant co-existence and diversity, while pollination has enhanced plants life, growth and diversity (Vail 1992; Coley and Barone 1996; Viola et al. 2010). Moreover, their association with particular forest types and host plants, the fluctuation in their richness and abundance according to seasonality and their pervasive presence on the territory makes them perfect study subjects for investigating and monitoring the conservation status of ecosystems (Lien 2007; Monastyrskii 2007; Habel et al. 2018). This co-existence and interplay between butterflies and plants offers a unique fundamental contribution to ecosystem functioning while presenting a huge potential in tropical forests biodiversity monitoring (Humpden and Nathan 2010). Vegetation diversity enhances ecological complexity by increasing the availability of nectar sources, larval host plants and microclimatic niches (Vu et al. 2015). Structural characteristics, such as canopy height, foliage density and vertical stratification, further influence microhabitats and resource accessibility, directly impacting butterfly foraging behaviour, oviposition and survival (Collinge et al. 2003). Consequently, areas with high vegetation heterogeneity are often associated with greater butterfly species richness and more stable community structures. Despite this well-established relationship, the extent to which vegetation diversity and structure shape butterfly assemblages remains poorly documented in many tropical and subtropical ecosystems. This is more pronounced especially in East Africa, a region known for its ecological heterogeneity and high biodiversity. The Arabuko-Sokoke Forest (ASF) in coastal Kenya presents a unique opportunity to investigate these relationships, as it encompasses a mosaic of distinct forest types within a relatively compact landscape, enabling detailed comparisons of butterfly community assembly across environmental gradients. The forest hosts four butterfly species endemic to the forests of Kenya and Tanzania: Acraea matuapa, Baliochila latimarginata, Baliochila stygia and Charaxes blanda, 50 nationally and globally rare plant species, three rare endemic mammals and is home to 230 bird species, 15 of which are rare and endemic to the Kenyan coast (ASF Management Team 2002). The forest also plays a crucial role as a global eco-tourism site, while locally supporting survival of the forest adjacent indigenous people livelihoods who depend on the forest for butterfly farming, collecting medicinal plants and wood production. Moreover, although ASF is rich in plant diversity and butterfly diversity, little is known about their interaction. Limited butterfly studies carried out in ASF have looked at the butterfly diversity across the forest and forest types or seasonality influence on butterfly diversity (Ayiemba 1995; Habel et al. 2018). However, to our knowledge, there is no study that has thoroughly investigated the influence of the plant species diversity on butterfly diversity in ASF. Moreover, the need to regularly assess and monitor its continued fragmentation and biodiversity is therefore fundamental for long-term conservation efforts (Azeria et al. 2007; MacFarlane et al. 2015; Habel et al. 2017; Busck-Lumholt and Treue 2018). In this study, we investigate the relationships between vegetation structure and butterfly community structure and species diversity within the Arabuko-Sokoke Forest (ASF). This was achieved through systematic sampling along 100-m transects across different forest types, conducted over a four-month period, incorporating a range of butterfly functional traits to enhance ecological interpretation. In particular, we investigate: (i) how the dominant forest types influence butterfly species diversity, composition and abundance in ASF; (ii) how plant species diversity influence or correlate with butterfly diversity and composition and (iii) how butterfly wingspan traits vary across different forest types. We synthesise these results to better guide the conservation policy formulations for sustainable forest use and management of the forest, especially in the dry season when this study was conducted. Contributions to Entomology 75 (2) 2025, 299–318 301 Materials and methods Study area and forest types The Arabuko Sokoke Forest (ASF) is the largest forest fragment remaining within the Kenyan coastal forests covering an area of 42,000 ha, the second being Shimba Hills Forest (25,300 ha; Fig. 1A; Burgess and Clarke (2000); Fungomeli et al. (2020a)). It is globally valued as a world biodiversity hotspot of the Eastern Arc and Coastal Forests of Kenya and Tanzania (Myers et al. 2000). ASF is a centre of endemism, hosting a conspicuous number of threatened and endangered species and recently declared a UNESCO Biosphere Reserve (UNESCO 2019). Anthropogenic pressure and biodiversity loss, together with climate change are heavily impacting tropical forests, such as the Arabuko Sokoke Forest in Kenya (Burgess and Clarke 2000; Newton and Echeverría 2014; FAO 2018; Fungomeli et al. 2025). As a dry lowland coastal forest, ASF spreads between the cities of Kilifi in the south and Malindi in the north, positioned between 39°48'E and 40°00'E longitude and between 3°11'S and 3°29'S latitude (Fanshawe 1995; Muchiri et al. 2001). It lies on a flat coastal plain at sea level and the area is divided by a low escarpment which crosses the forest from south-west to north-east (Moomaw 1960; Fanshawe 1995). The climate consists of rainy and dry seasons, with two rainfall seasons of long and short rains. The long rainy season occurs from April to July; short rains from October to December, while the dry season lasts from December to March and in August/September (Burgess and Clarke 2000; Omenge 2002). The annual rainfall ranges from 600 to 1,000 mm, with rainfall decreasing from east to west within the Forest (Omenge 2002; Habel et al. 2017). Temperature ranges from monthly averages of 24 °C and 30 °C and humidity is about 60% annually (Burgess and Clarke 2000). Several water pools exist within the Forest during the rainy season with most drying out in the dry season and there are no rivers within the forest (Fungomeli et al. 2001; Kanga 2002; Muriithi and Kenyon 2002). A defining characteristic of the Arabuko-Sokoke Forest is the presence of distinct vegetation types, usually referred to as forest types (Fig. 1B–E). These forest types are closely associated with the underlying soil characteristics. The area features two predominant soil types: light, white sandy soils and heavier, red clay soils (Fanshawe 1995; Muchiri et al. 2001). These contrasting soil conditions have significantly influenced the distribution and composition of the forest’s vegetation communities. The four distinct forest types are the Cynometra forest, Brachystegia forest, Mixed forest and the forest edge (Fig. 1B–E). Cynometra forest The Cynometra forest, which occupies the western sector of the ASF, is the most extensive of the forest types, accounting for over 50% of the forest area (Fanshawe 1995). This zone is found predominantly on red clay soils and is characteried by a dense, lowstatured canopy, composed mainly of Cynometra Figure 1. A. Map of Arabuko Sokoke Forest, Kenya, showing the distribution of the 108 studied butterfly transects within the four forest types: Brachystegia, Cynometra, Mixed forest and Forest edge. B–E. The four forest types study sites within Arabuko Sokoke Forest, Kenya showing. B. Cynometra forest; C. Brachystegia forest; D. Mixed forest; E. Forest edge. Photo credits: Maria Fungomeli. AB C D E Maria Fungomeli et al.: Butterfly and vegetation diversity interactions in Arabuko Sokoke Forest Kenya302 webberi, Cynometra suhalensis and Manilkara sulcata, with occasional emergents, such as Euphorbia candelabrum (Fig. 1). The undergrowth is sparse due to the closed canopy and low light penetration, creating a relatively stable microclimate with reduced temperature fluctuations and high humidity. Brachystegia forest Brachystegia forest is located centrally within the ASF, on the nutrient-poor, white sandy soils and covers approximately 18% of the forest (Fig. 1; Muchiri et al. 2001). It is dominated by Brachystegia spiciformis, characterised by an open canopy structure interspersed with grasses and shrubs, resulting in a more sunlit and drier environment than other forest types. The floristic composition is adapted to dry conditions and the habitat is important for butterfly species, reptiles and birds that depend on high light availability and open understoreys. Additionally, the openness of this zone creates microhabitats favourable for thermoregulation and basking, crucial for many invertebrate and herpetofauna species. Mixed forest The mixed forest type is located in the eastern part of the Forest, where it grows on grey sandy soils that retain more moisture than the white sands, but are lighter than the red clay soils. This forest type represents about 17% of the Reserve and is notable for its high plant species richness and vertical stratification (Fanshawe 1995; Muchiri et al. 2001; Arabuko Sokoke Management Team 2002). It is dominated by mixed plant species of Afzelia quanzensis, Hymenaea verrucosa, Newtonia hildebrandtii and Manilkara sansibarensis. Forest edge The forest edge was selected along the transition from the mixed forest of the Arabuko-Sokoke Forest (ASF) to adjacent agricultural lands. This ecotone represents a gradual shift from intact forest ecosystems to human-modified landscapes and coastal habitats (Fig. 1; Muchiri et al. 2001). It is characterised by a heterogeneous mosaic of land uses, including subsistence farms, scattered shrubs and open grassy clearings. Structurally, it exhibits increased light penetration, higher temperature fluctuations and reduced canopy cover compared to the forest interior. These conditions create a distinctive microclimate that supports a unique assemblage of flora and fauna adapted to edge environments. Data collection Field sampling and data collection were conducted during the dry season months (January-April) of 2019 across the four forest types of ASF: Cynometra forest, Brachystegia woodland, mixed forest and forest edge (Fig. 1). Butterflies were sampled by using a standard number of 27 transects within each vegetation type leading to a total of 108 transects. Each transect measured 10 m × 100 m. Butterflies were recorded in each transect by using a standard count technique performed by walking at slow constant pace for approximately 15 min. All butterfly species seen on both sides of the path were recorded. Each transect was surveyed once per day, every day, throughout the four-month dry season (January-April 2019), ensuring comprehensive and exhaustive species detection in accordance with established butterfly monitoring protocols (Pollard 1977; Pollard and Yates 1993). Butterflies were identified and recorded at species level. Specimens that could not immediately be identified in the field were caught with a butterfly net and placed in numbered envelopes or photographed for further identification in the lab. Identification was carried out using the butterfly references for the area (Larsen 1991) and supported by taxonomic counter checks from published sources. All transects were geo-referenced, with details of date, hour of start and end. Vegetation field sampling was performed by using 27 plots each measuring 10 m × 100 m (same used for butterfly transects hereafter referred to as plots) and internally subdivided into 20 subplots of 10 m × 5 m. Each vegetation plot corresponded to a butterfly transect. Within the plots and subplots, we identified and measured the height and diameter at breast height (DBH) for each individual woody plant species (trees, lianas and shrub) with DBH ≥ 5 cm. Plants with DBH < 5 cm, such as small shrubs, were identified in two subplots of each plot (see Fungomeli et al. 2020a, 2020b). Butterfly traits: wingspan sizes We compiled and obtained wingspan sizes for our sampled butterfly species from published data sources of Woodhall (2005), Woodhall (2020), Schmitt (pers. comm), Barcode of Life Data System database (https://v3.boldsystems. org) and from the collection of the Senckenberg German Entomological Institute, Müncheberg. All butterflies encountered along the transects were classified according to their ecological traits and distribution. The larval diet of each species was determined, based on host plant use and categorised into one of three trophic breadth classes: (1) monophagous, restricted to a single host plant genus; (2) oligophagous, restricted to host plants within a single plant family; or (3) polyphagous, utilising host plants from multiple plant families. A further classification into endemic status was assigned to species according to Larsen (1996). Data analysis A community matrix was prepared for the butterfly species abundances and another matrix was prepared for the woody plant species across the forest types. Contributions to Entomology 75 (2) 2025, 299–318 303 Butterfly species diversity Butterfly species diversity was analysed in terms of species richness, Shannon index and Simpson index across forest types: Shannon Index: Simpson Index: For both indices, k represents the total number of species, while pi indicates the relative abundance of each species that is calculated as ni/N (in which ni indicates the number of individuals of the i-species and N indicates the number of individuals of all species within the transect. Butterfly species richness and mean abundance distributions across forest types were visualised using boxplots. Mann-Whitney U test for pairwise comparisons was used to compare species richness and abundance amongst forest types. Additionally, rank-abundance curves were constructed for each forest type to illustrate patterns of species dominance and evenness (Whittaker 1965). Rarefaction curves and species diversity estimation To assess and compare species diversity across forest types, we employed sample coverage-based rarefaction and extrapolation within the Hill numbers framework (Chao et al. 2014). We analysed abundance data for each forest type using the iNEXT() function from the iNEXT package in R (Hsieh et al. 2016), specifying datatype = “abundance”. This function performs both interpolation (rarefaction) and extrapolation of diversity estimates, based on individualbased abundance data. Confidence intervals (95%) were derived using 1,000 bootstrap replicates. Following Chao et al. (2014), extrapolation was constrained to a maximum of twice the reference sample size to maintain estimate stability. Correlation between butterfly and plant species diversity We applied a symmetric Co-correspondence analysis (CoCA) to quantify relationships between the plant species community with the butterfly species community across the forest types. Co-correspondence analysis is useful for comparing biological communities where observations have been made at the same locations (Braak and Schaffers 2004). We did this by a weighted average of species abundance values for plant species and separately for butterfly species within each of the forest types. We used ‘coca’ function of the ‘cocorresp’ R package (Simpson 2009) to correlate the butterfly and plant communities using the ‘symmetric’ method. All graph plotting was performed using R package ggplot2 (Wickham 2016) and ggrepel (Slowikowski 2020). Butterfly species composition We square-root transformed butterfly community abundances prior to the analysis to reduce effects of dominant species. Transformed community abundances were then used to generate a Bray-Curtis dissimilarity matrix (Bray and Curtis 1957). We tested for species composition differences in the butterfly community structure amongst forest types by an analysis of similarities (ANOSIM) using the ‘anosim’ function of the ‘vegan’ R package (Oksanen et al. 2020). We also tested for significant differences between forest types using the permutational analysis of variance (PERMANOVA), using the ‘adonis’ function of the ‘vegan’ R package. All tests were conducted using 999 permutations. Butterfly species contributing to similarities across forest types were determined using similarity percentages analysis (SIMPER). P-values were adjusted using the Benjamini-Hochberg test to control the false discovery rate. This approach was selected for its ability to limit type I errors, while retaining greater statistical power than more conservative methods, such as the Bonferroni correction. Beta diversity partitioning To assess whether variability in butterfly community composition differed amongst the four forest types (Brachystegia, Cynometra, mixed forest and forest edge), we performed a permutation test for homogeneity of multivariate dispersions (PERMDISP), based on BrayCurtis dissimilarities using the ‘betadisper’ function in the vegan R package. We then evaluated beta diversity amongst the four forest types. We quantified multi-site beta diversity using the abundance-based extension of the ‘betapart’ framework (Baselga et al. 2017), implemented via the ‘beta.multi.abund’ function in the R package betapart (v. 1.6). This method partitions BrayCurtis dissimilarity into three components: (i) total beta diversity (β_total); (ii) balanced variation in abundance (β_balanced) and (iii) abundance gradients (β_gradient). We also assessed spatial heterogeneity of community composition within forest types by testing homogeneity of multivariate dispersion. Bray-Curtis dissimilarities, calculated from species abundance data, were used to compute distances of individual plots to their respective group centroids using the ‘betadisper’ function in the R package vegan (Oksanen et al. 2020). These distances reflect within-group variation in community composition. Differences in dispersion amongst forest types were tested using ANOVA, followed by Tukey’s Honest Significant Difference (HSD) post-hoc tests. Butterfly composition ‒ NMDS To visualise differences in butterfly species composition amongst forest types, we conducted a non-metric multidimensional scaling (NMDS) analysis, based on Bray-Curtis dissimilarities (Kruskal 1964). Prior to analysis, butterfly species abundance data were square-root transformed to reduce the influence of highly abundant species. NMDS was performed using the ‘metaMDS’ function from the vegan package (Oksanen et al. 2020) in R (R Core Team 2020). This function conducts automatic data standardisation, multiple random starts and iteration Maria Fungomeli et al.: Butterfly and vegetation diversity interactions in Arabuko Sokoke Forest Kenya304 to ensure a stable and optimal ordination solution. Groupings by forest type were visualised using convex hulls. Butterfly traits: wingspan sizes Using Pearson correlation, we correlated butterfly wingspan sizes across forest types, by first correlating for total abundances in all forest types and then second within each vegetation type. Following confirmation of normality, pairwise t-tests were conducted to compare average wingspan sizes across different forest types. To account for multiple comparisons and control the false discovery rate, p-values were adjusted using the Benjamin-Hochberg test. Results We recorded a total of 6,050 butterfly individuals belonging to 86 species, 38 genera and five families across the four forest types of Arabuko Sokoke Forest (Appendix 1). The plant species survey resulted in a total of 178 plant species belonging to 78 genera and 34 families. Butterfly species diversity Butterfly species diversity was primarily dominated by the Nymphalidae family, which had the highest number of species, followed by Pieridae, Papilionidae, Lycaenidae and Hesperiidae (Appendix 1). Analysis on the most abundant butterfly species revealed Phalanta phalantha, Appias epaphia, Catopsilia florella, Hypolimnas misippus and Coeliades forestan, as the most frequent across all forest types (Suppl. material 1: fig. S1). A strong positive correlation was observed between species richness and abundance across the forest types (R² = 0.89). The distribution of butterfly larval feeding habits showed oligophagous and polyphagous species being dominant in all forest types (Suppl. material 1: fig. S2). Rarefaction curves and species diversity estimation Rarefaction curves revealed the mixed forest exhibited the highest species richness, followed by Brachystegia forest, forest edge and Cynometra forests (Fig. 2). Rarefaction curves for all forest types approached asymptotes, indicating sufficient sampling and species richness capture, except in the Brachystegia forest, where the non-asymptotic curve suggests that further sampling may uncover additional species. Butterfly species richness and abundances across forest types showed that the mixed forest had the highest cumulative species richness, followed by Brachystegia and forest edge, while Cynometra had the lowest value (Table 1). Average species richness and abundance per plot varied across forest types, with significantly higher abundance values observed at the forest edge and in mixed forest habitats (ANOVA, P < 0.05; Fig. 3). According to ANOVA, differences amongst species richness per transect were at the significance threshold amongst forest types (P = 0.05), while species abundances per transect were significantly different (P = 0.001; Fig. 3). Pairwise comparisons of butterfly abundance revealed significant differences between Cynometra forest and forest edge (P = 0.001), Brachystegia forest and forest edge (P = 0.013), as well as between Brachystegia forest and forest edge (P = 0.001; Fig. 3). The diversity indices indicated relatively similar levels of butterfly diversity across vegetation types. Shannon index values ranged from 2.91 ± 0.40 at the forest edge to 2.82 ± 0.42 in the mixed forest, while the Simpson index values ranged from 0.93 ± 0.04 to 0.92 ± 0.03. Beta diversity partitioning Multivariate dispersions revealed significant difference in multivariate dispersion across the four forest types (F = 3.893, P = 0.007). Therefore, variation in butterfly community composition may in part be influenced by differences in within-forest type heterogeneity. Multi-forest type beta diversity analysis (β) revealed consistently high total dissimilarity across forest types, with β_total values ranging from 0.885 to Table 1. The butterfly species diversity across forest types, showing cumulative species richness and abundance, Shannon index and Simpson index per vegetation type in Arabuko Sokoke Forest, Kenya. Species diversity Brachystegia Cynometra Forest edge Mixed forest Cumulative species richness 50 40 52 80 Cumulative species abundance 1022 1112 2141 1775 Shannon’s H Index 2.38 2.58 2.87 2.66 Simpson’s 1-D Index 0.9 0.9 0.93 0.91 Figure 2. Rarefaction curves showing species richness as a function of number of individuals across the sampled forest types of Brachystegia, Cynometra, Mixed forest and Forest edge in the Arabuko Sokoke forest, Kenya. Each solid line represents actual sampled species (interpolated), and the dashedline represents extrapolated individuals (extrapolated). Shaded areas represent 95% confidence interval. Contributions to Entomology 75 (2) 2025, 299–318 305 0.910 (Table 2). The largest contribution to beta diversity came from balanced variation in species abundance (β_balanced: 0.803‒0.837), indicating that community compositional differences were primarily driven by species turnover in abundance rather than nestedness (β_gradient: 0.067‒0.096) (Table 2). Brachystegia forest exhibited the highest total beta diversity (β_total = 0.910), suggesting substantial heterogeneity in community composition across plots. Forest edge had the lowest β_total (0.885), while Cynometra and Mixed forests showed intermediate values (Table 2). Spatial dispersion of community composition Spatial heterogeneity within forest types, assessed via multivariate dispersion, varied significantly (Table 2). Brachystegia forest exhibited the highest dispersion (mean distance = 0.45), indicating greater spatial variability in species composition. These results reflect substantial spatial heterogeneity in species abundances in Brachystegia. In contrast, forest edge had the lowest dispersion (median ~ 0.33), indicating more homogeneous community structure. Tukey HSD tests confirmed dispersion in Brachystegia was significantly greater than at forest edges (P = 0.006), while other pairwise comparisons were not statistically significant (Table 2). These results reflect that forest edge communities are more similar to each other, while Brachystegia forest is more ecologically diverse. Overall, beta diversity results indicate that abundance-based species turnover drives community differentiation across forest types, with Brachystegia forests supporting more spatially heterogeneous communities, while forest edges harbour more homogenised assemblages. Correlation between butterfly and plant species diversity Co-correspondence analysis (CoCA) revealed a strong correlation between the species composition of plants and butterflies across the forest types. The correlation coefficients for Axis 1 and Axis 2 between the butterfly and plant communities were 0.991 and 0.994, respectively. The eigenvalues for the first and second axes indicated the contribution of each axis to the total inertia, with values of 0.022 and 0.012, representing a variance of 57.3% and 32.6%, respectively. This resulted in a total explained variance of 89.9% (Fig. 4), highlighting a robust and highly significant correlation between the community matrices of plants and butterflies. Butterfly species composition The NMDS analysis of butterfly species composition across forest types revealed considerable overlap, with no clear separation observed amongst the different forest types, with Cynometra forest covering a wider NMDS space that overlaps Brachystegia, forest edge and mixed forest (Fig. 5, Table 2). Butterfly assemblages across different forest types showed substantial overlap, with many species occurring in more than one forest type within the ASF. A pairwise permutational multivariate analysis of variance (PERMANOVA) revealed statistically significant compositional differences in butterfly assemFigure 3. Boxplots showing butterfly species richness and abundances comparison across the four forest types of Brachystegia, Cynometra, Forest edge and Mixed forest in the Arabuko Sokoke Forest, Kenya. Forest type with different letter denotes statistical significant differences (P < 0.01). Table 2. Summary of beta diversity metrics across the four forest types, including abundance-based partitioning components of total beta diversity (β_total); balanced variation (β_balanced); and abundance gradient (β_gradient), as well as multivariate dispersion (measured by the mean distance to centroid. Tukey HSD post hoc tests were used for pairwise comparisons amongst forest types (n.s. = no significant difference). Forest type n(plots) β_total β_balanced β_gradient Mean dispersion (distance to centroid) Significant difference (Tukey HSD) Brachystegia 27 0.910 0.825 0.085 0.452 higher than forest edge (P = 0.006) Cynometra 27 0.903 0.837 0.067 0.425 n.s. vs. other types Mixed forest 27 0.903 0.807 0.096 0.418 n.s. vs. other types Forest edge 27 0.885 0.803 0.083 0.364 Lower than Brachystegia (P = 0.006) Maria Fungomeli et al.: Butterfly and vegetation diversity interactions in Arabuko Sokoke Forest Kenya306 Figure 4. Symmetric co-correspondence analysis (CoCA) ordination bi-plots showing correlations between (a) butterfly and plant species and (b) plant and butterfly species within the four forest types of Brachystegia, Cynometra, Mixed forest and Forest edge in Arabuko Sokoke Forest, Kenya. The Axis-1 eigen value of 0.022 explains a variance of 57.3% and Axis-2 eigen value of 0.012 explains a variance of 32.6%. Total explained variance by Axis 1 and 2 is 89.9%. Figure 5. Non-metric multidimensional scaling (NMDS) for butterfly species composition within the four forest types of Arabuko Sokoke Forest, Kenya. Different colours represent different forest types as follows: Brachystegia (red), Cynometra (blue), Mixed forest (green), Forest edge (yellow). Figure 6. Butterfly average wingspan sizes across the four forest types of Brachystegia, Cynometra, Mixed forest and Forest edge in Arabuko Sokoke Forest. Forest type with different denotes significant difference (P < 0.01). Contributions to Entomology 75 (2) 2025, 299–318 307 blages amongst the forest types (R2 = 0.07; P = 0.006). Additionally, SIMPER results show species composition differences amongst forest types that contribute up to 70% of the observed dissimilarities (Appendix 2). Butterfly traits: wingspan sizes Average wingspan sizes were significantly larger at the forest edge compared to the Cynometra forest (P < 0.01; Fig. 6). However, no significant correlation was found between wingspan size and species abundance across forest types (Fig. 7). Butterfly traits: larval feeding habits The majority of larvae from the species encountered were classified as oligophagous, followed by polyphagous species and a smaller number classified as monophagous (Suppl. material 1: fig. S2). Overall, Oligophagous species constituted the largest share of individuals across all forest types, accounting for 63.4% of butterflies at the forest edge and reaching up to 67.5% in the mixed forest (Suppl. material 1: fig. S2). Discussion This study investigated the influence of vegetation diversity and structural complexity on butterfly community composition and species richness within Arabuko Sokoke Forest (ASF), a coastal biodiversity hotspot in East Africa. Our results provide new insights into butterfly community composition across habitat types within the Arabuko Sokoke Forest and their associations with plant communities. By examining species distributions alongside vegetation data, we highlight both broad and fine-scale patterns relevant to biodiversity conservation in tropical forest mosaics. Figure 7. Butterfly wingspan sizes correlation across the four forest types of Arabuko Sokoke Forest, Kenya. Showing wingspan correlation for (a) The total correlation in the four forest types (b) the correlation for each forest type of Brachystegia, Cynometra, Mixed forest and Forest edge. Maria Fungomeli et al.: Butterfly and vegetation diversity interactions in Arabuko Sokoke Forest Kenya314 Species Family Charaxes jahlusa Trimen, 1862 Nymphalidae Charaxes jasius saturnus Butler, 1866 Nymphalidae Charaxes lasti Grose-Smith, 1889 Nymphalidae Charaxes protoclea Feisthamel, 1850 Nymphalidae Charaxes sp. Nymphalidae Charaxes varanes (Cramer, 1764) Nymphalidae Charaxes violetta Grose-Smith, 1885 Nymphalidae Charaxes zoolina Westwood, 1850 Nymphalidae Coeliades forestan Stoll, 1782 Hesperiidae Colotis amata (Fabricius, 1775) Pieridae Colotis auxo (Lucas, 1852) Pieridae Colotis danae (Fabricius, 1775) Pieridae Colotis eris (Klug, 1829) Pieridae Colotis euippe (Linnaeus, 1758) Pieridae Colotis ione (Godart, 1819) Pieridae Colotis protomedia (Klug, 1829) Pieridae Colotis regina (Trimen, 1863) Pieridae Colotis vesta (Reiche, 1850) Pieridae Cupidopsis iobates (Hopffer, 1855) Lycaenidae Danaus chrysippus dorippus Klug, 1845 Nymphalidae Dixeia charina (Boisduval, 1836) Pieridae Eronia cleodora Hübner, 1823 Pieridae Euphaedra neophron Hopffer, 1855 Nymphalidae Eurema sp. Pieridae Eurytela dryope Cramer, 1779 Nymphalidae Euxanthewakefieldi (Ward, 1873) Nymphalidae Graphium angolanus (Goeze, 1779) Papilionidae Graphium antheus (Cramer, 1779) Papilionidae Graphium colonna (Ward, 1873) Papilionidae Graphium kirbyi (Hewitson, 1872) Papilionidae Graphium leonidas (Fabricius, 1793) Papilionidae Graphium philonoe (Ward, 1873) Papilionidae Graphium policenes (Cramer, 1775) Papilionidae Graphium polistratus (Grose-Smith, 1889) Papilionidae Graphium porthaon (Hewitson, 1865) Papilionidae Harma theobene Doubleday, [1848] Nymphalidae Hypolimnas anthedon (Doubleday, 1845) Nymphalidae Hypolimnas deceptor Trimen, 1873 Nymphalidae Hypolimnas misippus (Linnaeus, 1764) Nymphalidae Junonia hierta (Fabricius, 1798) Nymphalidae Junonia natalica Felder, 1860 Nymphalidae Junonia oenone Linnaeus, 1764 Nymphalidae Leptosia alcesta (Stoll, [1782]) Pieridae Libythea labdaca Westwood, 1851 Nymphalidae Melanitis leda Linnaeus, 1758 Nymphalidae Mylothris agathina (Cramer, 1779) Pieridae Nepheronia thalassina (Boisduval, 1836) Pieridae Neptis sp. Nymphalidae Papilio constantinus Ward, 1871 Papilionidae Papilio dardanus Brown, 1776 Papilionidae Papilio demodocus Esper, 1798 Papilionidae Papilio nireus Linnaeus, 1758 Papilionidae Pardopsis punctatissima Boisduval, 1833 Nymphalidae Phalanta phalantha Drury, 1773 Nymphalidae Physcaeneura leda Gerstaecker, 1871 Nymphalidae Pinacopteryx eriphia (Godart, 1819) Pieridae Pseudacraea boisduvali (Doubleday, 1845) Nymphalidae Pseudacraea lucretia (Cramer, 1775) Nymphalidae Salamis anacardii Linnaeus, 1758 Nymphalidae Salamis parhassus Drury, 1782 Nymphalidae Tirumala petiverana Doubleday, 1847 Nymphalidae Contributions to Entomology 75 (2) 2025, 299–318 315 Appendix 2 SIMPER analysis for butterfly species composition dissimilarities results. Highlighted are species that cumulatively contribute up to 70% of the observed dissimilarities. Table A2. Butterfly species contributing up to 70% of the observed dissimilarities across the sampled forest types of Brachystegia, Cynometra, Mixed forest and Forest edge in the Arabuko Sokoke forest, Kenya. Where average = the average contribution of a species to the dissimilarity between groups; Sd = standard deviation of the species’ contribution, showing variability in how much that species contributes across different sample comparisons; Ratio = the average contribution divided by its standard deviation (average / sd). A higher ratio indicates that the species consistently contributes to dissimilarity (less variable); ava = the average abundance or value of the species in forest A (e.g. Brachystegia); avb = the average abundance or value of the species in forest type B (e.g. Cynometra); cumsum = the cumulative sum of the contributions up to the current species, usually expressed as a percentage of total dissimilarity explained so far. This helps identify which species collectively contribute to a specified threshold (e.g., 70%); P-value = statistical significance testing the contribution of that species to the dissimilarity, lower values (typically < 0.05) suggest the species contributes significantly to differences between groups. Species average sd ratio ava avb Cumsum % of total dissimilarity P-value Brachystegia vs Cynometra Phalanta phalantha 0.030 0.022 1.330 2.143 1.564 0.049 4.9 0.001 Catopsiliaflorella 0.027 0.023 1.160 1.234 1.646 0.094 9.4 0.002 Appias epaphia 0.026 0.022 1.147 1.549 1.828 0.137 13.7 0.001 Hypolimnas misippus 0.025 0.021 1.184 0.959 1.151 0.178 17.8 0.002 Colotis regina 0.023 0.021 1.121 0.397 1.122 0.217 21.7 0.001 Graphium philonoe 0.022 0.019 1.181 0.606 1.190 0.254 25.4 0.001 Graphium antheus 0.022 0.020 1.112 1.010 1.018 0.291 29.1 0.002 Papilio demodocus 0.022 0.018 1.202 1.106 0.977 0.327 32.7 0.006 Neptis sp. 0.022 0.019 1.154 0.884 1.001 0.363 36.3 0.001 Junonia oenone 0.020 0.017 1.195 0.958 0.841 0.397 39.7 0.031 Colotis euippe 0.018 0.018 1.034 0.784 0.569 0.427 42.7 0.025 Eronia cleodora 0.018 0.017 1.080 0.700 0.785 0.458 45.8 0.023 Graphium porthaon 0.017 0.017 1.013 0.553 0.665 0.487 48.7 0.116 Colotis auxo 0.017 0.015 1.123 0.427 0.739 0.515 51.5 0.001 Hypolimnas deceptor 0.017 0.018 0.968 0.000 0.741 0.544 54.4 0.001 Coeliades forestan 0.017 0.017 0.974 0.628 0.668 0.572 57.2 1.000 Acraea sp. 0.017 0.016 1.072 0.612 0.729 0.600 60.0 0.006 Charaxes candiope 0.016 0.015 1.036 0.037 0.705 0.625 62.5 0.001 Pardopsis punctatissima 0.015 0.016 0.946 0.552 0.501 0.651 65.1 0.001 Bicyclussafitza 0.014 0.016 0.910 0.154 0.616 0.675 67.5 0.001 Eurema sp. 0.014 0.023 0.632 0.639 0.117 0.699 69.9 0.573 Brachystegia vs Forest edge Coeliades forestan 0.033 0.018 1.828 0.628 2.529 0.055 5.5 0.001 Hypolimnas misippus 0.021 0.015 1.364 0.959 1.738 0.090 9.0 0.183 Junonia oenone 0.021 0.015 1.331 0.958 1.743 0.124 12.4 0.022 Papilio demodocus 0.020 0.017 1.142 1.106 1.910 0.158 15.8 0.084 Catopsiliaflorella 0.020 0.019 1.034 1.234 1.415 0.191 19.1 0.809 Phalanta phalantha 0.019 0.023 0.846 2.143 2.479 0.224 22.4 0.941 Appias epaphia 0.019 0.018 1.063 1.549 2.052 0.255 25.5 0.489 Eurema sp. 0.019 0.018 1.052 0.639 1.176 0.287 28.7 0.006 Graphium antheus 0.018 0.016 1.126 1.010 1.316 0.317 31.7 0.445 Colotis ione 0.018 0.016 1.126 0.295 1.185 0.347 34.7 0.001 Colotis euippe 0.017 0.015 1.144 0.784 1.144 0.375 37.5 0.261 Eronia cleodora 0.017 0.014 1.182 0.700 1.213 0.403 40.3 0.509 Papilio nireus 0.016 0.012 1.310 0.641 1.299 0.430 43.0 0.023 Graphium philonoe 0.016 0.015 1.090 0.606 0.993 0.457 45.7 0.901 Graphium porthaon 0.016 0.013 1.202 0.553 1.087 0.483 48.3 0.660 Danaus chrysippus 0.015 0.014 1.117 0.276 0.991 0.509 50.9 0.002 Euphaedra neophron 0.015 0.012 1.261 0.482 1.105 0.534 53.4 0.007 Belenois creona 0.015 0.015 1.010 0.191 0.974 0.559 55.9 0.001 Papilio constantinus 0.014 0.015 0.930 0.000 0.836 0.584 58.4 0.001 Maria Fungomeli et al.: Butterfly and vegetation diversity interactions in Arabuko Sokoke Forest Kenya316 Species average sd ratio ava avb Cumsum % of total dissimilarity P-value Neptis sp. 0.014 0.014 1.019 0.884 0.548 0.607 60.7 0.957 Charaxes varanes 0.014 0.012 1.104 0.574 0.871 0.630 63.0 0.027 Nepheronia thalassina 0.013 0.012 1.125 0.268 0.872 0.652 65.2 0.345 Acraea sp. 0.013 0.012 1.058 0.612 0.719 0.674 67.4 0.980 Colotis regina 0.012 0.013 0.980 0.397 0.700 0.694 69.4 0.974 Brachystegia vs Mixed forest Phalanta phalantha 0.026 0.025 1.055 2.143 2.320 0.045 4.5 0.073 Hypolimnas misippus 0.022 0.019 1.189 0.959 1.449 0.082 8.2 0.029 Catopsiliaflorella 0.022 0.020 1.114 1.234 1.560 0.120 12.0 0.285 Appias epaphia 0.021 0.018 1.195 1.549 2.229 0.155 15.5 0.123 Papilio demodocus 0.019 0.018 1.070 1.106 1.518 0.188 18.8 0.192 Graphium porthaon 0.019 0.017 1.105 0.553 1.144 0.220 22.0 0.008 Graphium antheus 0.018 0.017 1.088 1.010 1.087 0.251 25.1 0.285 Eronia cleodora 0.018 0.016 1.147 0.700 1.032 0.281 28.1 0.072 Junonia oenone 0.018 0.015 1.226 0.958 0.934 0.311 31.1 0.795 Graphium philonoe 0.018 0.015 1.175 0.606 1.201 0.341 34.1 0.433 Colotis euippe 0.017 0.014 1.208 0.784 1.142 0.370 37.0 0.262 Papilio nireus 0.017 0.015 1.144 0.641 1.185 0.399 39.9 0.002 Neptis sp. 0.016 0.015 1.060 0.884 0.926 0.427 42.7 0.347 Nepheronia thalassina 0.016 0.015 1.060 0.268 0.940 0.454 45.4 0.001 Acraea sp. 0.016 0.015 1.075 0.612 0.999 0.481 48.1 0.033 Coeliades forestan 0.016 0.016 1.019 0.628 0.921 0.508 50.8 0.999 Eurema sp. 0.016 0.020 0.797 0.639 0.669 0.535 53.5 0.332 Belenois thysa 0.015 0.013 1.120 0.265 0.922 0.560 56.0 0.001 Papilio constantinus 0.015 0.015 1.008 0.000 0.843 0.585 58.5 0.001 Euphaedra neophron 0.013 0.014 0.990 0.482 0.710 0.608 60.8 0.403 Charaxes varanes 0.013 0.012 1.078 0.574 0.823 0.630 63.0 0.092 Junonia natalica 0.012 0.013 0.953 0.845 1.201 0.651 65.1 0.157 Danaus chrysippus 0.012 0.013 0.896 0.276 0.668 0.671 67.1 0.424 Colotis auxo 0.012 0.013 0.874 0.427 0.547 0.691 69.1 0.831 Cynometra vs Forest edge Coeliades forestan 0.030 0.016 1.934 0.668 2.529 0.051 5.1 0.001 Phalanta phalantha 0.021 0.018 1.162 1.564 2.479 0.087 8.7 0.771 Papilio nireus 0.020 0.009 2.146 0.000 1.299 0.121 12.1 0.001 Junonia oenone 0.020 0.015 1.317 0.841 1.743 0.154 15.4 0.116 Catopsiliaflorella 0.020 0.018 1.067 1.646 1.415 0.187 18.7 0.849 Papilio demodocus 0.018 0.014 1.349 0.977 1.910 0.218 21.8 0.422 Graphium philonoe 0.018 0.015 1.197 1.190 0.993 0.247 24.7 0.499 Hypolimnas misippus 0.017 0.014 1.228 1.151 1.738 0.275 27.5 0.972 Colotis ione 0.017 0.014 1.153 0.445 1.185 0.303 30.3 0.004 Appias epaphia 0.017 0.015 1.129 1.828 2.052 0.331 33.1 0.902 Colotis regina 0.016 0.014 1.161 1.122 0.700 0.359 35.9 0.153 Eurema sp. 0.016 0.015 1.091 0.117 1.176 0.386 38.6 0.209 Colotis euippe 0.016 0.014 1.151 0.569 1.144 0.413 41.3 0.663 Graphium antheus 0.016 0.014 1.126 1.018 1.316 0.440 44.0 0.932 Eronia cleodora 0.015 0.013 1.160 0.785 1.213 0.465 46.5 0.968 Danaus chrysippus 0.015 0.013 1.134 0.163 0.991 0.490 49.0 0.004 Neptis sp. 0.015 0.014 1.035 1.001 0.548 0.514 51.4 0.870 Graphium porthaon 0.014 0.013 1.151 0.665 1.087 0.538 53.8 0.931 Euphaedra neophron 0.014 0.011 1.309 0.379 1.105 0.562 56.2 0.097 Belenois creona 0.014 0.014 0.994 0.000 0.974 0.586 58.6 0.001 Charaxes varanes 0.014 0.012 1.129 0.000 0.871 0.609 60.9 0.016 Papilio constantinus 0.013 0.014 0.979 0.000 0.836 0.632 63.2 0.003 Acraea sp. 0.013 0.012 1.102 0.729 0.719 0.654 65.4 0.982 Hypolimnas deceptor 0.013 0.012 1.014 0.741 0.503 0.675 67.5 0.114 Nepheronia thalassina 0.012 0.011 1.141 0.465 0.872 0.695 69.5 0.777 Contributions to Entomology 75 (2) 2025, 299–318 317 Species average sd ratio ava avb Cumsum % of total dissimilarity P-value Cynometra vs Mixed forest Phalanta phalantha 0.026 0.019 1.330 1.564 2.320 0.045 4.5 0.112 Catopsiliaflorella 0.021 0.018 1.170 1.646 1.560 0.080 8.0 0.604 Papilio nireus 0.020 0.012 1.649 0.000 1.185 0.115 11.5 0.001 Hypolimnas misippus 0.020 0.017 1.159 1.151 1.449 0.149 14.9 0.425 Colotis regina 0.019 0.017 1.073 1.122 0.429 0.181 18.1 0.003 Appias epaphia 0.018 0.014 1.259 1.828 2.229 0.212 21.2 0.687 Graphium porthaon 0.018 0.016 1.154 0.665 1.144 0.243 24.3 0.048 Graphium philonoe 0.017 0.015 1.162 1.190 1.201 0.273 27.3 0.636 Papilio demodocus 0.017 0.014 1.224 0.977 1.518 0.302 30.2 0.832 Eronia cleodora 0.017 0.014 1.169 0.785 1.032 0.330 33.0 0.417 Neptis sp. 0.017 0.015 1.124 1.001 0.926 0.359 35.9 0.267 Junonia oenone 0.017 0.014 1.182 0.841 0.934 0.387 38.7 0.990 Colotis euippe 0.017 0.013 1.263 0.569 1.142 0.416 41.6 0.492 Graphium antheus 0.017 0.015 1.090 1.018 1.087 0.444 44.4 0.857 Acraea sp 0.015 0.014 1.101 0.729 0.999 0.470 47.0 0.214 Coeliades forestan 0.015 0.014 1.062 0.668 0.921 0.496 49.6 1.000 Nepheronia thalassina 0.015 0.014 1.080 0.465 0.940 0.521 52.1 0.023 Belenois thysa 0.014 0.012 1.153 0.444 0.922 0.545 54.5 0.002 Papilio constantinus 0.014 0.014 1.025 0.000 0.843 0.569 56.9 0.001 Hypolimnas deceptor 0.014 0.014 1.018 0.741 0.513 0.593 59.3 0.009 Charaxes varanes 0.014 0.011 1.282 0.000 0.823 0.616 61.6 0.026 Colotis auxo 0.013 0.012 1.092 0.739 0.547 0.639 63.9 0.202 Bicyclussafitza 0.013 0.013 0.951 0.616 0.447 0.661 66.1 0.015 Charaxes candiope 0.012 0.012 1.008 0.705 0.179 0.682 68.2 0.001 Euphaedra neophron 0.012 0.012 0.997 0.379 0.710 0.703 70.3 0.912 Forest edge vs Mixed forest Coeliades forestan 0.025 0.015 1.612 2.529 0.921 0.047 4.7 0.003 Junonia oenone 0.018 0.014 1.352 1.743 0.934 0.082 8.2 0.648 Phalanta phalantha 0.017 0.019 0.901 2.479 2.320 0.114 11.4 0.995 Catopsiliaflorella 0.017 0.015 1.070 1.415 1.560 0.145 14.5 0.998 Eurema sp. 0.015 0.013 1.116 1.176 0.669 0.174 17.4 0.471 Colotis ione 0.015 0.014 1.084 1.185 0.662 0.201 20.1 0.189 Graphium philonoe 0.015 0.012 1.231 0.993 1.201 0.229 22.9 0.995 Colotis euippe 0.014 0.011 1.250 1.144 1.142 0.256 25.6 0.994 Eronia cleodora 0.014 0.012 1.185 1.213 1.032 0.283 28.3 0.995 Hypolimnas misippus 0.014 0.013 1.091 1.738 1.449 0.310 31.0 1.000 Graphium antheus 0.014 0.013 1.103 1.316 1.087 0.336 33.6 0.999 Graphium porthaon 0.013 0.012 1.115 1.087 1.144 0.361 36.1 0.999 Papilio demodocus 0.013 0.013 1.005 1.910 1.518 0.387 38.7 0.997 Danaus chrysippus 0.013 0.012 1.097 0.991 0.668 0.411 41.1 0.143 Appias epaphia 0.013 0.011 1.117 2.052 2.229 0.434 43.4 1.000 Acraea sp. 0.013 0.011 1.104 0.719 0.999 0.458 45.8 0.991 Belenois creona 0.012 0.013 0.995 0.974 0.175 0.482 48.2 0.001 Euphaedra neophron 0.012 0.010 1.154 1.105 0.710 0.504 50.4 0.948 Papilio constantinus 0.012 0.011 1.054 0.836 0.843 0.526 52.6 0.087 Nepheronia thalassina 0.012 0.011 1.046 0.872 0.940 0.548 54.8 0.919 Neptis sp. 0.011 0.011 1.056 0.548 0.926 0.570 57.0 1.000 Belenois thysa 0.011 0.010 1.109 0.329 0.922 0.592 59.2 0.520 Graphium colonna 0.011 0.011 0.993 0.771 0.577 0.612 61.2 0.581 Charaxes varanes 0.011 0.009 1.157 0.871 0.823 0.633 63.3 0.960 Colotis regina 0.011 0.011 0.964 0.700 0.429 0.653 65.3 0.998 Junonia natalica 0.010 0.011 0.912 0.992 1.201 0.671 67.1 0.929 Melanitis leda 0.010 0.009 1.035 0.648 0.574 0.689 68.9 0.174 Hypolimnas deceptor 0.010 0.011 0.859 0.503 0.513 0.707 70.7 0.882 Maria Fungomeli et al.: Butterfly and vegetation diversity interactions in Arabuko Sokoke Forest Kenya318 Supplementary material 1 Suppl. figures S1–S3 Authors: Maria Fungomeli, Martin Wiemers, Lucia Calderini, Alessandro Chiarucci Data type: pdf Explanation note: figure S1. Frequency ranking of butterfly species abundance within the forest types of Brachystegia, Cynometra, Mixed forest and Forest edge in Arabuko Sokoke Forest, Kenya. figure S2. Abundance proportions of butterfly species with monophagous, oligophagous and polyphagous larval feeding habits across the four forest types of Brachystegia, Cynometra, Mixed forest and Forest edge in Arabuko Sokoke Forest, Kenya. figure S3. Butterflies in Arabuko Sokoke Forest, in the mixed forest vegetation type, feeding from elephant dung during the field sampling in the dry season. Photo credits: Maria Fungomeli. Copyright notice: This dataset is made available under the Open Database License (http://opendatacommons. org/licenses/odbl/1.0). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited. Link: https://doi.org/10.3897/contrib.entomol.75.e155016. suppl1