Journal of Biogeography
SUPPORTING INFORMATION
Unrivalled specialization in a pollination network from South Africa reveals that
specialization increases with latitude only in the Southern Hemisphere
Anton Pauw and Rosanne Stanway
Appendix S1 Analysis of the plant–visitor network, including non-pollinating visits.
In the main text we presented the analysis of the plant–pollinator network for a 1-ha
plot (Sevilla) in Cederberg, Cape Floristic Region, South Africa, which excluded all
visits where no contact was observed between the animal and the anthers or stigma.
Non-pollinating visits are, nevertheless, important from the perspective of the animal
because the visitor obtains floral resources. In this appendix the analyses are shown
for the full plant–visitor network. The data were collected by observing 76 plant
species. Ten plant species received no visits and were excluded from the analysis.
Plants: 66
Animals: 272
Visits: 11,796
Connectance = 495 realized links/17,952 possible links = 0.028
Average plant degree (i.e. number of interaction partners) = 7.500 (Fig. S1)
Average animal degree = 1.820 (Fig. S1)
12 plants species received visits from a single animal species
200 animal species were recorded on a single plant species
Number of one-to-one interactions = 6
Sampling intensity = 0.81
We compared the Sevilla network with a global dataset of 58 networks collated by
Schleuning et al. (2012). The analytical methods used for making these comparisons
were identical to those described in the main body of the paper, where all metrics are
defined. The current study (Sevilla) had the second highest number of animal species
and third highest sampling intensity (0.81). The plants at Sevilla had more interaction
partners than the sample of global values (one-sample Wilcoxon signed rank test of
the hypothesis that the median = 7.51, V = 332, P = 5.14 × 10–5; Fig. S2). The average
number of interaction partners per animal at Sevilla was smaller than expected from
the sample of global values (one-sample Wilcoxon signed rank test of the hypothesis
that the median = 1.82, V = 1562, P = 4.60 × 10–8; Fig. S2). The shape of the
interaction matrix was strongly asymmetrical in favour of animals and it was very
unlikely that the Sevilla datum (network asymmetry = 0.61) was the median of the
sample of global network asymmetry values (one-sample Wilcoxon signed rank test,
V = 71, P = 1.28 × 10–9; Fig. S3).
Mean specialization of both plants (mean d′ = 0.762; d′ weighted = 0.822) and
animals (mean d′ = 0.571; d′ weighted = 0.796) was high compared with the global
dataset (Fig. S4). When the raw number of visits (rather than visits flower–1 h–1) was
used as the input data, the results were very similar (mean d′ plants = 0.722; d′
weighted plants = 0.794; mean d′ animals = 0.506; d′ weighted animals = 0.679). Part
of the variation in specialization across sites was accounted for by variation in web
asymmetry, i.e. the ratio of animals to plants (see the Materials and Methods). Across
the sites, animal specialization was negatively correlated (linear regression, adjusted
R2 = 0.05, d.f. = 57, P = 0.04) and plant specialization was strongly positively
correlated (linear regression, adjusted R2 = 0.34, d.f. = 57, P = 6.49 × 10–7) with
network asymmetry. In an analysis of the residual of these regressions, Sevilla was
still more specialized than expected from the distribution of global values (Fig. 4). For
all four of the comparisons reported in Fig. 4, the global values followed a normal
distribution (Shapiro–Wilk normality test, P > 0.05) and in every case it was highly
unlikely that the South African datum was the mean of this sample (one-sample ttests, P < 0.005).
Overall, the Sevilla network was significantly specialized relative to the benchmark
set by a null model in which species strengths (i.e. marginal totals) were constrained
(H2′ = 0.835, mean H2ran = 0.005, P < 0.001). In addition, standardized network-wide
specialization was the highest reported (ΔH2′ = 0.830). The global collection of ΔH2′
values followed a normal distribution (Shapiro–Wilk normality test, W = 0.99,
P = 0.91) and it was very improbable that the South African datum was the mean of
this sample (one-sample t-test, t = –31.47, d.f. = 57, P < 2.2 × 10–16). Two alternative
methods for calculating H2′ produced similar results: when an algorithm that was
designed for continuous values was used, H2′continuous = 0.835; when the raw number
of visits (rather than visits flower–1 h–1) was used as the input data, H2′ = 0.798 and
ΔH2′ = 0.748.
10 12
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Figure S1 Histogram of the number of species of interaction partners observed across
66 flowering plant species and 272 flower-visiting animal species in a 1-ha plot in
South Africa.
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Figure S2 Histogram of the average number of species of interaction partner per plant
or animal species observed at 58 sites from across the world. The vertical line is the
South African datum.
15
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Frequency
−0.8
−0.4
0.0
0.4
0.8
Web asymmetry [(animals − plants)/(animals + plants)]
Figure S3 Histogram of visitor network asymmetry, i.e. the ratio of flower visitor
species to plant species present in the community, as observed at 58 sites from across
the world. The vertical line is the South African datum. Positive values indicate more
animal than plant species.
Figure S4 Histograms of average species-level specialization in flower–visitor
interaction networks from 58 sites from all over the world. The top two figures show
average specialization (d′) weighted by the marginal totals of the quantitative
interaction matrix. Because d′ is correlated with network asymmetry (Fig. S3), we
compared the residuals of the regression of d′ (weighted) on network asymmetry in
the bottom two figures. The vertical line is the South African datum.
Appendix S2 Sampling accumulation analysis.
We used interaction accumulation curves to assess the thoroughness of sampling for
the Sevilla (Cederberg, Cape Floristic Region, South Africa) plant–visitor network
(Gibson et al., 2011). We created a matrix with observation sessions as rows and
unique pairwise interactions (e.g. Melianthus major–Pycnonotus capensis) as
columns, and populated the matrix with the number of visits observed. To examine
how unique interactions accumulated with sampling, we rarefied the data to a range of
increasingly smaller samples using the VEGAN package in R (Fig. S5; Oksanen et al.,
2013; R Core Team, 2013). Cubic spline interpolation of the data points showed that a
doubling in sampling effort was predicted to increase the number of unique
interactions detected from 495 to 610. Thus, additional sampling would yield greatly
diminishing returns.
Figure S5 Decline in the rate of discovery of new interactions with increasing
sampling effort. The spread around the prediction is the confidence interval.
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