Changes in interaction network topology and species composition of ...

Received: 17 October 2017

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Revised: 5 March 2018

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Accepted: 30 April 2018

DOI: 10.1111/aje.12527

ORIGINAL ARTICLE

Changes in interaction network topology and species composition of flower-visiting insects across three land use types Opeyemi Adedoja

| Temitope Kehinde

Department of Zoology, Obafemi Awolowo University, Ile-Ife, Nigeria Correspondence Adedoja Opeyemi, Department of Zoology, Obafemi Awolowo University, Ile-Ife, Nigeria. Email: [email protected]

Abstract Land use change is a leading factor in the cause of pollinator decline globally. The response of flower-visiting insects to habitat transformation and disturbance has been well studied. However, the effect of these anthropogenic disturbances on the structure of insect–flower interaction networks is not well known. We examined how insect–flower interaction network topology changes across land use types with varying levels of disturbance. Three replicates of agricultural lands, grasslands and secondary forest habitats were sampled on a monthly basis for 12 months. The highest mean abundance of flower-visiting insects and flowering plants was observed in the secondary forest and the lowest record was found on agricultural lands. Furthermore, networks in secondary forests had a higher mean number of interactions, network size and nestedness compared to those observed in agricultural fields. Flower abundance was positively correlated with the number of interactions and network specialization. The significantly higher value of the qualitative and quantitative network indices in the secondary forests underscores the ecological importance of this land use type for conservation of insect–flower interactions in highly fragmented landscapes in the tropics such as the one studied here. Conservation efforts should concentrate on increasing the abundance of floral resources as changes in important network indices were mediated by flower abundance across the land use types.


sume
Re
nagement du territoire est un facteur majeur du de
clin des Le changement d’ame
ponse des insectes qui visitent les fleurs aux pollinisateurs de par le monde. La re
tudie
e. Mais l’effet des transformations et aux perturbations de l’habitat est bien e
seaux d’interactions insectesperturbations d’origine humaine sur la structure des re
tudie
comment la topologie du re
seau d’interacfleurs est mal connu. Nous avons e
tions insectes-fleurs change en fonction du type d’utilisation des sols, selon le degre
pliques de terres agricoles, de prairies et de fore ^ts secondaide perturbation. Trois re
te
e
chantillonne
es de facßon mensuelle pendant 12 mois. La plus grande res ont e
te
abondance moyenne d’insectes visitant les fleurs et de plantes en fleurs a e
e dans la fore ^t secondaire et la plus faible dans les terres agricoles. De plus, observe
seaux dans les fore ^ts secondaires comptaient un plus grand nombre moyen les re
seau et une meilleure imbrication que ceux d’interactions, une plus grande taille de re

Afr J Ecol. 2018;1–8.

wileyonlinelibrary.com/journal/aje

© 2018 John Wiley & Sons Ltd

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s dans les terres agricoles. L’abondance de fleurs e
tait positivement lie
e au observe
cialisation du re
seau. La valeur significativement nombre d’interactions et a la spe
seaux qualitatifs et quantitatifs dans les fore ^ts seconplus grande des indices de re
cologique de ce type d’utilisation du territoire pour la daires souligne l’importance e
servation des interactions insectes-fleurs dans des paysages fortement segmente
s pre
tudie
ici. Les efforts de conservation devraient des tropiques, comme celui qui est e se concentrer sur l’augmentation de l’abondance de ressources florales, alors que les
seau e
taient arbitre
s par l’abondance de changements des indices importants de re fleurs selon le type d’utilisation des sols. KEYWORDS

flower abundance, habitat disturbance, network nestedness, pollinators, secondary forest

1 | INTRODUCTION

Gruber, 2009). Asymmetry in food webs indicates that for example, the effect of a pollinator on a plant can vary from the effect of the

Habitat modification and alteration through human land use change

same plant on the pollinator species in terms of number of links

are lead causes of biodiversity loss and decline in ecosystem services

observed per species (Bascompte, Jordano, Meli
an, & Olesen, 2003;

(Aguilar, Ashworth, Galetto, & Aizen, 2006; Potts et al., 2010). While

Guimar~aes, Rico-Gray, Dos Reis, & Thompson, 2006) as well as the

some studies have reported decline in diversity, richness and abun-

strength of the interaction (Bascompte, Jordano, & Olesen, 2006).

dance of flower-visiting insects in disturbed habitats (Cairns, Vil-

The distribution of the interactions between a specialist and a


rrez, Koptur, & Bray, 2005; Kremen, Williams, & Thorp, lanueva-Gutie

generalist in a network can be explained by the nestedness of the

2002; Potts et al., 2010), others did not (Hagen & Kraemer, 2010;

network (Ings et al., 2009). Nestedness is an important metric within

Winfree, Griswold, & Kremen, 2007). It is, however, certain that land

a network that is used to describe the robustness of a network to

use change does alter the composition of flower-visiting insects and

loss of species (Fortuna & Bascompte, 2006). The value ranges from

€thgen, 2011). flowering plants (Weiner, Werner, Linsenmair, & Blu

1 to 100, and more nested networks are more resilient to the effect

Furthermore, the full extent of these effects on the functionality of

of changes in environmental factors. These and other network

the ecosystem may not be clearly shown by observing the abun-

indices illustrate the configuration of networks (Okuyama & Holland,

dance, species richness and diversity of flower-visiting insects and

2008) and can help explain how these networks respond to land use

flowering plants alone. However, the use of interaction networks in

change. This study assessed the response of insect–flower interac-

biodiversity assessment is attracting more attention because it pro-

tions to land use change across forest and nonforest land use types

vides more ecological details on the response of biodiversity to vari-

around the landscape of Obafemi Awolowo University (OAU) in

ous anthropogenic stressors such as land use change (Kehinde &

Southwest Nigeria where loss of indigenous forest is a threat to the

Samways, 2014; Macfadyen, Gibson, Polaszek, et al., 2009; Power &

diversity of native and endemic insects (Kehinde, Amusan, Ayansola,

Stout, 2011).

Oyelade, & Adu, 2014).

The relationship between plants and flower visitors are established by the interaction of co-occurring species in same space and time. According to Bascompte and Jordano (2007), the distribution of species interaction in networks is crucial to the understanding of the community structure. Understanding the architecture of complex

2 | MATERIALS AND METHODS 2.1 | Study area

networks of species interactions such as insect–flower networks is

The study was carried within the heterogeneous landscape of OAU,

crucial to our ability to interpret the responses of communities to

Ile-Ife, Nigeria, located between latitudes 07°260 N and 07°320 N and

extinction events, habitat loss and other aspects of global change

longitudes 004°310 E and 004°350 E (Figure 1). OAU campus has a

(Fortuna et al., 2010; Memmott, Waser, & Price, 2004; Tylianakis,

total land area of approximately 5,605 hectares and lies within the

Didham, Bascompte, & Wardle, 2008).

tropical rainforest region of Nigeria. This region is within the West

The structure and topology of insect–flower interaction networks

African Rainforest biodiversity hotspot (Myers, Mittermeier, Mitter-

are defined by the network indices and this can explain the relation-

meier, Da Fonseca, & Kent, 2000). The weather of the region is

ships across different land management systems (Macfadyen, Gibson,

characterized by wet and dry seasons which last from February to

Raso, et al., 2009). The difference in the strength of relationship

September and October to January, respectively. The study area is

between species within these interactions can be quantified by Inter-

constantly influenced by human activities such as urbanization and

€nd, Blu €thgen, & action Strength Asymmetry (ISA) (Dormann, Fru

agriculture resulting in landscape fragmentation. The fragmented

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landscape has resulted in three dominant land use types in terms of

mechanical tillage and removal of noncrop plants. The third land use

the vegetation cover along a gradient of deforestation. This includes

type is secondary forest habitats which are characterized by high

grasslands within the core urban areas, agricultural land and patches

canopy and dense vegetation. The common trees found in this land

of secondary forests.

use type include Azadirachta indica A. Juss., Alstonia boonei De Wild., Hildegardia barteri (Mast.) Kosterm, Leucaena leucocephala (Lam) de

2.2 | Study sites

Wit. The dense forest understorey has patches of flowering plants most of which are similar to those found on the grassland habitats.

Study sites were selected based on the three dominant land use

Sampling in the secondary forest was carried out in the understorey

types identified. Three replicates of each land use type were sam-

vegetation and along forest edges.

pled. The grassland vegetation cover was made up of common flowering plant species such as Sida acuta L., Ageratum conyzoides L., Chromolaena odorata L., Tridax procumbens L. and Aspilia Africana Pers. The grassland habitats in the study area are constantly modi-

2.3 | Sampling of flowering plants and flowervisiting insects

fied by periodic mowing activities. The second land use type

Sampling was carried out on a monthly basis for 12 months between

selected is agricultural lands which are managed by local peasant

May 2014 and April 2015. This is to account for temporal and spa-

farmers. Common crops planted on these lands include cassava

tial variation during the tropical flowering season which is usually

(Manihot esculentus) and maize (Zea mays). Some of the grasses

long (Kaiser-Bunbury et al., 2017). Sampling was conducted on days

located on grasslands were also found within and around the agricul-

with favourable weather condition, that is days without rainfall and

tural lands. Management activities on the farms include application

with little or no cloud cover; however, sampling was conducted on

of agrochemicals such as pesticides and fertilizers as well as

each study site once every month. Insect–flower interactions were

F I G U R E 1 Study sites and land use types on Obafemi Awolowo University (OAU) Campus. Sport = Grassland site1, GR2 = Dam, Quarters = Grassland site3, Chemical Eng = Agricultural land 1, Mozambique = Agricultural land 2, Oxidation pond = Agricultural land 3, Computer ICT = Secondary forest 1, Bank area = Secondary forest 2, Market = Secondary forest 3

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observed and recorded along transects on the study sites within the

were further compared among land use types using a Generalized

hours of 09.00 h–14.00 h. Two 100 m 9 5 m transects were sam-

Linear Model (GLM, Poisson error distribution). A linear regression

pled on each study site using methodology in Roulston, Smith, and

analysis was carried out to determine the effect of plant abundance

Brewster (2007). Insects visiting the floral part of flowering plants

on the network indices. All analyses were conducted using R (version

along transects were observed and collected for later identification.

3.3.3, R development core team 2016).

An interaction was deemed to have occured when the insect touched the reproductive part of the flowering plant. Vegetation sampling was carried out on the same transects where insect–flower

3 | RESULTS

interactions were sampled. Flowering plants were sampled in four replicates of 2 m 9 2 m quadrats along transects on all sampling

Nine interaction network plots were computed for the sites sampled

sites.

comprising a total of 717 interactions and 201 links. Seven flowering plant species were observed to be in interaction with 30 insect spe-

2.4 | Network analyses Data obtained from insect–flower interactions were used to con-

cies (Table 1). Flower abundance differed significantly across land use types. The highest mean abundance of flowers was recorded on the secondary forest while the least mean abundance was found on

struct illustrative interaction network plots. These plots show the

the agricultural lands (z = 173.42, p < 0.001, Figure 2a). However,

number of interactions between plant species and the interacting

no significant difference was observed in the species richness of

insect species. The descriptive properties of these plots include the

flowering plants across land use types. Furthermore, flower-visiting

number of plant species (P), number of insect species (J), total num-

insects differed significantly in mean abundance and species richness

ber of interactions observed (I), network size (Z) which is the product

among the three land use types. Secondary forest had the highest

of the plant and insects species in the network (P * J), connectance

mean abundance (z = 92.80, p < 0.001, Figure 2b) and species rich-

value (C) is the record of observed links divided by the number of

ness (z = 51.48, p < 0.001, Figure 2c) of flower-visiting insects while

potential links between all plant and insects in the network (I/Z).

the least mean abundance and species richness was recorded on the

Network-level specialization and generalization indices were also

agricultural lands. Flower abundance significantly influenced the

computed. Specialization or generalization can be deduced by the

number of interactions (R = 0.674, p < 0.05, Figure 3a) and special-

number of links formed between interacting partners in a food web.

ization (R = 0.660, p < 0.05, Figure 3b) of the networks. However,

More generalized networks account for higher niche complementar-

there was no significant effect of the flower abundance on other

ity of flower-visiting insects which increases the possibility of having

quantitative network indices.

€thgen, Menzel, & Blu € thgen, 2006; the most effective pollination (Blu

There was a significant difference in the mean number of

Tur, Castro-Urgal, & Traveset, 2013). Network specialization index

observed interactions across land use types. The mean number of

(H2`) is obtained from Shannon entropy and it describes the pattern

interactions was highest in secondary forest but lowest on agricul-

of deviation of observed visitation from the expectations of random

tural lands (z = 45.72, p < 0.001, Figure 4a). Similarly, there was a

€thgen, 2014). ISA as interactions (Benadi, Hovestadt, Poethke, & Blu

significant difference in mean network size across land use types

defined by V
azquez et al. (2007) is the mismatches in the effect of

with the largest network in the secondary forest and the smallest in

species on its interacting partner and also the reciprocal effect of

agricultural lands (z = 49.83, p < 0.001, Figure 4b). Furthermore, the

the interacting partner on the focal species.

nestedness of the interaction network differed significantly across

The nestedness indices which shows how much the species that

land use types with the highest mean nestedness on secondary for-

are in interaction with specialist species are subsets of other species

est and the lowest on agricultural lands (z = 16.07, p < 0.001, Fig-


, interacting with generalists was also computed (Tylianakis, Laliberte

ure 4c). However, no significant difference was observed in the

Nielsen, & Bascompte, 2010). NODF metric for nestedness has been

connectance, ISA, generalization and specialization of networks

proposed as a more stable and more consistent metric owing to its

across land use types.

robustness to change especially in matrix configuration of data (Almeida-Neto, Guimaraes, Guimaraes, Loyla, & Ulrich, 2008). NODF accounts for the consequences of overlap in the interaction matrix

4 | DISCUSSION

and can thus reduce the chances of obtaining type I error (Joppa, Montoya, Vicente, Sanderson, & Pimm, 2010). Unlike the conven-

In contrast to studies that have shown differences in the species

tional nestedness, NODF does not indicate nestedness between

richness of flowering plants across land use types (Diniz, Prado, &

matrixes where there is no paired nested structure between columns

Lewinsohn, 2010) and how this affects the composition of flower-

and rows, and also it shows how columns and rows contribute to

visiting insects (Holzschuh, Steffan-Dewenter, Kleijn, & Tscharntke,

the nestedness of the matrix by calculating the nestedness for col-

2007; Kehinde & Samways, 2012), this study shows no significant

umns and rows independently (Almeida-Neto et al., 2008). The net-

difference in the species richness of flowering plants across land

work analyses and plots were computed using the bipartite package

use types. However, species richness and abundance of flower-vis-

in R (version 3.3.3, R development core team 2016). Network indices

iting insects differ across land use types. Differences in the

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T A B L E 1 List of plants and insects sampled during the study

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Rafferty, 2013; Stanley & Raine, 2016). The lower abundance and species richness of flower-visiting insects in the agricultural land

Plant species

Insect species

Chromoleana odorata

Musca domestica

Aspilia africana

Apis mellifera

Ageratum conyzoides

Acraea sp1

Sida acuta

Chrysomya chloropyga

Memosa pudica

Papilio demodocus

Tridax procumbens

Xylocopa aolivacea

Emilia praetermissa

Pseudapis(Pachynomia) sp1

Seymour, Veldtman, & Nicolson, 2010; Patr
ıcio-Roberto & Campos,

Syrphidae sp1

2014).

compared to other land use types may be attributable to the application of pesticides which is a characteristic practice on farm lands owned by local farmers in the study area. In addition, continuous weeding on the farmland will reduce the ability of the habitat to meet the floral requirements of the flower-visiting insects and thus a decline in their population. Secondary forest, on the other hand, can serve as a good refuge to flower-visiting insects (Carvalheiro,

Acraea eponia

The number of interactions has been described as one of the

Sphecidae sp1

major factors that define the complexity of mutualistic networks

Eumenidae sp3

(Okuyama & Holland, 2008). The variation in the distribution of

Meliponula bocandei

flower-visiting insects across land use types may also bring about

Acraea sp2

variation in the number of interactions between flower visitors

Syrphidae sp2

and flowering plants across land use types. Flower abundance sig-

Belonogaster sp

nificantly affected the number of interactions in the insect–flower networks in this study. This was mostly observable in the sec-

Syrphidae sp4

ondary forest and grasslands with higher mean number of interac-

Sphecidae sp5

tions and network size compared to the agricultural lands. Flower-

Mylabris sp1

visiting insects inhabit natural areas as well as less disturbed man-

Syrphidae sp3

aged areas. Constant removal of natural areas through land man-

Acraea eponia

agement may lead to a potential decline in the population and

Aspidimorpha dissentanea

diversity of these insects through nest disruption and removal of

Blephariceridae sp1

the source of floral requirements. This, in turn, may indirectly

Polistes sp2

influence the availability of interacting partners in intensively man-

Mutillidae sp

aged land use types as flower-visiting insects are known to track

Chrysomelidae sp1

flower resources as well as natural areas (Winfree, Aguilar,

Danaus chrysippus

V
azquez, LeBuhn, & Aizen, 2009) . This may explain the positive

Halictus sp

effect of flower abundance on frequency of interactions in this

Lassioglossum sp2

study as the highest interaction and network size were observed

Nepheronia sp

in the less disturbed secondary forest as contrasted with the intensive management activities in the agricultural lands through soil tillage and agrochemical use. Similar results showing a higher number of interactions in less disturbed natural or seminatural

intensity of anthropogenic disturbances could result in variation in

habitats compared to habitats with higher degree of anthropogenic

habitat specific factors such as microclimatic conditions and effect

disturbance have been reported (Kaiser-Bunbury et al., 2017;

of agrochemicals which may cause variation in the diversity of

Kehinde & Samways, 2014). However, Hagen and Kraemer (2010)

flower-visiting insects across different land use types (Scaven &

reported larger networks and higher number of interactions in

(a)

b

(b)

b

(c)

b

a ab

ab

a a

FIGURE 2

ab

Difference in Flower abundance (a), Insect abundance (b) and Insect species richness (c) among land use types

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F I G U R E 3 Effect of flower abundance on the number of interactions (Interactions = 27.38 + 0.14Plant abundance) and specialization (Specialization = 0.12 + 0.0002Plant abundance) of the interaction network

(a)

b

(b)

b a

abb

(c)

a ab

b

ab a

FIGURE 4

Difference in network number of interactions (a), network size (b) and nestedness (c) among land use types

farmland compared to forest understorey habitats. The secondary

The network structure showed a positive relationship with flower

forests in this study are characterized by high undergrowth and

abundance, however, flower richness had no effect. This may show

porous canopy covers which allow sunlight penetration (personal

the presence of more generalized plant species in the network.

observation). This could aid the presence of noncrop flowering

Although the specialization of the interaction network did not differ

plants as well as good nesting sites and suitable microhabitat that

significantly across land use types, this was positively influenced by

promote the foraging activities of flower-visiting insects thereby

flower abundance. Flower abundance may be of more importance in

increasing the frequency of interactions (Nicholls & Altieri, 2013).

predicting insect–flower interaction network structure and properties

Agricultural lands in the study area are known to be highly

(Winfree et al., 2009). This implies that with more flowers on the

disturbed as a result of intensive farming practices and manage-

habitats, the mean number of insects found visiting a plant may

ment involving the use of synthetic pesticides and mechanical til-

increase.

lage that may possibly reduce floral quality and quantity of

This relationship was also reflected in the difference observed

resources. The negative effect of pesticides and other harmful

in nestedness of networks among land use type. Highest nested-

synthetic compounds on flower-visiting insects have been reported

ness value observed in secondary forest showed that specialized

(Brittain, Vighi, Bommarco, Settele, & Potts, 2010; Stanley &

species interact with subsets of species interacting with generalists

Raine, 2016; Stanley, Russell, Morrison, Rogers, & Raine, 2016).

in the network (Saavedra, Stouffer, Uzzi, & Bascompte, 2011, Bas-

Noncrop flowering plants provide floral rewards to flower-visiting

compte et al., 2003) . This may also be linked to the availability

insects in terms of nectar and pollen resources and attraction to

of more flower resources per insect species which is essential for

the visual cues of insects in the natural areas and patches of sem-

specialist and rare species in the network (Tiedeken & Stout,

inatural vegetation around farmlands (Nicholls & Altieri, 2013).

2015). This also implies that the interaction networks in secondary

According to Kleijn and Van Langevelde (2006), decline in the

forests are more robust to change and may be less susceptible to

patches of natural vegetation reduces conservation benefits for

anthropogenic disturbance and displacement of flower-visiting

flower-visiting insects.

insects (Bascompte & Jordano, 2006; Okuyama & Holland, 2008).

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Similarly, Piazzon, Larrinaga, and Santamaria (2011) also reported that habitats with large network size and nestedness are more robust to disturbances and this was directly influenced by the abundance of the interacting species. Flower abundance was highest in secondary forest and this corresponds to the highest abundance of flower-visiting insect in the same land use type. In tropical ecosystems such as the area in this study, secondary forest patches as shown here are crucial for promoting the diversity of flower-visiting insects, flowering plants and their interactions. These interactions are crucial for promoting delivery of pollination ecosystem service in tropical ecosystems (KaiserBunbury et al., 2017). Conservation planning should also involve paying attention to the intensity of management in grasslands and small-scale agricultural fields which are often dominant land use types, especially in urban and suburban areas. Also, local farmers should be enlightened on the importance of noncrop flowering plants on their farmland as this will help conserve communities of pollinators in areas with moderate disturbance.

ACKNOWLEDGEMENT We thank Monica Frisch and Kevin Wallace for proof-reading the manuscript and for English language review. We also thank Zachariah Orofin for the design of the map of the study area.

ORCID Opeyemi Adedoja

http://orcid.org/0000-0001-6163-3424

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How to cite this article: Adedoja O, Kehinde T. Changes in interaction network topology and species composition of flower-visiting insects across three land use types. Afr J Ecol. 2018;00:1–8. https://doi.org/10.1111/aje.12527