Apidologie 40 (2009) 634–650 Available online at: c INRA/DIB-AGIB/EDP Sciences, 2009 www.apidologie.org DOI: 10.1051/apido/2009062 Original article

Complex structure of - interaction-webs: random, nested, with gradients or modules?*

Anselm Kratochwil1,MarionBeil2, Angelika Schwabe2

1 Ecology, University of Osnabrück, Barbarastr. 13, 49069 Osnabrück, 2 Vegetation Ecology, Darmstadt University of Technology, Schnittspahnstraße 4, 64287 Darmstadt, Germany

Received 25 October 2008 – Revised 22 March 2009 – Accepted 7 April 2009

Abstract – We analysed the interaction web of a plant- pollinator community (: , honeybees excluded) for two years. Based on the ordination of the incidence matrix, both webs showed coherence and clumping but no species turnover. While this may indicate a moderate set of nested subsets and sub-communities, further analysis of nestedness did not reveal uniform results. A null-model analysis of different nestedness metrics showed no evidence despite the asymmetric structure of bipartite graphs. However, further analysis revealed significant modularization within the community with connected hub species within modules and module-interlinking connector species. The web is characterized by 4–6 dom- inant connector plant species, representing four main flower types. The pattern depends on the year. DCA demonstrates that the connector plant species support resources for of different body sizes and be- haviour. The pattern is characterized by modularity and the existence of specific connector plant species. coherence / nested subsets / bipartite web / modularity / real network structure

1. INTRODUCTION Bascompte et al. (2003), Bascompte and Jordano (2006), and others point out that mutualistic networks are generally nested. On a community level, interactions between This was shown by nested subset analy- plant species and flower-visiting species can ses based predominantly on the concept of be characterized as “plant-flower-visitor net- the “Nested Temperature Calculator Program” works” (Dupont et al., 2003). There have been (NTC) elaborated by Atmar and Patterson many studies on such networks, mostly cov- (1993, 1995). The matrix temperature T is ering one or parts of one season and in- a percentage that measures how much the cluding only selected visited plant species or presence-absence matrix departs from per- selected flower-visiting (Kratochwil, fect nestedness. It is used in certain disci- 1984; Memmott, 1999;Olesenetal.,2002; plines (e.g., biogeography) to quantify the ef- Jordano et al., 2003; Philipp et al., 2006; Forup fects of fragmentation on metacommunities et al., 2008). In recent times, investigations (Ganzhorn and Eisenbeiß, 2001; Armbrecht have focused more intensively on the structure et al., 2001; Fischer and Lindenmayer, 2005a) of flower-visitor network structures, such as or spatio-temporal changes of communities nestedness, compartmentation, or modularity (Bloch et al., 2007). According to Bascompte (Bascompte et al., 2003; Ollerton et al., 2003; et al. (2003) and Dupont et al. (2003), plant Lewinsohn et al., 2006; Olesen et al., 2008). species may be regarded as “resource is- lands” for flower-visiting species, thus en- Corresponding author: A. Kratochwil, abling a nested subset analysis of the net- [email protected] work of plant- and flower-visiting species. * Manuscript editor: Jacqueline Pierre For this purpose, a presence-absence matrix Complex structure of a plant-bee community 635 is established, with bee species aligned in methods for analysing the species pattern by columns (called “species”) and plant species coherence, species turnover, and boundary- in rows (called “islands”). Many studies use clumping analysis to detect random patterns NTC as “ideally suited to explore various fea- (Simberloff, 1983), checkerboards (Diamond, tures of nestedness” and hence used as the ba- 1975), nested subsets (Patterson and Atmar, sis for far-reaching conclusions from the as- 1986), or different gradient types. Clementsian sumed nestedness (e.g., Ibáñez et al., 2005). gradients (Clements, 1916) are characterized The demonstration of nestedness is inter- in discrete subcommunities in a web that re- preted as follows: bipartite networks are nested place each other as a group, Gleasonian gradi- if those species with fewer interactions are as- ents (Gleason, 1926) result in species turnover sociated with a subset of species that inter- with random arrangement of species ranges act with the most connected ones (Bascompte along the gradient, and Tilmanian gradients et al., 2003). Therefore, plant species that are (Tilman, 1982) exhibit no discrete communi- characterized by only limited interaction pro- ties but arrange species more evenly than ex- cesses are often associated with those flower pected by random chance. visitors that are widespread, abundant, and It is possible to visualize and test patterns generalists. In contrast, specialized flower visi- by bipartite networks, co-occurrence analy- tors (oligolectic species) are often closely con- sis (Muller, 2008;Olesenetal.,2008), or nected with widespread and abundant plant multivariate sets (Lewinsohn et al., 2006; species, which are visited by broad spectra Almeida-Neto et al., 2007). Here, we use of bees and are characterized by a high de- a data set sampled in a model ecosystem: gree of interaction. Moreover, bee and plant species-rich sand grassland of the temper- generalists tend to interact with other gener- ate zone. If networks are not nested, the alists, thus creating a close interactive net- consequences for system stability are serious work (Lewinsohn et al., 2006). Such asymmet- because such non-nested systems should be ric structures in plant-visitor interactions seem highly vulnerable against disturbances. How- likely to be the rule (Ashworth et al., 2004; ever, the ecological significance of nestedness Vázquez and Aizen, 2004; Blüthgen et al., has been disputed (Blüthgen et al., 2007)since 2007;Olesenetal.,2008). Many such net- it might be seen as a derived property of the works most probably depend on generalists bipartite network rather than a first-order prop- (Waser et al., 1996; Memmott, 1999). erty (Dormann et al., 2009). It is possible to quantify the pattern of The questions we pose here are as fol- nestedness with different programs, such as lows: (1) Are the network structures within NTC (Atmar and Patterson, 1995), Binmatnest the studied plant-bee webs distributed ran- (Rodriguéz-Gironés and Santamaría, 2006), domly, nested, or characterized by a gradient Nestedness (Ulrich, 2006a). Alternatively, one (and, if so, by a Clementsian, Gleasonian, or can use discrepancy indices (e.g., “BR”; Tilmanian gradient)? (2) Is it possible to de- Brualdi and Sanderson, 1999), a suitable in- tect modularity in this pollination network? dicator for the presence of nestedness (Ulrich (3) Which functional traits (e.g., use of spe- and Gotelli, 2007), or “d1” (Greve and Chown, cific flower types, pollen-collecting structures, 2006), all of which take into consideration or body size) determine the community struc- the occurrence of “singletons” (= data oc- ture of the bee web? curring only once in a matrix) leading to higher nestedness values. Recently, a new met- ric (“NODF”) has been proposed by Almeida- 2. MATERIALS AND METHODS Neto et al. (2008) quantifying neglected prop- erties of nestedness (Ulrich, 2006b). 2.1. Study area Besides nestedness, a non-random interac- tion matrix can also be detected as a gradi- The study areas are situated in the northern Up- ent, a compartmented, or a combined struc- per Rhine Valley near Darmstadt (Germany) in two ture. Leibold and Mikkelson (2002)present nature protection areas (70 ha, 8◦ 35’E, 49◦ 51’N; 636 A. Kratochwil et al.

45 ha, 8◦ 34’E, 49◦ 50’N). The vegetation in both n = 542). The structure of the bee community in study areas is characterized by plant communities of one year is regarded as a distinct data set, reflecting sandy ecosystems (Beil et al., 2008) and dominated the environmental conditions of the current year and by important flower resources for bees (e.g., Echium the year prior. vulgare, Helichrysum arenarium, Hieracium pi- losella, Potentilla argentea, Medicago minima). The meteorological conditions during the study pe- 2.4. Testing coherence, species turnover, riods 2004 and 2005 were slightly warmer and drier and boundary clumping than the long-term data of mean annual temperature 9–10 ◦C and mean precipitation of about 700 mm/a ◦ ◦ To identify gradients or compartments, we used (2004: 10.7 C, 2005: 11.0 C, annual average pre- the approach of Leibold and Mikkelson (2002). cipitation 2004: 556 mm and 2005: 524 mm; Walter This method is based on ordination of the inci- and Lieth (1967), Deutscher Wetterdienst Frank- dence matrix to identify the dominant axis of vari- / furt Main Airport). ation. “Coherence” represents the degree to which the web pattern can be collapsed into a single di- mension. “Species turnover” describes the replace- 2.2. Study design and sampling ments and “boundary clumping” defines the species distribution along this dimension. All significance The study was conducted from April to Septem- values are based on iterations (n = 100) of the ber in 2004 and 2005 on 45 circular permanent plots respective null model. Significance in coherence with radius 8 m (36 in a 50 ha area, 9 in a 12 ha but not in species-turnover indicates nested subsets, area of open sand vegetation). Both areas belong significance in coherence, turnover, and bound- to a formerly large sand area which had been frag- ary clumping characterizes a Clementsian gradi- mented about 50 years ago. We used a grid sys- ent, significance in coherence and turnover with a tem with minimum plot distances of 50 m. Both Morisita Index I > 1 characterizes a Gleasonian gra- the community structure of vegetation and the bee dient, and significance in coherence and turnover community structure are well represented by this but no significant boundary clumping characterizes approach. For vegetation, we were able to com- a Tilmanian gradient. Checkerboards and random pare the grid approach with vegetation maps of the patterns show no significant coherence. area; the same is true for the distribution maps of single bee species (unpublished data). Additionally, the plots are far enough apart to avoid negative sam- pling effects. Flower-visiting bees (Hymenoptera 2.5. Additional analyses to test Apoidea) were caught by sweep nets from flow- nestedness ers mainly once a week during a period of 15 min- utes/plot in both years under sunny conditions, with The software Nestedness (Ulrich, 2006a) over- no or only slight wind and temperatures exceed- comes almost all deficiencies of NTC (Atmar and ing 12 ◦C. Bee species that could clearly be iden- Patterson, 1995), which detects nestedness in many tified in the field were not caught. The flower re- cases as an artefact (Fischer and Lindenmayer, source visited by each individual bee was recorded, 2002, 2005b; Rodriguéz-Gironés and Santamaría, and the plots were sampled in changing order. The 2006; Ulrich, 2006a; Ulrich and Gotelli, 2007; semi-domesticated honey bee (Apis mellifera)was Almeida-Neto et al., 2007, 2008). Binmatnest excluded. “Parasites” refer to bee species practic- (Rodriguéz-Gironés and Santamaría, 2006)intro- ing nest parasitism (e.g., cuckoo bees). The vege- duces a new order for the isoclines as well as fur- tation of each plot was sampled using the cover- ther null models and uses a genetic algorithm, but abundance scale of Barkman et al. (1964). some fundamental problems are not solved. There- fore, we preferentially used the software Nested- ness (using the fixed-fixed null model; W. Ulrich 2.3. Data matrix unpubl. data) but additionally NTC (Random00 model) and Binmatnest (null model Type I and The data matrix includes the number of bees, Type II errors). The cells situated at the border- the number of entomophilous plant species, and line were calculated in Nestedness on the basis of the number of interactions. In total, 1714 individ- the matrix fill. The “Fixed row and column con- ual bees were recorded (2004: n = 1172; 2005: straints sequential swap” accounts best for passive Complex structure of a plant-bee community 637

sampling (Ulrich, 2006a). The number of iterations For bee species, Mann-Whitney-U tests were for computing standard deviations of the null model used to detect differences in interaction number (I) is 1000. The minimum distance to borderline is 0.5, and distribution frequency (D) between polylec- which means cells nearer to the borderline will be tic and oligolectic species, whereas plant species’ excluded from the computation of the matrix tem- flower types were tested with a one-way-analysis perature. The matrix is packed according to ma- of variance (ANOVA) followed by Tukey post-hoc trix temperature. We quantify the pattern of nest- tests and Kruskal-Wallis tests when data were not edness with the discrepancy index BR of Brualdi normally distributed or satisfied variance assump- and Sanderson (1999) and the discrepancy value d1, tions. Data sets were always log (x+1) transformed. which can be tested with the help of the software Statistical analysis was carried out using the pro- written by B. Harper for Greve and Chown (2006). gram Statistica 6.0 for Windows. We used the new metric proposed by Almeida- We analysed the pattern of our webs with the Neto et al. (2008), which quantifies whether “fills” Bipartite Package 0.73 of Dorman et al. (2008) differ among columns and rows, and whether the and present the data set from 2005 (at present, the presences in less-filled columns and rows coincide data set of 2004 was not suitable for the program; with those found in the more-filled columns and Gruber, unpubl. data). Three different null models rows. This test was conducted with the programs with different constraints about marginal totals, di- CoOccurrence (Ulrich, 2006b)andAninhado 3.0 mensions, and connectance were calculated. The (Guimarães and Guimarães, 2006) with fixed-fixed Patefield algorithm (null model 1) produces null null model for randomization: random sampling ac- models with marginal totals that are identical to cording to the observed frequencies of occurrence those of the observed web. Thus the distribution of (1000 iterations). rare and common species is equal, but the number of links is usually reduced compared to the observed matrix. Null model 2 (Shuffle) keeps the number of 2.6. Bipartite graph: Interaction links constant but shows variation in the marginal structure, linkage levels, totals. Finally, the Swap-algorithm (null model 3) abundance, degree of specialization keeps the marginal totals and the number of links identical to those of the observed matrix. To check for significance of the matrix temperature (degree of The bipartite graph presents species in columns nestedness) between the observed data and the null or rows facing each other. The interactions are models, a genetic algorithm with 2000 generations drawn as links and grouped by the number of inter- was used while the number of calculated null mod- actions in decreasing order. For measuring the de- els per class was fixed to 100. Calculations were gree of generalization, linkage levels (ln) of plant performed using the “nestedness” module provided (P) and bee species (B) were assessed (Olesen et al., by the bipartite library within R 2.8.1 (R Develop- 2002; Dupont et al., 2003). Linkage levels charac- ment Core Team, 2008). terize the number of interactions per species (link- age level Lm of a bee species m is the number of plant species visited by m, linkage Ln of a plant species n is the number of bee species visiting n). 2.7. Modularity For comparisons, the linkage level was standardized as relative linkage of bee species lm = Lm/Pand We tested modularity with the software Net- relative linkage of plant species ln = Ln/B. Species Cutter 1.0 (Muller, 2008). This software permits with high linkage levels utilize more species than do identifying and analyzing co-occurrence networks. species with low linkage levels. Spearman’s Rank- NetCutter was individually modified by H. Muller Correlations were used to analyze possible correla- for our tasks. We used edge-betweeness clustering tions between the degree of generalization (“linkage proposed by Girvan and Newman (2002), which level”) and abundance. The distribution of a species produces best results by visualization of the data is documented by the number of plots on which sets like ours (Muller, unpubl. data). It is based the species was detected. The degree of specializa- on an iterative process of removing linkages to tion in plant species is characterized by their flower reach the highest modularity value. To prove sig- type. For bee species, we differentiated polylectic nificance, we randomized the original data set by and oligolectic species (as well as cuckoo bees) and edge-swapping (1000 iterations), tested 50 edge- three body-size groups. swapping randomized graphs by edge-betweeness 638 A. Kratochwil et al.

clustering, and calculated the modularity value in- Table I. Coherence as indicated by the occur- cluding standard deviation for comparison with the rence of embedded absences in ordinated matrices; real data set. Edge swapping is the best null model species turnover as indicated by the number of times for co-occurrence analysis (Muller, unpubl. data), one species replaces another between two sites, and shuffling the linkages of a bipartite graph randomly boundary clumping as indicated by Morisita’s Index (usually 100 iterations) while preserving the ver- (Leibold and Mikkelson, 2002). tex degrees and avoiding linkage of the same plant species to the same bee species more than once. The Coherence 2004 2005 graph structure and the presence of communities are Embedded absences visualized with CircleLayout. The size of the points Actual number 729.0 408.0 representing the linked species grows in diameter Expected number 1135.1 685.7 with increasing linkage level. Standard deviation 72.1 47.8 Z-score –5.632 –5.810 P < 0.001 < 0.001 2.8. DCA-analysis and functional group correlation Turnover 2004 2005 Replacements The functional relationships between plant and Actual number 50970.0 23566.0 bee species are visualized by the detrended corre- spondence analysis (DCA) with PC-ORD 5.0 un- Expected number 51204.4 24617.7 der “no downweighting of rare species”, “rescaling Standard deviation 5426.1 2121.0 of axes”, “taking 26 segments”, and without sin- Z-score –0.043 –0.495 gletons to eliminate multireferences and to clarify P 0.4801 0.3123 the graph. This linear ordination method is recom- mended in the case of gradient lengths more than Clumping 2004 2005 3 SD and a powerful multivariate tool (Kent and Coker, 1992; Ejrnaes, 2000). DCA with singletons Morisita’s index produced the same result. Actual value 2.636 2.402 All methods are based on presence-absence data Expected value 1.000 1.000 to guarantee comparability. P < 0.001 < 0.001

3. RESULTS 3.2. Additional analyses to test nestedness 3.1. Coherence, species turnover The software Nestedness did not reveal sig- and boundary clumping nificant nestedness in contrast to NTC and Binmatnest; only after masking (singletons Both data sets (2004, 2005) are significantly of plant-bee interactions were removed from coherent and clumped according to the method columns and rows), weakly significant nested- of Leibold and Mikkelson (2002) but not char- ness was indicated for one year (Tab. II). The acterized by species turnover (Tab. I). Com- discrepancy index BR, d1, and NODF did not bining these three results, the studied web pat- prove significant nestedness with one excep- tern tends to be similar to a mixture between a tion (NODF for the year 2005; see Tab. III). nested subset pattern and a Clementsian gradi- A second examination of the masked data ent. Nested subset pattern (some species form concerning BR, d1, and NODF did not re- a set of nested subsets) is detected by posi- veal any significant nestedness (Tab. III). The tive coherence plus negative turnover, and a temperature values of NTC and Binmatnest Clementsian gradient by positive coherence showed clear differences. The temperature plus positive boundaries (the argument posi- values determined by Binmatnest were always tive turnover is not fulfilled). lower than those of NTC. Complex structure of a plant-bee community 639

Table II. Nested subset-analysis of the pollination web calculated via NTC (Atmar and Patterson, 1995), BINMATNEST (Rodriguéz-Gironés and Santamaría, 2006) and NESTEDNESS (Ulrich, 2006a), T = tem- perature in degrees, Trandom = randomized mean temperature (1000 iterations), N = degree of nestedness, P = level of significance.

Year Bee Plant Interactions T TSim N Z-score P species species NTC 2004 69 42 225 6.6 24.3 ± 2.0 0.933 –8.85 < 0.001 2005 60 33 171 9.2 25.2 ± 2.4 0.908 –6.66 < 0.001 BINMATNEST 2004 69 42 225 4.2 13.8 ± 1.8 0.958 –5.33 < 0.001 2005 60 33 171 5.7 14.7 ± 2.2 0.943 –4.09 < 0.001 NESTEDNESS 2004 69 42 225 5.2 4.1 ± 0.6 0.948 1.83 0.9656 2005 60 33 171 6.5 5.5 ± 0.7 0.935 1.42 0.9222 NESTEDNESS 2004 35 30 181 18.4 17.5 ± 1.3 0.816 0.69 0.7549 without singletons 2005 31 18 128 18.9 21.5 ± 1.6 0.811 –1.63 0.0475

Table III. Discrepancy indices: “BR” (Brualdi and Sanderson, 1999), “NODF” (Almeida-Neto et al., 2008) and “d1” (Greve and Chown, 2006), BRSim, NODFSim = randomized value (1000 iterations), P = signifi- cance level.

Year BR BRSim Z-score P Entire data set 2004 119 118.7 ± 5.3 0.056 0.5239 2005 88 89.3 ± 4.2 –0.310 0.3745 Without singletons 2004 88 87.1 ± 4.6 0.196 0.5723 2005 58 55.7 ± 3.5 0.657 0.7475

Year NODF NODFSim Z-score P Entire data set 2004 24.0 24.4 ± 0.7 –0.63 0.264 2005 21.8 23.7 ± 0.7 –2.62 0.004 Without singletons 2004 34.1 34.6 ± 0.6 –0.87 0.192 2005 39.0 39.2 ± 0.7 –0.30 0.370

Year d1 d1Sim Z-score P Entire data set 2004 0.529 0.528 ± 0.016 0.745 0.7704 2005 0.520 0.546 ± 0.018 0.815 0.7939 Without singletons 2004 0.492 0.477 ± 0.017 0.808 0.8081 2005 0.445 0.446 ± 0.019 0.491 0.6879

3.3. Bipartite graph: Interaction to 0.42 in 2005, the relative linkage levels structure, linkage levels, of bees (lm) from 0.02 to 0.52 in 2004 and abundance, degree of specialization from 0.03 to 0.33 in 2005 (Fig. 1a, b). More- over, there were positive correlations between In 2004, 42 plant species were used as flo- plant species abundance and ln (2004: n = ral resources by 69 bee species (225 interac- 42; rS = 0.56; P < 0.001; 2005: n = 33; tions); in 2005, 33 plant species were visited rS = 0.29; P = 0.102), which did not show by 60 bee species (171 interactions). The rela- significant values only in 2005. Also for bee tive linkage levels of plant species (ln) ranged species (bee species abundance and lm: 2004: from 0.01 to 0.38 in 2004 and from 0.02 n = 69; rS = 0.93; P < 0.001; 2005: n = 60; 640 A. Kratochwil et al.

Figure 1. Web structure (bipartite graph) of the studied plant-bee system in 2004 (a) and 2005 (b). S = specialization (flower types: A = actinomorphic, Z = zygomorphic, AS= Asteroideae, CI = Cichorioideae; pollen collecting: P = polylectic, oligolectic in = AST, Boraginaceae = BOR, = FAB, Malvaceae = MAL, Salicaceae = SAL; C = cuckoo bees); Lm = plant linkage level; ln: relative plant linkage level; D = distribution; number of plots with occurrence; B = body size group (a = small [< 10 mm], b = medium [> 10–12 mm], c = large (> 12 mm)); Lm = bee linkage level; ln: relative bee linkage level lm. For abbreviations of plant and bee species, see Appendix.

rS = 0.91; P < 0.001), the analyses revealed The bipartite graphs of 2004 and 2005 that the most common species also show the (Fig. 1a, b) demonstrate a highly asymmet- most frequent interactions with other species. ric pattern. Plant species with high interac- The number of interactions differed sig- tion numbers were correlated with almost all nificantly between polylectic and oligolectic bee species and vice versa. There were only species only in 2004 (Mann-Whitney U-Test; a few exceptions (e.g., jacobaea, Vi- 2004: P = 0.016; 2005: P = 0.07), whereas cia angustifolia or Lasioglossum minutissi- the frequency of polylectic and oligolectic bee mum,andNomada fulviventris). Within plant species did not differ (Mann-Whitney U-Test; species, there was no correlation between dis- 2004: P = 0.086; 2005: P = 0.385). tribution frequency and interaction number or Concerning plant species, differences of flower type. In contrast, the highest interac- interaction numbers were only detected be- tion numbers in bee species were correlated tween Asteroideae and Cichorioideae in 2004 with frequency. Polylectic bee species dom- (2004: ANOVA, df = 3, F = 3.389, P = inated in the group of species with many interactions and high distribution frequency. 0.027; 2005: Kruskal-Wallis test: H3 = 2.274, P = 0.517). There was no difference in the Oligolectic species (most of which occurred presence of flower types (Kruskal-Wallis test: in only one or a few plots) showed a low number of interactions; the same was true for 2004: H3 = 6.698, P = 0.082; 2005: H3 = 2.057, P = 0.561). parasites. Plant species with low interaction Complex structure of a plant-bee community 641

Figure 2. Modularity graph (Software NetCutter 1.0; Muller, 2008) of 2004 and 2005, which is based on edge-betweenness clustering (further explanations see text).

Table IV. Temperature of nestedness analysed by and P = 0.01, respectively) the Shuffle- The bipartite Package 0.73 (Dorman et al., 2008) (null model 2) and the Swap-algorithm (null for the data set of 2005: real data, null-model 1 model 2) generate marginally higher average (Patefield algorithm), null-model 2 (Shuffle) and matrix temperatures compared to the observed null-model 3 (swap-algorithm). data of 39.5 and 38.5, respectively.

TTSim P Observed data 33.4 3.4. Modularity Null-model 1 46.1 ± 5.2 < 0.001 Null-model 2 39.6 ± 0.9 0.02 The analysis of co-occurrence networks with NetCutter showed a modularized pattern Null-model 3 38.5 ± 5.0 0.01 of 5–7 in 2004 and only 2 modules in 2005 (Fig. 2a, b; Tab. V). Within one module, the species were strongly connected (hub number and frequency were visited mostly by species), between-module connector species those bee species with higher connectivity and interlinked the modules and more or less sep- wider distribution in the study area. arated interactions. These connector species The Patefield algorithm (null model 1) con- represent key species of the network. Our structs networks that have significantly higher system is characterized by 4–6 dominant temperature (< 0.001) than the observed connector plant species, changing partly be- network (2005; see Tab. IV). This implies a tween years but representing the 4 important lower degree of nestedness for null model 1. flower types: in 2004: incana (acti- While still significantly different (P = 0.02 nomorphic), stoebe (Asteroideae 642 A. Kratochwil et al.

Figure 3. Detrended correspondence analysis of the plant-bee web 2004 with plant species (a) and bee species (b) plotted by PC-ORD 5.0 and characterization by flower types and bee body sizes. Key hub and connector plant species in bold and italics. For abbreviations of plant species, see Appendix.

Table V. Modularity degrees of the real data sets module pattern in both years was significantly (2004, 2005), of the 50 randomized graphs by edge- modularized (Tab. VI). swapping (1000 iterations) according to NetCut- ter 1.0 (Muller, 2008). 3.5. DCA-analysis and functional group Modularity 2004 2005 correlation Actual value 0.4121 0.4452 The DCA diagrams for year 2004 Expected value 0.3785 0.3959 (Fig. 3a, b) and 2005 (not printed) were Standard deviation 0.0014 0.0166 based on the data set of the plant-bee web Z-score 2.3384 2.9610 without singletons differentiated as plotted plant species (Fig. 3a) and plotted bee species P < 0.01 < 0.002 (Fig. 3b). Figure 3a revealed two main and two further traits of plant species with differ- ent flower types. The left side of the graph type), capillaris (Cichorioideae type), represents plant species with relatively small, Medicago falcata (zygomorphic), Sisym- actinomorphic flowers (e.g., in 2004: Berteroa brium altissimum (actinomorphic); in 2005: incana, Geranium molle, Alyssum alyssoides, Centaurea stoebe (Asteroideae type), Crepis Salsola kali, and in 2005: Potentilla taber- capillaris (Cichorioideae type), Cerastium naemontani, P. argentea, Sedum acre)or arvense (actinomorphic), repens inflorescences easily accessible to flower- (zygomorphic). Only in 2004 did connector visitors (Euphorbia cyparissias). Their flower species in bees exist ( visitors (Fig. 3b) were, above all, composed of and B. terrestris). The number of realized bee species with small body sizes (Andrena, modules and the pattern of hub-plant species Lasioglossum, Halictus). These species are showed high between-year variability. The characterized by hind legs (coxa, femur) bear- same was true concerning bee species. The ing hairs for pollen collecting. The right side Complex structure of a plant-bee community 643

Table VI. Web modules in 2004 and 2005 according to NetCutter 1.0 (Muller, 2008) analysed with edge- betweeness clustering. In parenthesis species numbers; key hub and connector plant species in bold and italics. N = number of interactions. For abbreviations of plant species, see Appendix.

2004 Module 1 (18) Module 2 (17) Module 3 (15) Module 4 (13) N Bees N Plants N Bees N Plants N Bees N Plants N Bees N Medfal 15 Halsma 11 Crecap 18 Lasleu 10 Armmar 4 Bomlap 22 Sisalt 11 Halleu 9 Medmin 10 Andfla 9 Hyprad 4 Dashir 4 Carnut 4 Bomter 19 Potarg 7 Halcon 6 Erocic 11 Andova 8 Camrap 3 Laspau 4 Sedacr 4 Bompas 6 Cerarv 4 Nommin 3 Triarv 7 Bomhum 5 Hiepil 2 Lasful 3 Salpra 3 Spheph 1 Salkal 4 Andbim 3 3livciV2rhcdnA1budarT4ramgeM6luvhcE 2reasaL 4pelleM4lupyhT 2ffocnA2nnalyH 1busdnA 2talsaL 2reppyH1parehC 1arpmoB 2ruamsO 1munleH1fidlyH 1arpsaL 1nuptnA 1ohrpaP1citalsaL 1furmsO Laspun 1 Osmspi 1 Papdub 1 Osmleu 1 Pancal 1 Tricam 1 Rhocan 1 Spheph 1 (6) (12) (5) (12) (11) (4) (4) (9)

2004 Module 5 (11) Module 6 (11) Module 7 (7) Plants N Bees N Plants N Bees N Plants N Bees N Censto 26 Andcar 5 Berinc 19 Andarg 1 Onorep 10 Antman 3 Dipten 8 Lascal 5 Germol 8 Lasluc 1 Starec 2 Bomsyl 3 Lasmor 5 Verphl 1 Haltum 3 Megrot 3 Halsex 4 Andalf 3 Megpil 2 Osmadu 2 Andbar 1 Megeri 1 Halsca 1 Halqua 1 Hercre 1 Lasgri 1 Hertru 1 Anddor 4 Megver 1

(2) (9) (3) (8) (2) (5)

2005 Module 1 (33) Module 2 (29) Plants N Bees N Plants N Bees N Crecap 19 Halleuc 11 Censto 25 Bomlap 8 Berinc 11 Halsma 10 Onorep 11 Bomsyl 5 Germol 10 Lasful 8 Carnut 11 Bomhum 5 Potarg 9 Halcon 8 Echvul 7 Mellup 3 Senver 8 Halsub 8 Armmar 5 Andcar 3 Sedacr 7 Lasmor 8 Hypper 2 Megwil 3 Pottab 3 Haltum 5 Medfal 1 Halsex 3 Sisalt 1 Lascal 5 Ancoff 1 Osmspi 3 Myoram 1 Lasalb 4 Cirarv 1 Lasaer 3 Medmin 1 Lasleu 4 Halsca 2 Conarv 1 Lasluc 3 Halqua 2 Andova 3 Bompas 2 Anddor 1 Dashir 2 Lasvil 1 Megrot 2 Sphmon 1 Stepun 1 Andarg 1 Osmadu 1 Hylang 1 Antlit 1 Andfal 1 Hercre 1 Sphcri 1 Coecon 1 Laslatic 1 Laspun 1 Halpol 1 Cercuc 1 (11) (22) (9) (20) 644 A. Kratochwil et al. of the graph (Fig. 3a) is dominated by plant not fulfilled. By the use of the methods sum- species characterized by zygomorphic flowers marized by Leibold and Mikkelson (2002), (e.g., 2004: Stachys recta, Ononis repens, we are only able to characterize our web Salvia pratensis, , and 2005: pattern as non-random, moderately nested, Echium vulgare, Vicia villosa). Their flower- and “clumped”. With additional methods, the visiting bee species (Fig. 3b) were of larger structure of the web concerning nestedness, body size and have longer proboscises and clumping, and gradients should be analysed other pollen-collecting structures (Bombus: more in detail. hind-leg corbiculae; : hair brushes on abdominal tergites). Halictus species with larger body sizes also occur (e.g., Halictus 4.2. Additional and supplementary quadricinctus, H. sexcinctus). The group analyses to test nestedness with medium body size comprises species of Andrena, Osmia and Megachile with an PimmandLawton(1980), Raffaelli intermediate position in the DCA. Moreover, and Hall (1992), Bascompte et al. (2003), there is another flower type, pseudanthia of Bascompte and Jordano (2006), and others the Cichorioideae, which is grouped on the point out that food and mutualistic networks left side of the DCA (Fig. 3a) and preferably are generally nested. This is, however, denied visited by smaller bee species or in the case by others (e.g., Paine, 1992; Corbet, 2000; of hirtipes and Osmia spinulosa Krause et al., 2003), and, in the case of Asteraceae specialists (Fig. 3b). This group complex flower-visitor networks (which are was only detected in 2004. Species visiting the characterized by several different taxa of Asteroideae type were grouped in the centre ), by Dicks et al. (2002). of the matrix. There was a compartmented The software Nestedness confirms (with gradient concerning flower type, body size of one exception – weakly significant without bees (which is mostly related with proboscis singletons in 2005) that our web is not nested. length), and pollen-collecting structures. The analysis demonstrates that, according to NTC and Binmatnest, significant nestedness of the plant-flower-visitor network is evident, 4. DISCUSSION whereas this is not proven by Nestedness. While the discrepancy values d1 also indicate 4.1. Coherence, species turnover non-nestedness of the system, the same is true and boundary clumping for NODF with the exception of 2005. Nested- ness shows a noticeable effect of singletons on The metaanalysis presented by Leibold and the evidence of nestedness in 2005; this could Mikkelson (2002) demonstrates that in most be proved by masking the data set. cases community structures are characterized Data sets of pollination networks published by coherence and not by randomness. Further- in the literature which, according to NTC more, communities tend to be either nested or Binmatnest, had shown nestedness (e.g., or characterized by a species turnover and to Dupont et al., 2003), do not show any signif- correspond either to a Clementsian or to a icant nestedness when re-examined with the Gleasonian gradient. This can be shown for software Nestedness and the index d1.This our data set (2004, 2005), which indicates sig- fact is also mentioned by Ulrich (2006a)and nificant coherence and a clumped pattern but Ulrich and Gotelli (2007). no significance in species turnover. A moder- In our dataset, the different methods used ate nested subset pattern is detected by pos- to determine nestedness give inconsistent and itive coherence and negative turnover, and a contradictory results, depending on the null Clementsian gradient is indicated by positive models used. It is impossible to decide which coherence and by positive boundaries, which of these results reflect the reality and whether means the existence of discrete subcommuni- the programs over- or underestimate nested- ties. The argument for “positive turnover” is ness. Further methods should be applied to test Complex structure of a plant-bee community 645 whether the studied plant-bee web shows nest- model 1. Given the significance of both the edness or other web structures. This was car- Shuffle- and the Swap-algorithm in generat- ried out by using the “bipartite network”, by ing temperatures that deviate even if slightly modularity tests, and by “multivariate sets (de- from the observed matrix, both the number of trended correspondence analysis)”. links (constraints of null model 3) and the dis- tribution of marginal totals (constraint of null model 2; i.e., the distribution of common and 4.3. Bipartite graph: interaction rare species within the network) need to be structure, linkage levels, taken into account. Thus nestedness is a prop- abundance, degree of specialization erty of the network rather than of first-order properties alone (Dormann et al., 2009). The visual analyses of the bipartite graph show that the studied network structure is too 4.4. Modularity und functional group complex to be assigned to only one of the types correlation (gradient, nested, compartmented, combined types) specified by Lewinsohn et al. (2006) In our plant-bee web, different numbers of and Almeida-Neto et al. (2007). However, modules are visible in both years but nearly the three methods mentioned by Lewinsohn identical plant key species mainly influence et al. (2006) are important for pattern recogni- the module pattern. These plant key species tion. The asymmetry displayed in the bipartite are hub species for those bee species that are graph shows lower numbers of plant species present in low numbers and important con- and higher numbers of bee species. Of all bee nector species between modules and separated species with low interaction numbers and low non-modularized species. An important fea- frequency, most are integrated into network ture of a hub and connector plant species is structures by visiting plant species having high high flower density and a specific flower type. interaction numbers and high frequency. The The combination of key plant species, which same is true for plant species. The bipartite includes the four main flower types (acti- graph shows an asymmetric pattern of high nomorphic, zygomorphic, Asteroideae-, and connectivity, where most of the rare species Cichorioideae type), guarantees that all bee with low interaction numbers are well inte- species – regardless of their body size or grated into the web. That the most common their pollen collecting behaviour – will have plant species show the most frequent inter- resources available. The core of the web is actions with bee species and vice versa (the structured by hub and connector species, with indication for partly nested) might be based all other species are gathered around. Corbet on statistical probability. But it is surprising (2000) emphasized the importance of various that, although the frequency of polylectic and parameters (e.g., flower type, flower size, body oligolectic bee species did not differ between ff size, adaptation to nectar absorption, pollen the years, there is a significant di erence be- collecting, and length of the bees’ proboscises) tween years according to the interaction num- for revealing the compartmentation of a plant- bers of polylectic and oligolectic species. Fur- flower-visitor network (see also Kratochwil, thermore, the bipartite graph demonstrates that 1988). The “detrended correspondence analy- specialization in flower type is independent sis” of our data set shows that these parameters of the degree of linkages. According to these are indeed important features of the structure results, we can hypothesize that the key fac- of the plant-flower-visitor web. tor of our system is the variety of plant re- sources between years affecting different in- teraction webs. This should be demonstrated 4.5. The consequences for community by the modularity degree of the web in 2004 stability and 2005. The observed nestedness of the real pol- Bascompte et al. (2003) and Bascompte linator web cannot be reproduced by null and Jordano (2006) conclude that mutualistic 646 A. Kratochwil et al. nested webs are “asymmetric coevolutionary The consequence is that the web structure is networks”, which guarantee a long-term co- asymmetric. existence of species and facilitate biodiversity According to the point of view of different maintenance (see Jordano et al., 2003). Con- authors, only nested communities should guar- sequently, proving that there are non-nested, antee the survival of rare and specialist species or only partly nested, patterns within the pol- by the existence of widely spread general- lination web would imply that conservation of ist species (e.g., Gibson et al., 2006). Recent uncommon and specialist species might not di- studies suggest that nested plant- net- rectly be ensured by the existence of general- works are more robust to environmental per- ist species. In our case with current methods, turbation (Fortuna and Bascompte, 2006). This nestedness is essentially impossible to detect, would imply that in nested webs the rare and but modularity can be stated as a stabilizing specialist species are just as little endangered factor which includes partial nestedness. as the common generalist species. Our results Which stabilizing features, minimizing the have shown that under conditions of a partly threat of extinction of rare and specialist nested but modularized web, most of the bee species in an only partly nested system, can species that are characterized by relatively few be assumed? As shown in Figure 1,thecom- interactions are connected with those plant munity structure is (1) dominated by polylec- species showing the highest degrees of in- tic bee species, and (2) most of the flower- teraction (hub and connector species) and specialized bees depend on floral resources of vice versa. Specialized flower visitors are also Fabaceae and Asteraceae species. The species closely connected with widely distributed and of these families are widespread in our study abundant plant species, which are generalists area, with high abundances. This is also true and characterized by a high degree of interac- for large regions in Central Europe, where spe- tion processes. cialized bees forage on frequently occurring Nestedness structure alone does not im- Fabaceae and Asteraceae species (in Germany: ply web stability, but modularity includes 140 oligolectic bee species, 60 of which de- partly nested subgroups of the web. Although pend on these two plant families; Kratochwil, our plant-bee web seems to be structured by 2003). These specialized bee species are an asymmetric pattern and modularity, which highly integrated in the bee-plant web of our produces stability, it should be taken into ac- study area. Moreover, (3) there are only a few count that not all rare, specialist, and even bee species in our system which depend on oligolectic bee species can always be bal- species of plant families with lower occur- anced by the existence of more common and rence (e.g., Tetralonia macroglossa on Malva, generalist plant species. This is true in our Osmia adunca on Echium). These species case (e.g., Tetralonia macroglossa, specialist are only slightly integrated within the web of Malvaceae). Specific analyses of the web structure. structure and functional groups (e.g., body Plant species in our system are not exclu- size, pollen-collecting type, flower type) focus sively dependent on one particular bee species. on biological traits of community patterns like They are also visited by numerous other bee our studied bee-plant-web. species, which are seldom oligolectic but in most cases polylectic (e.g., Echium vulgare and the specialist Osmia adunca as well as ACKNOWLEDGEMENTS numerous generalists, e.g., several Bombus species). The plant species are visited by Particular thanks to Till Eggers (University of higher percentages of generalists than by spe- Osnabrück, Germany) who contributed with im- cialized bee species. portant discussions and carried out the bipartite Various analyses have so far shown that analyses in R-statistics. Heiko Muller (IFOM-IEO the number of bee species in a real web is Campus, Milan, ) enabled us by his support mostly higher than the number of bee-visited to analyse modularity of our web. Michelle Greve plant species (e.g., Blüthgen et al., 2007). (University of Stellenbosch, South Africa) kindly Complex structure of a plant-bee community 647

made the software available to calculate the dis- 2005 jedoch keine signifikante Schachtelung für crepancy index d1, and Mathew Leibold (University das Gesamtsystem. Dagegen konnte ein signifikan- of Texas, USA) the Leibold and Mikkelson soft- tes modularisiertes Muster festgestellt werden mit ware. Thanks also to Bernd Gruber (UFZ Leipzig, einzelnen Arten, die innerhalb eines Moduls eine hohe Konnektivität besitzen („hub species“), aber Germany) for bipartite and W. Ulrich (Univer- auch solchen, die zwischen den Modulen vermit- sity Toruñ, ) for NODF support. We are teln („connector species“). In der Regel wird das grateful to A. Schanowski (Bühl/Baden) and to Netzwerk durch 4-6 dominante Pflanzenarten cha- H. Schwenninger (Stuttgart) for checking critical rakterisiert, die die Rolle von „hub“ oder „connec- specimens of bee species. We thank the regional ad- tor species“ einnehmen. Das Muster variiert in den ministrations for permission to collect specimens. beiden Jahren bezüglich der Anzahl der Module und Financial support was provided by the German Fed- Bindungen zwischen den Arten. Die dominierenden „hub“- und „connector“-Pflanzenarten treten jedoch eral Environmental Foundation (Deutsche Bundess- in beiden Jahren auf. Eine multivariate Analyse tiftung Umwelt, Osnabrück). zeigt, dass die „hub“ und „connector species“ vier Haupt-Blütentypen repräsentieren. Diese Blütenty- pen gewährleisten, dass Wildbienenarten mit un- Structure complexe des réseaux d’interactions terschiedlicher Körpergröße und unterschiedlichem plantes-pollinisateurs : au hasard, à emboîte- Ernährungsverhalten adäquate Ressourcen nutzen ments, avec gradients ou modules ? können. Modularität und Anwesenheit spezifischer „hub“ und „connector species“ kennzeichnen die cohérence / sous-ensemble imbriqué / réseau hier vorliegende Netzwerk-Struktur. Im Gegensatz bipartite / modularité / Apidae zu theoretischen Netzwerken (zufällig, mit Gradi- enten, modularisiert) zeigt dieses reale Wildbienen- Nahrungspflanzen-Netzwerk einen hohen Grad an Zusammenfassung – Komplexität eines Bes- Komplexität mit Übergängen zwischen verschiede- täuber-Pflanzen-Netzwerkes: zufällig, geschach- nen Netzwerk-Typen. Damit wird das Aussterben telt, mit Gradienten oder Modulen? An ei- lokal seltener Bienenarten minimiert. Im Blütenbe- nem Modellökosystem konnte innerhalb eines such spezialisierte Wildbienen-Arten kommen nur Zeitraumes von zwei Jahren das Wildbienen- in geringen Abundanzen vor. In der überwiegen- Nahrungspflanzen-Netzwerk (Hymenoptera Apoi- den Mehrzahl nutzen sie im Gebiet weit verbreitete, dea) analysiert werden. Die Honigbiene (Apis sowie arten- und individuenreiche Pflanzenfamilien mellifera) blieb dabei unberücksichtigt. Folgen- (Asteraceae, Fabaceae). Das Netzwerk wird von po- de Methoden kamen zum Einsatz: Nested-Subset-, lylektischen Bienenarten dominiert. Im Gegensatz Gradienten- (Clements, Gleason und Tilman) und zu Ergebnissen aus der Literatur, in denen Nested- Modularitätsanalysen. Wir untersuchten das Netz- ness mit Systemstabilität in Verbindung gebracht werk auf Kohärenz, Kompartimentierung und wird, ist unser Netzwerk primär durch Modularität Schachtelung und kamen zu dem Ergebnis, dass charakterisiert, wobei aber eine partielle Schachte- unser System modularisiert und teilweise auch lung in dieses System integriert ist. schwach geschachtelt ist. Geschachtelte Gemein- / / schaften von Höheren Pflanzen- und Wildbienen- Kohärenz Nested-Subset-Analyse bipartites / / arten garantieren das Überleben seltener und spe- Netzwerk Modularität reale Netzwerkstruk- zialisierter Arten. Nach der Nested-Subset-Theorie turen nutzen seltene und spezialisierte Blütenbesucher weitverbreitete und auf einen großen Blütenbe- sucherkreis eingerichtete Pflanzenarten. Das Glei- che gilt für seltene und im Blütenbesuch spe- zialisierte Pflanzenarten. Weiterführende Analysen APPENDIX: ABBREVIATIONS zum Nachweis von Nested-Subset-Strukturen er- brachten in der Mehrzahl den Hinweis, dass das Plant species:Alyaly:Alyssum alyssoides, gesamte Nahrungsnetz nicht geschachtelt ist. Der Ancoff: Anchusa officinalis, Armmar: Armeria bipartite Graph belegt eine asymmetrische, teil- weise geschachtelte Netzwerk-Struktur, wobei sel- maritima, Berinc: Berteroa incana,Camrap: tene Bienenarten und solche mit Präferenzen für Campanula rapunculus, Carnut: Carduus Korbblütler (Asteraceae) und Schmetterlingsblüt- nutans,Censto:Centaurea stoebe, Cerarv: ler (Fabaceae) die häufigeren Pflanzenarten oder Cerastium arvense,Cirarv:Cirsium arvense, solche mit einem breiten Blütenbesucherkreis nut- Conarv: Convolvulus arvense, Crecap: Crepis zen. Seltene Pflanzenarten werden durch häufige, ffi im Blütenbesuch sich generalistisch verhaltende capillaris, Cynglos: Cynoglossum o cinale, Wildbienenarten besucht. Eine Nullmodell-Analyse Diacar: Dianthus carthusianorum, Dipten: erbrachte für den bipartiten Graphen des Jahres Diplotaxis tenuifolia, Echvul: Echium vulgare, 648 A. Kratochwil et al.

Erocic: Erodium cicutarium, Eupcyp: Euphor- angustatus, Hylann: Hylaeus annularis, bia cyparissias,Germol:Geranium molle, Hyldif: Hylaeus difformis, Lasaer: Lasioglos- Helnum: Helianthemum nummularium, sum aeratum, Lasalb: Lasioglossum albipes, Helare: Helichrysum arenarium, Hiepil: Lascal: Lasioglossum calceatum,Lasful:La- Hieracium pilosella, Hypper: Hypericum sioglossum fulvicorne, Lasgri: Lasioglossum perforatum, Hyprad: radicata, griseolum, Laslatic: Lasioglossum laticeps, Malalc: Malva alcea, Medfal: Medicago Laslat: Lasioglossum lativentre,Lasleu: falcata,Medmin:Medicago minima,My- Lasioglossum leucozonium, Lasluc: La- oram: Myosotis ramossisima, Onorep: Ononis sioglossum lucidulum,Lasmin:Lasioglossum repens, Papdub: Papaver dubium, Paprho: minutissimum,Lasmor:Lasioglossum morio, Papaver rhoeas, Potarg: Potentilla argentea, Laspau: Lasioglossum pauxillum, Laspra: Pottab: Potentilla tabernaemontani,Salkal: Lasioglossum prasinum, Laspun: Lasioglos- Salsola kali, Salpra: Salvia pratensis, Sedacr: sum punctatissimum, Lasvil: Lasioglossum Sedum acre, Senjac: Senecio jacobaea,Sen- villosulum,Megeri:Megachile ericetorum, ver: Senecio vernalis, Silcon: Silene conica, Megmar: Megachile maritima, Megpil: Sisalt: Sisymbrium altissimum, Starec: Stachys Megachile pilidens,Megrot:Megachile rotun- recta, Thypul: Thymus pulegioides, Tradub: data,Megver:Megachile versicolor, Megwil: Tragopogon dubius,Triarv:Trifolium arvense, Megachile willughbiella, Mellep: Melitta Trifcam: Trifolium campestre, Verphl: Verbas- leporina,Nomful:Nomada fulvicornis, cum phlomoides, Vicang: Vicia angustifolia, Nommin: Nomioides minutissimus, Osmadu: Vicvil: Vicia villosa. Osmia adunca,Osmaur:Osmia aurulenta, Bee species: Andalf: Andrena alfkenella, Osmleu: Osmia leucomelana, Osmruf: Osmia Andarg: Andrena argentata, Andbar: Andrena rufa, Osmspi: Osmia spinulosa, Pancal: barbilabris, Andbim: Andrena bimaculata, Panurgus calcaratus, Rhocan: Rhophitoides Andcar: Andrena carbonaria agg., Andchr: canus, Sphalb: Sphecodes albilabris, Sphcri: Andrena chrysosceles, Anddor: Andrena Sphecodes cristatus, Spheph: Sphecodes dorsata, Andfal: Andrena falsifica, Andfla: ephippius, Sphlon: Sphecodes longulus, Andrena flavipes, Andmin: Andrena minutula, Sphmon: Sphecodes monilicornis, Stepun: Andova: Andrena ovatula, Andsub: Andrena Stelis punctulatissima, Tetmac: Tetralonia subopaca, Andsyn: Andrena synadelpha, macroglossa. Andtib: Andrena tibialis, Antman: Anthidium manicatum, Antlit: Anthidium lituratum, REFERENCES Antpun: Anthidium punctatum, Bomhum: Bombus humilis, Bomlap: Bombus lapidarius, Almeida-Neto M., Guimarães P.R. Jr., Lewinsohn T.M. Bompas: Bombus pascuorum, Bompra: Bom- (2007) On nestedness analyses: rethinking matrix temperature and anti-nestedness, Oikos 116, 716– bus pratorum,Bomsyl:Bombus sylvarum, 722. Bomter: Bombus terrestris, Cercha: Almeida-Neto M., Guimarães P., Guimarães P.R. chalybea, Cercuc: Ceratina cucurbitina,Cer- Jr., Loyola R.D., Ulrich W.A. (2008) A consis- cya: Ceratina cyanea, Cherap: Chelostoma tent metric for nestedness analysis in ecological rapunculi, Coecon: Coelioxys conoidea,Col- systems: reconciling concept and quantification, cun: cunicularius, Colfod: Colletes Oikos 117, 1227–1239. fodiens, Colsim: Colletes similis,Dashir: Armbrecht I., Tischer I., Chacon P. (2001) Nested subsets and partition patterns in ant assemblages Dasypoda hirtipes,Epevar:Epeolus varie- (Hymenoptera, Formicidae) of Colombian dry for- gatus, Halcon: Halictus confusus, Halleu: est fragments, Pan-Pac. Entomol. 77, 196–209. Halictus leucaheneus, Halpol: Halictus Ashworth L., Aguilar R., Galetto L., Aizen M.A. pollinosus, Halqua: Halictus quadricinctus, (2004) Why do pollination generalist and special- Halsca: Halictus scabiosae,Halsex:Halictus ist plant species show similar reproductive suscep- tility to habitat fragmentation? J. Ecol. 92, 717– sexcinctus, Halsma: Halictus smaragdulus, 719. Halsub: Halictus subauratus, Haltum: Halic- Atmar W., Patterson B.D. (1993) The measure of or- tus tumulorum, Hercre: Heriades crenulatus, der and disorder in the distribution of species in Hertru: Heriades truncorum, Hylang: Hylaeus fragmented habitats, Oecologia 96, 373–382. Complex structure of a plant-bee community 649

Atmar W., Patterson B.D. (1995) The nestedness Fischer J., Lindenmayer D.B. (2002) Treating the nest- temperature calculator. A visual basic program, edness temperature calculator as a “black box” can including 294 presence-absence matrices, AICS lead to false conclusions, Oikos 99, 193–199. Research, Inc., University Park, NM and The Field Fischer J., Lindenmayer D.B. (2005a) Nestedness in Museum, Chicago. fragmented landscapes: a case study on birds, ar- Barkman J.J., Doing H., Segal S. (1964) Kritische boreal marsupials and lizards, J. Biogeogr. 32, Bemerkungen und Vorschläge zur quantitativen 1737–1750. Vegetationsanalyse, Acta Bot. Neerlandica 13, Fischer J., Lindenmayer D.B. (2005b) Perfectly nested 394–419. or significantly nested – an important difference Bascompte J., Jordano P. (2006) The structure of for conservation management, Oikos 109, 485– plant-animal mutualistic networks, in: Pascual M., 494. Dunne J. (Eds.), Ecological networks, Oxford Forup M.L., Henson K.S.E., Craze P.G., Memmott J. University Press, Oxford, US, pp. 143–159. (2008) The restoration of ecological interactions: Bascompte J., Jordano P., Melián C.J., Olesen J.M. plant-pollinator networks on ancient and restored (2003) The nested assembly of plant-animal mutu- heathlands, J. Appl. Ecol. 45, 742–752. alistic networks, Proc. Natl Acad. Sci. (USA) 100, Fortuna M.A., Bascompte J. (2006) Habitat loss and 9382–9387. the structure of plant animal mutualistic networks, Beil M., Horn H., Schwabe A. (2008): Analysis Ecol. Lett. 9, 281–286. of pollen loads in a wild bee community Ganzhorn J.U., Eisenbeiß B. (2001) The concept of (Hymenoptera: Apidae) – a method for elucidat- nested species assemblages and its utlility for un- ing habitat use and foraging distances, Apidologie derstanding effects of habitat fragmentation, Basic 39, 456–467. Appl. Ecol. 2, 87–95. Bloch C.P., Higgins C.L., Willig M.R. (2007) Effects Gibson R.H., Nelson L., Hopkins G.W., Hamlett B.J., of large-scale disturbance on meta-community Memmott J. (2006) Pollinator webs, plant commu- structure of terrestrial gastropods: temporal trends nities and the conservation of rare plants: arable in nestedness, Oikos 116, 395–406. weeds as a case study, J. Appl. Ecol. 43, 246–257. Blüthgen N., Menzel F., Hovestadt T., Fiala B., Girvan M., Newman M.E. (2002) Community struc- Blüthgen N. (2007) Specialization, constraints, ture in social and biological networks, Proc. Natl and conflicting interests in mutualistic networks, Acad. Sci. (USA) A99, 7821–7826. Curr. Biol. 17, 341–346. Gleason H.A. (1926) The individualistic concept of the Brualdi R.A., Sanderson J.G. (1999) Nested species plant association, Bull. Torrey Bot. Club 53, 7–26. subsets, gaps, and discrepancy, Oecologia 119, Greve M., Chown S.L. (2006) Endemicity biases nest- 256–264. edness metrics: a demonstration, explanation and solution, Ecography 29, 347–356. Clements F.E. (1916) Plant succession. An analy- sis of the development of vegetation, Carnegie Guimarães P.R., Guimarães P. (2006) Improving the Institution of Washington, Washington, DC. analysis of nestedness for large sets of matrices, Environ. Modell. Softw. 21, 1512–1513. Corbet S. (2000) Conserving compartments in pollina- tion webs, Conserv. Biol. 14, 1229–1231. Ibáñez J.J., Caniego J., García-Álvarez A. (2005) Nested subset analysis and taxa-range size distri- Diamond J.M. (1975) Assembly of species communi- butions of pedological assemblages: implications ties, in: Cody M.L., Diamond J.D. (Eds.), Ecology for biodiversity studies, Ecol. Model. 182, 239– and evolution of communities, Harvard Univ. 256. Press, Cambridge Massachusetts, pp. 342–444. Jordano P., Bascompte J., Olesen J.M. (2003) Invariant Dicks L.V., Corbet S.A., Pywell R.F. (2002) properties in coevolutionary networks of plant- Compartmentalization in plant- flower animal interactions, Ecol. Lett. 6, 69–81. visitor webs, J. Anim. Ecol. 71, 32–41. Kent M., Coker P. (1992) Vegetation description and Dormann C.F., Fründ J., Blüthgen N., Gruber B. analysis. A Practical Approach, Boca Raton: CRC (2009) Indices, graphs and null models: analyzing Press. bipartite ecological networks, The Open Ecol. J. 2, Kratochwil A. (1984) Pflanzengesellschaften und 7–24. Blütenbesuchergemeinschaften: biozönologische Dormann C.F., Gruber B., Fründ J. (2008) Introducing Untersuchungen in einem nicht mehr bewirts- the bipartite package: analysing ecological net- chafteten Halbtrockenrasen (Mesobrometum) works, RNews 8, 8–11. im Kaiserstuhl (Südwestdeutschland), Phytoc- Dupont Y.L., Hansen D.M., Olesen J.M. (2003) oenologia 11, 455–669. Structure of a plant-flower-visitor network in the Kratochwil A. (1988) Co-phenology of plants high-altitude sub-alpine desert of Tenerife, Canary and entomophilous : a historical area- Islands, Ecography 26, 301–310. geographical interpretation, Entomol. Gen. 13, Ejrnaes R. (2000) Can we trust gradients extracted by 67–80. Detrended Correspondence Analysis? J. Veg. Sci. Kratochwil A. (2003) Bees (Hymenoptera: Apoidea) 11, 565–572. as key-stone species: specifics of resource and 650 A. Kratochwil et al.

requisite utilization in different habitat types, Ber. a pahoehoe lava desert of the Galápagos Islands, Reinh.-Tüxen-Ges. 15, 59–77. Ecography 29, 531–540. Krause A.E., Frank K.A., Mason D.M., Ulanowitz Pimm S.L., Lawton J.H. (1980) Are food webs divided R.E., Tayler W.W. (2003) Compartments revealed into compartments? J. Anim. Ecol. 49, 879–898. in food web structure, Nature 426, 282–285. R Development Core Team (2008) R: A lan- Leibold M.A., Mikkelson G.M. (2002) Coherence, guage and environment for statistical comput- species turnover, and boundary clumping: ele- ing R. – Foundation for Statistical Computing, ments of meta-community structure, Oikos 97, Vienna, . ISBN 3-900051-07-0, URL 237–250. http://www.R-project.org. Lewinsohn T.M., Ináco Prado P., Jordano P., Raffaelli D., Hall S. (1992) Compartments and preda- Bascompte J., Olesen J.M. (2006) Structure tion in an estuarine food web, J. Anim. Ecol. 61, in plant-animal interaction assemblages, Oikos 551–560. 113, 174–184. Rodriguéz-Gironés M.A., Santamaría L. (2006) A new Memmott J. (1999) The structure of a plant-pollinator algorithm to calculate the nestedness temperature food web, Ecol. Lett. 2, 276–280. of presence-absence matrices, J. Biogeogr. 33, Muller H. (2008) Identification and analysis of co- 924–935. occurrence networks with NetCutter. – NetCutter Simberloff D. (1983) Competition theory, hypothe- 1.0 software documentation. sis testing, and other community ecological buz- Olesen J.M., Eskildsen L.I., Venkatasamy S. (2002) zwords, Am. Nat. 122, 626–635. Invasion of oceanic island-pollination networks: Tilman D. (1982) Resource competition and commu- importance of invader complexes and endemic su- nity structure, Princeton Univ. Press, Princeton. per generalists, Diversity and Distribution 8, 181– 192. Ulrich W. (2006a) Nestedness – a FORTRAN program for calculating ecological matrix temperatures. Olesen J.M., Bascompte J., Dupont Y.L., Jordano P. (2008): The modularity of pollination networks, Ulrich W. (2006b) CoOccurence – a FORTRAN pro- PNAS 104, 19891–19896. gram for species co-occurrence analysis. Ollerton J., Johnson S.D., Cranmer L., Kellie S. (2003) Ulrich W., Gotelli N.J. (2007) Null model analysis of The pollination ecology of an assemblage of grass- species nestedness patterns, Ecology 88, 1824– land asclepiads in South Africa, Ann. Bot. 92, 1831. 807–834. Vázquez D.P., Aizen M.A. (2004) Asymmetric spe- Paine R.T. (1992) Food-web analysis through field cialization: a pervasive feature of plant-pollinator measurement of per capita interaction strength, interaction, Ecology 85, 1251–1257. Nature 355, 73–75. Walter H., Lieth H. (1967) Klimadiagramm Weltaltlas, Patterson B.D., Atmar W. (1986) Nested subsets and VEB Gustav Fischer Verlag, Jena. the structure of insular mammalian faunas and Waser N.M., Chittka L., Price M.V., Williams N.M., archipelagos, Biol. J. Linn. Soc. 28, 65–82. Ollerton J. (1996) Generalization in pollination Philipp M., Böcher J., Siegismund H.R., Nielsen L.R. systems and why it matters, Ecology 77, 1043– (2006) Structure of a plant-pollinator network on 1060.