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Current Biology 17, 341–346, February 20, 2007 ª2007 Elsevier Ltd All rights reserved
DOI 10.1016/j.cub.2006.12.039
Report
Specialization, Constraints, and
Conflicting Interests
in Mutualistic Networks
Nico Blüthgen,1,* Florian Menzel,1
Thomas Hovestadt,1,2 Brigitte Fiala,1
and Nils Blüthgen3,4
1
Department of Animal Ecology and Tropical Biology
University of Würzburg
Würzburg, 97074
Germany
2
Field Station Fabrikschleichach
University of Würzburg
Rauhenebrach, 96181
Germany
3
Institute of Molecular Neurobiology
Free University of Berlin
Berlin, 14195
Germany
Summary
The topology of ecological interaction webs holds
important information for theories of coevolution, biodiversity, and ecosystem stability [1–6]. However,
most previous network analyses solely counted the
number of links and ignored variation in link strength.
Because of this crude resolution, results vary with
scale and sampling intensity, thus hampering a comparison of network patterns at different levels [7–9].
We applied a recently developed [10] quantitative and
scale-independent analysis based on information theory to 51 mutualistic plant-animal networks, with interaction frequency as measure of link strength. Most networks were highly structured, deviating significantly
from random associations. The degree of specialization was independent of network size. Pollination
webs were significantly more specialized than seeddispersal webs, and obligate symbiotic ant-plant mutualisms were more specialized than nectar-mediated
facultative ones. Across networks, the average specialization of animal and plants was correlated, but is
constrained by the ratio of plant to animal species involved. In pollination webs, rarely visited plants were
on average more specialized than frequently attended
ones, whereas specialization of pollinators was positively correlated with their interaction frequency. We
conclude that quantitative specialization in ecological
communities mirrors evolutionary trade-offs and constraints of web architecture. This approach can be easily expanded to other types of biological interactions.
Results and Discussion
Ecological specialization in a food web or other interaction networks is commonly defined by the number of
*Correspondence: [email protected]
4
Present address: Manchester Centre for Integrative Systems Biology (MCISB), Manchester Interdisciplinary Biocentre (MIB), Manchester M1 7ND, United Kingdom.
realized ‘‘links.’’ For instance, predators are specialized
if they attack only a few prey species, and specialized
flowers are those that are visited by few pollinator species only. This concept has been extended to measure
the degree of specialization of entire networks (‘‘connectance’’), where associations are classified as ‘‘present’’ or ‘‘absent,’’ but all links are considered equally
important [1–3, 6, 8, 11–13]. However, such qualitative
measures ignore the importance of variation in interaction strength for community dynamics [5, 14, 15]. Moreover, they are highly sensitive to sampling intensity and
network size [7–10, 15]. Therefore, weighted links have
been included in quantitative descriptors of different
types of webs [5, 14, 16]. In bipartite ecological networks, the frequency of an interaction between two species is a meaningful measure of its strength (Figure 1)
and has been shown to represent a suitable surrogate
for mutualistic services such as pollination success
[17]. In this article, we use two measures inspired by
information theory to quantify specialization within and
across networks. Technical properties of these indices
have been explored in a recent methodology article
[10] showing that—in contrast to other quantitative
measures—they are scale independent and largely insensitive to sampling effort. Unlike previous measures,
we define the overall degree of specialization in each
web as the deviation from an expected probability distribution of interactions (evaluated by the standardized
two-dimensional entropy H0 2), and individual species’
specialization as the deviation from a conformity expected by the overall utilization of potential partners
(standardized Kullback-Leibler distance, d0 i) [10]. The
expected null distribution assumes that all species interact with their partners in proportion to their total frequencies, whereas the heterogeneity (evenness) of interactions in previously proposed quantitative metrics
such as diversity indices [16, 18] varies with the partner
availabilities in an uncontrolled way and is thus less suitable in the context of network analyses (see also [10,
19]). On the basis of these standardized quantitative
measures, we explored 51 networks, covering four
types of mutualistic plant-animal associations, for patterns of specialization on the level of the entire network
[2, 8], the community of each of the two parties (guild
level) [5], and the level of species [9].
Network Level
Across all networks, the overall degree of specialization
(H0 2) covered a broad range (Table S1 in the Supplemental Data available online). All networks showed a significantly higher degree of organization than simulated
networks, where partners were associated randomly
(all p % 0.001), except for a single network of loosely associated ants and bromeliads [20] (p = 0.31). Pollination
mutualisms were significantly more specialized than
seed-dispersal mutualisms (Figure 2), corroborating a
previous qualitative analysis [2] and expected on the
basis of evolutionary considerations [21]. Plants may
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342
Figure 1. Visualization of Two Quantitative Networks
A pollinator web and an ant-plant association are displayed (webs 6
and 37 in Table S1). Widths of links are scaled in relation to interaction frequencies, bar sizes to total interaction frequencies. Both
webs are regarded specialized, but the degree of specialization is
lower in the pollinator web (H0 2 = 0.46) compared to the myrmecophyte web (H0 2 = 0.84). Note that the former web is asymmetric
(more pollinator than plant species), whereas the latter is symmetric.
benefit disproportionately more from specialized pollinators, corresponding to the likelihood that each individual pollinator successively visits conspecific plants
to maintain both male and female reproductive success
of a plant, thereby reducing maladaptive heterospecific
pollen transfer ([22, 23], but see [11]). In contrast to pollination, the efficacy of seed dispersal to suitable sites
does not depend on the specialization of the dispersal
agent [21]. A broader spectrum of seed dispersers
may even be profitable from the plant’s perspective to
avoid aggregation of seeds [24] and generate fat-tailed
dispersal kernels [25], which should be favored by natural selection under many conditions [26]. Correspondingly, obligate specialized mutualisms are known from
a number of pollination systems [23] but seem to be
largely absent in seed-disperser systems [21].
In ant-plant networks, there is an important distinction
between completely facultative associations, based on
extrafloral nectaries, and symbiotic associations, where
ant colonies, often obligatory, inhabit plants (myrmecophytes) [27, 28]. For obligate and symbiotic mutualisms,
a higher degree of specialization is generally expected
[4, 29]. This differentiation is supported by our analysis:
Ant-plant mutualisms involving myrmecophytes were
significantly more specialized than those involving extrafloral nectaries (Figure 2). Obligate associations are
common among myrmecophytic associations, sometimes causing irreversible dependence on a single partner species. Myrmecophytes represent a gradient from
plants that offer neither specific structures nor specific
food rewards to support their facultative ant inhabitants
[20, 28] to cases where only few ant species are adapted
to actively bite small entrance holes into preformed domatia and where colonies are fully supplied by nutritious
plant-produced food bodies and never forage outside
their host plants [27, 30]. Obligate-myrmecophytic symbioses represent the most specialized networks across
all systems examined in this study. In contrast to other
networks, such associations often remain uninterrupted
for several generations, opening the opportunity for the
evolution of tight specialization. In contrast, extrafloral
nectaries usually attract a spectrum of largely opportunistic ants, where the accessible nectaries seem to
offer little structural plasticity to facilitate specialization
except for some degree of biochemical differentiation
[31, 32]. This dissimilarity between the two types of
ant-plant mutualisms is particularly evident between
nectary-bearing and myrmecophytic species from the
same genus [27, 32]. The gradient from facultative to
obligate mutualisms is thus largely associated with an
increasing H0 2.
The degree of specialization did not show a significant
trend across networks of different dimensions (Figure 3)
(Spearman rank correlations for each of the four network
types, all 20.48 % rS % 20.10, p R 0.15). Given that H0 2
is mathematically independent of web size [10], the lack
of a correlation between web size and H0 2 indicates that
species-rich and species-poor real biological systems
(or smaller fractions of a system) do not inherently differ
in their degree of specialization between partners. This
novel finding contrasts with the hyperbolic decline of
Figure 2. Network-Level Specialization
Overall specialization (H0 2) in 51 mutualistic
networks. Box plots show median, quartiles,
and range of the networks analyzed (number
of networks in parentheses). Asterisks show
significant difference between types according to a t test (*** p < 0.0001, both t R 5.2,
Welsh corrected for unequal variances).
Mutualistic Interaction Webs
343
Figure 3. Relationship between Network Size
and Specialization
Overall specialization (H0 2) of 51 networks
plotted over network size (plant plus animal
species, log scale). Networks include pollination (yellow), seed-dispersal (black), ant-myrmecophyte (green), and ant-nectar plant (red)
associations.
the qualitative connectance index over increasing network size in different studies [2, 8, 33, 34], a decline
that was also found if applied to the dataset used here
(see Supplemental Data).
Guild Level
Within mutualistic networks, differences between the
average degree of specialization of both parties (i.e.,
plants versus animals) could be a consequence of conflicting interests. Consumers would only benefit from increased specialization if this process went along with
greater resource-use efficacy and/or reduced interspecific competition, e.g., by improved resource detoxification, reduced handling effort, or specific search images,
and outweighed the costs of increased foraging time. If
resources were very similar, optimal foraging theory
would thus predict selection for generalization in both
frugivores and pollinators [11, 21, 23]—the latter conflicting with the plant’s interest in specialized pollinators. However, both parties did not vary independently
in their degree of specialization, and average specialization of plants hd0 ii and animals hd0 ji was largely reciprocal (Pearson’s r2 = 0.71, p < 0.0001, n = 51 webs). Moreover, differences between hd0 ii and hd0 ji are strongly
predicted by the asymmetry of the matrix (r2 = 0.62,
p < 0.0001) (Figure 4). In those webs where animal species were more numerous than plants, animals showed
a lower degree of specialization (hd0 ji < hd0 ii) and vice
versa. This effect was even stronger (r2 = 0.93) for simulated networks with randomly assigned associations
(Figure S2). Pollinator and ant-nectar webs were
highly asymmetric, involving a much higher number of
Figure 4. Relationship between Network Asymmetry and Specialization
Asymmetry of the number of plant (I) and animal (J) species in each web (network asymmetry) is given as (J 2 I) / (I + J) and equals zero for balanced webs (same number of animal and plant species). Specialization asymmetry between plants and animals is given as (hd0 ji – hd0 ii) / (hd0 ii +
hd0 ji), based on weighted means across all species (plants i or animals j) in a web. Real networks include pollination (yellow), seed-dispersal
(black), ant-myrmecophyte (green), and ant-nectar plant (red) associations. The regression line is plotted for randomly generated networks (fixed
total interactions per species, mean values from 100 randomizations per web, r = 20.97).
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344
pollinator species (usually insects) or ant species than
plant species (on average 3.6:1 and 3.8:1, respectively).
Consequently, pollinators were significantly less specialized on plants than plants on pollinators, and ants
were significantly less specialized on plants with nectaries than vice versa (paired t test; pollinators: t = 3.8,
p = 0.001; ants: t = 3.2, p = 0.01). In contrast, networks
involving seed dispersers (mostly vertebrates) as well
as ant-myrmecophyte associations were usually more
symmetric (1.2:1 and 1.6:1, respectively) and did not
show significant unequal specialization of both mutualists (both p R 0.38). Hence, the network architecture severely constrains average specialization between two
parties irrespective of the type of association, a result
that is expected given the mathematical relationships
of the indices in their unstandardized form [10]. Such
constraints on specialization have been largely overlooked so far, but are important in other network metrics
as well, including the ‘‘number of links’’ or quantitative
‘‘dependences’’ used elsewhere [5] (see Supplemental
Data). However, with architectural constraints accounted for, residual variation from the linear regression
(line shown in Figure 4) depicted differences between
networks depending on the type of association. Pollinators were significantly more specialized than expected
by the asymmetry (mean residuals > 0, t = 4.7,
p < 0.001), whereas ants visiting extrafloral nectaries
were more generalized than expected (residuals < 0,
t = 22.4, p < 0.05). In seed-dispersal and ant-myrmecophyte networks, differences between animals
and plants in specialization did not deviate significantly
from the expected on the basis of asymmetry (both
p R 0.10). The increased residual specialization observed in pollinators, but not seed dispersers, thus
corresponds to the plant’s differential interest in these
types of mutualists.
Species Level
Although the average degree of specialization in a community may be constrained to a large degree, this does
not apply to single elements of the network, i.e., the local
population of each species. For example, disparities in
specialization of pollinators and plants were particularly
pronounced for the rarely interacting species in a network. Across pollination networks, there was a significantly positive correlation between pollinator frequency
and specialization, but a significant negative correlation
between plant frequency and specialization (Table 1).
We also found a significantly positive correlation between ant frequency and specialization in ant-myrmecophyte webs but not in any of the other networks investigated. Previous qualitative network analyses showed an
invariable negative correlation between frequency and
specialization (estimated as the inverse of the number
of links), a correlation that can be explained by a null
model [9] and is strongly affected by sampling effort.
In contrast, our quantitative analysis demonstrates a
highly variable relationship between frequency and
quantitative specialization, one that differs between network types. Our results suggest that plant populations
with low visitation frequencies, presumably those that
occur in low densities in a community, have a particularly
unconventional spectrum of visitors. Rare plants may be
particularly sensitive for two fitness costs: subsequent
Table 1. Relationship between Frequency and Specialization of
Plant and Animal Species
Plants
Pollination
20.20* (20.30 2 20.11)
(n = 20)
Seed dispersal
0.06 (20.19 2 0.23)
(n = 7)
Ant-myrmecophyte 0.14 (20.16 2 0.40)
(n = 14)
Ant-nectar plant
20.10 (20.23 2 0.02)
(n = 7)
Animals
0.27* (0.15 2 0.37)
(n = 21)
0.00 (20.27 2 0.24)
(n = 8)
0.51* (0.24 2 0.71)
(n = 13)
20.04 (20.27 2 0.16)
(n = 8)
Effect sizes derived from linear correlation coefficients for each network by using meta analysis based on Fisher’s z-transformation.
Mean back-transformed r values are shown with range of 95% bootstrap confidence intervals and number of webs (n) in parentheses.
Asterisks indicate significant deviation from r = 0.
pollen deposition on (more common) plants and clogging of the stigma by pollen from (common) plants
[22]. Increased specialization and reduced overlap
with visitors of common flowers may reduce such costs.
The positive correlation between animal abundance and
specialization indicates that resource partitioning is particularly pronounced among the most active species,
whereas rarely interacting species use their resources
more opportunistically.
Conclusions
Three general conclusions can be drawn from the results. (1) The network-level specialization is unaffected
by network size and form and depicts biologically meaningful system-specific differences. Our results demonstrate that the plant’s interest in specialized pollen transfer but generalized fruit dispersal conformed to the
overall specialization of the respective networks. Networks involving facultative associations were less specialized than more obligate ones, particularly in ant-plant
webs. (2) The average degree of specialization of both
network parties is highly reciprocal, i.e., one party cannot
specialize or generalize on the other party without concomitant changes in the specialization within the other
party itself. Moreover, differences between network
parties are largely driven by constraints in the network
architecture (unequal species numbers). Such constraints cause unequal degrees of specialization as
well as asymmetric dependences between both parties.
Residual differences in specialization still contain meaningful information, e.g., pollinators were more specialized than expected from architectural constraints only.
(3) Species-level specialization is less affected by these
constraints and may indicate differential roles of rare
and common species in a network. Such patterns may
potentially unveil density-dependent selection pressures or feedback mechanisms between frequency and
specialization.
The hypothesis that natural selection drives specialization between interacting mutualists or antagonists
has been debated for a long time [4, 11, 35]. Whereas
generalists are obviously much less limited by resource
or partner availability, specialists are usually better
adapted to effectively use their selected resources. For
antagonistic relationships (e.g., predator-prey, hostparasite, and plant-herbivore interactions), defensive
Mutualistic Interaction Webs
345
mechanisms of hosts or prey substantially constrain the
choices of their enemies, enforcing specialization [36].
Trade-offs between specialization and generalization
may occur in food webs [6], but are also complex among
mutualists [37], where selective pressures on partner
choices may be variable and shaped by coevolutionary
complementarity or convergence [4]. Refined analyses
and more fine-grained empirical data, particularly at
the level of individuals, may reveal additional insights
into the evolution of a broad spectrum of interaction
webs and their ecological fragility.
Experimental Procedures
We analyzed the degree of specialization for 51 published and unpublished interaction webs that included frequency data, representing a broad range of mutualistic relationships between plant-based
resources and their consumers or inhabitants and covering six continents (Supplemental Data). Although all webs were obviously dominated by mutualists, several datasets may contain nonmutualistic
species, e.g., nectar robbers and seed predators. Twenty-seven datasets were obtained from the Interaction Web Database (http://
www.nceas.ucsb.edu/interactionweb). For each network containing
a total of I plant and J animal species, we obtained the two-dimensional Shannon entropy for the observed association matrix [10] as
H2 = 2
I X
J
X
I X
J
X
pij ,ln pij ; with
pij = 1:
i=1 j=1
pij = aij
X
I X
J
aij :
i=1 j=1
Our specialization index H0 2 normalized H2 between the minimum
and maximum entropy for associations leading to the same matrix
row and column totals as
H02 =
H2max 2 H2
:
H2max 2 H2min
For quantification of the degree of specialization of each species
(say plant i), the proportional distribution of the interactions with
each animal (j), p0 ij, was compared with the proportion of the total
number of interactions where j was involved, qj, by using the Kullback-Leibler measure
di =
J X
p0ij ,ln
j=1
where p0ij = aij =
J
X
p0ij ;
qj
aij ; thus
j=1
I
X
i=1
Supplemental Data
Supplemental Data include additional results and data sources,
three figures, and one table and are available with this article online
at: http://www.current-biology.com/cgi/content/full/17/4/341/DC1/.
i=1 j=1
In this equation, i represents one plant species and j one animal
species. The number of interactions between i and j (aij), e.g., the
number of recorded visits of pollinator j on plant i, is divided by
the total interaction frequencies recorded for the entire web, thus
and qj =
was compared to a null model (randomly associating all species
with the total number of interactions being fixed per species, 104
permutations) by using an established algorithm ([38], see [10]).
Fixed marginals have been advocated as suitable constraints for
null hypotheses in qualitative webs [6, 39]. In addition, we suggest
that total interaction frequencies may better reflect variation in animal activity or plant resource availability for the actual associations
than would external estimates of local population densities [10].
Such independent measures of the species’ local abundances for
both parties have not been provided by most empirical studies so
far. All calculations can be performed online at http://itb.biologie.
hu-berlin.de/wnils/stat/.
To analyze the relationship between specialization and interaction
frequency at the species level, we calculated linear correlation coefficients between log(total number of interactions) and arcsin(O d0 i)
across all species of a guild per network and then quantified the
combined mean effect size from all networks of the same type by
using standard meta-analysis tools (MetaWin 2.0; Fisher’s z-transformation, sample size as number of species, fixed effects); 95%
confidence intervals were based on bootstrapping with 999 iterations, bias-corrected. To reduce a bias due to single, very large networks, we removed, prior to analysis, datasets where the number of
species was more than twice as large as in the second-largest
network (four cases).
aij =
I X
J
X
J
X
p0ij = 1;
j=1
aij ; thus
i=1 j=1
J
X
qj = 1:
j=1
This measure was normalized as
d0i =
di 2 dmin
:
dmax 2 dmin
Specialization of the plant community (guild level) was obtained as
the weighted mean hd0 ii, for which each plant species i was weighted
by its total number of interactions. Specialization of animals was calculated in the same way (d0 j and hd0 ji). Maximum and minimum
values for H2, di, and dj were computed algorithmically by using
the fixed total number of interactions of each species as a constraint
[10]. Resulting H0 2, d0 i, and d0 j range between 0.0 for extreme generalization and 1.0 for extreme specialization. For each network, H2
Acknowledgments
We thank D. Vázquez, K. Fiedler, and K.E. Linsenmair for discussion
and helpful comments on earlier versions of the manuscript and the
Interaction Web Database for providing several of the networks
analyzed here. Field work of the N.B., T.H., and B.F. was supported
by the German Research Foundation (DFG).
Received: October 25, 2006
Revised: December 6, 2006
Accepted: December 6, 2006
Published online: February 1, 2007
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Please cite this article in press as: Blüthgen et al., Specialization, Constraints, and Conflicting Interests in Mutualistic Networks, Current Biology (2007), doi:10.1016/j.cub.2006.12.039
Supplemental Data
Specialization, Constraints, and
Conflicting Interests
in Mutualistic Networks
S1
Nico Blüthgen, Florian Menzel, Thomas Hovestadt,
Brigitte Fiala, and Nils Blüthgen
Supplemental Results and Data Sources
All 51 mutualistic networks used in this analysis and their
sources are listed in Table S1. Previous analyses invariably demonstrated a hyperbolic decline of the qualitative ‘‘connectance’’ index with increasing network size,
a pattern that has considerably constrained the usefulness of this qualitative measure [S1]. Connectance has
been defined as the number of realized links divided
by the number of cells in the association matrix (plant
species 3 animal species) [S2, S3]. For the dataset
used here, connectance declined significantly with increasing matrix size within each of the network types
(all Spearman rS % 20.64, p % 0.04) except for the small
set of ant-nectar webs (rS = 20.59, p = 0.13) (Figure S1).
Asymmetries in the average quantitative specialization of plants and animals are a function of the network
form (ratio of plant to animal species), an effect that is
particularly pronounced in random associations (Figure S2). Neither the reciprocity of average specialization
levels between guilds nor the effect of architectural constraints on specialization is confined to the quantitative
metrics used here. Specialization as defined in the traditional, qualitative sense (i.e., number of partner species)
is strictly dependent on the web architecture: In balanced webs, when an equal number of animal species
(J) and plant species (I) is involved (I = J), the mean number of associated partners (links) for each animal (LJ )
equals that for plants (LI ). Consequently, qualitative specialization is a direct function of the network asymmetry,
as LJ =LI = I=J. Differences in the number of partner species also affect quantitative ‘‘dependences’’ between
plants and animals that were used in previous analyses
by Bascompte et al. [S4], an effect that has not been
shown previously. The dependence of plant i on animal
j (bij) and the reciprocal dependence of j on i (bji) is estimated from of the interaction frequency (aij) between
them as
bij =
aij
J
P
aiq
and
bji =
q=1
aij
:
I
P
apj
p=1
It can be shown that the average dependence of a
guild across all putative interactions (I $ J, including all
cases where aij= 0) is a simple function of the number
of species, because
bij =
1=ðIJÞ 1
=
J
J
and
bji =
1=ðIJÞ 1
= :
I
I
Consequently, in square matrices (I = J), expected average differences between the two parties (bij 2 bji) are
zero, but become stronger with increasing asymmetry
in rectangular networks in both directions (I > J or J >
I). This effect is also evident across empirical webs
(Figure S3).
Supplemental References
S1. Blüthgen, N., Menzel, F., and Blüthgen, N. (2006). Measuring
specialization in species interaction networks. BMC Ecol. 6, 9.
S2. Jordano, P. (1987). Patterns of mutualistic interactions in pollination and seed dispersal: Connectance, dependence asymmetries, and coevolution. Am. Nat. 129, 657–677.
S3. Olesen, J.M., and Jordano, P. (2002). Geographic patterns in
plant-pollinator mutualistic networks. Ecology 83, 2416–2424.
Figure S1. Relationship between Network Size and Connectance
Qualitative connectance (proportion of realized links of the total number of possible links) of 51 networks plotted over network size (plant plus
animal species, log scale). Networks include pollination (yellow), seed-dispersal (black), ant-myrmecophyte (green), and ant-nectar plant (red)
associations.
Please cite this article in press as: Blüthgen et al., Specialization, Constraints, and Conflicting Interests in Mutualistic Networks, Current Biology (2007), doi:10.1016/j.cub.2006.12.039
S2
Figure S2. Relationship between Web Asymmetry and Specialization
Each point represents the mean value (6 standard deviation [SD]) of 100 randomized networks simulated from the set of 51 natural networks
(maintaining the same total interaction frequencies per species). Asymmetry of the number of plant (I) and animal (J) species in each web is given
as (J 2 I)/(I + J), asymmetry in specialization between plants i and animals j as (hd0 ji 2 hd0 ii) / (hd0 ji + hd0 ii). Regression line (r = 20.97) was used for
Figure 4 in the main text and for calculating residuals of real networks.
Figure S3. Relationship between Web Asymmetry and Dependence
Asymmetry of the number of plant (I) and animal (J) species in each web is given as (J 2 I)/(I + J), and asymmetry in dependence between plants i
and animals j is given as (bji 2 bij)/(bij + bji). Data were obtained from 26 networks [S4], including pollination (yellow) and seed-dispersal (black)
associations (means and 95% confidence intervals for all realized interactions). The mean dependence asymmetry across all realized interactions of a web (aij > 0) is strongly linearly predicted by network asymmetry (r = 0.97, p < 0.0001).
Please cite this article in press as: Blüthgen et al., Specialization, Constraints, and Conflicting Interests in Mutualistic Networks, Current Biology (2007), doi:10.1016/j.cub.2006.12.039
S3
Table S1. Mutualistic Networks Analyzed
Number
Reference
Taxonomic Focus
Location
(several families)
(several families)
(several families)
(several families)
(several families)
(several families)
(several families)
(several families)
(several families)
Asclepiadaceae
(several families)
(several families)
Syrphidae
(several families)
(several families)
(several families)
(several families)
(several families)
(several families)
(several families)
(several families)
Canada
Sweden
Australia
Japan
Britain
Canada
USA
Azores
Mauritius
South Africa
USA
Canada
Germany
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Argentina
Birds
Mammals
Birds, Mammals
Ants
Birds, Miconia,
Psychotria
Birds
Birds
Birds
Papua N.G.
Kenya
Ivory Coast
West Malaysia
Panama
Plants
Animals
m
hd0 Plantsi
hd0 Animalsi
H0 2
Pollination
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Barrett and Helenurm [S5]
Elberling and Olesen [S6]
Inouye and Pyke [S7]
Kato et al. [S8]
Memmott [S9]
Mosquin and Martin [S10]
Motten [S11]
Olesen et al. [S12]
Olesen et al. [S12]
Ollerton et al. [S13]
Schemske et al. [S14]
Small [S15]
Ssymank [S16]
Vázquez and Simberloff [S17]
Vázquez and Simberloff [S17]
Vázquez and Simberloff [S17]
Vázquez and Simberloff [S17]
Vázquez and Simberloff [S17]
Vázquez and Simberloff [S17]
Vázquez and Simberloff [S17]
Vázquez and Simberloff [S17]
12
23
42
91
25
11
13
10
14
9
7
13
88
10
9
9
10
8
8
7
8
102
118
91
679
79
18
44
12
13
56
32
34
75
29
33
27
29
35
26
24
27
550
383
1459
2392
2183
134
2225
1139
1512
594
299
992
4837
677
613
1130
515
719
286
761
592
0.61
0.48
0.54
0.65
0.27
0.51
0.43
0.50
0.19
0.43
0.33
0.54
0.45
0.65
0.64
0.90
0.56
0.53
0.78
0.65
0.50
0.40
0.29
0.59
0.37
0.16
0.36
0.34
0.46
0.25
0.27
0.18
0.39
0.47
0.63
0.55
0.65
0.39
0.26
0.69
0.84
0.52
0.55
0.33
0.60
0.48
0.24
0.46
0.43
0.53
0.38
0.43
0.34
0.55
0.47
0.71
0.78
0.85
0.60
0.57
0.74
0.79
0.70
31
219
34
33
17
9
33
48
51
20
1189
3730
17575
448
492
0.22
0.20
0.16
0.27
0.16
0.28
0.43
0.15
0.28
0.17
0.26
0.39
0.18
0.24
0.21
65
29
11
14
19
14
2180
19946
7434
0.20
0.21
0.38
0.28
0.29
0.21
0.30
0.30
0.47
4
5
7
8
9
13
5
14
18
51
39
71
111
242
388
0.23
0.16
0.44
0.89
0.51
0.14
0.23
0.23
0.76
0.26
0.23
0.27
0.40
0.89
0.47
Seed dispersal
22
23
24
25
26
Beehler [S18]
Engel [S19]
Hovestadt [S20]
Kaufmann [S21]
Poulin et al. [S22]
27
28
29
Snow and Snow [S23]
Snow and Snow [S24]
Sorensen [S25]
Trinidad
Britain
Britain
Ant-myrmecophyte
30
31
32
33
34
Blüthgen et al. [S26]
N.B., unpublished data
Cabrera and Jaffé [S27]
Davidson et al. [S28]
Dejean et al. [S29]
35
36
37
38
39
40
41
42
43
Fiala et al. [S30]
Fiala et al. [S30]
Fiala et al. [S30]
Fiala et al. [S30]
Fiala et al. [S30]
B.F., unpublished data
B.F., unpublished data
Fonseca and Ganade [S31]
Yu and Davidson [S32]
Bromeliaceae
Melastomataceae
Melastomataceae
(several families)
Bromeliaceae,
Orchidaceae
Macaranga
Macaranga
Macaranga
Macaranga
Macaranga
Macaranga
Macaranga
(several families)
Cecropia
Venezuela
Ecuador
Venezuela
Peru
Mexico
Borneo
Borneo
Borneo
Sumatra
West Malaysia
Borneo
Borneo
Brazil
Peru
9
10
6
7
4
6
7
16
7
7
8
6
4
5
7
5
25
4
349
173
98
78
183
88
88
417
155
0.72
0.74
0.74
0.57
0.71
0.96
0.53
0.72
0.45
0.77
0.87
0.76
0.79
0.77
0.97
0.72
0.78
0.54
0.80
0.86
0.84
0.80
1.00
0.99
0.83
0.80
0.61
Philodendron, Dioclea
(several fam.), incl.
flowers
(several families)
(several families)
(several families)
(several families)
Passiflora, Mimosa
Euphorbiaceae
Venezuela
Australia
6
51
53
41
180
644
0.36
0.18
0.11
0.20
0.31
0.13
Borneo
Borneo
West Malaysia
West Malaysia
French Guiana
Papua N.G.
15
22
11
24
3
5
14
28
16
35
37
17
267
324
121
315
1661
246
0.14
0.34
0.37
0.31
0.24
0.22
0.17
0.21
0.26
0.19
0.08
0.13
0.19
0.23
0.33
0.21
0.23
0.24
Ant-nectar plant
44
45
Blüthgen et al. [S33]
Blüthgen et al. [S34]
46
48
47
49
50
51
B.F., unpublished data
B.F., unpublished data
B.F., unpublished data
B.F., unpublished data
Hossaert-McKey et al. [S35]
Whalen and Mackay [S36]
References, taxonomic focus, number of plant and animal species, total interactions (m), weighted mean specialization of plants and animals
(hd0 Plantsi, hd0 Animalsi), and overall specialization (H0 2) are given.
Please cite this article in press as: Blüthgen et al., Specialization, Constraints, and Conflicting Interests in Mutualistic Networks, Current Biology (2007), doi:10.1016/j.cub.2006.12.039
S4
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