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- Publisher
- Taylor & Francis
- Copyright
- © Taylor & Francis
- ISSN
- 2151-3732
- eISSN
- 2151-3740
- DOI
- 10.1080/21513732.2012.709539
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Abstract
International Journal of Biodiversity Science, Ecosystem Services & Management, 2013 Vol. 9, No. 4, 298–310, http://dx.doi.org/10.1080/21513732.2012.709539 Drivers of species richness and abundance of butterflies in coffee–banana agroforests in Uganda a,b,c, M.B. Théodore Munyuli * Department of Agriculture, Biology and Environment, National Center for Research in Natural Sciences, CRSN-Lwiro, D.S. Bukavu, Kivu, Democratic Republic of Congo; Département de Nutrition et Diététiques, Centre de Recherche pour la Promotion de la Santé (CRPS), Institut Supérieur des Techniques Médicales, ISTM-Bukavu, Sud-Kivu, Democratic Republic of Congo; Department of Environmental and Natural Resource Economics, Faculty of Natural Resources and Environmental Sciences, Namasagali Campus, Busitema University, PO Box 236, Tororo, Eastern Uganda A study was conducted in 26 sites on agricultural landscapes in Central Uganda to collect baseline information about important drivers of butterfly richness and abundance. Data were collected for 1 year (2006) using line transects walk-and-counts, fruit-bait traps and handnets sampling methods. A total of 57,439 individuals belonging to 331 species were collected. Totals of 127, 131 and 299 species were recorded in transect counts, banana-bait and handnets, respectively. Of the 57,439 individuals registered, 75%, 19% and 6% were recorded in transect counts, handnet and banana-bait trap, respectively. Butterfly abundance and species richness were significantly (p < 0.05) affected by climatic factors (rainfall, temperature) in previous years (2004 and 2005) and richness and abundance of wild nectaring plants. Butterfly species rich- ness (not the abundance) decreased with land-use intensity (p < 0.05) and was positively related to the cover of semi-natural habitats. Both butterfly species richness and abundance declined sharply with forest distance. Nearby forest remnants and high cover of semi-natural habitats are thus important for conservation of butterflies in coffee–banana agroforestry systems and farmers should be encouraged to protect such resources. Keywords: butterfly diversity; drivers; farmlands; Lepidoptera conservation; habitats protection; East Africa Introduction by crops. In their larval stage, butterflies feed on leaves of several wild plants found in the agricultural systems Butterflies have been recognized as a useful biodiversity and therefore release faeces that contain large amounts indicator group of tropical land-use systems because they of nutrients (Munyuli 2010). In addition, butterflies are are sensitive and react quickly to subtle changes in environ- food to birds and other predators and are hosts to sev- mental and habitat conditions (Kremen 1994; Libert 1994; eral parasitoids that suppress crop pests (Summerville Brown 1997;Larsen 2008; Özden et al. 2008; Pozo et al. et al. 2001; Cardoso et al. 2008, 2009). Consequently, 2008). Due to their short life cycle, narrow niches and rel- their conservation is essential to sustaining the produc- atively low mobility, they are more sensitive to land-cover tivity of natural and agricultural landscapes. Despite their and land-use changes than long-lived animals. Butterflies diversity, ubiquity and ecological importance, butterflies are relatively easy to capture, manipulate and identify remain relatively little studied, particularly with regard (Rogo and Odulaja 2001; Fitzherbert et al. 2006;Marin to their ecology, behaviour and functional role in farm- et al. 2009; Nyamweya and Gichuki 2010), which makes land habitats (Marchiori and Romanowski 2006; Stireman them important candidates for monitoring changes in habi- et al. 2009). In agricultural systems, butterflies are sus- tat, biodiversity and environmental conditions (Kremen pected to be important pollinators of wild and cultivated 1992;Howardetal. 2000; Cleary 2004; Cleary and Mooers crop species (Munyuli 2010). Roughly, 90% of butterfly 2004), including the impact of landscape and habitat man- species live in the tropics (Boriani et al. 2005; Bonebrake agement practices and disturbance regimes in terrestrial et al. 2010). However, knowledge of butterflies inhab- ecosystems (Stork et al. 2003; Öckinger and Smith 2008). iting farmland habitats is fairly good in Mediterranean Butterfly activities are closely controlled by weather regions compared with the sub-Saharan Africa. The rela- and many species are constrained by climate, mostly tive scarcity of data on tropical butterfly populations ham- occupying a small part of the range of their host plants pers the ability to effectively conserve them, particularly as (Fitzherbert et al. 2006; Nyamweya and Gichuki 2010). pollinating agents (Bonebrake et al. 2010) in agricultural Butterflies play a significant ecological role in agricultural systems. landscapes. They perform essential ecosystem services Across sub-Saharan Africa, surveys of drivers of but- (Rogo and Odulaja 2001; Schmidt and Roland 2006), espe- terfly diversity in farmlands are rare in the literature. There cially in the recycling of nutrients (N, P, K) highly needed *Emails: tmunyuli@gmail.com; munyuli@yahoo.com © 2013 Taylor & Francis International Journal of Biodiversity Science, Ecosystem Services & Management 299 are a few published papers quantitatively documenting the of agricultural intensity (Bolwig et al. 2006). Detailed diversity of butterflies for some agricultural regions in East vegetation, environmental and landscape characteristics of Africa. In Uganda, most of the works on butterflies have the 26 sites are presented in Munyuli (2011a). been carried out mainly in natural areas, forest ecosys- tems and in protected areas (Tumuhimbise et al. 1998; Howard et al. 2000; Molleman et al. 2006; Tushabe et al. Butterfly sampling 2006). There exist no published data describing extensively Field sampling of butterflies was conducted in each of the diversity of butterflies found in agricultural landscapes the 26 study sites. In each study site, an area of 1 km in Uganda in relationship to climatic, regional, landscape was selected and divided into five linear parallel tran- and local drivers. However, such information is impor- sects 1000 m long and 200 m apart. Butterfly data were tant for butterfly biodiversity conservation in the rural collected on one of the five transects per study site, and landscapes. the selected transect was surveyed during five study site While several studies (Chay-Hernández et al. 2006; visits in 2006. Butterflies were sampled using three com- Kivinen et al. 2008; Pickens and Root 2008; Dover and plementary methods (transects walk-and-counts, handnets Settele 2009) indicate that climatic, regional and landscape and fruit-bait traps) universally recommended and exten- factors are important drivers for butterflies in farmland sively used to survey and monitor butterfly populations areas, other studies indicate that local factors (availabi- and communities (Pollard and Yates 1993; Raguso 1993; lity of nectaring resources) are of foremost importance in DeVries and Walla 2001; Kitahara and Sei 2001; Yahner explaining the variation in species richness and total den- 2001;Fermonetal. 2002; Kitahara 2004;Barlowetal. sity of butterflies in farmland regions (Pöyry et al. 2009). 2007; Kitahara et al. 2008; van Swaay et al. 2008;Marín It is not clear which local, landscape, regional and cli- et al. 2009;Vu 2009) in terrestrial ecosystems (Lehmann matic factors are important predictors of butterfly species and Kioko 2005) in the tropics. These sampling methods richness and abundance in Central Uganda. have been applied in Uganda in previous studies (Owen The aim of this study was to explore the relation- 1971; Coe et al. 1999; Molleman et al. 2006; Akite 2008). ships between butterfly assemblages (species richness and Butterfly specimens were identified by consulting litera- abundance) and local, landscape, regional and climatic ture, nomenclature and coloured plates of butterflies and drivers. by using the reference collection available at Makerere University Zoology Museum. To avoid a mix up of the Materials and methods results, specimens collected by each of three sampling Study area and selection of study sites methods applied were identified separately. The taxonomic characteristics of butterflies were obtained from standard This study was conducted in 26 different study sites in guides, including (i) Butterflies of Kenya (Larsen 1996), the banana–coffee system of Lake Victoria Arc, covering (ii) ‘Butterflies of West Africa’ (Larsen 2006) and (iii) several districts of Central Uganda (Munyuli 2009a, ‘Butterflies of Uganda’ (Carder and Tindimubona 2002). 2009b, 2009c, 2011a, 2012; Munyuli et al. 2009). The Identification of all voucher specimens was confirmed Lake Victoria Arc is characterized by ferrisoils with by a butterfly taxonomist based at Makerere University high to medium fertility level and receives on average Zoology Museum. All voucher specimens collected during 1000–1800 mm of rains in a bimodal pattern (rainy the present research were deposited at Makerere University seasons: March–May, September–November; semi-dry Zoology Museum. to dry seasons: June–August, December–February) with 22–28 C and 60–75% of mean annual temperature and relative humidity, respectively (Munyuli 2011b). Rainfall Measurement of local, landscape, regional and climatic amounts and patterns are unpredictable. Several food and drivers cash crops are grown in small-scale monoculture and/or Local drivers polyculture fields that are integrated into the coffee–banana agroforest production systems where coffee and banana Local drivers (richness and abundance of floral nectar- are the main crops (Munyuli 2011c). Rural Central Uganda ing resources) of importance for butterflies were mea- is a mosaic landscape where ‘islands’ of patches of natural sured following approaches described by Munyuli (2010). habitats (forest fragments, forest reserves, wetlands, wood- These included the percent cover of flowering plants lands) are found scattered within agricultural matrices (trees, shrubs, herbs and weeds), the number of nectaring dominated by linear and non-linear features of semi- plant species and the percentage cover of cultivated flo- natural habitats (fallows, hedgerows, grasslands, woodlots, ral resources (proportion of cultivated pollinator-dependent rangelands). All study sites were situated at approximately and non-pollinator-dependent crops) per 1 km area. Data 790–1000 m above sea level. A total of 26 sites were on herbs were collected from 5 m × 5m(25m ) selected to represent a range of vegetation and habitat quadrats, while those on shrub and trees were collected 2 2 types of varying degrees of anthropogenic disturbance and from 10 m × 50 m (500 m ) quadrats. A total of 2025 m management intensities. Selected study sites were grouped quadrats and 20,500 m quadrats were established per using human population density as a surrogate measure transect per study site. 300 M.B.T. Munyuli Landscape drivers classifies land use around Lake Victoria into four cat- egories: low, medium, high and very high. Land uses The area size of different land-use types in each 1 km site classified as low are areas where three-quarters of the land was determined using a global positioning system (GPS) are maintained uncultivated compared with one-quarter or using a tape measure for measurement of the dimen- that is under crop/animal production. Land uses classified sions of small fields (<50 × 50 m). Different land uses as medium are managed habitat types where there is an were grouped into types. Three landscape variables of almost equal amount of land cultivated and uncultivated, high ecological importance for butterfly community stud- whereas land uses classified as high are areas dominated ies in agricultural matrices were determined for each site: by crops or livestock and land uses classified as very high (i) the percentage of semi-natural habitat per square kilo- represent large monocultures (estates of tea, sugar, coffee, metre area; the term semi-natural habitats included all etc.). linear and non-linear semi-natural habitats such as fallows, hedgerows, field margins, grasslands, roadsides, wood- lands, woodlots, track sides and stream edges. Because fallows play a particular role (as butterfly reservoirs and Climatic drivers as foraging/breeding habitats) in the maintenance of many Data on regional climatic factors were obtained from butterfly species in rural landscapes, the proportion of meteorological stations located in the study area cover (%) of young fallows per site was calculated along- (Kamenyamigo, Entebbe, Jinja and Kiige meteoro- side the proportion of cover of semi-natural habitats; (ii) logical stations). Using raw data from the stations, monthly the percentage of the land area cultivated; and (iii) the for- means for 10 years (1997–2006) for temperature and est distance, that is, the distance from a given study site rainfall (Figure 1) were calculated to detect trends in to the nearest potential pollinators’ source. Distances up to the rainfall patterns and temperature, since such oscilla- 100 m were measured with a tape measure and distances tions may affect the patterns (trends in occurrence and greater than 100 m were measured with GPS (Garmin activities) of butterfly species richness and abundance International, Olathe, KS, USA; corrected to ± 1 m accu- in rural landscapes. Later, other variables (of relevance racy with Pathfinder v 2.0). to the study of patterns of butterfly communities in farmlands) were calculated: (i) the overall mean rains (means/month/10 years); (ii) the overall daily mean Regional land-use intensity factors minimum and maximum temperatures (mean of 10 years); Different land-use categories were obtained from the (iii) the mean monthly rainfall (2004, 2005, 2006); (iv) the Makerere University Geographic Information Service that mean monthly maximum temperature (2004, 2005, 2006); 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month of the year Overall monthly mean rainfall (rains) Overall daily mean maximum temperature (°C) Overall daily mean minimum temperature (°C) Figure 1. General pattern in temperature and rainfall data in the coffee–banana system, Central Uganda. Note: Data are means of five meteorological stations and means of 10 years: 1997–2006. Mean monthly rainfall (mm) Mean daily temperature International Journal of Biodiversity Science, Ecosystem Services & Management 301 and (v) the mean monthly minimum temperature (2004, Pierini (14.5%), Charaxini (12.7%), Mycalesini (10.9%) 2005, 2006). and Limenitidini (8.6%). The dominant (>5% of total indi- viduals recorded) butterfly species were Acraea acerata, Bicyclus safitza, Catopsilia florella and Junonia sophia. Data analysis These dominant and widespread species could be found Data from the three sources (transect walk-and-counts, in all study sites. The most abundant genera were Acraea banana-bait traps and handnets) were pooled to obtain total (17.6%), Junonia (17.4%), Bicyclus (11.1%), Catopsilia butterfly biodiversity per transect per study site per sam- (7.9%), Eurema (7.7%), Belenois (6.0%) and Ypthima pling round. Lumping data together helped to minimize (5.1%), whereas the most abundant tribes included variance associated with individual sampling methods Junoniini (22.6%), Acraeini (22.4%), Mycalesini (14.2%), (Bossart et al. 2006). Butterfly abundance was calculated Pierini (10.2%), Polyommatini (7.4%) and Satyrini (5.2%). as the total number of individuals recorded per transect Around 91.2% (331 observed species against 360 expected per study site each sampling day, whereas butterfly species species) of the species present in the study area were richness was calculated as the total numbers of butterfly found. The results suggested that a complete inventory species recorded per transect per study site each sampling of the regional species pool was obtained. Fifteen species round. To determine the suite of explanatory variables most were identified as the most ecologically important species closely related to butterfly species richness and abundance (‘characteristic species’) of the coffee–banana system but- measurements, a correlation analysis was conducted. Thus, terfly assemblages, since they occurred with high con- correlation among dependent variables (butterfly species stancy value (>50%). These were C. florella, J. sophia, richness and abundance) and independent variables (cli- B. safitza, A. acerata, Eurema hecabe, Ypthima albida, matic, local, landscape and regional variables) characteriz- Zizula hylax, Acraea ventura, Eurema brigitta, Neptidopsis ing the 26 study sites was tested using Pearson correlation ophione, Junonia eonone, Zizeeria knysna, Cupidopsis after transformation (log10 and arcsine square root trans- cissus, Junonia chorimene and Acrae uvui. They should formation) of the raw data (Munyuli 2009a, 2009b, 2009c; serve as reliable and efficient indicators of change in Munyuli et al. 2009). Based on the correlation matrix conditions in agroecological environments in further sur- of all variables measured, only non-collinear independent veys. They may also be good candidates for monitoring variables that were significantly (p < 0.05) related to the state of butterflies of the farmland habitats in East dependent variables were selected for further analyses in Africa. simple regression analyses. These also illustrated the trends and magnitude of the effects of independent factors on dependent variables. Scatter plots were used to illustrate Local drivers of butterfly richness and abundance the scale dependency of butterflies on the different drivers. The results of simple linear and non-linear regressions For all simple regression models obtained, the coefficient indicated that mass flowering wild plant resources (nec- of determinations (R ) were plotted to demonstrate the tar flower abundance) were significantly positively related level of influence of the type of drivers that correlate with to both species richness (Figure 2A) and abundance the response variable (abundance and species richness of (Figure 2B) of butterflies. Overall, mass flowering plants butterflies). The value of the R was also used to iden- explained 48% and 62% of variations in butterfly species tify variables with the most predictive effects. Thus, for richness and abundance, respectively. Similarly, the num- all explanatory variables, the best regression model was ber of nectaring plant species (all weeds, herbs, shrubs, selected based on the highest cross-validated R . trees) was positively related to both species richness To analyse the effects of land-use categories on butter- (Figure 2C) and abundance (Figure 2D) of butterflies. fly abundance and diversity, a general linear model analysis The number of nectaring plant species accounted for of variance was conducted with butterfly community vari- 38% and 22% of variations in butterfly species richness. ables (abundance, species richness) as the dependent vari- This result suggests that diverse flowering plant species ables, and the regional land-use categorical variables (low, stimulated attraction of a species-rich flower-visiting but- medium, high and very high) as fixed factors. The least sig- terfly community in the farm landscapes. Unexpectedly, nificant difference (LSD) tests were used as post hoc tests cultivated pollinator-dependent floral resources were not for multiple comparisons of means. related to butterfly species richness. Similarly, the rela- tionship between cultivated pollinator-dependent crops and butterfly abundance was not significant (p > 0.05), and Results this indicated that some butterfly species could not visit Characteristics of butterfly communities recorded during animal-pollinated crop species. Apparently, butterflies do the surveys not benefit much from available mass flowering pollinator- In total, 331 species and 95 genera were registered com- dependent crops. However, cultivated non pollinator- prising a total of 57,439 individuals in 26 study sites. dependent floral resources were significantly negatively Genera with the greatest number of species were Acraea related to butterfly species richness (Figure 2E). The per- (10.8%), Charaxes (8.9%), Bicyclus (7.6%) and Neptis centage of cultivated non-pollinator crops explained 21% (5.7%). The most rich tribes were Acraeni (15.5%), of the variation in butterfly species richness. Butterfly 302 M.B.T. Munyuli (A) (B) y = 0.5384x + 10.028 y = 20.254x – 2.2314 R = 0.484, n = 26, p < 0.001 R = 0.6246, n = 26, p < 0.001 0 0 020 40 60 5 10 15 20 25 30 35 40 45 50 Percentage cover of mass flowering plants Percentage cover of mass flowering plants (nectar flower abundance)/500 m (nectar flower abundance)/500 m (C) (D) y = 7.1129x + 266.43 y = 0.31x + 13.815 2 1200 2 50 R = 0.3863, n = 26, p < 0.001 R = 0.2225, n = 26, p < 0.001 0 0 020 40 60 80 0 20 40 60 80 Number of nectaring plant species Number of nectaring plant species (weeds/herbs/trees/shrubs)/site (weeds/herbs/trees/shrubs)/site (E) (F) y = –7.3297x + 612.17 y = –0.3587x + 29.66 R = 0.2172, n = 22, p < 0.05 R = 0.1023, n = 22, p = 0.63 5 0 5 15 25 35 45 5 15 25 35 45 Proportion (%) of cover of cultivated Proportion (%) of cover of cultivated non-pollinator dependent crops/site non-pollinator dependent crops/site Figure 2. Relationship between (i) nectar flower abundance (percentage of mass flowering plants), (ii) the abundance of cultivated floral resources (percentage of cultivated non-pollinator-dependent crops), (iii) the number of flowering plant species (weeds, herbs, trees, shrubs) and the number of butterfly species (A, C, E) and butterfly abundance (B, D, F) per 2 ha transect/study site. Note: Pollinator-dependent crops included all annual, biannual and perennial non-entomophilous crops potentially offering nectar to butterflies. abundance was not significantly (p > 0.05) related to richness. Similarly, the proportion of semi-natural habitats the proportion of pollinator-dependent crops, although in a 1 km area was significantly positively related to there was a negative trend. These results indicate that butterfly species richness (Figure 3C) and not related to the occurrence of butterflies in the agricultural landscapes butterfly abundance (Figure 3D). Overall, the proportion of Central Uganda was largely influenced by wild flo- of semi-natural habitats accounted for 23% of the varia- ral resources and by cultivated non-pollinator-dependent tion in butterfly species richness. These results indicated crops rather than by cultivated pollinator-dependent floral that the increase in amount of semi-natural habitats in the resources. landscape was likely to lead to increased species richness (not the abundance) of butterflies in agricultural matri- ces of Central Uganda. Forest distance was significantly Landscape drivers of butterfly richness and abundance negatively related to both butterfly abundance (Figure 3F) and butterfly species richness (Figure 3E). Thus, the for- Cultivation intensity was significantly negatively related to est distance explained 69% and 27% of variations in butterfly species richness (Figure 3A), but not to butterfly butterfly species richness and abundance, respectively abundance (Figure 3B). Proportion of cultivation inten- (Figure 3). sity explained 48% of the variation in butterfly species Butterfly species/site Butterfly species/site Butterfly species/site Butterfly individuals/site Butterfly individuals/site Butterfly individuals/site International Journal of Biodiversity Science, Ecosystem Services & Management 303 (A) y = –22.269x + 39.77 y = –520.7x + 869.43 (B) R = 0.1302, n = 26, p = 0.89 50 R = –0.2178, n = 26, p < 0.05 0.4 0.6 0.8 1 0.4 0.6 0.8 1 Proportion of cultivation intensity Proportion of cultivation intensity (C) (D) y = 6.4643x + 303.82 R = 0.1464, n = 26, p = 0.61 y = 0.2754x + 15.781 R = 0.2357, n = 26, p < 0.05 020 40 60 020 40 60 Percentage of semi-natural habitats/site Percentage of semi-natural habitats/site (E) (F) y = –0.0.129x + 34.663 y = –0.2136x + 652.38 R = 0.6969, n = 26, p < 0.001 R = 0.2732, n = 26, p < 0.001 0 500 1000 1500 2000 0 500 1000 1500 2000 Rainforest distance (m) Rainforest distance (m) Figure 3. Relationship between landscape drivers (cultivation intensity, distance to forest margins and percentage of semi-natural habitats) per square kilometre area and butterfly species richness (A, C, E) and abundance (B, D, F). Regional drivers of butterfly richness and abundance Climatic drivers of butterfly richness and abundance There were no significant variations in butterfly abundance There were significant correlations between climatic vari- among the four land-use intensity categories (p > 0.05, ables and butterfly community parameters (Table 1). Figure 4B), indicating that butterfly abundance was not Rainfall of the year 2004 was negatively correlated to very sensitive to the degree of agriculture intensifica- both species richness (r = –0.47, p < 0.05, n = 26) and tion. However, there were significant effects of the land- abundance (r = –0.49, p < 0.05, n = 26) of butter- use intensity on butterfly species richness (p < 0.05; flies. Similarly, the mean rainfall of the year 2005 was Figure 4A) that were significantly higher in low to medium significantly negatively correlated to both species rich- land-use intensity categories than in high and very high ness (r = –0.48, p < 0.001, n = 26) and abundance land-use categories (Figure 4A). Study sites located in the (r = –0.53, p < 0.05, n = 26) of butterflies. However, low land-use category had almost double the number of butterfly abundance and species richness were not signif- species. icantly (p > 0.05) associated with the mean rainfall of the Butterfly species/site Butterfly species richness/site Butterfly species/site Butterfly individuals/site Butterfly individuals/site Butterfly individuals/site 304 M.B.T. Munyuli (A): GLM-ANOVA, F = 4.09, P = 0.019, DF = (3100) LSD(0.05) = 3.89 abundance and species richness. Both the number and cover of wild blooming plant species were more impor- 40 28.8 tant local drivers for butterflies than the abundance of 27.7 cultivated floral resources in the coffee–banana agrofor- 17.1 est systems in Central Uganda. It is likely that cultivated 13.4 floral resources (pollinator-dependent and non-pollinator- dependent crops) are not the primary food plants for the majority of butterfly species inhabiting farmlands proba- bly because locally grown plants are less rich in nectar, the main floral resource collected by most butterflies forag- ing in agricultural landscapes. A significant strong positive Very high High Medium Low relationship between butterfly species richness and nectar- Regional land-use intensity gradient ing plant species richness was found in Central Uganda. (B): GLM-ANOVA, F = 1.64, P = 0.208, DF = (3100) 1200 This result is consistent with Simonson et al. (2001) who 592.5 found that butterfly species richness was positively corre- lated with total vascular plant species richness and native 564.2 plant species richness in Colorado (USA). Worldwide, pos- itive relationships between butterfly diversity and plant 344.3 a species richness are reported (e.g. Kumar et al. 2009): plant 289.6 species serve as important food plants for larvae, flow- ers providing nectar resources for adult butterflies and the physical structure of plants create microhabitats providing shelter to numerous species of butterflies in rural land- scapes. Thus, high plant species diversity is accepted to Very high High Medium Low be an indicator of good habitat quality for generalist and Regional land-use intensity gradient specialist butterfly species (Kumar et al. 2009) inhabiting Figure 4. Effects of the regional land-use intensity categories farmland habitats. on the species richness (A) and abundance (B) of butterflies col- In this study, it was observed that butterfly species lected from farmlands of Central Uganda in 2006. richness was sensitive to land-use intensity whereas the Notes: Means (x ± SE) followed by different letters are signifi- abundance was not, probably because very few adapted cant at p < 0.05 according to the LSD test. GLM, general linear farmland species can easily be represented by a high num- model; ANOVA, analysis of variance. ber of individuals in the farmlands while the majority (>70%) of species may be ‘rare species’ represented by year 2006 or the overall mean rainfall of 10 years (Table 1). one to two individuals only. While studying the effect In addition, the mean maximum temperature of 10 years of agricultural intensification on the homogenization of was positively correlated to both species richness (r = 0.51, Lepidoptera community in Finland, it was observed that the p < 0.05, n = 26) and abundance (r = 0.48, p < 0.05, diversity of butterfly and day-active geometrid moth com- n = 26) of butterflies. In addition, the mean butterfly abun- munities found within 134 differently fragmented land- dance was positively correlated to both the maximum tem- scapes decreased with increasing agricultural intensity perature of the year 2004 (r = 0.51, p < 0.05, n = 26) and with the consequence: promotion of habitat generalists in the maximum temperature of the year 2005 (r = 0.46, landscapes with moderate to intensive agriculture com- p < 0.05, n = 26) (Table 1). These results indicated that pared to region with low agriculture intensity (Ekroos et al. current butterfly abundance and/or species richness were 2010). likely being affected by past events. In other words, but- The results from Central Uganda revealed that forest terfly communities found in farmlands of Central Uganda distance was the most significant landscape driver fol- may be sensitive to climate change. Although rainfall is lowed by the proportion of semi-natural habitats. Both expected to affect positively the number and cover of the abundance and species richness of butterflies declined wild and cultivated flowering plant species, no significant linearly with increasing forest distance. This result is con- (p > 0.05) correlations were found between the abundance sistent with observations recorded in India where a study (and the diversity) of cultivated and wild floral resources on adult butterfly communities visiting coffee plantations and the mean rainfall of 10 years, or the mean rainfall of around a protected area in the Western Ghats found that the year 2004 or 2005 (Table 1). both butterfly species composition and species richness declined with increasing forest distance (Dolia et al. 2008) up to 2000 m. Distance of up to 1700 m from mature Discussion forests did not have any significant effect on butterfly Local, landscape and regional drivers of butterfly species richness in a mosaic landscape in Central Sulawesi abundance and diversity in Indonesia (Veddeler et al. 2005), whereas in Central Among local drivers, the abundance and species richness Uganda, butterfly species richness declined sharply up of wild flowering plants significantly affected butterfly to a distance of 2000 m. The fact that forest distance Butterfly individuals/2 ha/site Butterfly species/2 ha/site International Journal of Biodiversity Science, Ecosystem Services & Management 305 Table 1. Cross-correlation matrix showing naïve multiple correlations of Lepidoptera (butterfly) variables with environmental, local and landscape variables. BC D E F H I J K L M N O P Q R S T U V W X ∗∗ ∗ ∗ ∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗ ∗∗ ∗∗ ∗∗ A 0.74 −0.52 −0.44 0.46 0.71 −0.68 −0.74 −0.70 −0.50 0.64 0.62 0.57 ∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗ ∗∗ ∗ ∗ B −−0.49 −0.57 0.77 0.66 −0.94 −0.95 −0.97 −0.46 0.69 0.49 0.44 ∗∗ ∗ ∗∗ ∗∗ ∗ C −−0.59 0.52 0.57 0.61 0.47 ∗∗ ∗ ∗∗ ∗∗ ∗∗ ∗ D − 0.57 0.52 0.55 0.69 0.78 −0.50 ∗∗ ∗∗ ∗∗ ∗ E −−0.78 −0.79 −0.72 0.52 ∗∗ ∗∗ ∗∗ ∗ ∗ F −−0.64 −0.72 −0.58 −0.46 0.45 ∗∗ ∗∗ ∗∗ ∗ ∗∗ H − 0.93 0.91 0.83 0.52 −0.58 ∗∗ ∗ ∗∗ ∗ ∗ I − 0.87 0.47 −0.67 −0.46 −0.45 ∗∗ ∗ J − −0.67 −0.49 ∗∗ ∗ ∗ ∗ ∗ K − 0.83 −0.50 0.51 −0.47 −0.48 ∗ ∗ ∗ ∗ ∗ L − 0.48 0.51 0.46 −0.49 −0.53 ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ M −0.84 0.84 −0.76 −0.96 −0.85 −0.75 −0.79 0.76 0.67 ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ N −−0.69 0.69 0.85 0.67 0.56 0.64 −0.55 −0.58 ∗∗ ∗∗ ∗∗ ∗ ∗∗ O −−0.56 0.64 0.58 0.43 −0.77 ∗∗ ∗∗ ∗∗ ∗ ∗ ∗∗ ∗∗ P −−0.93 −0.95 −0.93 −0.52 −0.47 0.94 0.93 ∗∗ ∗∗ ∗∗ ∗∗ Q − 0.90 0.89 −0.78 −0.98 ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ R − 0.93 0.61 0.63 −0.84 −0.84 ∗∗ ∗∗ ∗∗ ∗∗ S − 0.60 0.54 −0.97 −0.88 ∗∗ ∗∗ ∗∗ T − 0.83 0.97 −0.64 ∗∗ U − 0.87 ∗∗ V −−0.54 ∗∗ W − 0.80 X − ∗ ∗∗ Notes: Different levels of significance of Spearman rank of correlation coefficients: p < 0.05; p < 0.001, otherwise not significant when no value is given. A, human population density; B, cultivation intensity; C, flowering plant species richness; D, density of plant species (abundance of floral resources); E, cultivated with floral resources (pollinator-dependent crops); F, cultivated without resources (pollinator non-dependent crops); H, forest distance; I, percentage of semi-natural habitats/site; J, percentage of young fallows per site; K, mean butterfly species richness; L, mean butterfly abundance; M, overall mean rainfall (10 years); N, overall daily mean maximum temperature (mean 10 years); O, overall daily mean minimum temperature (mean 10 years); P, mean monthly rainfall (2006); Q, mean monthly maximum temperature (2004); R, mean monthly maximum temperature (2005); S, mean monthly maximum temperature (2006); T, mean monthly minimum temperature (2004); U, mean monthly minimum temperature (2005); V, mean monthly minimum temperature (2006); W, mean monthly rainfall (2004); and X, mean monthly rainfall (2005). 306 M.B.T. Munyuli was negatively related to butterfly species richness and Yates 1997; Roy and Sparks 2000; Watt and McFarlane abundance suggests that surrounding natural forests of 2002). Climatic variables may therefore be very impor- agricultural matrices remain an irreplaceable habitat for tant in determining emergency (van Asch and Visser 2007; several butterfly species belonging to different functional Saastamoinen and Hanski 2008), foraging, reproduction groups (e.g. breeding strategy) and life history traits in and breeding activities of many butterfly species in sub- Central Uganda. In addition, this observation suggests Saharan Africa. In fact, seasonal flight activity, as well that the ability of different agricultural land uses (human- as species diversity and overall abundance vary greatly dominated landscapes) to support Lepidoptera communi- between sampling rounds (Munyuli 2010) in a study site. ties can be enhanced if tree plantations/crop fields are It is well established that temperature is a key factor established in proximity to surrounding primary forests increasing daily activities (flight, foraging movements) (Hawes et al. 2009). Obviously, a contiguous distribution of butterflies, whereas rainfall affects indirectly butterfly of large secondary forest patches with many host trees is through enhancing the availability of nectaring resources. very important to conserve butterfly species in fragmented Most butterfly species need resources like host plants (for agricultural landscapes (Kobayashi et al. 2009). The obser- larvae), nectar plants (for adults) and sites for resting, vation made in Central Uganda about the effect of forest and both natural/semi-natural habitats and nectar plant fragments is consistent with the conclusion of Koh (2008) resources are also affected by rainfall pattern (Öckinger who clearly demonstrated that natural forests serve as an et al. 2009; Rossi and van Halder 2010). important population sources for butterfly species occur- In this study, the abundance and species occurrence of ring in nearby agricultural matrices. In terms of habitat butterflies were significantly related to rainfall and mean use, butterflies were found more in forest margins, forest maximum temperature in previous years (2004, 2005), fallows, wetlands, swampy habitats, woodlots, woodlands, not the year of study (2006). The mean precipitation of grasslands and in adjacent fields (Munyuli 2011a) as com- the past 10 years was not significantly associated with pared with fields that were located far from such natural any measure of richness and abundance of butterflies, but and semi-natural habitats. Forest is a primary factor dictat- the mean maximum temperature in the same period was. ing the occurrence of butterflies in agricultural landscapes The fact that butterfly community variables were related of Central Uganda. to mean maximum temperature and rainfall in previous In this study, both the abundance and diversity of but- years rather than current has also been found in tem- terflies increased with increasing amount of semi-natural perate regions (e.g. Pollard 1988). In relation to rainfall, habitats per square kilometre. Similarly, in southeastern results obtained from Uganda coincide at least partially Sweden (Jonason et al. 2010), butterfly species richness with results from Malaysia, where it was found (Brehm (not the abundance) was found to be positively related to 2005) that monthly rainfall variation correlated with moth increasing tree cover in the farm landscape. Apart from populations in previous months of the year, weather factors land use and farm management methods or farming prac- and plant phenology in the lowland dipterocarp forest. The tices (Weibull and Östman 2003), vegetation structure, intensity of tree flowering in the previous months was an quality of the matrix surrounding an agricultural habi- important environmental factor that correlated positively tat (Binzenhöfer et al. 2008; Summerville et al. 2008), with the numbers of species and individuals of moths that diversity and types of habitats (Dessuy and de Morris emerged in any subsequent month (Intachat et al. 2001). 2007;Ngaietal. 2008; Kumar et al. 2009), landscape High rainfall in the previous 3 months led to an increase in heterogeneity and habitat connectivity (Davis et al. 2007) moth abundance in the following month (perhaps by stim- are important factors determining occurrence, movements, ulating an increase in fresh plant material), whereas high population dynamics, seasonality, persistence and long- rainfall and relative humidity thereafter served to decrease term survival of Lepidoptera faunal communities in the abundance, possibly by encouraging the spread and activity agricultural landscapes (Dennis 2003; Greza et al. 2004; of pathogens that impacted on early life-stage survivorship Chay-Hernández et al. 2006; Kivinen et al. 2008; Öckinger (Intachat et al. 2001). It was concluded that the diversity of and Smith 2008; Pickens and Root 2008; Stasek et al. geometrid moths correlated more with weather parameters 2008; Dover and Settele 2009; Brückmann et al. 2010). than with tree phenology (Intachat et al. 2001) in tropi- Generally, agricultural matrices that are more resembling a cal regions, and important weather parameters that influ- nearby forest patch maintain higher butterfly diversity than enced moth abundance include monthly rainfall, relative matrices with lesser shade cover (Summerville et al. 2001; humidity and minimum temperature in previous months. Kitahara and Watanabe 2003; Weibull and Östman 2003; In addition, it is generally agreed that weather variables can Boriani et al. 2005;Avironetal. 2007; Ohwaki et al. 2007, have long-term effects on population fluctuations of dif- 2008; Barlow et al. 2008; Bergman et al. 2008; van Halder ferent butterfly taxa (Sei-Woong 2003). Weather variables et al. 2008; Marín et al. 2009). can help in detecting potential butterfly population changes following changes (Sei-Woong 2003) in climatic factors (changes in temperature regimes, rainfall patterns, relative Climatic drivers of butterfly richness and abundance humidity) that are believed to affect negatively/positively It is generally accepted that climate factors regulate most species richness, abundance and distribution of butterflies insect species’ life cycles, including butterflies (Sparks and regionally as well as locally (Sei-Woong 2003; Kivinen International Journal of Biodiversity Science, Ecosystem Services & Management 307 et al. 2007). Although in Uganda the study was conducted Lepidoptera. It was also found that butterfly communi- on butterflies in agricultural systems, while in Malaysia ties were affected more by previous climatic events than the study was conducted on moths, the two studies were current ones. Landscape managers should advocate for able to show that Lepidoptera biodiversity can be related to the maintenance of high-quality habitat matrix in agricul- previous events (historical events) in weather factors. tural mosaic farm landscapes to protect native butterfly Practically, weather factors affect the production of faunal diversity and reduce their vulnerability to fur- floral resources in current and previous years. High pro- ther environmental/climatic changes in the region. This duction of flowers in the previous wet months of the year could be supported by policies that enhance protection may guarantee the availability of food for different butter- and restoration of forest fragments and promotion of inter- fly taxa (larval and adult stages), and this in turn may tend ventions that enable farmers to adopt butterfly friendly to increase larval survivorship and therefore the abundance conservation and farming practices, such as agroforestry of adult butterflies. In some cases, the abundance of adult systems with multipurpose tree species, to sustain butterfly butterflies can increase with high abundance and diversity communities and services in rural landscapes of Uganda. of flowering plants in the current and not in previous years. To provide incentives to the conservation of butter- An increase in the number of host trees flowering would fly fauna in agricultural landscapes in Uganda, further also mean an increased number of oviposition sites for the research is needed to understand the behavioural ecology butterflies (particularly forest specialist species). of butterflies and to determine the pollination efficiency High rainfall in the months of the current year may (Martins and Johnson 2009) of different butterfly species deter butterflies from flying, thus resulting in a low catch (as well as for other Lepidoptera taxa). Such studies will in traps. High rainfall in the previous months of the year help in highlighting the importance of butterflies (and may also decrease the survivorship of the larvae, resulting moths) as a group providing pollination services of high in lower adult butterfly catch in the current month. During economic importance to both wild and cultivated plants in very rainy periods, larvae are more likely to be washed off Uganda and in East and Central Africa. their host plants. Combination of a wetter period and a high relative humidity in the months before adult emergence Acknowledgements may also increase activities of disease microorganisms dur- I am very grateful to the Darwin Initiative (Defra, UK; project ing that period, resulting in higher risk of larval mortality reference: 14-032; project title: Conserving biodiversity in mod- from disease and therefore reduction in adult abundance ernized farm landscapes in Uganda) for funding this study. I am in the current month. Rainfall in the current month may also very grateful to project leaders Dr Juliet Vickery (RSPB- also encourage disease at the pupal stage and cause mor- Cambridge University, UK), Dr Phil Atkinson (British Trust for Ornithology, UK), Prof. Derek Pomeroy (Makerere University, tality if heavy during the vulnerable eclosion period. High Uganda), scientific supervisors Prof. Simon Potts (University rainfall, on the other hand, can encourage blooming of of Reading, UK) and Prof. Philip Nyeko (Makerere University, wild and cultivated plants in most tropical agroecosys- Uganda). I am also grateful to Dr Bwinja M and to Mr Maurice tems for the benefit of butterflies (Munyuli 2010) and Mutabazi (research assistant) for his assistance in the field. I am grateful to Akite Perpetra who helped during identification of but- other Lepidoptera. Heavy blossoms in the previous months terfly to species level at Makerere University Zoology Museum. would encourage adult and larval survivorship and there- I am also grateful to anonymous reviewers of this journal for fore increase the number of emergence adult butterflies in constructive criticisms and comments on earlier versions of this the following months of the following year. article. In summary, if the annual and seasonal changes in rain- fall and temperature continue to oscillate and if rainfall continue diminishing while temperature continue rising in References Uganda (Munyuli 2010) this may, combined with already Akite P. 2008. Effects of anthropogenic disturbances on the existing negative impacts of environmental degradation, diversity and composition of the butterfly fauna of sites in the Sango Bay and Iriiri areas, Uganda: implications for lead to a high loss/decline in butterfly biodiversity while conservation. 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Journal
International Journal of Biodiversity Science, Ecosystem Services & Management – Taylor & Francis
Published: Dec 1, 2013
Keywords: butterfly diversity; drivers; farmlands; Lepidoptera conservation; habitats protection; East Africa