The Successional Pathway of the Tree Community and How It Shapes the Fruit-Feeding Butterﬂy Community in an Afrotropical Forest

Home , Bebearia cocalia, Bersama abyssinica, Catuna crithea, Erythrina abyssinica, Euphaedra harpalyce, Oncoba spinosa

Journal of Tropical Ecology (2017) 33:12–21. © Cambridge University Press 2016 doi:10.1017/S0266467416000560

The successional pathway of the tree community and how it shapes the fruit-feeding butterﬂy community in an Afrotropical forest

Anu Valtonen1,2,∗, Geoffrey M. Malinga1,3, Margaret Nyafwono1,4, Philip Nyeko5, Arthur Owiny1 and Heikki Roininen1

1 Department of Environmental and Biological Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland 2 Center for Ecological Research, Kyoto University, Hirano 2, Otsu, Shiga 520–2113, Japan 3 Department of Biology, Gulu University, P.O. Box 166, Gulu, Uganda 4 Department of Environment and Natural Resources Management, Gulu University, P.O. Box 166, Gulu, Uganda 5 Department of Forestry, Biodiversity and Tourism, Makerere University, P.O. Box 7062, Kampala, Uganda (Received 1 August 2016; revised 28 October 2016; accepted 29 October 2016)

Abstract: The relative importance of different bottom-up-mediated effects in shaping insect communities in tropical secondary forests are poorly understood. Here, we explore the roles of vegetation structure, forest age, local topography (valley vs. hill top) and soil variables in predicting fruit-feeding butterfly and tree community composition, and tree community composition in predicting fruit-feeding butterfly community composition, in different-aged naturally regenerating and primary forests of Kibale National Park, Uganda. We also examine which variables are best predictors of fruit-feeding butterfly species richness or diversity. Butterflies (88 species) were sampled with a banana-baited trap and trees (98 taxa) with a 40 × 20-m sampling plot at 80 sampling sites. The environmental variables explained 31% of the variation in the tree community composition, the best predictors being local topography, forest age and cover of Acanthus pubescens (a shrub possibly arresting succession). The fruit-feeding butterfly community composition was better predicted by tree community composition (explaining 10% of the variation) rather than vegetation structure, local topography or soil factors. Environmental variables and tree species richness (or diversity) were poor predictors of butterfly species richness (or diversity). Our results emphasize the importance of tree community to recovery of herbivorous insect communities in tropical secondary forests.

Key Words: community composition, diversity, forest regeneration, insects, Lepidoptera, primary forest, secondary forest, species richness, Uganda, vegetation structure

INTRODUCTION changes in animal communities, because species vary in their requirements for resources and habitats (Pinotti The relative importance of different bottom-up- et al. 2012). Increases in accumulated biomass and mediated effects in shaping insect communities in tree height should lead to increases in the variety of tropical secondary forests recovering from anthropogenic feeding, resting, oviposition sites, shelter and hiding disturbances are poorly understood. The severity of a places from enemies (Lawton 1983). Changes taking disturbance, vegetation surviving after the disturbance, place in plant community composition, or differences in landscape matrix (proximity of seed sources), soil fertility plant communities emerging on different soil types and and texture, altitude, local topography, climate and local topography, can impact the emerging herbivore microclimate play important roles in the recovery communities because the majority of tropical herbivorous processes of plant communities (reviewed by Chazdon insects seem to be host-specialized to at least some degree, 2003, 2008; Guariguata & Ostertag 2001). Changes typically at the plant genus level (Dyer et al. 2007, Forister taking place in vegetation structure, plant community et al. 2015, Novotny & Basset 2005). composition and microclimate are expected to lead to Here, we explore the successional pathway of a tree community and how it shapes the fruit-feeding butterﬂy community in different-aged naturally regenerating and ∗ Corresponding author. Email: anu.valtonen@uef.ﬁ primary forests of Kibale National Park, Uganda. We

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hypothesized that the tree community composition dataset was collected with 80 banana-baited butterfly can be predicted not only by forest age but also by traps, each located in one sampling site (see map and soil characteristics and local topography (because they details in Nyafwono et al. 2014a). The traps were hung could produce divergence in community composition 40–50 cm above the ground (i.e. understorey traps), during succession; Chazdon 2008), or cover by plant and were sampled monthly for 1 y from May 2011 species potentially arresting succession and preventing to April 2012. The 80 sampling sites were randomly the establishment of seedlings (Chapman & Chapman located (but always > 100 m apart; Nyafwono et al. 1999, Chapman et al. 1999) (Hypothesis 1). Fruit-feeding 2014a) within: (1) four regenerating clear-cut forests butterflies are heterotrophs dependent on plants as larvae (9, 11, 14 and 19 y old; previously conifer plantations; and rotting fruits on the forest floor as adults (Molleman RAC9–19 of Nyafwono et al. 2014a; coded here as R); et al. 2005). Larval hosts of some of these species are (2) two 43-y-old selectively logged forest compartments known (Molleman 2012). If most fruit-feeding butterflies (K13 and K15 of Nyafwono et al. 2014a; coded here are relatively specialized on their larval hosts (and are as K); and (3) two forest compartments in continuous relatively local in their movements), or specialized in primary forests (K30 and K31 of Nyafwono et al. 2014a, their adult feeding requirements, or both, then tree coded here as P). The dataset included 16,728 individuals community composition will be a better predictor of representing 88 fruit-feeding butterfly species. For the butterfly communities than vegetation structure and analyses, the number of butterflies within each species other physical conditions of the forest (Hypothesis 2). and each sampling site were summed (over the year). The opposite would be true if the species are specialized more strongly to certain physical conditions of the forest (e.g. canopy closure). If the fruit-feeding butterflies are Tree community, vegetation structure, local topography and strongly host-specialized, an increasing species richness soil datasets and diversity of trees will be associated with an increasing species richness (Lewinsohn et al. 2005; Hypothesis 3) At the same sampling sites where traps were located, data or diversity (Hypothesis 4) of fruit-feeding butterflies, on tree community composition, vegetation structure, respectively. local topography and soil variables were collected from April to October 2011 (Owiny et al. 2016). The tree community dataset included 98 tree taxa (92 identified METHODS at species level, four at genus level and two unidentified), and 8200 counted stems, collected within a 40 × 20-m Study area plot located at each sampling site (see details in Owiny et al. 2016). For each species in each plot, the density of Our study was conducted in Kibale National Park stems (number ha−1), and basal area (m2 ha−1) (of trees (0°13–0°41N, 30°19–30°32E), which is a moist, with diameter at breast height ࣙ 5 cm) was estimated (the evergreen medium-altitude (approximately 1500 m asl) community composition datasets). tropical forest in western Uganda, East Africa. The mean Five vegetation structure variables were recorded at daily minimum temperature is 14.9°C, the mean daily each sampling plot: (1) total estimated stem density and maximum temperature is 20.2°C and the mean annual (2) total estimated basal area which were calculated as rainfall is 1749 mm (Chapman et al. 2005). The soils sums across species from the community composition are lixic ferralsols (Majaliwa et al. 2010). The vegetation datasets described above; (3) tree canopy closure (the in Kibale represents mainly moist evergreen forests, but proportion of the sky covered by vegetation when viewed includes also regenerating forests, grasslands, swamps from a single point; Jennings et al. 1999); (4) gap cover and woodland thickets (Chapman & Lambert 2000, (area not covered by trees and shrubs); and (5) cover Struhsaker 1997). Within forest gaps created by logging, of A. pubescens. The cover values were estimated visually shrubs (mainly Acanthus pubescens Engl.) can form a withinthe40×20-mplotsonascale:0(0%),0.5(<10%), dense cover, reducing the light and nutrient availability 1 (10%), 2 (20%), 3 (30%), . . . , 10 (100%). to potential colonizers (Duclos et al. 2013), potentially Local topography and 11 soil variables were measured arresting the forest succession. at each sampling site. Local topography was measured as height (m) above the lowest recorded elevation (range 1433–1604 m asl), i.e. the lowest sampling site on a Fruit-feeding butterfly dataset valley bottom received a value of 0. In each plot, five soil samples (450 g) were excavated; four soil samples were We used the fruit-feeding butterfly dataset collected in taken from the border points of the 40 × 20-m plot, and naturally regenerating and primary forests of Kibale one from the middle. Samples were collected from depths National Park, published in Nyafwono et al. (2014a). The of 0–15 cm and 15–30 cm using a 2-cm-diameter soil

auger. Samples were separately stored in polythene bags, (Warton & Hui 2011), the total basal area of trees air dried to halt biological transformation, and sieved square-root transformed, and P, K, Ca and Mg loge- through a 2-mm screen to remove stone particles and transformed to reduce the skewness in their distributions. roots. Samples from each plot and depth were then bulked Four sampling sites were excluded from CCA analyses and 5 g were analysed for: (1) pH; (2) soil organic matter due to missing values in soil variables. To perform CCAs, (Walkley-Black Method); (3) N (total nitrogen, Kjeldahl first, we selected all predictor variables and tested how method); (4) available phosphorus (P; Bray and Kurtz No. manyconstrainedCCAaxesweresignificantinexplaining 1method); (5) K; (6) Na; (7) Ca (flame photometer); (8) Mg the community composition (499 permutations). This (AAS); and soil texture (hydrometer method), as percent- was done to determine how many axes to keep in the ages of (9) sand, (10) clay and (11) silt. The soil analyses final model. Second, to determine whether the tree or wereconductedatthesoilsciencelaboratoryoftheMaker- butterfly community composition was associated with ere University. For all soil variables the average of the the predictors of the final model, a permutation test was values from the two depths was calculated for each plot. conducted (499 permutations). To adjust for probable spatial autocorrelation in community composition, we conducted the conditional variation partitioning analyses Data analysis of Canoco (ter Braak & Smilauerˇ 2012) (Appendix 1). To answer the question whether tree community To determine whether the variables describing vegetation composition predicts the butterfly community compos- structure, forest age, local topography and soil factors ition, we used predictive co-correspondence analysis predicted tree and butterfly community compositions, we (CoCA; Schaffers et al. 2008, ter Braak & Schaffers used predictive canonical correspondence analysis (CCA; 2004). CoCA is an ordination method which relates ter Braak 1986). CCA is a multivariate analysis technique two types of community, and attempts to identify the that relates community composition to environmental patterns which are common to both communities (ter variables. The age of the forest was added to CCA models Braak & Schaffers 2004). The influence of the most as a factor with six levels: 9, 11, 14, 19, 43 y, and primary abundant species was decreased by performing loge(x+1) forest. To avoid collinearity, we included only continuous transformations on the butterfly community and tree variables that did not correlate strongly with each other stem density datasets prior to the analyses. We assessed (Pearson ρ ࣘ 0.5). The tree community composition was theaccuracyoftheCoCAmodelusingthecross-validatory predicted with forest age, gap cover, A. pubescens cover, fit percentage, with values above zero indicating that local topography (which correlated with soil organic the model prediction is better than expected by chance matter, ρ = 0.53) and the soil variables including pH, N, alone (ter Braak & Schaffers 2004). The cross-validatory P, Na, K (which correlated with Ca, ρ = 0.51), Mg (which fit describes the percentage of explained variation but correlated with Ca, ρ = 0.63), clay (which correlated it is not comparable with the percentages of explained with sand, ρ =−0.88) and silt (which correlated with variation in CCA (Schaffers et al. 2008). The number of sand, ρ =−0.51). The tree community composition at CoCA ordination axes for the final model was selected each study site was first described as the stem density based on both cross-validatory fit (the local maximum of each tree species, emphasizing the smaller trees, and was selected to keep the model as simple as possible; ter then as the estimated basal area of each tree species, Braak & Schaffers 2004) and by testing the significance emphasizing the larger trees. The fruit-feeding butterfly of each ordination axis with a permutation test (499 community composition (the number of individuals of permutations; ter Braak & Schaffers 2004); if the two each butterfly species at each study site) was predicted methods disagreed, the smaller of the two values was using forest age, canopy closure (which correlated with selected. stem density, ρ = 0.54; basal area, ρ = 0.59; soil organic To test whether tree community composition is a matter, ρ =−0.57; and soil N, ρ =−0.52), gap cover, better predictor of butterfly communities than vegetation A. pubescens cover, local topography (which correlated structure and physical conditions of the forest, we first re- with soil organic matter) and soil variables including fitted the CCA model excluding the factor forest age. We pH, P, Na, K (which correlated with Ca), Mg (which then ran a simple randomization test to judge whether correlatedwithCa),clay(whichcorrelatedwithsand)and differences among the model fits that predicted fruit- silt (which correlated with sand). Prior to the analyses, the feeding butterfly communities (CCA vs. CoCA) were response datasets, i.e. stem densities of trees, and counts of statistically significant (van der Voet 1994). From each fruit-feeding butterflies were loge(x+1) transformed, and model, the predicted loge(x+1) transformed count of tree basal areas loge(100x+1) transformed to decrease individual fruit-feeding butterflies of each species at the influence of the most abundant species. The tree each site was extracted. The difference in mean-squared canopyclosure,gapcoverandA.pubescenscoverwerefirst predictionerrorswascalculatedandusedasateststatistic, transformed to proportions and then logit transformed and the mean-squared prediction errors of the sites were

re-arranged 999 times. We tested a two-sided alternative Mae Pr Cai Agr Cp Hr hypothesis, i.e. mean-squared prediction error of CCA 0.6 A. pubescens model ࣔ mean-squared prediction error of CoCA model Fsu Cm (van der Voet 1994). Mc To determine how well tree species richness or diversity gap Agu Nm and environmental variables predicted fruit-feeding Fb Paf 43 y Ea butterfly species richness or diversity, linear regression Bu Ec Vam Ra Adi Pal Ee Ba Bm models were fitted. For each sampling site, fruit-feeding Cru Pe Sr 14 y MaVau Nb Caf Ad silt Cat S Mdu butterfly and tree species richness were estimated using Xm Tp Md Can Pa Ml Cr Cal Ss Ka Ru Zl Rb U2 Ag HoP CCA Axis 2 At Vn Fa Cg SgpH Va Dm DabOc C 11 y Colwell’s combined rarefaction-extrapolation method Lc PmTd Ca Fe K Pp De Mlu Car Bun F Pan Bw To prim. forest Osp Ae Uc Mg ScMe Cs Lsw Mb Pf Fv Se 9 y (species richness estimated for 120 butterfly individuals Rw Na TmLm Ls Dk Os Mm or 1428 tree stems, i.e. extrapolated to maximum three Da Cc 19 y clay Pt Fs N U1 times the smallest observed number). Diversity was Sm = − − − measured as Simpson’s D 1 ((Ni(Ni 1))/(N(N 1)), -0.4 local topography where Ni = number of individuals in species i,and N = total number of individuals, a measure of the -0.6CCA Axis 1 0.8 probability that two individuals drawn at random belong to different species (Maurer & McGill 2011). Tree species Figure 1. Tree community predictors: Canonical correspondence analysis (CCA) ordination diagram showing how community richness and diversity were calculated from the estimated compositionoftreeschangedalonggradientsofenvironmentalvariables stem densities dataset, rounded to integers. For linear in secondary and primary forests of Kibale National Park, Uganda. regression models, we included only variables that did Community composition at the 76 sampling sites (40 × 20-m plots) was not correlate strongly with each other (Pearson ρ ࣘ measured as stem densities of trees. Tree species names are abbreviated 0.5). Butterfly species richness was explained by tree (see full names in Appendix 2), continuous predictors are shown as arrows, and the factor levels of forest age with triangles. Symbols of species richness (which correlated with stem density, certain species are moved slightly to improve readability and the few ρ = 0.66; basal area, ρ = 0.73; canopy closure, ρ = 0.57 outliers are located outside the graph edge. The first CCA axis explained and local topography, ρ =−0.59), A. pubescens cover 9% and the second, 5% of the variation. and gap cover. Butterfly diversity was explained by tree diversity, total basal area of trees (which correlated with stem density, ρ = 0.69 and canopy closure, ρ = 0.59), A. K, Mg, clay and silt) explained 31.0% of the variation in pubescens cover, gap cover and local topography. The fits the tree community composition (stem densities of trees of all possible combinations of these explanatory variables as the response dataset; final CCA model with two axes: were compared using an Information Theoretic approach permutation test for all axes, pseudo-F = 1.7; P = 0.002). (AICc) (Anderson 2008). The linear regression models The CCA ordination presents the patterns in community assume independence of the samples (here sampling sites) composition explained by the environmental variables but we assumed this was not violated because no evidence (arrows in Figure 1) and forest age (triangles in Figure 1), of spatial autocorrelation among the sampling sites in the length of each arrow showing the proportional terms of species richness values was found (Mantel test; importance. The first CCA axis is the local topography Nyafwono et al. 2014a). gradient (intra-set correlation = 0.74) but also separates CCA and variation partitioning analyses were the tree communities of young recovering forests from conducted with Canoco, version 5 (ter Braak & Smilauerˇ older forests (intra-set correlation with forest age group 2012), CoCA analyses with the package cocorresp primary forest =−0.64). The forest age group 43-y-old (http://cran.at.r-project.org/web/packages/cocorresp/ forests (0.54) and cover of A. pubescens (0.53) correlated cocorresp.pdf)inR(http://www.r-project.org/), species most strongly with the second CCA axis. For example, the richness values were estimated with EstimateS (http:// tree species Craterispermum schweinfurthii and Leptonychia viceroy.eeb.uconn.edu/estimates/), Simpson’s D was mildbraedii were relatively most abundant in valley- calculated with Primer-E, version 6 (Clarke & Gorley bottom primary-forest sites; Albizia grandibracteata and 2006) and all other analyses were conducted with R Bridelia micrantha in young hill-top sites; Carapa procera version 2.14.1. and Hallea rubrostipulata in sites with high coverage by A. pubescens; Craterispermum schweinfurthii and Pancovia turbinata in sites with low coverage by A. pubescens RESULTS (Figure 1; Appendix 2). This analysis (stem densities of Predictors of tree community composition (Hypothesis 1) trees used as the response dataset) emphasizes small trees but the CCA ordination was very similar if basal areas of The environmental variables (forest age, gap cover, A. trees were included as the response dataset, emphasizing pubescens cover, local topography and soil pH, N, P, Na, large trees (final CCA model with three axes explaining

Agr Pr Mae K Hr K

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U1 K Bm Ml R14 Fv R11 R14Me R9ToPa R9 Cm Paf R14R19 Nm Ho Se Sc R11 R9 DkR11 R9 R19 C R14 Mdu Oc Rb R11 R14 R14R19 R14 K R9 R9 R19U2 K K R9Bw R11 Ba Fa R11 Zl Sr SCan Pf R14 R9R9 Bu K R14 R9 MdPm CafAdDabPan Ca Cai P KEc AdiPe P K R11 F KR14 R11 Va Ee PR19 PDm R11 R19 Mlu Cal K K K K Cg R14 Sg P Bun R14 CoCA Axis 2 Nb Cat PP TpAgu Ma K AeMb Ra CrK KXm Uc KKK Car Ka Ru Ss Osp Fb Vn At PR19 Td Ag Os Pp De P PMm Lc Rw Cc Fs Pal Cru Da LsPP P Vam Lm Tm Lsw Pt P P Vau P Cs P Sm P R19 P P Fsu Fe Ea R14 021

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K R14 R11 R14 Cz CpR9 Bf R9 Figure 2. Butterfly community predictors: Canonical correspondence Bsu R14R19 Pt R11 R9 Eer CsJw analysis (CCA) ordination diagram showing how community R11 R9 R19Sc R14 Cce R11 R19 CprR14 R9 Ae R14 R14 VdCe KK ApR9R19 composition of fruit-feeding butterflies changed along gradients of Bb Bi K SoR11 R9 R11 ChoEalCcsR14 R14 R9R9 Cl Hp K BgEd NoAd R9 environmental variables in secondary and primary forests of Kibale Bm BvCnBd Sb BcErP PKBsAgR11 K Ehi R14 R11 PR19 BcmR11 Js K R19 Ac P Gc Bsm CtHpe National Park, Uganda. Community composition at the 76 sampling PlBsaKKK PK Cf Cb Bca R14 R14 Ha P BgrHt CoCA Axis 2 Cc PMl BaGbKrBauCpo sites was collected with one banana-baited butterfly trap. Butterfly KK KBse EmCh HsPpa KK GnEu Bme Ech R19KEa Bmo Ez Ecr Ccy species names are abbreviated (see full names in Appendix 2), PpCbrPP P La Eh Ei Ec Ee Ep PP P continuous predictors are shown as arrows, and the factor levels of Eho Ma Ek Cpl Cca P P forest age with triangles. Symbols of certain species are moved slightly P Es P to improve readability and the few outliers are located outside the graph P R19 PP edge. The first CCA axis explained 10% and the second axis, 4% of the Czo R14 variation. 021 CoCA Axis 1

30.6% of variation: permutation test for all axes, pseudo-F Figure 3. Predictive co-correspondence analysis (CoCA) bi-plots = 1.6; P = 0.002). showing the common gradient in tree (a), and butterfly community composition (b), in naturally regenerating and primary forests of Kibale National Park, Uganda. Species names are abbreviated (see full names in Appendix 2); tree species predict the butterfly species which are located Predictors of butterfly community composition in the same part of the plots. Sampling sites (red) are indicated with (Hypothesis 2) symbols: P = primary forests; K = 43-y-old forests; R9–19 = young forests. Some symbols are moved slightly to improve readability and the The environmental variables (forest age, canopy closure, few outliers are located outside the graph edge. gap cover, A. pubescens cover, local topography and soil pH, P, Na, K, Mg, clay and silt) explained 31.8% of the variation in the fruit-feeding butterfly community The tree community composition (measured as stem composition (final CCA model with two axes: pseudo- densities of trees) significantly explained the fruit-feeding F = 1.7; P = 0.002). The first CCA axis is the butterfly community composition (cross-validatory fit canopy-closure gradient which separates primary-forest of the CoCA model > 0). The final model (including communities from the secondary-forest communities; four significant axes) explained 10.2% of the variation forest age primary forest (intra-set correlation = 0.82) in butterfly community composition, but note that the and tree canopy closure (0.75) were the most important percentage represents the cross-validatory fit and is not variables correlating with CCA axis 1 (Figure 2). The comparable with the percentages of explained variation forest age group 43-y-old forests (−0.57) correlated most in CCA. The CoCA biplots identify the pattern which strongly with the second CCA axis. For example, species is common to both tree and butterfly communities of Bicyclus and Charaxes were relatively most abundant (Figure 3). In these plots, tree species predict the butterfly in young or intermediate-aged secondary forests and low species which are located in the same part of the plots. canopy closure, and of Euphaedra in primary forests with The pattern revealed follows the disturbance gradient high canopy closure (Figure 2; Appendix 2). where the primary forest sites (P) receive mostly low

values, 43-y-old forests (K) intermediate values, and Chapman 2006). On the other hand, dense cover by A. the youngest forests (R) high values for CoCA axis 1 pubescens can change light and nutrient availability and (although 9–19-y-old forests do not show a clear age- may be an important factor explaining seedling growth gradient among themselves). Only a few butterfly species in Kibale (Duclos et al. 2013). were found at the young end of the gradient (all being The strong relationship between tree and butterfly singletons or doubletons), predicted by several tree species community composition suggests that most fruit-feeding such as Bridelia micrantha, Macaranga capensis, Maesa butterflies could be relatively specialized in their larval lanceolata and Trema orientalis. Species of Euphaedra were hosts or trees used for adult feeding, or both. The most common at the old end of the gradient, predicted, abundance of lepidopteran species is known to correlate for example, by tree species Trilepisium madagascariensis, with the biomass of their host plants (Yamamoto et al. Craterispermum schweinfurthii, Leptonychia mildbraedii and 2007), but among Central European moths this is true Pancovia turbinata. only for monophagous species (Lepsˇ et al. 1998)andthe According to the randomization test, the tree association between macromoths and plant community community composition was a significantly better composition was stronger for specialists than for gen- predictor(P=0.001)ofbutterflycommunitycomposition eralists (Muller¨ et al. 2011). An alternative explanation (CoCA; mean of squared prediction errors = 0.18) than could be that the tree community composition captures vegetation structure, local topography and soil variables the complex habitat conditions better than the measured (CCA; mean of squared prediction errors = 0.60). vegetation structure, local topography and soil variables (Schaffers et al. 2008). Plant community composition has been found to be an important predictor of the What explains species richness and diversity of butterflies communitycompositionofvariousgroupsofherbivorous, (Hypotheses 3 and 4)? carnivorous and omnivorous arthropods (Gioria et al. 2011, Schaffers et al. 2008, ter Braak & Schaffers We found no significant associations between butterfly 2004), and fruit-feeding butterflies in young restored and species richness (or diversity) and the tree species richness primary forests in Kibale National Park, where it was an (or diversity) or the environmental variables. The highest- equally good predictor as vegetation structure (Nyafwono ranked models (i.e. the models with the lowest AICc) et al. 2014b, 2015). Across various arthropod groups, explaining butterfly species richness (P = 0.24; R2 adj. plant community composition has been found to explain = 0.005) or diversity (P = 0.10; R2 adj. = 0.03) were not 2–18%ofthevariation(cross-validatoryfit;Schaffersetal. significant. 2008), hence the explained variation in our study (10%) is substantial. The locations of tree and butterfly species on DISCUSSION the corresponding gradient (Figure 3) suggest that the gradient could be, at least partly, explained by Our results illustrate a possible pathway shaping tree and specialization in larval host use. For example, some insect communities in tropical secondary forests: the tree butterfly species, especially species of Euphaedra whose community composition showed stratification along the larval host-plants are known (Molleman 2012), had their local topographic gradient and was also explained by highest densities near their host trees, such as Lepisanthes succession (forest age) and factors potentially arresting senegalensis, Blighia unijugata, Pancovia turbinata, Parinari the succession (A. pubescens), confirming Hypothesis 1. excelsa and Uvariopsis congensis. Some of the studied The fruit-feeding butterfly community composition was fruit-feeding butterflies also use non-tree larval hosts stronglyassociatedwiththetreecommunitycomposition, (Molleman 2012), in which case the tree-butterfly confirming Hypothesis 2. It was no surprise perhaps that associations could be either explained by specialization the tree communities showed stratification according to in adult resources or indirectly by light or moisture the local topography which is associated with variation in conditions preferred both by trees and the non-tree edaphic conditions and soil, i.e. hilltops are considered to host plants of the butterflies. However, if the gradient be more prone to drought, while soils on slopes and valley was mainly explained by specialization in larval host bottoms are likely to be moister (Ghazoul & Sheil 2010). use, this would require that the adults are relatively Our results also suggest that A. pubescens, a shrub that local in their movements. Little is known about the arrives in forest gaps created by logging and potentially spatial structure and dispersal capabilities of tropical arrests the succession (Chapman et al. 1999), can shape adult butterflies, but the few studies indicate that they the tree community composition, independent of forest can vary from highly local to those that can disperse age. The mechanism could be twofold. It is known that or migrate long distances (Bonebrake et al. 2010). The elephants can suppress tree regeneration in Kibale, and corresponding gradient of butterflies and trees might they may be attracted to gaps by A. pubescens (Lawes & also be explained by the strong associations between

fruit-feeding butterflies and their adult food resources of Makerere University Biological Field Station (Kibale (fruit-bearing trees). For example, fruits of Ficus spp., National Park). Funding was provided by the Academy Mimusops bagshawei, Uvariopsis congensis, Diospyros of Finland (grants no. 138899 and 14956 to H.R.), abyssinica and Strychnos mitis are used by the adults of the Joensuun Yliopiston Tukisaäti¨ o¨ and Finnish Cultural studied butterfly species (Molleman et al. 2005), some of Foundation (to A.V.). Permission to conduct this research which are also their larval host plants (Molleman 2012). was given by the Office of the President (Uganda), the Finally, it is also theoretically possible that since our National Council for Science and Technology (Uganda) sampling covered understorey only, canopy specialists and the Uganda Wildlife Authority. would only be found where canopy cover is low and understorey specialists only where canopy cover is high, but in our case no such division was found (see species LITERATURE CITED categorizations to different forest strata in Molleman et al. 2006), although the relatively few canopy specialists ADUSE-POKU, K., LEES, D. C., BRATTSTROM,¨ O., more typically were found in the younger rather than KODANDARAMAIAH, U., COLLINS, S. C., WAHLBERG, N. & in the older ends of the CCA and CoCA gradients. BRAKEFIELD, P. M. 2016. 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In second- Conservation Biology 13:1301–1311. ary and mature forests of Mt Kilimanjaro, the diversity of CHAPMAN, C. A. & LAMBERT, J. E. 2000. Habitat alteration and the Geometridae and the species number of Dicotyledoneae conservation of African primates: a case study of Kibale National correlated negatively (Axmacher et al. 2004). Park, Uganda. American Journal of Primatology 50:169–186. Understanding the patterns and mechanisms of CHAPMAN, C. A., CHAPMAN, L. J., KAUFMAN, L. & ZANNE, A. E. succession in animal communities is needed when 1999.PotentialcausesofarrestedsuccessioninKibaleNationalPark, assessing the potential of regenerating forests to prevent Uganda: growth and mortality of seedlings. African Journal of Ecology or slow down the mass extinction of tropical forest fauna 37:81–92. (Gibson et al. 2011, Wright & Muller-Landau 2006). CHAPMAN, C. A., CHAPMAN, L. J., STRUHSAKER, T. T., ZANNE, A. Based on our results, if tree communities are altered E., CLARK, C. 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communities. Proceedings of the National Academy of Sciences USA Appendix 2. Continued. 104:10524–10529. Abbreviation Tree species Cm Croton macrostachyus Delile C Cupressus spp. Appendix 1. Variation partitioning analyses De Dasylepis eggelingii J. B. Gillett Da Dictyandra arborescens Hook. f. To adjust for probable spatial autocorrelation in community Dab Diospyros abyssinica (Hiern) F. White composition in CCA analyses, we conducted the conditional Dk Dombeya kirkii Mast. variation partitioning analyses of Canoco (ter Braak & Smilauerˇ Dm Dovyalis macrocalyx (Oliv.) Warb. 2012). With variation partitioning it is possible to test if the Ec Ehretia cymosa Thonn. effects of vegetation structure, forest age, local topography Ea Erythrina abyssinica DC. Ee Euadenia eminens Hook. f. and soil are significant after spatial arrangement (latitude and Fa Fagaropsis angolensis (Engl.) Dale longitude) of the sampling sites is taken into account, and vice Fb Ficus barteri Sprague versa. Fe Ficus exasperata Vahl Vegetation structure, forest age, local topography and soil Fs Ficus sansibarica Warb. explained variation in tree communities independent of spatial Fsu Ficus sur Forssk. arrangement (latitude and longitude) of the sampling sites Fv Ficus vallis-choudae Delile (P = 0.002; 81% of the explained variation). Also latitude F Funtumia spp. and longitude explained variation independent of vegetation Hr Hallea rubrostipulata (K. Schum.) J.-F. Leroy structure, forest age, local topography and soil (P = 0.002; 18% Ho Hoslundia opposita Vahl of the explained variation), indicating spatial autocorrelation. Ka Kigelia africana (Lam.) Benth. Ls Lepisanthes senegalensis (Poir.) Leenh. Vegetation structure, forest age, local topography and soil Lm Leptonychia mildbraedii Engl. explained variation in butterfly communities independent of Lsw Lovoa swynnertonii Baker f. spatial arrangement (latitude and longitude) of the sampling Lc Lychnodiscus cerospermus Radlk. sites (P = 0.002; 82% of the explained variation). Also the Mc Macaranga capensis (Baill.) Sim latitude and longitude of the sampling sites explained variation Ml Maesa lanceolata Forssk. independentofvegetationstructure,forestage,localtopography Me Maesopsis eminii Engl. and soil (P = 0.002; 27.0% of the explained variation), Md Margaritaria discoidea (Baill.) G. L. Webster indicating spatial autocorrelation. Mlu Markhamia lutea (Benth.) K. Schum. Mdu Millettia dura Dunn Appendix 2. Tree and butterfly species recorded in the studied Mb Mimusops bagshawei S. Moore secondary and primary forest sites in Kibale National Park, Uganda. Mm Monodora myristica (Gaertn.) Dunal Abbreviations indicate codes in Figures 1–3. Ma Myrianthus arboreus P. Beauv. Mae Mystroxylon aethiopicum (Thunb.) Loes. Abbreviation Tree species Nm Neoboutonia macrocalyx Pax Ae Aeglopsis eggelingii M. Taylor Nb Newtonia buchananii (Baker) G. C. C. Gilbert & Ag Albizia grandibracteata Taub. Boutique Agu Albizia gummifera (J. F. Gmel.) C. A. Sm. Oc Olea capensis L. Ad Allophylus dummeri Baker f. Os Oncoba spinosa Forssk. Agr Anthocleista grandiflora Gilg Osp Oxyanthus speciosus DC. At Antiaris toxicaria Lesch. Pt Pancovia turbinata Radlk. Adi Apodytes dimidiata Arn. Pe Parinari excelsa Sabine Bw Balanites wilsoniana Dawe & Sprague Pa Persea americana Mill. Bu Beilschmiedia ugandensis Rendle Pr Phoenix reclinata Jacq. Ba Bersama abyssinica Fresen. Pp Pleiocarpa pycnantha (K. Schum.) Stapf Bun Blighia unijugata Baker Pf Polyscias fulva (Hiern) Harms Bm Bridelia micrantha (Hochst.) Baill. Pal Pouteria altissima (A. Chev.) Baehni Cp Carapa procera DC. Pan Premna angolensis Gurke¨ Cr Casearia runssorica Gilg Paf Prunus africana (Hook. f.) Kalkman Cru Cassipourea ruwensorensis (Engl) Alston Pm Pseudospondias microcarpa (A. Rich.) Engl. Ca Celtis africana Burm. f. Ra Ritchiea albersii Gilg Cg Celtis gomphophylla Baker Ru Rothmannia urcelliformis (Hiern) Robyns Car Chaetacme aristata Planch. Rw Rothmannia whitfieldii (Lindl.) Dandy Caf Chionanthus africanus (Knobl.) Stearn Rb Rytigynia beniensis (De Wild.) Robyns Cal Chrysophyllum albidum G. Don S Sapium spp. Cat Citropsis articulata (Spreng.) Swingle & Kellerm. Sr Scolopia rhamniphylla Gilg Can Clausena anisata (Willd.) Benth. Se Senna spp. Cc Coffea canephora A. Froehner Sc Spathodea campanulata P. Beauv. Cai Cordia africana Lam. Ss Strombosia scheffleri Engl. Cs Craterispermum schweinfurthii Hiern Sm Strychnos mitis S. Moore Sg Symphonia globulifera L. f. Tp Tabernaemontana pachysiphon Stapf

Appendix 2. Continued. Appendix 2. Continued. Abbreviation Tree species Abbreviation Butterfly species To Trema orientalis (L.) Blume Cz Charaxes zelica Butler, 1869 Td Trichilia dregeana Sond. Czo Charaxes zoolina (Westwood, [1850]) Tm Trilepisium madagascariensis DC. Cce Cymothoe caenis (Drury, 1773) U1 Unidentified 1 Ch Cymothoe herminia (Grose-Smith, 1887) U2 Unidentified 2 Cho Cymothoe hobarti Butler, 1900 Uc Uvariopsis congensis Robyns & Ghesq. Cl Cymothoe lurida (Butler, 1871) Va Vangueria apiculata K. Schum. Ea Euphaedra alacris Hecq, 1978 Vn Vepris nobilis (Delile) Mziray Ec Euphaedra christyi Sharpe, 1904 Vam Vernonia amygdalina Delile Ee Euphaedra edwardsi (van der Hoeven, 1845) Vau Vernonia auriculifera Hiern Eh Euphaedra harpalyce (Cramer, 1777) Xm Xymalos monospora (Harv.) Warb. Eho Euphaedra hollandi Hecq, 1974 Zl Zanthoxylum leprieurii Guill. & Perr. Ei Euphaedra imitans Holland, 1893∗ Ek Euphaedra kakamegae van Someren, 1934 Butterfly species Em Euphaedra medon (Linnaeus, 1763) Ad Antanartia delius (Drury, 1782) Ep Euphaedra preussi Staudinger, 1891 Ac Apaturopsis cleochares (Hewitson, 1873) Eu Euphaedra uganda Aurivillius, 1895 Ae Ariadne enotrea (Cramer, 1779) Ez Euphaedra zaddachi Dewitz, 1879 Ap Ariadne pagenstecheri (Suffert, 1904) Er Euriphene ribensis (Ward, 1871) Ag Aterica galene (Brown, 1776) Es Euriphene saphirina (Karsch, 1894) Ba Bebearia absolon (Fabricius, 1793) Eal Euryphura albimargo Joicey & Talbot, 1921 Bc Bebearia cocalia (Fabricius, 1793) Ech Euryphura chalcis (Felder & Felder, 1860) Bs Bebearia sophus (Fabricius, 1793) Ed Eurytela dryope (Cramer, 1775) Bau Bicyclus auricruda (Butler, 1868) Ehi Eurytela hiarbas (Drury, 1770) Bb Bicyclus buea (Strand, 1912) Ecr Euxanthe crossleyi (Ward, 1871) Bca Bicyclus campina (Aurivillius, 1901) Eer Euxanthe eurinome (Cramer, [1775]) Bcm Bicyclus campus (Karsch, 1893) Gb Gnophodes betsimena (Boisduval, 1833) Bd Bicyclus dentata (Sharpe, 1898) Gc Gnophodes chelys (Fabricius, 1793) Bf Bicyclus funebris (Guerin-M´ eneville,´ 1844) Gn Gnophodes new∗∗ Bg Bicyclus golo (Aurivillius, 1893) Ht Harma theobene Doubleday, 1848 Bgr Bicyclus graueri (Rebel, 1914) Hp Heteropsis peitho (Plotz,¨ 1880)∗∗∗ Bi Bicyclus istaris (Plotz,¨ 1880) Hpe Heteropsis perspicua (Trimen, 1873)∗∗∗ Bm Bicyclus mandanes Hewitson, 1873 Ha Hypolimnas anthedon (Doubleday, 1845) Bme Bicyclus mesogena (Karsch, 1894) Hs Hypolimnas salmacis (Drury, 1773) Bmo Bicyclus mollitia (Karsch, 1895) Js Junonia stygia (Aurivillius, 1894) Bsa Bicyclus sambulos (Hewitson, 1877) Jw Junonia westermanni Westwood, 1870 Bsu Bicyclus saussurei (Dewitz, 1879) Kr Kallimoides rumia (Doubleday, 1849) Bse Bicyclus sebetus (Hewitson, 1877) La Lachnoptera anticlia (Hubner,¨ 1819) Bsm Bicyclus smithi (Aurivillius, 1928) Ma Melanitis ansorgei Rothschild, 1904 Bv Bicyclus vulgaris (Butler, 1868) Ml Melanitis leda (Linnaeus, 1758) Cc Catuna crithea (Drury, 1773) No Neptidopsis ophione (Cramer, 1777) Cb Charaxes bipunctatus Rothschild, 1894 Pp Phalanta phalantha (Drury, 1773) Cbr Charaxes brutus (Cramer, 1779) Ppa Protogoniomorpha parhassus (Drury, 1782) Cca Charaxes candiope (Godart, 1824) Pt Protogoniomorpha temora (Felder & Felder, 1867) Ccs Charaxes castor (Cramer, 1775) Pl Pseudacraea lucretia (Cramer, [1775]) Ccy Charaxes cynthia Butler, 1866 Sa Salamis cacta (Fabricius, 1793) Ce Charaxes etheocles (Cramer, 1777) Sb Sevenia boisduvali (Wallengren, 1857) Cf Charaxes fulvescens (Aurivillius, 1891) So Sevenia occidentalium (Mabille, 1876) Cn Charaxes numenes (Hewitson, 1859) Vd Vanessa dimorphica Howarth, 1966 Cp Charaxes paphianus Ward, 1871 Cpl Charaxes pleione (Godart, 1824) ∗ Cpo Charaxes pollux (Cramer, 1775) Euphaedra eusemoides imitans (Molleman 2012). ∗∗ Cpr Charaxes protoclea Feisthamel, 1850 Molleman (2012). ∗∗∗ Cs Charaxes smaragdalis Butler, 1866 now Brakefieldia (Aduse-Poku et al. 2016). Ct Charaxes tiridates (Cramer, 1777)