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Rodriguésia

Print version ISSN 0370-6583On-line version ISSN 2175-7860

Rodriguésia vol.67 no.2 Rio de Janeiro Apr./June 2016

http://dx.doi.org/10.1590/2175-7860201667203 

Functional Pollination Ecology

Floral traits as potential indicators of pollination vs. theft

Camila Silveira de Souza1  3 

Camila Aoki2 

Augusto Ribas1 

Arnildo Pott1 

Maria Rosângela Sigrist1 

1Universidade Federal de Mato Grosso do Sul, Centro de Ciências Biológicas e da Saúde, Lab. Polinização, Reprodução e Fenologia de Plantas, Cidade Universitária s/n, , 79070-900, Campo Grande, MS, Brazil.

2Universidade Federal de Mato Grosso do Sul, Campus Universitário de Aquidauana, R. Oscar Trindade de Barros 740, Bairro Serraria, 79200-000, Aquidauana, MS, Brazil.

Abstract

Floral visitation does not necessarily mean pollination, as several animals utilize floral resources without transferring pollen. Since pollinators and thieves can affect the reproduction, morphology and diversification of flowering plants, we here investigated if attributes of flowers and flowering of plant species collected in the central Brazilian vereda would predict the pollination (pollen seeking) or theft (pollen/nectar theft) during the visits. It was hypothesized that non specialized flowers would have a higher incidence of thievery, where as specialization, for example, the presence of large and medium flowers with long corolla, making it difficult to access nectar, would lead to increased pollination. As a result, four attributes were mainly associated with illegitimate visits, and in order of importance, they are size (small), quantity of flowers per plant (large), flowering time (< 10 months) and floral type (inconspicuous). The richest and most abundant visitor groups, including bees, flies and wasps, acted mainly as potential pollinators, while cockroaches, butterflies, beetles, ants and hemipterans acted as thieves. However, further studies are required to confirm that this pattern is repeated in other larger and more diverse communities, thus confirming the possible preference for floral thieves.

Key words: vereda; bees; anthophilous fauna; flowering; generalist flower

Resumo

Tipos florais de plantas como potenciais indicadores da ocorrência de polinização vs. pilhagem. A visitação floral não é sinônimo de polinização, vários animais utilizam recursos florais sem transferir pólen. Essas visitas podem ser consideradas pilhagem. Polinizadores e pilhadores podem afetar a diversificação morfológica, taxonômica e reprodutiva das espécies de plantas, e nós investigamos se os atributos florais e de floração das espécies de plantas amostradas em vegetação de vereda nos dariam evidência de visitas de polinização (transporte de pólen) ou de pilhagem (roubo de néctar/pólen). Esperamos que as flores não especializadas tenham uma maior incidência de pilhagem, enquanto flores especializadas, por exemplo com tamanho grande e médio e corolas longas, com néctar menos acessível, tenderiam a ser polinizadas e menos pilhadas. Quatro atributos foram associados principalmente com visitas ilegítimas, tamanho da flor (pequeno), quantidade de flores por planta (muitas flores), tempo de floração (<10 meses) e tipo floral (inconspícuo). Os grupos mais ricos e mais abundantes de visitantes, incluindo abelhas, moscas e vespas, atuaram principalmente como potenciais polinizadores, enquanto baratas, borboletas, besouros, formigas e hemípteros atuaram como pilhadores. No entanto, mais estudos são necessários para confirmar se esse padrão se repete em outras comunidades maiores e mais diversificadas, confirmando assim a eventual preferência dos pilhadores.

Palavras-chave: vereda; abelhas; fauna antófila; floração; flor generalista

Introduction

Mutualistic relationships occur in all ecosystems and contribute to the generation and maintenance of diversity in different habitats or communities (Rech & Brito 2012). These interactions can be categorized as trophic, defensive or dispersive, as well as obligate or facultative (Ricklefs 2001; Thompson 2005). Interactions between flowers and the animals that visit them are an example of dispersive mutualism. In this case, animals obtain floral resources, e.g., nectar, resins, or oils, and, in return, transport and disperse pollen between flowers and plants, thus performing pollination (Rech & Brito 2012).

Pollination units, such as flower blossoms or inflorescences (sensu Faegri & van der Pijl 1979), are visited by a large variety of floral visitors (Faegri & van der Pijl 1979; Corlett 2004). Importantly, however, flower visitation does not necessarily mean pollination (Waser et al. 1996; Waser et al. 2015). Thus, in some cases, only floral visitors reap benefits since they are not all effective pollinators (Inouye 1980; Strauss & Whittall 2006; Irwin et al. 2010). In this sense, floral visitors can either act as pollinators or thieves, indicating that these mutualistic and antagonistic interactions are part of a continuum and that visitor species can have multiple behaviors within their respective repertories (Rech & Brito 2012). Within such continuum, flower morphology is fundamental because floral traits can restrict access to floral resources and thus prevent visits from the antagonists visits (Olesen et al. 2007; Vázquez et al. 2009).

Therefore, within a community, plant species compete for pollination services (Waser 1983), but also present barriers against would be thieves (Gonzálvez et al. 2013). In this sense, the floral or flowering traits of a given species derive from a set of distinct selective pressures when pollinators and thieves may share the same preferences. Althought we have clues on selection pressure exerted by thieves (Strauss & Whittall 2006), few studies have explored the relative selection pressure of this group or its floral preferences (e.g., Maruyama et al. 2015). Furthermore, while robbers may negatively influence plant reproduction by reducing floral attractiveness to pollinators, they may also have a positive influence by either increasing the number of flowers the pollinators must visit or by increasing travel distances to obtain their daily energy requirements (Irwin et al. 2010; Maruyama et al. 2015).

Since pollinators and thieves can affect the morphology and diversification of plants (Strauss & Whittall 2006), as well as reproduction, we herein investigated whether attributes of flowers and flowering of plant species collected in the central Brazilian vereda would be useful to predict pollinating or antagonistic visits. To accomplish this, we conducted a survey of floral visitors and verified their pollination performance on the sampled species. We considered some floral attributes as less specialized, e.g., small nectariferous flowers, with resources accessible to all visitors, assembled in collective or congested pollination units, and produced in large quantity. We hypothesized that such attributes would tend to be less restrictive and thus allow the indiscriminate visitation of several groups of visitors, or generalists, to the floral resources, thereby increasing the chance of antagonistic visits.

Material and Methods

Study area

This study was carried out from September 2012 to August 2013 in a vereda wetland of the Área de Proteção Ambiental (APA) Guariroba (20o32'39''S, 54o23'54''W), which supplies water to the urban area of Campo Grande, Mato Grosso do Sul, Brazil, and is located 35 km to the north (Dias 2005) (Fig. 1a). The study area consists of approximately 1.44 ha and is surrounded by pasture, cerrado stricto sensu, cerrado woodland and eucalyptus. Typical of vereda vegetation, the palm Mauritia flexuosa is the most abundant tree in the area (Fig. 1b).

Figure 1 a. Map of the Área de Proteção Ambiental (APA) Guariroba, Campo Grande, Mato Grosso do Sul, Brazil. b. front view of vegetation of the studied vereda wetland, showing the "buriti" palm Mauritia flexuosa (arrow), a typical vegetation type of this area, in the background. c. aerial view showing the approximate distribution of plots in the study area (source: Google Earth). 

The study area consists of three distinct stages: an outer part with an open grassland, an intermediate shrubland, and an innermost part with tree species. In the innermost part, the water table is lower, and running water is found, as well as humid soil (Fig. 1b).

The climate of the region is tropical Aw (Köppen 1948), with a warm and rainy season from October to March, a warm and dry period from April to September, and a transitional season in July. Annual rainfall is approximately 1400 mm, with mean temperatures ranging from 21 and 26oC (Vilas Boas et al. 2013).

Sampling

Fieldwork was conducted monthly in eight fixed plots of 50 m × 3 m (10 m apart), crossing the vereda in a westerly direction (Fig. 1c). Sampling of the diurnal floral visitors was conducted on all flowered plants between 07h30 and 17h30. Each flowering plant found in the transect was sampled for ten minutes; however, sampling time depended on species occurrence and abundance, resulting in an observation time for each individual ranging from 10 to 9900 minutes (565.66 ± 846.26) (Tab. 1).

Table 1 Plant species, habit, flowering time, floral traits, size of flowers, pollination unit, symmetry, color, resource, observation time and CGMS number recorded in a vereda vegetation, Campo Grande, Mato Grosso do Sul, Brazil, from September 2012 to August 2013. Habit: A = arboreal, S = shrub, H = herb, Sb = sub-shrub, C = climbing; Floral traits: D = dish; T = tube, G = gullet, In = inconspicuous, F = flag; Size of flower: S = small, B = big, M = medium; Pollination unit: F = flower, Inf = inflorescence; Symmetry: Ac = actinomorphic, Z = zigomorphous; Resource: N = nectar, P = pollen; CGMS = Herbarium CGMS. * = plant species with nocturnal anthesis. 

Species Habit Flowering time Floral traits Size of flower Pollination unit Symmetry Color Resource Observation time (minutes) CGMS number
Alismataceae
Echinodorus longipetalusMicheli H 1 D B F Ac white P 10 38665
Sagittaria rhombifoliaCham. H 7 D B F Ac white P 180 38648
Alstroemeriaceae
Alstroemeria longistylaSchenk H 1 T B F Ac white N 10 38658
Apiaceae
Eryngium ebracteatumLam. H 4 In Sm Inf Ac purple N 60 38659
Aquifoliaceae
Ilex affinisGardner A 10 D Sm F Ac white N 9900 38637
Araceae
Urospatha sagittifolia(Rudge) Schott H 1 In Sm Inf Ac brown P 20 38663
Asteraceae
Achyrocline alata(Kunth) DC. H 3 T M Inf Ac yellow P, N 60 38674
Chromolaena maximilianii(Schrad. exDC.) R.M. King & H. Rob. Sb 1 T Sm Inf Ac white P, N 20 38686
Clibadium armanii(Balb.) Sch. Bip. exO.E. Schulz Sb 2 T Sm Inf Ac white P, N 50 38670
Elephantopus palustrisGardner H 3 T M Inf Ac white P, N 90 38672
Erechtites hieraciifolius(L.) Raf. exDC. H 1 In Sm Inf Ac green P, N 10 38684
Raulinoreitzia crenulata(Spreng.) R.M. King & H. Rob. Sb 1 T Sm Inf Ac white P, N 10 38682
Lessingianthus bardanoides(LeSb.) H.Rob. S 4 T Sm Inf Ac purple P, N 80 38671
Mikania psilostachyaDC. C 1 T Sm Inf Ac white P, N 10 38679
Mikania stenophyllaW.C. Holmes C 1 T Sm Inf Ac white P, N 20 38680
Vernonanthurasp. S 1 T Sm Inf Ac white P, N 20 38642
Chloranthaceae
Hedyosmum brasilienseMart. A 1 In Sm Inf Ac green N 30 38645
Cyperaceae
Ascolepis brasiliensis(Kunth) Benth. ex C.B. Clarke H 5 In Sm Inf Ac white P 90 38667
Rhynchospora globosa(Kunth) Roem. & Schult. H 7 In Sm Inf Ac brown P 410 38651
Rhynchospora nervosa(Vahl) Boeckeler H 8 In Sm Inf Ac white P 390 38639
Rhynchospora robusta(Kunth) Boeckeler H 3 In M Inf Ac brown P 190 38673
Eriocaulaceae
Comanthera xeranthemoides(Bong.) L.R. Parra & A.M. Giuletti H 12 In Sm Inf Ac white N 980 38634
Eriocauloncf. magnumAbbiatti H 10 In Sm Inf Ac white N 1150 38635
Syngonanthus caulescens(Poir.) Ruhland H 7 In Sm Inf Ac white N 210 38654
Syngonanthus gracilis(Bong.) Ruhland H 6 In Sm Inf Ac white N 90 38657
Gentianaceae
Chelonanthus alatus(Aubl.) Pulle * S 8 F B F Z green N 330 38650
Gesneriaceae
Sinningia elatior(Kunth) Chautems H 1 G B F Z red N 10 38676
Iridaceae
Trimezia spathata(Klatt) Baker H 1 D B F Ac yellow O 10 38678
Lamiaceae
Hyptis brevipesPoit. Sb 1 F Sm F Z white N 10 38677
Malpighiaceae
Heteropterys coriaceaA. JuSb. S 2 D M F Z yellow O 90 38647
Heteropterys eglandulosaA. Juss. S 1 D M F Z yellow O 10 38660
Melastomataceae
Desmoscelis villosa(Aubl.) Naudin Sb 1 D B F Ac purple P 10 38675
Miconia albicans(Sw.) Steud. A 1 D M F Ac white P, N 10 38681
Miconia chamissoisNaudin S 6 D M F Ac white P, N 7200 38644
Tibouchina gracilis(Bonpl.) Cogn. H 3 D B F Ac purple P 40 38666
Tococa guianensisAubl. Sb 2 D M F Ac purple P 40 38649
Ochnaceae
Ouratea floribunda(A.St.-Hil.) Engl. S 1 D B F Ac yellow P 10 38640
Sauvagesia racemosaA. St.-Hil. Sb 9 D B F Ac pink P 540 38643
Onagraceae
Ludwigia nervosa(Poir.) H. Hara Sb 7 D G F Ac yellow P, N 5500 38638
Ludwigia octovalvis(Jacq.) P.H. Raven Sb 2 D G F Ac yellow P, N 40 38683
Rubiaceae
Coccocypselum lanceolatum(Ruiz & Pav.) Pers. Sb 4 T P F Ac blue N 80 38668
Diodella radula(Willd. & Hoffmanns. ex Roem. & Schult.) Delprete H 3 T P F Ac white N 230 38661
Emmeorhiza umbellata(Spreng.) K. Schum. C 4 T P F Ac white N 230 38641
Ferdinandusa speciosa(Pohl) Pohl A 4 T G F Ac red N 40 38662
Psychotria tenerior(Cham.) Müll. Arg.* S 2 T M F Ac white N 40 38669
Sipanea pratensisAubl. H 2 T M F Ac pink N 40 38664
Smilacaceae
Smilax fluminensisSteud. C 2 In M F Ac green N 40 38653
Solanaceae
Brunfelsia obovataBenth. S 3 T G F Ac purple N 80 38655
Cestrum axillareVell.* Sb 2 T M F Ac white N 20 38685
Styracaceae
Styrax camporumPohl A 2 D M F Ac white N 40 38652
Urticaceae
Cecropia pachystachyaTrécul A 1 In P Inf Ac green N 10 38646
Xyridaceae
Abolboda egleriL.B. Sm. & Downs H 3 T M F Ac blue P, N 100 38636
Xyris jupicaiRich. H 8 T M F Ac yellow P 1090 3865

We excluded Poaceae spp., which are generally anemophilous (sensu Faegri & van der Pijl 1979; Proctor et al. 1996). Most sampled species have herbaceous habit (47%), followed by sub-shrubs (19%), shrubs (16%), trees (12%) and climbers (7%) (sensu Guedes-Bruni et al. 2002). Vouchers of the sampled plant species were collected, dried, identified and deposited in the Herbarium CGMS of the Universidade Federal de Mato Grosso do Sul, Campo Grande (Tab. 1). Botanical identification was achieved after consulting a specialized bibliography, the herbarium CGMS, and specialists. Plant names followed APGIII (2009) and Mobot (2011).

For all species, the following attributes were verified: number of flowering months, number of flowers per individual, organization (solitary flowers or assembled in inflorescences) and dimension (large: >10 mm; medium: >5 mm and <10 mm; small: <5 mm) of the pollination units (visited flower or inflorescence), main color (yellow, blue, white, brown, pink, purple, green), symmetry (actinomorphic, zygomorphic). Following this analysis, the flowers were classified into floral types as described in Machado & Lopes (2004) (Tab. 1).

During the study period, we recorded only insects in pollination units. These insects were collected with entomological nets and kept in glass jars with ethyl acetate or alcohol 70%. Later, they were pinned or stored in alcohol, morphotyped and sent to specialists for identification. Classification of insect groups followed Corlett (2004). Performance of the floral visitor as a pollinator was determined through observations of intrafloral behavior, analysis of photographic records, floral morphology and/or the size relationship between flower and visitor. The visitor was considered a potential pollinator when it touched anthers to receive pollen and stigma to deposit it. The visitor was considered a thief of pollen or nectar (sensu Inouye 1980; adapted as described in Irwin et al. 2010) if it did not show such behavior while collecting floral resource. Specimens of floral visitors were deposited in the Zoological Collection of the Universidade Federal de Mato Grosso do Sul (ZUFMS), Museu de Zoologia of Universidade de São Paulo (MZUSP), Museu de História Natural/Zoologia of Universidade Federal da Bahia (MHNBA/MZUFBA) and private collections of some taxonomists (e.g., Ayr de Moura Bello).

Data Analysis

Classification trees are suited for the analysis of complex ecological data, which, in our case, involved a set of continuous and categorical predictors. This method can also treat nonlinear relationships and high-order interactions, which inflate the number of parameters needed for regression models commonly used (De'Ath Fabricius 2000). Therefore, we utilized this method of recursive partitioning to generate a classification decision tree, as suggested by Breiman et al. (1984). This method aims to ordinate, in a more parsimonious way, the behaviors of pollination and theft based on 10 attributes and 401 samples. The utilized attributes were (1) number of flowers per plant, (2) flower color, (3) floral symmetry, (4) floral type, (5) anthesis, (6) pollen as resource, (7) nectar as resource, (8) flowering time, (9) size of flower and (10) type of inflorescence (Tab. 2).

Table 2 Floral attributes, classification of variable type and characteristics used in the construction of the decision tree with the plant species of the vereda in Campo Grande, Mato Grosso do Sul, Brazil, from September 2012 to August 2013. 

Attributes Variable type Characteristics and number of samples observed Total of each partition to the decision tree
(1) Number of flowers per plant Numerical Minimum 1; Maximum 1900; Median 32; Average 156 38,43
(2) Flower color Categorical Yellow - 99; Blue - 1; White - 263; Brown - 21; Pink - 3; Purple - 4; Green - 10 10,71
(3) Floral symmetry Categorical Actinomorphic - 397; Zygomorphic - 4 0
(4) Floral type Categorical Inconspicuous - 98; Dish - 258; Tube - 42; Gullet - 1; Flag - 3 15,81
(5) Anthesis Categorical Diurnal - 396; Nocturnal - 5 0
(6) Resource pollen Categorical Absent - 204; Present - 197 8,14
(7) Resource nectar Categorical Absent - 95; Present - 306 1,86
(8) Flowering time (number of months) Numerical Minimum 1; Maximum 11; Median 8 20,96
(9) Flower size Categorical Big - 120; Medium - 66; Small - 215 6,54
(10) Inflorescence type Categorical Collective = 24; No collective = 29 2,96

We also ran a 10 k-fold cross-validation to evaluate the fitness of the model compared with other possible models and determine if the model improved our predictability of the behaviors of pollination and theft. We computed the accuracy and cross-tabulated the observed and predicted classes, calculating their probabilities for the model we present here. To calculate the importance of each attribute for the decision tree, the reduction in the mean square error contributed by each attribute to each partition was calculated, and the sum is presented in Table 2. The attributes which were candidates for partition, but not utilized, were also considered for each partition, as proposed by Quinlan (1992). All analyses were performed utilizing R language (R Core Team 2015) with the rpart package (Therneau et al. 2015) to generate the classification tree and caret packages (Kuhn 2015) for cross-validation and importance attribute calculations.

Results

In the 53 sampled plant species, flowering time varied from one to eleven months, with most plants typically flowering between one and four months (73.1%). The number of pollination units per plant varied from 1 to 1900, most plants having 1-10 flowers (60%) or 200-500 (38%) (Tab. 1). Solitary flowers predominated (54%) over those assembled in inflorescences (45%). With respect to size, small flowers (43.3%) were the most representative, followed by large (32%) and medium flowers (24.5%). Most species displayed white (45.3%), yellow (15.1%) or lilac/violet/purple pollination units (13.2%); the others were brown, pink, red, blue or green (26.4%). Five floral types were recorded: tube (37.7%), dish (30.1%), flag (3.77%), inconspicuous (26.4%) and gullet (1.88%). Flowers with nectar (65%) were more frequent than those with only pollen (29.3%) or oil (5.7%). Actinomorphic flowers (96.2%) with diurnal anthesis (90%) predominated. Visits to individual flowers (54.7%) were slightly higher than visits to inflorescence (45.2%) (Tab. 1).

The pollination units of thirty plant species were visited by 138 species of insects, herein categorized into ten groups in decreasing order of richness: flies, bees, wasps, crickets, beetles, ants, hemipterans, butterflies, spiders and cockroaches. Bees, flies and wasps were the most abundant groups and visited the largest number of plant species, together with ants (Tab. 3). Most studied groups behaves as thieves, including a high percentage of butterflies, all species of ants, hemipterans and cockroaches. In this study, all floral thieves were observed collecting nectar or pollen and exhibited one morphological mismatch between flower and the thieves body, thus preventing pollination from taking place. Spiders probably predated other floral visitors, and crickets ate floral parts, including stamens (Tab. 3); therefore they are not proper flower visitors and so they were not included in the analyzes (Tab. 3). Bees, flies and wasps acted as potential pollinators.

Table 3 Group, richness, abundance and behavior of floral visitors sampled in plant species of vereda vegetation, in Campo Grande, Mato Grosso do Sul, Brazil, from September 2012 to August 2013. The last two columns are the percentage of the species in the group that perform with thieves or pollinators of the plant species visited. 

Group Richness (n) Abundance (n) Number of plant species visited (n) Pollination (%) Thief (%)
Bees 37 131 19 100 0
Spidersa 3 3 3 - -
Cockroaches 1 1 1 0 100
Beetles 6 17 7 58,8 41,2
Butterflies 4 20 9 35 65
Ants 6 60 13 0 100
Cricketsb 6 8 4 - -
Hemipterans 5 13 3 0 100
Flies 46 77 12 93,5 6,5
Wasps 20 75 12 100 0

apredation;

bfeeding on floral parts

Based on our hypothesis, which holds that decreasing specialization correlates with theft, while increasing specialization correlates with pollination, species with small, inconspicuous flowers, plants with a high number of flowers (>500) and flowering time shorter than 10 months would have a greater chance of illegitimate visits to the pollination units. In contrast, large or medium-sized flowers with tube or dish floral type, long flowering period (>10 months) and plants with a variable number of flowers (but < 500) would stand a greater chance of becoming pollinated (Tab. 2; Fig. 2). The classification tree had an accuracy of 82%, and the cross-validation results are as follows: 10% for true theft, 72.1% true pollination, 4.2% false theft and 13.7% for false pollination.

Figure 2 Decision tree with the floral attributes and the probability of pollination or theft in a vereda community, Campo Grande, Mato Grosso do Sul, Brazil. Percentage indicates the probability of occurrence of theft or pollination. NF = number of flowers; NMF = number of months in flowering. 

Discussion

Composition

Data obtained from floral and flowering characteristics in the studied vereda community are in accordance with results observed in other savanna communities (Freitas & Sazima 2006) and other physiognomies of the cerrado biome, i.e, predominance of diurnal, nectariferous, actinomorphic and light-colored flowers (e.g., Silberbauer-Gottsberger & Gottsberger 1988; Barbosa 1997; Oliveira & Gibbs 2000).

Considering anthophilous fauna, the groups of insects recorded in the community were similar to those found in other vegetation types of cerrado (Aoki & Sigrist 2006), grasslands (Freitas & Sazima 2006; Pinheiro et al. 2008) or anthropic habitats, such as eucalyptus plantations (Lopes et al. 2007). In our study, the richest and most abundant groups, including bees, flies and wasps, were mainly potential pollinators of the sampled plant species. Bees are known as the main and most efficient pollinators in tropical vegetation types (Oliveira & Gibbs 2000; Freitas & Sazima 2006; Silva et al. 2012). Nectar and pollen constitute the main source of carbohydrates and proteins for bees, respectively, for nourishment of brood and adults (Faegri & van der Pijl 1979; Barbola et al. 2000). Flies are one of the most important groups of floral visitors (Kevan & Baker 1999; Larson et al. 2001), as adults can consume large amounts of pollen and nectar (Larson et al. 2001; Morales & Köhler 2008), and some groups of diptera are highly specialized flower visitors and important pollinators of several plant species (e.g., Endara et al. 2010; Kearnes 2001). Furthermore, appropriate behavior and morphological adjustment on these groups in the flower visitation, causes them to be important pollinators of plant species.

In contrast, it was surprising that a high percentage of theft was recorded for butterflies, particularly because this group depends on floral nectar and has historically been considered as pollinators (Faegri & van der Pijl 1979; Proctor et al. 1996). The other studied insects, are commonly sampled on flowers and are generally not considered as "habitual" pollinators, except beetles (Proctor et al. 1996). Hence ants were the main thieves recorded in our study, tending to visit flowers with exposed nectar (Herrera et al. 1984; Ballantyne & Willmer 2012).

Floral traits and thieves

In this study, nonspecialized flowers were those having a higher frequency of thieves. The traits more related to thieving were: plants with small and inconspicuous flowers, high number of flowers, and flowering time under 10 months. These results confirm our hypothesis that nonspecialized flowers have a higher incidence of thievery, whereas specialization, for example, the presence of large and medium flowers with long corolla, making it difficult to access nectar, leads to increased pollination (Stang et al. 2006). Thus, theft correlates with the incompatible morphology between plant and animal visitor that gains access to the resource without offering pollen transfer (Irwin et al. 2001). In small and inconspicuous pollination units, including flowers and inflorescences, the floral resource is generally more accessible to the visiting fauna, in particular those with short mouthparts, such as ants, beetles, cockroaches and hemipterans, all groups with a high percentage of theft in this study.

The number of flowers per plant and flowering time were more important features than flower shape as indicators of frequent illegitimate visits. For example, while a larger number of flowers could help to increase floral display, acting to attract a wider range of visitors, legitimate or illegitimate, such displays could also indirectly represent higher amounts of available resources, a situation which seems to increase the chances of theft in this vereda community. In contrast, a smaller quantity of flowers may be more efficient in reducing geitonogamous pollination and promote cross pollination, in addition to reducing stigma obstruction with unsuitable pollen in self-incompatible species (Wyatt 1982).

Flowering time can promote the temporal variation of the resources, and in this study, most species flowered for up to four months (> 70%), although some flowered for up to 11 months, which is favorable for maintenance of floral visitors. In an assessment of impact of floral theft at different levels, Irwin et al. (2001) reported that theft could be related to flowering season of the plant species. Irwin & Maloof (2002) suggest that the direction and magnitude of theft could depend on the relative temporal and spatial abundance of thieves and pollinators and their synchrony with the flowering period of the visited species.

Thus, the net effects of cheating for plant reproduction can be negative, positive, or neutral. Thieves (morphological uncoupling) may have more subtle indirect effects on plant fitness, for example by altering the interaction between plants and pollinators. The result may be to decrease plant fitness if larceny reduces floral rewards sufficiently for pollinators to avoid a plant or to desert it after a brief visit (Inouye 1983; Wootton 1993; Maloof and Inouye 2000; Irwin et al. 2001).

According to Irwin et al. (2001), the evolution of floral traits surely must be understood, in part, with reference to pollinators. However for the plant, the selection environment includes larcenists, and other plant enemies, as well as mutualists. The net direction and magnitude of selection will likely depend on the relative abundances in space and time of robbers and pollinators.

In conclusion, other researches including color measurement, functional characteristics of nectar, quantitative measurement of odor and the corolla tubes are encouraged to better understand the floral traits that can predict the cheating in pollination. Also, efficiency measures for each visitor should be performed in the future. Furthermore, the pollinators in vereda community and other formations are part of a bigger network of interactions where plants, pollinators and larcenists are embedded. Plants with robbed flowers grow sympatrically with other plant species that share larcenists and/or pollinators (Irwin et al. 2001). Thus, future studies investigating the role of these thieves in community interactions network should be encouraged in order to better understand the pressures and effects of thieves in the vereda community.

Acknowledgements

Programa de Pós-graduação em Biologia Vegetal for logistical support, CAPES, for financial support, anonymous reviewers for their valuable suggestions. Eric Okiyama Hattori, Geraldo Alves Damasceno-Junior, Nara Mota Furtado, Suzana Neves Moreira and Vali Joana Pott, for identification of plant species. Favízia Freitas de Oliveira (bees), Ramon Mello, Daniel Maximo C. de Alcantara, Carlos José Einicker Lamas and Mirian Nunes Morales (flies), Rodrigo Aranda (wasps), Ayr de Moura Bello (beetles, hemiptera), Andressa Figueiredo (hemiptera), Renan da Silva Olivier (crickets), Danilo Ribeiro (butterflies), Paulo Robson de Souza (ants), for identification of floral visitors. Flávia Maria Leme, Tamires Soares Yule, João Roberto Fabri, Tiago G. de Freitas, Thiago Henrique Stefanello, Aline Parreira da Costa, Damião Teixeira de Azevedo, Danielle Boin Borges, Evaldo Benedito de Souza, Fabio Kochanovscki Junior, Franciélle Oliveira, Jacqueline A. Rotta, Jéssica Placência, Milton Omar Cordova Neyra, Muryel Furtado de Barros, Rafaela Thaller and Vivian Almeida Assunção, for help with fieldwork. Hannah Doerrier, for English revision.

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Received: June 15, 2015; Accepted: April 16, 2016

3Author for correspondence: souza.camila.bio@gmail.com

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