Albrecht, M; Riesen, M; Schmid, B (2010). Plant-pollinator network assembly along the chronosequence of a glacier foreland. Oikos, 119(10):1610-1624. Postprint available at: http://www.zora.uzh.ch University of Zurich Posted at the Zurich Open Repository and Archive, University of Zurich. Zurich Open Repository and Archive http://www.zora.uzh.ch Originally published at: Oikos 2010, 119(10):1610-1624.
Winterthurerstr. 190 CH-8057 Zurich http://www.zora.uzh.ch
Year: 2010
Plant-pollinator network assembly along the chronosequence of a glacier foreland
Albrecht, M; Riesen, M; Schmid, B
Albrecht, M; Riesen, M; Schmid, B (2010). Plant-pollinator network assembly along the chronosequence of a glacier foreland. Oikos, 119(10):1610-1624. Postprint available at: http://www.zora.uzh.ch
Posted at the Zurich Open Repository and Archive, University of Zurich. http://www.zora.uzh.ch
Originally published at: Oikos 2010, 119(10):1610-1624. Plant-pollinator network assembly along the chronosequence of a glacier foreland
Abstract
Forelands of retreating glaciers offer an ideal model system to study community assembly processes during primary succession. As plants colonize the area that is freed from ice they should be accompanied by their pollinators to successfully reproduce and spread. However, little is known about the assembly of plant-pollinator networks. We therefore used quantitative network analysis to study the structure of plant-pollinator interactions at seven sites representing a chronosequence from 8 to 130 years since deglaciation on the foreland of the Morteratsch glacier (southeastern Switzerland). At these sites, individual visits of plant fl owers by insects were recorded throughout the fl owering season. Species richness of insect-pollinated plants and plant-pollinating insects, together with measures of interaction diversity and evenness, increased along the chronosequence at least for the fi rst 80 years after deglaciation. Bees were the most frequent fl ower visitors at the two youngest sites, whereas fl ies dominated in mature communities. Pollinator generalization (the number of visited plant species weighted by interaction strength), but not plant generalization, strongly increased during the primary succession. This was reflected in a pronounced decline in network level specialization (measured as Blüthgen's H2') and interaction strength asymmetry during the fi rst 60 years along the chronosequence, while nestedness increased along the chronosequence. Thus, our findings contradict niche-theoretical predictions of increasing specialization of pollination systems during succession, but are in agreement with expectations from optimal foraging theory, predicting an increase in pollinator generalization with higher plant diversity but similar flower abundance, and an increase in diet breadth at higher pollinator densities during primary succession. Albrecht et al. Plant–pollinator network assembly 1 ______1 2 Plant–pollinator network assembly along the chronosequence of a 3 glacier foreland 4
5
6 Matthias Albrecht 1, Matthias Riesen 1 and Bernhard Schmid 1
7
8
9 1 Institute of Evolutionary Biology and Environmental Studies, University of Zurich,
10 Winterthurerstrasse 190, 8057 Zurich, Switzerland
11
12
13
14
15 Corresponding author:
16 Matthias Albrecht
17 E-mail: [email protected]
18 Fax: +41 44 635 57 11
19
20
21 Albrecht et al. Plant–pollinator network assembly 2 ______22 Abstract 23 Forelands of retreating glaciers offer an ideal model system to study community
24 assembly processes during primary succession. As plants colonize the area that is
25 freed from ice they should be accompanied by their pollinators to successfully
26 reproduce and spread. However, little is known about the assembly of plant–
27 pollinator networks. We therefore used quantitative network analysis to study the
28 structure of plant–pollinator interactions at seven sites representing a chronosequence
29 from 8 to 130 years since deglaciation on the foreland of the Morteratsch glacier (SE-
30 Switzerland). At these sites, individual visits of plant flowers by insects were
31 recorded throughout the flowering season. Species richness and Shannon diversity of
32 insect-pollinated plants and plant-pollinating insects, together with measures of
33 interaction diversity and evenness, increased along the chronosequence at least for the
34 first 80 years after deglaciation. Bees were the most frequent flower visitors at the
35 two youngest sites, whereas flies dominated in mature communities. Pollinator
36 generalization (the number of visited plant species weighted by interaction strength),
37 but not plant generalization, strongly increased during the primary succession. This
38 was reflected in a pronounced decline in network level specialization (measured as
39 Blüthgen’s H2’) and interaction strength asymmetry during the first 60 years along the
40 chronosequence, while nestedness increased along the chronosequence. Thus, our
41 findings contradict niche-theoretical predictions of increasing specialization of
42 pollination systems during succession, but are in agreement with expectations from
43 optimal foraging theory, predicting an increase in pollinator generalization with
44 higher plant diversity but similar flower abundance, and an increase in diet breadth at
45 higher pollinator densities during primary succession. Albrecht et al. Plant–pollinator network assembly 3 ______46 Introduction
47 How communities assemble is a central issue in ecology (e.g. Clements 1916, Hubell
48 2001, Lortie et al. 2004, Sargent and Ackerly 2008). Primary successions, in
49 particular glacier forelands with a well-known chronology of glacier retreats, have a
50 long history as natural model systems for the study of plant community assembly
51 (reviewed in Matthews 1992; Walker and del Moral 2003). The chronosequence
52 across a glacier foreland can be interpreted, with the required caution, as a space for
53 time substitution of primary succession (Foster and Tilman 2000, Kaufmann 2001,
54 Walker and del Moral 2003). Previous studies have focused on a single trophic level,
55 typically plant (Matthews 1992) and less often animal community assembly
56 (Kaufmann 2001, Hodkinson et al. 2001, Hodkinson et al. 2004). Very little is known
57 about multitrophic community assembly and how plant communities interact with
58 animal communities during this process (Sargent and Ackerly 2008). Here, we study
59 a plant–pollinator community assembly process during primary succession on a
60 glacier foreland.
61 Most of the research on plant–pollinator interactions has tended to focus on
62 single species or guilds of related plants and their pollinators, giving little attention to
63 the larger community context (Waser et al. 1996). Recently, however, plant–
64 pollinator interactions at the community level have attracted considerable attention
65 (Waser and Ollerton 2006 and references therein), largely fueled by new
66 developments in the analysis of complex networks (Proulx et al. 2005). Complex
67 network analysis has provided important insight into the structure of plant–pollinator
68 networks (e.g. Jordano 1987, Olesen and Jordano 2002, Jordano et al. 2003, Stang et
69 al. 2006, Bascompte and Jordano 2007, Blüthgen et al. 2008, Ings et al. 2009,
70 Vasquez et al. 2009a,b). For example, an increasing number of studies suggest that Albrecht et al. Plant–pollinator network assembly 4 ______71 the structure of most plant–pollinator networks is asymmetric (Vazquez and Aizen
72 2004, Bascompte et al. 2006) and nested (Bascompte et al. 2003), whereby
73 specialized species interact with a subset of the interaction partners of more
74 generalized species. Furthermore, large interaction networks may show some degree
75 of modularity (Olesen et al. 2007). Several ecological and evolutionary determinants
76 of these network patterns are currently discussed. The concept of forbidden links (e.g.
77 Jordano et al. 2003) refers to the possibility that some interactions may not occur
78 because of mismatches in the morphology, phenology or spatial distribution of
79 species (Stang et al. 2006, Bascompte and Jordano 2007, Santamaría and Rodríguez-
80 Gironés 2007). Conversely, the neutrality hypothesis claims that network patterns are
81 mainly driven by random encounters, so that abundant species interact more
82 frequently and with a higher number of species than rare species (Vazquez 2005).
83 Recent findings suggest that both of these mechanisms contribute to observed plant–
84 pollinator network structures (Blüthgen et al. 2006, Stang et al. 2006, Bascompte and
85 Jordano 2007, Vazquez et al. 2009a,b).
86 Much less is known about the dynamics of plant–pollinator networks (but see
87 e.g. Petanidou et al. 2008, Olesen et al. 2008, Bascompte and Stouffer 2009). A
88 fundamental open question is how network structure changes during the process of
89 network assembly (Olesen et al. 2008, Bascompte and Stouffer 2009). Some studies
90 used simulation models to predict network disassembly regarding robustness and
91 stability (see Bascompte and Jordano 2007, Bascompte and Stouffer 2009 and
92 references therein), concluding that the heterogeneous degree distribution and the
93 cohesive organization in nested systems, as typically found for mutualistic networks,
94 may primarily account for the high robustness predicted for networks that are
95 structured in such a way. In an empirical study Olesen et al. (2008) recorded the day- Albrecht et al. Plant–pollinator network assembly 5 ______96 to-day dynamics of an arctic pollination network to analyze the attachment process of
97 new species (plant species that initiated flowering and pollinator species that started
98 to actively forage) to the network over two seasons. Studying plant–pollinator
99 networks during real succession, as done in the present paper, represents a new
100 approach to get a deeper insight into network assembly processes.
101 Understanding the patterns of specialization and generalization in plant–
102 pollinator interactions is a central and hotly debated topic in pollination ecology and
103 plant–pollinator network research (Waser et al. 1996, Johnson and Steiner 2000,
104 Waser and Ollerton 2006, Blüthgen et al. 2008, Petanidou et al. 2008). However,
105 there is a paucity of theory predicting the determinants of the degree of specialization
106 in pollination systems and how the latter may change during assembly processes
107 (Johnson and Steiner 2000). Successional status has been considered as such a
108 determinant (Parrish and Bazzaz 1979). From a plant’s perspective, niche theory
109 predicts that mature plant communities should be more specialized (have narrower
110 pollinator niches) than species of early successional communities, because the
111 expected higher density and diversity of plant species should result in more
112 interspecific competition and thus reduced niche overlap, at least in saturated systems
113 (Parrish and Bazzaz 1979). Furthermore, higher unpredictability of pollinator
114 availability should favor more generalized use of pollinators by plants (Waser et al.
115 1996) in early- as compared with late-successional plant communities.
116 In contrast to plants, pollinators only profit from increased specialization if
117 this is associated with increased resource-use efficiency or reduced interspecific
118 competition, e.g. through reduced handling time (e.g. Heinrich 1976, Pyke 1984).
119 However, if resources are similar and accessible, optimal foraging theory predicts
120 that, with an expected increase in resource-plant diversity during succession, Albrecht et al. Plant–pollinator network assembly 6 ______121 pollinators should be more generalized in mature stages to minimize foraging time
122 (Heinrich 1976, Pyke 1984, Waser et al. 1996, Blüthgen et al. 2007). Furthermore,
123 optimal foraging theory predicts that a decrease in resources provided by single plant
124 species should result in an increase in diet breadth (MacArthur and Pianka 1966).
125 Furthermore, increasing pollinator density during succession may result in a more
126 rapid depletion of floral resources and, as a consequence, also lead to a higher diet
127 breadth of individual pollinators (Fontaine et al. 2008).
128 In this paper we quantify plant–pollinator interactions at seven sites along a
129 chronosequence from 8 to 130 years since deglaciation at the Morteratsch glacier
130 foreland (SE-Switzerland). We examine how (1) plant diversity and flower
131 abundance, (2) pollinator diversity and visitation rate, (3) diversity of plant–pollinator
132 interactions, and (4) different aspects of specialization in plant–pollinator interactions
133 change along the chronosequence of the glacier foreland.
134
135 Material and Methods
136 Study site
137 The glacier “Morteratsch” is located at the south-facing slope of the Bernina Range in
138 the subalpine belt of the Central Alps of Switzerland (Fig. 1). Its foreland (9°56' E,
139 46°26' N) is approximately 2.2 km long and extends over altitudes from 1900 to 2010
140 m above sea level. Since the beginning of monitoring the glacier dynamics in 1881,
141 the glacier has continuously retreated at a rate of approximately 18 m per year (VAW
142 2007). The entire glacier foreland lies below the upper tree line of Larix decidua
143 Miller and Pinus cembra L. forest which reaches approximately 2400 m above sea
144 level in the study region. The plant primary succession along the chronosequence of
145 the glacier foreland is dominated by herbaceous plants in a pioneer stage, followed Albrecht et al. Plant–pollinator network assembly 7 ______146 after about 50 years by a stage with shrubs (Ziefle 2006). An open forest stage is
147 reached after around 150 years. Annual mean temperature at Samedan, 10 km north-
148 west of the glacier foreland at an altitude of 1700 m above sea level, is 1.2 °C, and
149 yearly average precipitation is 700 mm. Snow cover on the glacier foreland usually
150 lasts from October to May.
151 The foreland of the Morteratsch glacier was selected as model system for this
152 study because pronounced side moraines separate the glacier foreland from the
153 surrounding vegetated slopes, thus reducing plant propagule pressure from the slopes,
154 and because the history of the glacier retreat since 1857 is well documented (Elsener
155 2006 and references therein). Due to these factors the foreland of the Morteratsch
156 glacier has been a preferred study site for previous research on plant succession
157 (Ziefle 2006).
158
159 Study design
160 Plant–pollinator interactions were studied along 14 transects located at 7 sites of
161 increasing chronological age across the Morteratsch glacier foreland. Maps providing
162 exact information on past glacier movements and snout positions (Elsener 2006) were
163 digitalized and imported into a GIS. Next, the entire chronosequence from the last
164 glacier expansion in 1878 until present was subdivided into 7 strata limited by
165 isochrones of age since deglaciation, separated by time steps of roughly 20 years. The
166 isoclines represent the glacier extensions from 1878 until 2000 (Fig. 2). This map was
167 then merged with a map providing data about the characteristic vegetation for each of
168 110 intersection points of grid with a mesh width of 40 m and covering the entire
169 glacier foreland (Ziefle 2006, Elsener 2006). Then the known flooding areas close to
170 the glacial stream were excluded as potential areas for choosing sites. Finally, the Albrecht et al. Plant–pollinator network assembly 8 ______171 most representative vegetation unit was identified for each site as the unit with the
172 most intersection points per stratum.
173 At each site two transects that were perpendicular to each other were
174 established at a randomly selected intersection point of the most representative
175 vegetation nearby the delimiting isocline of the corresponding stratum. The seven
176 sites in 2008 corresponded to a chronological age since deglaciation of 8, 28, 49, 63,
177 84, 109 and 130 years (Fig. 2). Each intersection point was the center of a cross
178 consisting of a 50-m long transect in the east–west direction and a 30-m long transect
179 in the north–south direction. At the second oldest site (109 years since deglaciation),
180 sampling was not possible along the shorter transect due to very dense tree and shrub
181 vegetation (dominated by Betula sp., Salix sp. and Rhododendron ferrugineum L.).
182 Therefore, only the longer transect was sampled and analyzed at this site. Each
183 transect was marked with cord during the entire sampling season.
184
185 Sampling protocol for plant–pollinator interactions
186 Plant–pollinator interactions were observed during the entire flowering season (10
187 June to 21 August 2008) at roughly 2-week intervals. This resulted in seven sampling
188 rounds for each transect. The snow melted at roughly the same time at each sampling
189 site. Flower-visiting insects were sampled between 9:30 and 17:30 h in sunny weather
190 conditions. Because of potential temporal differences in pollinator activity, each site
191 was sampled at least twice in the morning (9:30–12:30), twice in the early afternoon
192 (12.30–15:30) and twice in the late afternoon (15:30–17:30). Time of day in which
193 sampling took place was varied randomly across sites and sampling rounds. During
194 each sampling round, all flowering vascular plants and their insect visitors were
195 sampled within a belt of 1 m along the transects. While slowly walking along the Albrecht et al. Plant–pollinator network assembly 9 ______196 transects every insect visitor landing on an open flower was caught with a sweep-net
197 immediately after landing and put into a 50 ml tube containing ethyl acetate. Thus,
198 the number of individual flowers within the same inflorescence that an insect visited
199 was not considered in the analyses. Each transect was walked twice during each
200 sampling round. The time spent for each transect and sampling round was 15 min for
201 the short transects and 25 min for the long transect, resulting in total sampling time of
202 4 hours and 40 min for each site. If possible, flower visitors were identified to species
203 level, otherwise to genus or family level (see Appendix 1). Visitation rate was defined
204 as the total number of flower visits observed along a transect during one sampling
205 round. Flower visitors were assumed to be pollinators. Flower abundance of vascular
206 plants was recorded as the number of inflorescences of each flowering plant species,
207 including species that did not receive any pollinator visits.
208
209 Quantifying plant–pollinator network structure
210 The pooled, quantified plant–pollinator networks for each site were plotted. To detect
211 changes in the interaction structure of the plant–pollinator networks along the
212 chronosequence, the following quantitative, weighted properties (i.e. properties that
213 account for variation in interaction strength) were calculated for each transect
214 (sampling rounds pooled): linkage density (LDq), interaction diversity (IDq),
215 pollinator generality (Gpoll,q, the mean number of plant species visited by a pollinator
216 species weighted by interaction strength; also referred to as generalization in the
217 following text) and plant generality (Gplant,q, the mean number of pollinator species
218 visiting a plant species weighted by interaction strength, equivalent to vulnerability
219 Vq of Bersier et al. 2002). Plant species that were not visited by pollinators were not
220 included in these calculations. These metrics were calculated following Bersier et al. Albrecht et al. Plant–pollinator network assembly 10 ______221 (2002) and Albrecht et al. (2007). They are based upon the Shannon indices of
222 entropy to measure the diversity of flowering plants (HN) and pollinator individuals
223 (HP) for each taxon k:
s b b H =− ik log ik N,k ∑ b 2 b 224 Error! Bookmark not defined. i=1 • k • k
s b b 225 H =− kj log kj P,k ∑ b 2 b j=1 k• k•
226 Here b•k is the number of flowering plants visited by visitor species k, and bk• is the
227 number of flower visitors visiting plant species k. The network metrics are based on
228 the “reciprocals” of HN,k and HP,k (Bersier et al. 2002):
⎪⎧ 2HN,k n = ⎨ if b = 0 N,k 0 •k 229 ⎩⎪
⎪⎧ 2HP,k n = ⎨ if b = 0 230 P,k 0 k• ⎩⎪
231 The weighted linkage density (LDq) was calculated for each) as:
s s 1 ⎛ bk• b• k ⎞ LDq = ∑ nP,k + ∑ nN,k 2 ⎝⎜ b b ⎠⎟ 232 k=1 •• k=1 ••
233 where b•• is the total number of visited flowering plants. Weighted pollinator
234 generality (Gpoll,q) and plant generality (Gplant,q) were calculated as:
s b G = • k n poll,q ∑ b N,k 235 k =1 ••
s b G = k• n poll,q ∑ b P,k 236 k =1 ••
237 To measure network level generalization, we calculated the recently
238 developed index H2’ by Blüthgen et al. (2006). This index characterizes the degree of
239 specialization among the two trophic levels in bipartite networks as the deviation Albrecht et al. Plant–pollinator network assembly 11 ______240 from an expected probability distribution of interactions, evaluated by the
241 standardized two-dimensional entropy. For a detailed description of the mathematical
242 derivation of the index see Blüthgen et al. (2006). The index accounts for variability
243 in interaction frequencies and is not only robust against variation in the size of a
244 network, but also its shape, i.e. the skewness of the frequency distribution of the
245 recorded species (Blüthgen et al. 2006, 2008).
246 An additional aspect of generalization in weighted, bipartite networks is
247 interaction strength asymmetry, calculated as the grand mean of the imbalance
248 between the interaction strengths of each species pair within a network (Bascompte et
249 al. 2006). We used the modified version of interaction strength asymmetry proposed
250 by Dormann et al. (2009), in which all singleton species are omitted from the network
251 to avoid that these species receive a very high influence.
252 The measure of the diversity of interactions (IDq) was calculated using the
253 Shannon index:
ID = p log p 254 q ∑ i 2 ()i
255 where pi is the proportion of the interaction i of the total number of interactions.
256 Furthermore, we adapted Hurlbert’s probability of inter-specific encounters (PIE) as a
257 measure for evenness in ecological communities, which is unbiased by the number of
258 species in a sample (Hurlbert 1971), to measure interaction evenness across networks
259 as:
⎡ 2 ⎤ ⎡ L ⎤ ⎛ Li ⎞ PIE = ⎢1− ∑⎜ ⎟ ⎥ ⎣⎢L+ 1⎦⎥ ⎝ L ⎠ 260 ⎣⎢ i ⎦⎥
261 where L is the total number of individual plant–pollinator interactions and Li the
262 number of individual interactions of the i-th link between a specific plant–pollinator
263 pair. Albrecht et al. Plant–pollinator network assembly 12 ______264 We believe that the above mentioned weighted network properties are the
265 most appropriate metrics currently available to investigate our research questions
266 (Bersier et al. 2002, Blüthgen 2006, Dormann et al. 2009). However, in order to make
267 it easier to compare the network properties of this study with those of other plant–
268 pollinator network studies that give only qualitative network metrics we also
269 calculated these traditionally used, unweighted properties (number of links per
270 species, connectance, average pollinator species degree, average plant species degree,
271 average network level degree and nestedness). Connectance is the realized proportion
272 of all possible links. Species degree is the number of different species a species
273 interacts with. Nestedness is a measure of departure from systematic arrangement of
274 species by niche width (Bascompte et al. 2003). The nestedness “temperature” T (0°–
275 100°) measures the departure from a perfectly nested interaction matrix, with
276 maximum nestedness defined as T = 0°.
277 For the calculation of the more complex network properties H2’, interaction
278 strength asymmetry and nestedness the data of the two transects and the seven
279 sampling rounds were pooled for each of the seven sites (to avoid small network sizes
280 potentially affecting the functioning of the more complex algorithms used for the
281 calculations of these metrics). To test whether pooling of the two transects of a site
282 would lead to different results for other measures, all analyses for those other
283 measures were also performed using these pooled data (fitting linear models with the
284 single continuous explanatory variable site age). The results of these analyses were
285 very similar to those obtained by the mixed-effect model analyses presented here. H2’
286 was calculated on the webpage http://nils.mib.man.ac.uk/~nils/stat/. For the
287 calculations of all other network metrics (except Hurlbert’s PIE) the bipartite package
288 supplied in the R-system of statistical computing was used (Dormann et al. 2009). Albrecht et al. Plant–pollinator network assembly 13 ______289
290 Data analysis
291 We analyzed variation in the dependent variables of plant and pollinator species
292 richness, plant and pollinator Shannon diversity indices, flower abundance, visitation
293 rate of pollinators and the above-mentioned network properties with linear mixed-
294 effects models. For the Shannon diversity indices we used the formula described
295 above for interaction diversity IDq, but with pi as the proportion of inflorescences or
296 flower visiting individuals belonging to the i-th plant or pollinator species,
297 respectively. Site age (number of years since deglaciation) was treated as fixed
298 continuous explanatory variable. The remaining variation among sites (i.e. site fitted
299 after site age), transect nested within site, sampling round and the site x sampling
300 round interaction were treated as random effects. For the analyses of the network
301 properties data from all sampling rounds were pooled, because some networks of
302 single sampling rounds were too small for a reliable analysis (thus, sampling round
303 was not included in the analyses of network properties). According to the
304 specification of the random terms for the mixed-model analysis the fixed term site age
305 was tested against the residual variation among sites. This avoided a potential pseudo-
306 replication problem that would have occurred if site age would have been tested
307 against variation among transects or among residuals (= transect x sampling round
308 interactions). Transect length was included as a covariate in the models to account for
309 any potential effects of transect length. Finally, to test whether variation in pollinator
310 species richness and visitation rate along the chronosequence were explained by
311 variation in plant species richness and flower abundance, these two measures were
312 used as further covariates in the models with pollinator visitation rate, pollinator Albrecht et al. Plant–pollinator network assembly 14 ______313 species richness and pollinator diversity as the dependent variables and sampling
314 round, site age and transect length as explanatory variables.
315 We analyzed similarity between the networks at the different sites in observed
316 plant–pollinator interactions by calculating the modified Simpson’s index proposed
317 by Koleff et al. (2003). This index measures the similarity once differences in number
318 of interactions have been removed. It is calculated as the number of interactions of
319 two sites divided by the minimum number of interactions present in one of the two
320 sites (Petanidou et al. 2008). This modified Simpson’s index for interaction similarity
321 was calculated for each of the 7 x 6 / 2 = 21 site pairs. To test whether plant–
322 pollinator networks of closer sites were more similar in interactions than those of sites
323 further apart from each other along the chronosequence we fitted a linear model with
324 the response variable modified Simpson’s index for interactions and the explanatory
325 variables closeness (in years since deglaciation, i.e. 20 years for the pair consisting of
326 the 8- and 28-year old sites), mean age of the site pair (i.e. 18 years for the
327 aforementioned site pair), indicating whether similarity changed along the
328 chronosequence, and their interaction.
329 Mixed effect models were fitted using the lme-function of the nlme package
330 supplied in the R-system of statistical computing (R Development Core Team 2008).
331 Arithmetic means ± 1 standard error (SE) are reported. Due to the low number of sites
332 the power of the F1,5-test for site age was low and thus marginal significances at P <
333 0.1 are also reported as a protection against making too many type-II errors (Toft and
334 Shea 1983). The effect sizes in these analyses can easily be calculated from the
335 reported F-values and the degrees of freedom by taking the square-root of the ratio r2
336 = (df numerator * F) / (df numerator * F + df denominator) (Rosenthal and Rosnow Albrecht et al. Plant–pollinator network assembly 15 ______337 1985), thus the square-root of F / (F + 5). For example, an F-value of 5 results in an
338 effect size of 0.50, whereas an F-value of 10 results in an effects size of 0.67.
339
340 Results
341 Pooled, quantified plant–pollinator networks for all sites along the chronosequence
342 are shown in Figure 2. Over the entire season a total of 54 plant species were
343 flowering and 37 of them (67%) were visited by a total of 92 pollinator species (Fig.
344 2; Appendix 1). In total, 506 plant–pollinator interactions (pollinator visits) were
345 recorded. Whereas flower abundance was not significantly higher at late- than at
346 early-succesional sites (F1,5 = 3.98, P = 0.103; Fig. 3a), species richness of flowering
347 plants (F1,5 = 12.89, P = 0.016), Shannon diversity of flowering plants (F1,5 = 8.77, P
348 = 0.032), visitation rate (F1,5 = 8.47, P = 0.033), species richness of pollinators (F1,5 =
349 8.37, P = 0.034) and network size (total number of interacting plant and pollinator
350 species; F1,5 = 7.87, P = 0.038) all increased along the chronosequence (Fig. 3b–e).
351 Species richness and Shannon diversity of pollinators peaked at intermediate
352 successional age (84 years since deglaciation; Fig. 3e,f).
353 To test if the observed pattern in pollinator species richness and visitation rate
354 along the chronosquence was related to plant species richness and flower abundance,
355 we introduced these covariates stepwise into the linear models. Because plant species
356 richness and flower abundance were highly correlated (F1,6 = 58.84, P < 0.001), we
357 only report results of the stronger explanatory variable, which was plant species
358 richness. Plant species richness was highly significant in explaining variation in
359 pollinator richness (F1,76 = 52.78, P < 0.001) and pollinator visitation rate (F1,76 =
360 48.83, P < 0.001). Moreover, site age was not significant anymore in explaining
361 variation in pollinator richness or visitation rate when plant species richness was Albrecht et al. Plant–pollinator network assembly 16 ______362 included in the model (results not shown). This suggests that increasing site age
363 influenced pollinators indirectly via its effects on plant species richness, although
364 pollinator species richness and pollinator diversity showed a saturating or peaked
365 pattern, respectively, while pollinator visitation rate continued to increase in the late-
366 succesional stages.
367 Diptera accounted for 74%, Hymenoptera (almost exclusively Apidae)
368 accounted for 24% and other insect orders accounted for 2% of the visits to flowers
369 (Fig. 2, Appendix 2). The number of visits by Diptera significantly increased along
370 the chronosequence (F1,5 = 14.77, P = 0.012), whereas that of Hymenoptera showed a
371 non-significant decrease (F1,5 = 3.74, P = 0.111). This resulted in a significant change
372 in the relative number of visits by Diptera and Hymenoptera along the
373 chronosequence (decline in log-transformed Hymenoptera/Diptera-ratio: F1,5 = 14.43
374 P = 0.013) from the youngest site (55% Hymenoptera, 45% Diptera) to the oldest site
375 (4% Hymenoptera, 96% Diptera).
376 At the youngest two sites (8 and 28 years since deglaciation) two plant
377 species, Epilobium fleischeri (Onagraceae; species code 1268: Fig. 2, Appendix 2)
378 and Saxifraga bryoides (Saxifragaceae; species code 909: Fig. 2, Appendix 2),
379 received by far the most pollinator visits. Bumblebees almost exclusively visited E.
380 fleischeri at these two sites (Fig. 2). The interaction structure at these sites was
381 characterized by the dominant interaction between the bumblebee Bombus sichelii
382 and E. fleischeri (Fig. 2). At site 3 (49 years since deglaciation), 44% of the pollinator
383 individuals were recorded on Saxifraga paniculata (Saxifragaceae; species code 887:
384 Fig. 2, Appendix 2), which produced flowers most abundantly (34%) at this site. The
385 percentage of flowering plant species for which pollinator visits were observed was
386 lower at the two pioneer sites (24% at the 8-years old site and 31% for the 28-years Albrecht et al. Plant–pollinator network assembly 17 ______387 old site), while at older sites this percentage was much higher (54% on average,
388 ranging from 47% to 59%; Fig. 2).
389 Interaction diversity increased with site age (F1,5 = 7.38, P = 0.041), at least up
390 to 84 years (Fig. 4a). Interaction evenness tended to increase throughout the entire
391 130-year period covered by the chronosequence (F1,5 = 6.38, P = 0.053; Fig. 4b).
392 However, weighted linkage density (F1,5 = 2.12, P = 0.205; Fig. 4c) remained more or
393 less constant along the chronosequence, while the unweighted number of links per
394 species increased with site age (F1,5 = 8.42, P = 0.034; Appendix 2). Connectance
395 tended to decrease with site age (F1,5 = 4.13, P = 0.098; Appendix 2).
396 Weighted network-level specialization measured as Blüthgen’s H2’
397 significantly decreased along the chronosequence (F1,5 = 8.62, P = 0.032; Fig. 4d).
398 The strongest decline occurred between the 28- and the 63-year old sites. This
399 decrease in network-level specialization reflected the higher average number of
400 pollinator species visiting a plant species (increase in pollinator generality with site
401 age; F1,5 = 39.08, P = 0.002; Fig. 4e), while the weighted average number of plants
402 visited by a pollinator did not change (F1,5 = 0.12, P = 0.747; Fig. 4f). The qualitative,
403 unweighted equivalents of the above mentioned weighted metrics showed
404 qualitatively identical patterns (increase in average network level and pollinator
405 species degree: F1,5 = 8.04, P = 0.037; F1,5 = 7.86, P = 0.001; no directional trend in
406 average plant species degree: F1,5 = 1.18, P = 0.327; Appendix 2). Examples of
407 pollinator species that occur at most sites and that visited more plant species at older
408 sites compared to earlier ones include Tricops sp., Paonia sp. or Bombus sichelii. For
409 most of the abundantly flowering plant species the highest degree values (number of
410 visitor taxa per plant species) were recorded at the site with highest flower abundance
411 of the corresponding plant species. For example, for Epilobium fleischeri this was the Albrecht et al. Plant–pollinator network assembly 18 ______412 8-year old site, for Saxifraga paniculata the 49-year old site or for Achillea erba-rotta
413 the 84-year old site.
414 The contrasting patterns of plant and pollinator generalization in early-
415 successional stages resulted in a decline in interaction strength asymmetry along the
416 chronosequence (F1,5 = 9.39, P = 0.028; Fig. 4g), with pollinators being more
417 specialized in these early assembly stages than plants. Conversely, networks at
418 mature successional stages showed a more nested interaction structure than those at
419 early stages (F1,5 = 9.39, P = 0.028; Fig. 4h).
420 Interaction similarity between networks (the modified Simpson’s index) was
421 higher for closer sites than for sites further apart from each other along the
422 chronosequence (F1,17 = 13.31,12 P = 0.002; Appendix 3). However, average
423 interaction similarity did not change with site age (F1,17 = 0.04, P = 0.854; Appendix
424 3).
425
426 Discussion
427 Our study shows pronounced changes in the structure of plant–pollinator networks
428 along the chronosequence of a primary glacier foreland succession. These changes
429 can be interpreted as community assembly processes leading to increasingly diverse
430 plant–pollinator systems at least during the first 80 years after deglaciation. With
431 increasing site age, the average pollinator visited more plant species (increasing
432 pollinator generalization, but not plant generalization), suggesting that plant
433 community assembly may have been the driver in this diversity-begets-diversity
434 assembly process. As a consequence, network level specialization (measured as
435 Blüthgen’s H2’) and interaction strength asymmetry declined up to a mid-successional
436 stage. Albrecht et al. Plant–pollinator network assembly 19 ______437
438 Plant and pollinator community assembly
439 Species richness and Shannon diversity of plants with open flowers increased from
440 the youngest to the mid- and late-successional sites along the chronosequence (see
441 Fig. 3). This confirms findings of previous studies on plant primary succession on the
442 foreland of the Morteratsch glacier (Ziefle 2006) and most other studied glacier
443 forelands (Matthews 1992, Walker and del Moral 2003).
444 Pollinator community assembly seemed to follow plant community assembly.
445 Thus, the increase in plant diversity along the chronosequence was paralleled by an
446 increase in visitation rate and species richness of pollinators and, at least up to a mid-
447 successional stages, pollinator Shannon diversity (see Fig. 3). Recent experimental
448 work also indicates that pollinator visitation rate increases linearly with plant species
449 richness, while pollinator diversity scales with plant species richness along a
450 saturating curve (Ebeling et al. 2008). Considering the relatively high mobility of
451 most pollinator taxa, colonization of freshly available, early-successional habitats by
452 pollinators was probably not dispersal limited. In addition, the increasing
453 generalization of pollinators suggests that the diversity of available floral resources
454 was the crucial factor affecting pollinator community assembly on the local scale of
455 our seven sites (Potts et al. 2003b). Evidence from studies on secondary successions
456 also indicates that pollinator diversity mainly mirrors plant diversity, which can lead
457 to increasing pollinator diversity with increasing age of meadows (Gathmann et al.
458 1994) or decreasing pollinator diversity along recovering vegetation gradients after
459 fire, with highest plant diversity levels in the first few years after a burn (Potts et al.
460 2003a). Albrecht et al. Plant–pollinator network assembly 20 ______461 In addition to the changes in pollinator species richness, the species
462 composition of pollinator communities also changed along the chronosequence of the
463 primary succession of our glacier foreland. Bees, particularly bumblebees, dominated
464 in pioneer stages and flies in mature stages. These changes in pollinator species
465 composition probably reflect those in species composition of the plant communities
466 along the chronosequence. For example, the abundance of bumblebees closely
467 mirrors the flower abundance of Epilobium fleischeri, a pioneer species with highest
468 abundances at the youngest two sites.
469
470 Plant–pollinator network assembly
471 Interaction diversity and less so interaction evenness increased along the
472 chronosequence up to mid-successional stages. These trends were consistent with an
473 increase in pollinator generality and a decrease in network-level specialization along
474 the chronosequence (see Fig. 4). In contrast to some measures used previously to
475 characterize generalization or specialization, such as the average species degree
476 (number of taxa interacting with one partner species; e.g. Herrera 2005) or
477 connectance (proportion of realized of all possible links; Jordano 1987, Olesen and
478 Jordano 2002), the quantitative, weighted network metrics used in this study are
479 largely independent of network size and sampling effort (Bersier et al. 2002, Blüthgen
480 et al. 2006). The metric H2’ additionally controls for variation in skewness of the
481 frequency distribution of the data, which may affect some weighted properties such as
482 generality or interaction strength asymmetry (Blüthgen et al. 2008). In spite of their
483 deficiencies, the qualitative metrics of plant and pollinator generality and network
484 level generalization (average plant, pollinator and network level degree) produced
485 similar results as their quantitative, weigthed counterparts. Only for connectance, a Albrecht et al. Plant–pollinator network assembly 21 ______486 qualitative metric that has often been used to describe generalization in networks,
487 there was a tendency to decline with site age, while the results for quantitative H2’
488 (and the qualitative average network degree) indicated a significant increase in
489 network level generalization. Connectance as a qualitative metric to characterize
490 generalization across networks has been criticized (Blüthgen et al. 2006, 2008 and
491 references therein) because it is not only sensitive to sampling intensity but also
492 because it is negatively correlated with network size (e.g. Olesen and Jordano 2002).
493 Since network size increased with site age in our study, a decrease in connectance
494 with site age is expected only due to this mathematically inherent scale dependence
495 (Blüthgen et al. 2006). This emphasizes the importance of using properties that are
496 more robust against such effects when comparing networks differing in size (Bersier
497 et al. 2002, Blüthgen et al. 2006).
498
499 Why does plant generality not decrease with successional age?
500 Our results do not support niche-theoretical predictions that plant species in mature
501 communities should be more specialized with respect to their pollinators than those in
502 early-successional communities because of increased interspecific competition and
503 reduced niche overlap (Parrish and Bazzaz 1979). Although some characteristic plant
504 species of the two pioneer sites, such as Epilobium fleischeri or Saxifraga bryoides
505 were visited by a relatively high number of different pollinator species (Fig. 2),
506 overall plant generalization did not show a significant directional trend along the
507 chronosequence. Interestingly, however, some abundantly flowering plant species
508 such as Arabis alpina at the pioneer sites received no or very few pollinator visits.
509 Most studies on plant–pollinator networks do not provide data regarding the
510 percentage of flowering plant species for which no pollinator visits are observed. In Albrecht et al. Plant–pollinator network assembly 22 ______511 our study, approximately one third of all flowering species received no pollinator
512 visits, although most of them are known to be normally pollinated by insects. This
513 percentage was roughly twice as high for the two youngest sites compared with the
514 more mature ones, indicating that a considerably lower number of flowering plant
515 species is integrated into the pollination network at early than at late stages of
516 network assembly.
517
518 Why does pollinator generality increase with successional age?
519 Because plant diversity increased yet total flower abundance remained relatively
520 constant along the chronosequence, the abundance of flowers of individual plant
521 species declined. Under these circumstances and according to optimal foraging
522 theory, pollinators should become generalists to minimize foraging time (MacArthur
523 and Pianka 1966, Heinrich 1976, Pyke 1984). Thus, the increase in pollinator
524 generalization and the decrease in network-level specialization found in this study
525 may reflect the increase in heterospecific floral resources during the primary
526 succession. Another prediction of optimal foraging theory that is supported by our
527 findings is that the increase in pollinator abundance during the succession, indicated
528 by the positive correlation between visitation rate and site age, should result in a
529 higher diet breadth of individual pollinators as a response to more rapid depletion of
530 floral resources at high pollinator abundance (Fontaine et al. 2008). Such a parallel
531 increase in abundance and generalization along the chronosequence was found for
532 example for several taxa of flies or the bumblebee species Bombus sichelii. However,
533 also many plant species showed a positive correlation between flower abundance and
534 generalization level (see Fig. 4). A simple mechanism of network assembly that is
535 compatible with such a pattern is the preferential attachment model (Barabasi and Albrecht et al. Plant–pollinator network assembly 23 ______536 Albert 1999). According to this model, new species have a higher probability to link
537 to species that have already many links in a network, leading to a “rich-get-richer”
538 process. Studying the day-to-day attachment of new species (i.e. species that started
539 to flower or actively forage) to an arctic plant–pollinator network during two seasons,
540 Olesen et al. (2008) found that new species indeed tended to attach to already well-
541 connected species, although this was constrained by some morphological and
542 phenological mismatching of plants and pollinators. Although we did not study such
543 factors as potential drivers of the observed patterns of network assembly in the
544 present study, we found at least some evidence that abundance was positively linked
545 with the degree of many plant and pollinator species.
546 Our findings are consistent with the view that plant–pollinator networks, at
547 least in later successional stages, may be not so much niche-structured, but may rather
548 reflect a high degree of opportunism with respect to resource use and thus a more
549 neutral way of plant–pollinator networks assembly (Vazquez 2005, Petanidou et al.
550 2008). Such opportunism is, however, obviously limited by phenological,
551 morphological and co-evolutionary constraints (Jordano et al. 2003, Stang et al. 2006,
552 Bascompte and Jordano 2007, Santamaría and Rodríguez-Gironés 2007, Vazquez et
553 al. 2009a,b). On glacier forelands and other systems structured by primary
554 succession, plants often occur in scattered and sparse populations, in particular during
555 pioneer stages, and thus may depend much more than those of later assembly stages
556 on specialized pollinators that show high levels of fidelity (Johnson and Steiner 2000
557 and references therein). On the other hand, a plant species typically found in early
558 stages of primary succession that relies to a large degree on animal pollination, such
559 as E. fleischeri (Albrecht, unpublished data), should attract a diverse pollinator
560 community considering the unpredictability of pollinator availability during this stage Albrecht et al. Plant–pollinator network assembly 24 ______561 (Parrish and Bazzaz 1979). This should lead to exactly the pattern found here, with
562 higher pollinator specialization but not plant specialization in the pioneer stages of the
563 succession, resulting in a higher interaction strength asymmetry in these early stages.
564
565 Increased nestedness of plant–pollinator networks with successional age
566 Most plant–pollinator networks that have been studied so far were characterized by a
567 highly nested interaction structure, i.e. more specialized species tend to interact with
568 interaction partners that are highly generalized, and with nestedness levels
569 significantly higher compared with those of null models (Bascompte et al. 2003,
570 Bascompte and Jordano 2007). Our finding of high nestedness levels in the mature
571 assembly stages is in a line with the existing studies typically investigating mature,
572 relatively stable systems. The early assembly stages, however, were significantly less
573 nested, indicating that nestedness is a typical feature of rather developed and stable
574 plant–pollinator networks, but not necessarily of still evolving ones at the beginning
575 of assembly processes. This view would also be in agreement with simulation models
576 suggesting that a highly nested interaction structure makes ecological networks more
577 cohesive and stable (Bascompte et al. 2003, 2009, Bascompte and Jordano 2007).
578
579 Conclusions
580 It is a well-known fact that ecological network data so far have always been
581 constructed of incomplete samples (Blüthgen et al. 2008). Also in the present study,
582 sampling thoroughness may not have been extremely high. However, here we were
583 primarily interested in the relative change of network properties and their pattern
584 along a primary successional gradient rather than estimate values, for example of
585 fundamental specialization, most accurately. Despite potential limitations inherent in Albrecht et al. Plant–pollinator network assembly 25 ______586 the space-for-time substitution approach and the above mentioned uncertainties in
587 sampling thoroughness typical for ecological network data, the analysis of networks
588 along chronosequences provide unique insights into the assembly of multitrophic
589 communities and their interaction structure that cannot be gained by short-term
590 experimental studies (Matthews 1992, Hodkinson et al. 2004). To our knowledge, this
591 is the first study that used a network approach to explore plant–pollinator assembly
592 during primary succession. We found pronounced changes in the interaction structure
593 of the plant–pollinator communities during the studied glacier foreland succession,
594 which was characterized by a strong increase in pollinator generality, but not plant
595 generality, during network assembly, resulting in a decline of network level
596 specialization and interaction strength asymmetry. An important implication of this
597 study is that the network structure of evolving ecological communities in early
598 assembly stages can be significantly different to that of the usually studied, rather
599 mature and stable ones. Further research is needed to unravel the exact mechanistic
600 processes underlying the observed patterns in the evolution of plant–pollinator
601 network properties during this type of primary succession. Ecological network
602 analysis along successional gradients is a promising tool to gain new insights in how
603 networks of multitrophic communities assemble.
604
605 Acknowledgements
606 We dedicate this paper to Christine Müller, who died after the start of this study; she
607 was a great inspiration for us. We thank Ulrich Schneppat for valuable advice on
608 insect preparation and the following taxonomic specialists for help with the species
609 identification: Jean-Paul Haenni (Diptera) and Hermann Blöchlinger (Diptera), Ruth
610 Bärfuss (Syrphidae), Rainer Neumeyer and Felix Amiet (Apidae), Seraina Klopfstein Albrecht et al. Plant–pollinator network assembly 26 ______611 and Hannes Baur (Ichnneumonidae), Jürg Schmid (Lepidoptera), Peter Herger
612 (Coleoptera) and Denise Wyniger (Heteroptera). We are grateful to Stefan Elsener for
613 providing GIS data and Roland Schmid for help with drawing the plant–pollinator
614 networks. We also thank Martina Stang and two anonymous reviewers for their
615 valuable comments on an earlier version of this manuscript. This work was supported
616 by the Swiss National Science Foundation (grant 631-065950 to Christine Müller). Albrecht et al. Plant–pollinator network assembly 27 ______617 References
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732 Albrecht et al. Plant–pollinator network assembly 32 ______733 Figure legends
734 Fig. 1. Location and map of the Morteratsch glacier foreland at the south-facing slope
735 of the Bernina Range (Engadine, Switzerland). Positions of sampling sites
736 representing a chronosequence from 8 to 130 years since deglaciation together with
737 isoclines of glacier extensions from 1857 to 2008 (grey lines) and the glacial stream
738 (black line).
739
740 Fig. 2. Quantified plant–pollinator networks at seven sites of increasing distance from
741 the glacier snout across the Morteratsch glacier foreland, representing a
742 chronosequence from 8 to 130 years since deglaciation. Lower bars represent flower
743 abundance of vascular plants and upper bars pollinator visitation rates drawn at
744 different scales. The width at the basis of the wedges represents interaction frequency.
745 Taxa are coded by numbers (see Appendix 2 for names).
746
747 Fig. 3. Flower abundance (a), plant species richness (b), plant diversity (Shannon
748 diversity index) (c), pollinator visitation rate (d), pollinator species richness (e) and
749 pollinator diversity (Shannon diversity index) (f) at seven sites of increasing distance
750 from the glacier snout across the Morteratsch glacier foreland, representing a
751 chronosequence from 8 to 130 years since deglaciation. The broken lines connect the
752 mean values of the two transects of each site (for better visualisation they are slightly
753 jittered; at the 109-year old site only one transect could be sampled). For the
754 calculation of (a) to (c), all flowering plant species, including species that did not
755 receive any pollinator visits, were used. Plant–pollinator interactions were sampled
756 during a total of four hours and 40 min per site during seven sampling rounds from 10
757 June to 21 August 2008. Albrecht et al. Plant–pollinator network assembly 33 ______758
759 Fig. 4. Changes in quantitative, weighted network properties at seven sites of
760 increasing distance from the glacier snout across the Morteratsch glacier foreland,
761 representing a chronosequence from 8 to 130 years since deglaciation: (a) interaction
762 diversity (Shannon diversity index), (b) interaction evenness (measured as Hurlbert’s
763 probability of interspecific encounters), (c) linkage density, (d) network level
764 specialization (measured as Blüthgen’s H2’ using the pooled networks of a site, see
765 “Material and Methods”), (e) pollinator generality and (f) plant generality. The broken
766 lines connect the mean values of the two transects of each site (for better visualisation
767 they are slightly jittered; at the 109-year old site only one transect could be sampled).
768 Plant species that were not visited by pollinators were not included in these analyses.
769 For description and calculation of the network properties see “Material and Methods”
770 section.
771 Albrecht et al. Plant–pollinator network assembly 34 ______772 Figures
773 Figure 1
774
775 Albrecht et al. Plant–pollinator network assembly 35 ______776 Figure 2
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779 Albrecht et al. Plant–pollinator network assembly 36 ______780 Figure 3
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797 Albrecht et al. Plant–pollinator network assembly 37 ______798 Figure 4
799
800
801 Albrecht et al. Plant–pollinator network assembly 38 ______802 Appendix 1. List of codes for pollinator and plant species used in Fig. 2.
Code Pollinator Code Plant 3001 Anthidium montanum Morawitz 1864 221 Papaver croceum Ledebour 3002 Anthomyiidae sp. 385 Cerastium fontanum ssp. fontanum Baumgarten 3003 Bombus monticola Smith 1849 390 Cerastium arvense ssp. strictum Koch 3004 Bombus mucidus Gerstaecker 1869 419 Silene vulgaris ssp. vulgaris Garcke 3005 Bombus pyrenaeus Perez 1879 447 Oxyria digyna Hill 3006 Bombus sichelii Radoszkowski 1859 472 Rumex scutatus L. 3007 Bombus sp. 583 Salix sp. 3008 Calliphoridae sp. 652 Cardamine resedifolia L. 3009 Cantharis pagana Rosenhauer 1847 667 Arabis alpina L. 3010 Cauchas fibulella Denis & Schiffermüller 1775 778 Myrica germanica L. 3011 Chamaesyrphus scaevoides Fallén 1817 786 Rhododendron ferrugineum L. 3012 Cheilosia albitarsis Meigen 1822 789 Vaccinium vitis–idaea L. 3013 Cheilosia sp. Meigen 1822 803 Pyrola minor L. 3014 Cheilosia variabilis Panzer 1798 818 Primula latifolia Lapeyrouse 3015 Coenosia sp. Meigen 1826 879 Sempervivum montanum L. 3016 Coenosiinae subfam. 880 Sempervivum arachnoideum L. 3017 Colletes impunctatus Nylander 1852 882 Sempervivum tectorum ssp. Alpinum L. 3018 Coreus sp. Fabricius 1794 887 Saxifraga paniculata Miller 3019 Cryptocephalus aureolus Suffrian 1847 901 Saxifraga aizoides L. 3020 Dasysyrphus friuliensis van der Goot 1960 909 Saxifraga bryoides L. 3021 Dilophus femoratus Meigen 1804 910 Saxifraga aspera L. 3022 Empididae sp. 936 Geum reptans L. 3023 Empis sp. L. 1758 969 Potentilla aurea L. 3024 Empria fletcheri Cameron 1878 1129 Trifolium pratense ssp. pratense L. 3025 Episyrphus balteatus De Geer 1776 1135 Trifolium pallescens Schreber 3026 Eristalis tenax L. 1758 1145 Trifolium badium Schreber 3027 Eupeodes corollae Fabricius 1794 1150 Anthyllis vulneraria ssp. alpestris Schultes 3028 Eupeodes latilunulatus Collin 1931 1158 Lotus alpinus Ramond 3029 Eupeodes nitens Zetterstedt 1843 1268 Epilobium fleischeri Hochstetter 3030 Halictus rubicundus Christ 1791 1297 Thesium alpinum L. 3031 Helina annosa Zetterstedt 1838 1499 Laserpitium halleri Crantz 3032 Hybomitra sp. Enderlein 1922 1585 Myosotis scorpioides L. 3033 Hybotidae sp. Enderlein 1922 1701 Thymus sp. 3034 Exochus sp. Gravenhorst 1829 1761 Linaria alpina ssp. alpina (L.) Miller 3035 Lasioglossum alpigenum Dalla Torre 1877 1796 Veronica fructicans Jacques 3036 Lasioglossum fratellum Perez 1903 1824 Euphrasia minima Schleicher 3037 Leucozona lucorum L. 1758 1846 Rhiantus minor L. 3038 Malthodes sp. Kiesenwetter 1852 1853 Melampyrum silvaticum L. 3039 Meliscaeva auricollis Meigen 1822 1900 Campanula barbata L. 3040 Miridae sp. 1908 Campanula cochleariifolia Lamarck 3041 Muscidae sp. 1917 Phyteuma hemisphaericum L. 3042 Myopa sp. Fabricius 1775 1923 Phyteuma betonicifolium Villars 3043 Nematus bohemani Thomson 1871 1954 Galium anisophyllon Villars 3044 Osmia inermis Zetterstedt 1838 2012 Valeriana tripteris L. 3045 Osmia loti Morawitz 1867 2028 Adenostyles leucophylla (Willd.) Reichenbach 3046 Paragus punctulatus Zetterstedt 1838 2030 Solidago virgaurea ssp. minuta (L.) Archangeli 3047 Phaonia hybrida Schnabl 1888 2051 Erigeron acer ssp. politus (Fr.) Lindberg 3048 Phaonia meigeni Schnabl 1888 2067 Anthennaria dioica (L.) Gaertner 3049 Phaonia serva Meigen 1826 2114 Achillea erba-rotta ssp. moschata (Wulfen) Vacc. Albrecht et al. Plant–pollinator network assembly 39 ______
3050 Phaonia sp. Robineau-Desvoidy 1830 2139 Leucanthemopsis alpina (L.) Heywood 3051 Phoridae sp. Robineau-Desvoidy 1830 2271 Leontodon helveticus Mérat 3052 Phratora vitellinae L. 1758 2297 Taraxacum sp. 3053 Pipiza bimaculata Meigen 1822 2343 Hieracium staticifolium Allioni 3054 Pipizella sp. Rondani 1856 2357 Hieracium murorum L. 3055 Platycheirus albimanus Fabricius 1781 3056 Platycheirus melanopsis Loew 1856 3057 Platycheirus scutatus Meigen 1822 3058 Platypalpus sp. Le Peletier & Serville 1828 3059 Psilidae sp. 3060 Rhamphomyia sp. Meigen 1822 3061 Rhingia campestris Meigen 1822 3062 Sarcophagidae sp. 3063 Scaeva pyrastri L. 1758 3064 Scaeva selenitica L. 1758 3065 Sciaridae sp. 3066 Siphona sp. Meigen 1803 3067 Sphaerophoria bankowskae Goeldlin 1989 3068 Sphaerophoria laurae Goeldlin 1989 3069 Sphaerophoria scripta L. 1758 3070 Sphaerophoria sp. Le Peletier & Serville 1828 3071 Ichneumon sp. L. 1758 3072 Spilogona sp. Schnabl 1911 3073 Stomoxys calcitrans L. 1758 3074 Syrphus ribesii L. 1758 3075 Syrphus torvus Osten-Sacken 1875 3076 Syrphus vitripennis Meigen 1822 3077 Tachinidae sp. 3078 Tenthredo brevicornis Konow 1886 3079 Thricops aculeipes Zetterstedt 1838 3080 Thricops cunctans Meigen 1826 3081 Thricops longipes Zetterstedt 1845 3082 Thricops nigritellus Zetterstedt 1838 3083 Thricops rostratus Meade 1882 3084 Thricops semicinereus Wiedemann 1817 3085 Thricops separ Zetterstedt 1845 3086 Thricops sp. Zetterstedt 1845 3087 Thricops sudeticus Schnabl 1888 3088 Pipizella annulata Macquart 1829 3089 Bombus pratorum L. 1761 3093 Cryptinae subfam. 3094 Tryphoninae subfam. 3095 Campopleginae subfam. 803
804 Albrecht et al. Plant–pollinator network assembly 40 ______
805 Appendix 2. Qualitative (unweighted) network properties of plant–pollinator networks at seven sites of increasing distance from the glacier
806 snout across the Morteratsch glacier foreland, representing a chronosequence from 8 to 130 years since deglaciation. T1 = value for the 50 m
807 long transect; T2 value for the 30 m long transect; Site = value for the pooled data of the two transect for a site. At the 109-year old site only one
808 transect could be sampled. For the calculation of the network properties only the number of flowering plant species visited by pollinators were
809 used.
Site age (years)
82849 63 84 109 130
T1 T2 Site T1 T2 Site T1 T2 Site T1 T2 Site T1 T2 Site T1 T2 Site T1 T2 Site
Qualitative network properties
Network size 17 15 25 16 18 27 31 27 42 24 24 37 33 37 54 26 NA 26 41 24 49 Number of visited plant species 4 3 4 4 3 5 9 10 12 11 11 16 12 9 15 17 NA 17 26 14 20 Number of not visited plant species 12 8 13 10 7 11 15 7 13 9 12 12 12 6 10 2 NA 2 17 11 3 Number of pollinator species 13 12 21 12 15 22 22 17 30 13 13 21 21 28 39 9 NA 9 15 10 29 Links per species 0.77 0.87 0.92 0.75 0.83 0.85 0.94 0.78 1.02 0.75 0.71 0.89 0.94 0.92 1.11 1.08 NA 1.08 1.07 0.96 1.2 Connectance 0.25 0.36 0.27 0.25 0.33 0.21 0.15 0.12 0.12 0.13 0.12 0.10 0.12 0.13 0.10 0.18 NA 0.18 0.11 0.16 0.10 Average degree 1.53 1.73 1.84 1.5 1.67 1.7 1.87 1.56 2.05 1.5 1.42 1.78 1.88 1.84 2.22 2.25 NA 2.25 2.15 1.92 2.41 Plant degree 3.25 4.33 5.75 3 5 4.6 3.22 2.1 3.58 1.64 1.55 2.06 2.58 3.78 4 3.11 NA 3.11 2.93 2.3 2.95 Pollinator degree 1 1.08 1.1 1 1 1.05 1.32 1.24 1.43 1.38 1.31 1.57 1.48 1.21 1.54 1.65 NA 1.65 1.69 1.64 2.03
810 Albrecht et al. Plant–pollinator network assembly 41 ______811 Appendix 3. Values of the modified Simpson’s index as a measure of interaction
812 similarity of the plant–pollinator networks for each site pair. Plant–pollinator
813 interactions were sampled at seven sites of increasing distance from the glacier snout
814 across the Morteratsch glacier foreland, representing a chronosequence from 8 to 130
815 years since deglaciation. The numbers in the site pair column indicate the ages of the
816 two sites in years.
817
Site pair Modified Simpson's index
8 – 28 0.30 8 – 49 0.22 8 – 63 0.00 8 – 84 0.13 8 – 109 0.04 8 – 130 0.00 28 – 49 0.17 28 – 63 0.09 28 – 84 0.17 28 – 109 0.13 28 – 130 0.04 49 – 63 0.12 49 – 84 0.16 49 – 109 0.20 49 – 130 0.02 63 – 84 0.24 63 – 109 0.12 63 – 130 0.12 84 – 109 0.17 84 – 130 0.08 109 – 130 0.14
818