Bipartite provides functions to visualise webs and calculate a series of indices commonly used to describe pattern in (ecological) networks, a.k.a. webs. It focusses on webs consisting of only two levels, e.g. pollinator-visitation or predator-prey webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the webs topology.
Input for most analyses is an interaction matrix of m species from one group (“higher”) with n species from another group (“lower”), i.e. a n x m matrix, where higher level species are in columns, lower level species in rows. Column and row names can be provided. This is fundamentally different from “one-mode” webs, which are organised as k x k matrix, i.e. one group of species only, in which each species can interact with each other. Such a format is incompatible with the functions we provide here. (Note, however, that functions
web2edges are convenience functions to morph bipartite networks into one-mode webs. Furthermore, some indices build on one-mode networks and are called from bipartite.)
Before you start with the network, you have to get the data into the right shape. The function
frame2webs aims to facilitate this process. Arranging a web, e.g. by size, is supported by
The typical first step is to visualise the network. Two functions are on offer here: one (
visweb) simply plots the matrix in colours depicting the strength of an interaction and options for re-arranging columns and rows (e.g. to identify compartments or nesting). The other function (
plotweb) plots the actual web with participants (as two rows of rectangles) connected by lines (proportional to interaction strength). Both can be customised by many options.
The second step is to calculate indices describing network topography. There are three different levels this can be achieved at: the entire web (using function
networklevel), at the level of each group (also using function
networklevel) or the individual species (using function
specieslevel). Most other functions in the package are helpers, although some can be called on their own and return the respective result (
The third step is to compare results to null models. Many interaction matrices are very incomplete snapshots of the true underlying network (e.g. a one-week sampling of a pollination network on a patch of 4 x 4 meters). As a consequence, many species were rarely observed, many are singletons (only one recording). To make analyses comparable across networks with different sampling intensity and number of species per group, we need a common yardstick. We suggest that users should use a null model, i.e. an algorithm that randomises entries while constraining some web properties (such as dimensions, marginal totals or connectance). The function
nullmodel provides a few such null models, but this is a wide field of research and we make no recommendations (actually, we do: see Dormann et al. 2009 and Dormann 2011, both shipping in the doc-folder of this package). You can also simulate networks using
Finally, bipartite comes with 23 quantitative pollination network data sets taken from the NCEAS interaction webs data base (use
data(package="bipartite") to show their names) and it has a few miscellaneous functions looking at some special features of bipartite networks (such as modularity:
computeModules or apparent competition:
Speed: The code of bipartite is almost exclusively written in R. You can increase the speed a bit (by 30 to 50 %, depending on the functions you use) by compiling functions on-the-fly. To do so, you need to load the compiler package and type:
enableJIT(3). The first time you call a function, it will be compiled to bytecode (just-in-time: jit), which takes a few seconds, but the second call will be substantially faster than without compilation. In the few tests we have run, this improvement was NOT substantial (i.e. a few tens of percent), indicating, I guess, that our R code wasn't too bad. See compiler-help files or http://www.r-statistics.com/2012/04/speed-up-your-r-code-using-a-just-in-time-jit-compiler/ for details.
See help pages for details and examples.
For an overview of other computing resources, data, books, journals etc. check out this page: https://github.com/briatte/awesome-network-analysis.
Please see help page
versionlog for all changes and updates prior to version 2.00. This page will only list most recent changes.
2.08 (release date: 30-Mar-2017)
Although the excellent algorithm
DIRT_LPA_wb_plus by Stephen Beckett has been around for a year, I never managed to find the time to put it into bipartite. By now, Stephen has even written a wrapper code so that the output is fully compatible with existing code for plotting (
plotModuleWeb) there was really no argument left to postpone it. Stephen's DIRT_LPA_wb_plus will be the new default, replacing 'QuanBiMo', which remains available under method='DormannStrauss'. While DIRT could be called recursively, thereby making modules-within-modules computable, this is not packaged yet. So currently the much slower DormannStrauss-option is the only way to get recursive modules. Many thanks to Stephen for making this code available!
returned different values for secondary extinction, because the former by default purged empty columns/rows, while the latter didn't. It does now. Thanks to Gianalberto Losapio for bringing this to my attention.
2.07 (release date: 08-Nov-2016)
filled the matrix with 1s instead of 0s (although it was a ‘sophisticated’ logical mistake I made, not a simple typo). Thanks to Sandra Bibiana Corea for reporting!
for compatibility with clang. Thanks to Brian Ripley for fixing one of these C++-things that I never will understand! (In this case, the original code (by Miguel Rodríguez-Gironés) defined a pointer to "vector", which caused ambiguities in which "vector" should be used during compilation: the such defined pointer, or the std::vector.)
and referencing corrections/additions (e.g. in plotPAC).
2.06b (release date: 10-May-2016)
where a z-value of NA is returned if a species is alone (in its trophic level) in a module. This is due to the way z-values are computed, and not a bug.
was not exported in the namespace. Fixed. Thanks to various people reporting this.
2.06 (release date: 29-Sep-2015)
which did not compute the maximum number of possible checkerboards correctly, and hence let the normalised C-score to be incorrect. Now it uses a brute-force approach, which works fine but takes its time.
was not exported (i.e. not listed in the namespace file). Fixed. Thanks to Wesley Dátillo for reporting.
now correctly described a species' degree as sum of its links. Thanks to Bernhard Hoiß for the correction!
outcommented some unused variables in dendro.h and removed some fprintf-warnings in bmn5.cc
Threw an error when matrix was without 0s. Thanks to Thais Zanata for reporting.
2.05 (release date: 24-Nov-2014)
which computes the contribution of each species to the overall nestedness, based on Bascompte et al. 2003 and as used by Saavedra et al. 2011. Many thanks to Daniel Stouffer for contributing this function!
this function is based on R-code written by Diego Vázquez (many thanks for sending the code), with a bit of brushing up options by CFD. The function takes a probability matrix generated by whatever mechanism and builds a null model from it. This is a niffty little idea, making null modelling concerned with generating ideas on what makes an interaction probable and leaving the step of producing and integer-network of simulated interactions to this function.
“weighted connectance” was only returned when “linkage density” was in “index” call; now also available on its own. Also sligthly revised the help file.
nestedwith option weighted NODF called the unsorted version of this function,
while calling the same index in
networklevel called the sorted. This is not nice (although not strictly wrong). Now both call the sorted version and users have to directly invoke
nestednodf for the unsorted option. Many thanks to Julian Resasco for reporting!
I (CFD) misread the original paper introducing this null model and hence assumed that
vaznull would constrain marginal totals and connectance. However, this was not intended in Diego Vázquez original implementation and never stated anywhere (except in the help pages of this function here in bipartite). Hence, the help pages were changed to now reflect both intention and actual action of this function. This also means that currently only one null model with constrained marginal totals and connectance is available:
swap.web. Many thanks to Diego for clearing this up!
to adapt to the upcoming/new structure of vegan, which got rid of function
commsimulator (replaced by
simulate). Many thanks to Jari Oksanen for informing me about this!
second.extinct for the case that a user wants to provide an extinction sequence for both trophic levels. There is no obvious way to simulate this across the two groups, and hence it is not implemented. Also added error messages for non-matching vector/web dimensions and alike.
2.04 (release date: 25-Mar-2014)
This bug has been a constant thorn in my side. Somehow the C-code behind
computeModules could only be called once. On second call, it returned an error because somehow it kept some old files in memory. So far, I used a work-around (unloading and re-loading the dynamic library), which only worked on Windows and Mac. I still don't fully understand it, but thanks to Tobias Hegemann (whom I paid for being more competent than myself) we now have a function running bug-free on all platforms. (Deep sigh of relief.)
networkleveldid not work.
Fixed this legacy issue, which was due to a confusion created by the index' earlier name of “functional diversity”.
specieslevelgave incomplete name for one index:
Should be interaction push pull; also the function itself had the “push pull”-bit messed up. Thanks to Natacha Chacoff for reporting!
Both should be the same and should fit the description in the help file. Thanks to Jimmy O'Donnell for reporting!
2.03 (release date: 15-Jan-2014)
Ghost text deleted. Thanks to Brian Ripley of the R-Team and CRAN for not only reporting the issue but also pointing to its solution!
Similar to the argument in
networklevel; non-interacting species from the network were always excluded so far; new option FALSE not fully tested yet.
“pollination support index” returned “PSI”; “PDI” now referenced correctly as “paired differences index”.
groupleveland correspondingly in
"vulnerability" was identical to
"effective partners" with option
weighted=TRUE, but different for
weighted=FALSE (to which only
"effective partners" responded). We reduced this to one index called "generality" or "vulnerability" (depending on the focal group), but which will now give the non-weighted mean if option
weighted=FALSE. It can still be called by "effective partners" for backward compatibility.
Instead of returning the value for rows, it returned the functional diversity for columns (and vice versa). We also used the opportunity to rename the index to its correct name: “functional complementarity” and the function to
fc. Help pages for
grouplevel were adapted accordingly. Thanks to Mariano Devoto for pointing out this mistake!
This index is simply computed as linkage density divided by number of species in the network. Note that using empty.web=TRUE will affect this value (which is intended). Thanks to Becky Morris for suggesting to add this index here.
Thanks to Timothy Poisot for reporting some issues in the help page.
2.02 (release date: 30-Sep-2013)
grouplevel(thus also affecting
Networks with only one species in one of the two levels resulted in errors, rather than simply return NA for C-score and secondary extinction computation. Thanks to whoever it was for reporting (at the INTECOL workshop).
Gave error messages for closeness and betweenness if the network had no shortest path. Now returns a warning and NAs instead. Reported: JF.
Failed to work when an index was listed twice in the function call. Reported: JF.
This function is a null model algorithm like Patefields (
r2dtable, but it excepts externally measured abundances to compute the null model-expectation. Experimental.
Because some object was not deleted, memory consumption of this function shot through the roof (with time). Since R has a somewhat weird way of handling memory, I think that also subsequent operations were slower (because the dynamically expanded memory is not being shrunken again, which is a problem if you use the hard drive as RAM). Thanks to Florian Hartig for investing the time to fix it!
2.01 (release date: 28-Jun-2013) This release features smoothing of various glitches that were introduced when we cleaned up the code for version 2.00.
Computes the nestedness rank (as proposed by Alarcon et al. 2008). Can also be employed directly using the new function
nestedrank with options for weighting for number of interactions per link, normalising the rank and different method to compute the nestedness-arranged matrix.
Now returns an error message if the index is not recognised, instead of an empty list.
received an option to plot additional individuals of a species in different ways. For a host-parasitoid network, some hosts are not parasitised. This data vector can now be interpreted in two ways, making the plotting function a bit more flexible.
can now be invoked for each level separately. Also arguments can be passed to the plotting options.
a nice and large pollination network. Thanks to Robert Junker for providing this data set!
for random extinction sequences. This was so far not possible, because the function did not combine extinction sequences of different lengths. This was simply an oversight, reported by Richard Lance. (Thanks!)
2.00 (release date: 15-Mar-2013)
A new version number usually indicates substantial changes. In this case, we have re-named and re-grouped some of the output of
specieslevel for greater consistency and transparency. Beware! Running the same functions now (2.00 and up) will yield different results to <2.00 (because the same values are now in a different sequence).
We also started carefully renaming indices and re-writing help files. The main reason is that we started this work thinking of pollination networks. Over time, however, other types of ecological networks came into focus, and now also non-ecological networks are on the table. Thus, we started (and shall continue) referring to lower and higher levels, rather than plant and pollinators, hosts and predators or even trophic levels. Thus, in our emerging nomenclature the two levels are referred to as “groups” (their members remain “species” interacting with their “partners” in the other group).
Please read (or at least skim) the help pages before using a function of version 2.00 for the first time.
specieslevel indices can now be computed for levels separately (or together). Few user-visible changes, but complete re-structuring under the hood. Option species number was moved to
grouplevel as number of species.
In the new function
grouplevel we collected all indices that can be computed for each of the two groups (i.e. trophic or other levels). Indices can be computed for each group separately or for both simultaneously. All group-level indices are also accessible through
In the new function
linklevel we collected all indices that can be computed for each cell of the bipartite matrix. Currently, there are few such indices, however.
networklevel we dropped the plotting options. Users wanting to plot degree distributions or extinction slopes are encouraged to use the functions
Furthermore, due to licensing issues, we copy-pasted several functions from the package tnet, created and maintained by Tore Opsahl, to bipartite. We have so far called these functions from tnet, but only recently did R start to enforce license compatibility, which caused this step (bipartite being GPL and tnet being CC by-NC 3.0). We are really very grateful to Tore for allowing us to include the following functions:
Here a more detailed list of changes:
Function call and output now more consistent in naming and sequence. When higher and lower level indices are given (e.g. extinction slopes, number of shared partners), the first will always be the one referring to the property of the lower level. From a pollinator network perspective, the first value in such a pair describes a plant-level index, the second a pollinator-level index.
Indices mean interaction diversity dropped from
networklevel. We found no reference to this metric and saw little use for it. It is very similar to vulnerability/generality and can easily be computed from the output of
Now also accepts non-integer values as input. The argument H2_integer will then automatically be set to FALSE. Will return NA for those indices that cannot be computed (e.g. Fisher's alpha). As a knock-on effect,
H2fun had to be slightly adapted to round to machine precision when searching for H2min. (A somewhat technical detail, but making
H2fun getting caught sometimes.)
in which we collected indices that can be computed for each of the two groups (i.e. trophic or other levels). Indices can be computed for each group separately or for both simultaneously. All group-level indices are also accessible through
in which we collect indices that can be computed for each cell of the bipartite matrix.
normalise=FALSE offers the option of using the index as originally proposed, although we prefer to use TRUE and made this the default.
Network was actually the transpose of the correct network and hence wrongly had plant species as columns.
computes end-point degrees following Barrat et al. (2004); one of the indices computed at
helps organising data into one or more webs.
helps organising webs into one array.
gained two new indices (thanks to Jochen Fründ): proportional similarity and proportional generality. See help page of that function for details.
Experimental function to analyse more-than-2-level networks.
now obeys the label size to make sure labels are always in the plotting area. Thanks to Zachary Grinspan for drawing our attention to this issue.
Function failed for argument participant="both" because I filled the extinction sequence with the wrong number of 0s (to achieve always the same dimensionality of results in repeated runs). Thanks to Carine Emer for reporting!
failed to work for non-matrix data (i.e.
data.frames). It now coerces
matrix as a first step and hence should work also on
data.frames. Thanks to Marina Wolowski for drawing our attention to this problem.
When external abundances were provided with a 0 in it,
dfun could throw up
Inf-values. Reported by Indrani Singh and fixed by Jochen Fründ.
are now enshrined in stone. The initial reason was to set only the default for one function (
nestedness) to a faster setting (null.models=FALSE), but then I decided to restrict all settings to the defaults of the functions called (except for this one option).
Did not work if only one index was given.
Carsten F. Dormann, Jochen Fründ and Bernd Gruber, with additional code from many others (referred to in the respective help file), noticeably from Tore Opsahl's tnet package.
Maintainer: Carsten Dormann [email protected]
Alarcon, R., Waser, N.M. and Ollerton, J. 2008. Year-to-year variation in the topology of a plant-pollinator interaction network. Oikos 117, 1796–1807
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Bascompte, J., Jordano, P. and Olesen, J. M. (2006) Asymmetric coevolutionary networks facilitate biodiversity maintenance. Science 312, 431–433
Beckett, S.J. 2016. Improved community detection in weighted bipartite networks. Royal Society open science 3, 140536
Bersier, L. F., Banasek-Richter, C. and Cattin, M. F. (2002) Quantitative descriptors of food-web matrices. Ecology 83, 2394–2407
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Loading required package: vegan Loading required package: permute Loading required package: lattice This is vegan 2.4-3 Loading required package: sna Loading required package: statnet.common Loading required package: network network: Classes for Relational Data Version 1.13.0 created on 2015-08-31. copyright (c) 2005, Carter T. Butts, University of California-Irvine Mark S. Handcock, University of California -- Los Angeles David R. Hunter, Penn State University Martina Morris, University of Washington Skye Bender-deMoll, University of Washington For citation information, type citation("network"). Type help("network-package") to get started. sna: Tools for Social Network Analysis Version 2.4 created on 2016-07-23. copyright (c) 2005, Carter T. Butts, University of California-Irvine For citation information, type citation("sna"). Type help(package="sna") to get started. This is bipartite 2.08 For latest changes see versionlog in ?"bipartite-package". For citation see: citation("bipartite"). Have a nice time plotting and analysing two-mode networks. Attaching package: 'bipartite' The following object is masked from 'package:vegan': nullmodel connectance web asymmetry 0.16049383 0.50000000 links per species number of compartments 1.08333333 2.00000000 compartment diversity cluster coefficient 1.49621915 0.11111111 nestedness weighted nestedness 19.88853204 0.38901213 weighted NODF interaction strength asymmetry 11.60637382 0.39072657 specialisation asymmetry linkage density -0.16331384 1.60253736 weighted connectance Fisher alpha 0.04451493 7.83379039 Shannon diversity interaction evenness 1.58249027 0.28808894 Alatalo interaction evenness H2 0.40981947 0.85373072 number.of.species.HL number.of.species.LL 27.00000000 9.00000000 mean.number.of.shared.partners.HL mean.number.of.shared.partners.LL 0.47293447 0.44444444 cluster.coefficient.HL cluster.coefficient.LL 0.15309735 0.28167814 weighted.cluster.coefficient.HL weighted.cluster.coefficient.LL 0.38109672 0.06065982 niche.overlap.HL niche.overlap.LL 0.32776461 0.04794504 togetherness.HL togetherness.LL 0.16068376 0.03529412 C.score.HL C.score.LL 0.56160969 0.77042484 V.ratio.HL V.ratio.LL 0.58064516 6.49285714 discrepancy.HL discrepancy.LL 21.00000000 16.00000000 extinction.slope.HL extinction.slope.LL 2.62186579 1.33478953 robustness.HL robustness.LL 0.71584997 0.57436742 functional.complementarity.HL functional.complementarity.LL 1683.80420725 1627.11857340 partner.diversity.HL partner.diversity.LL 0.17686749 0.60896198 generality.HL vulnerability.LL 1.24808506 1.95698966 $`higher level` degree normalised.degree species.strength Policana albopilosa 1 0.1111111 0.851898734 Bombus dahlbomii 2 0.2222222 1.670940171 Ruizantheda mutabilis 2 0.2222222 0.539240506 Trichophthalma amoena 1 0.1111111 0.400000000 Syrphus octomaculatus 3 0.3333333 0.360149573 Manuelia gayi 1 0.1111111 0.033653846 Allograpta.Toxomerus 4 0.4444444 0.988141026 Trichophthalma jaffueli 1 0.1111111 0.014423077 Phthiria 2 0.2222222 1.038461538 Platycheirus1 2 0.2222222 0.009870983 Sapromyza.Minettia 1 0.1111111 0.004807692 Formicidae3 1 0.1111111 0.400000000 Nitidulidae 1 0.1111111 0.050000000 Staphilinidae 2 0.2222222 0.219230769 Ichneumonidae4 2 0.2222222 1.001265823 Braconidae3 1 0.1111111 0.100000000 Chalepogenus caeruleus 1 0.1111111 0.750000000 Vespula germanica 1 0.1111111 0.019230769 Torymidae2 1 0.1111111 0.450000000 Phthiria1 1 0.1111111 0.004807692 Svastrides melanura 1 0.1111111 0.028846154 Sphecidae 1 0.1111111 0.004807692 Thomisidae 1 0.1111111 0.004807692 Corynura prothysteres 2 0.2222222 0.015688900 Ichneumonidae2 1 0.1111111 0.019230769 Ruizantheda proxima 1 0.1111111 0.019230769 Braconidae2 1 0.1111111 0.001265823 interaction.push.pull nestedrank PDI Policana albopilosa -0.1481012658 0.34615385 1.0000000 Bombus dahlbomii 0.3354700855 0.07692308 0.9456169 Ruizantheda mutabilis -0.2303797468 0.11538462 0.9931818 Trichophthalma amoena -0.6000000000 0.73076923 1.0000000 Syrphus octomaculatus -0.2132834758 0.03846154 0.7750000 Manuelia gayi -0.9663461538 0.46153846 1.0000000 Allograpta.Toxomerus -0.0029647436 0.00000000 0.8333333 Trichophthalma jaffueli -0.9855769231 0.65384615 1.0000000 Phthiria 0.0192307692 0.19230769 0.9843750 Platycheirus1 -0.4950645083 0.26923077 0.9687500 Sapromyza.Minettia -0.9951923077 0.80769231 1.0000000 Formicidae3 -0.6000000000 0.42307692 1.0000000 Nitidulidae -0.9500000000 0.84615385 1.0000000 Staphilinidae -0.3903846154 0.23076923 0.9062500 Ichneumonidae4 0.0006329114 0.15384615 0.9916667 Braconidae3 -0.9000000000 0.76923077 1.0000000 Chalepogenus caeruleus -0.2500000000 0.69230769 1.0000000 Vespula germanica -0.9807692308 0.53846154 1.0000000 Torymidae2 -0.5500000000 0.38461538 1.0000000 Phthiria1 -0.9951923077 0.88461538 1.0000000 Svastrides melanura -0.9711538462 0.50000000 1.0000000 Sphecidae -0.9951923077 0.92307692 1.0000000 Thomisidae -0.9951923077 0.96153846 1.0000000 Corynura prothysteres -0.4921555501 0.30769231 0.9583333 Ichneumonidae2 -0.9807692308 0.57692308 1.0000000 Ruizantheda proxima -0.9807692308 0.61538462 1.0000000 Braconidae2 -0.9987341772 1.00000000 1.0000000 resource.range species.specificity.index PSI Policana albopilosa 1.000 1.0000000 0.851898734 Bombus dahlbomii 0.875 0.7243424 0.798038249 Ruizantheda mutabilis 0.875 0.9432075 0.152728066 Trichophthalma amoena 1.000 1.0000000 0.400000000 Syrphus octomaculatus 0.750 0.5038120 0.109577228 Manuelia gayi 1.000 1.0000000 0.033653846 Allograpta.Toxomerus 0.625 0.4683885 0.331639194 Trichophthalma jaffueli 1.000 1.0000000 0.014423077 Phthiria 0.875 0.8819171 0.145299145 Platycheirus1 0.875 0.8000000 0.005012171 Sapromyza.Minettia 1.000 1.0000000 0.004807692 Formicidae3 1.000 1.0000000 0.400000000 Nitidulidae 1.000 1.0000000 0.050000000 Staphilinidae 0.875 0.6700594 0.096703297 Ichneumonidae4 0.875 0.9317532 0.937579114 Braconidae3 1.000 1.0000000 0.100000000 Chalepogenus caeruleus 1.000 1.0000000 0.750000000 Vespula germanica 1.000 1.0000000 0.019230769 Torymidae2 1.000 1.0000000 0.450000000 Phthiria1 1.000 1.0000000 0.004807692 Svastrides melanura 1.000 1.0000000 0.028846154 Sphecidae 1.000 1.0000000 0.004807692 Thomisidae 1.000 1.0000000 0.004807692 Corynura prothysteres 0.875 0.7603453 0.011133763 Ichneumonidae2 1.000 1.0000000 0.019230769 Ruizantheda proxima 1.000 1.0000000 0.019230769 Braconidae2 1.000 1.0000000 0.001265823 node.specialisation.index.NSI betweenness Policana albopilosa 1.863636 0.00000000 Bombus dahlbomii 1.272727 0.00000000 Ruizantheda mutabilis 1.636364 0.04807692 Trichophthalma amoena 2.090909 0.00000000 Syrphus octomaculatus 1.227273 0.02307692 Manuelia gayi 1.272727 0.00000000 Allograpta.Toxomerus 1.136364 0.41730769 Trichophthalma jaffueli 1.272727 0.00000000 Phthiria 1.272727 0.00000000 Platycheirus1 1.090909 0.24423077 Sapromyza.Minettia 1.272727 0.00000000 Formicidae3 1.000000 0.00000000 Nitidulidae 1.000000 0.00000000 Staphilinidae 1.227273 0.02307692 Ichneumonidae4 1.863636 0.00000000 Braconidae3 1.000000 0.00000000 Chalepogenus caeruleus 2.090909 0.00000000 Vespula germanica 1.272727 0.00000000 Torymidae2 1.000000 0.00000000 Phthiria1 1.272727 0.00000000 Svastrides melanura 1.272727 0.00000000 Sphecidae 1.272727 0.00000000 Thomisidae 1.272727 0.00000000 Corynura prothysteres 1.090909 0.24423077 Ichneumonidae2 1.272727 0.00000000 Ruizantheda proxima 1.272727 0.00000000 Braconidae2 1.863636 0.00000000 weighted.betweenness closeness weighted.closeness Policana albopilosa 0.00000000 0.031499203 0.007653246 Bombus dahlbomii 0.27133479 0.045454545 0.016935668 Ruizantheda mutabilis 0.18161926 0.035885167 0.007635430 Trichophthalma amoena 0.00000000 0.026315789 0.002008598 Syrphus octomaculatus 0.26805252 0.046650718 0.009803161 Manuelia gayi 0.00000000 0.045454545 0.007645691 Allograpta.Toxomerus 0.17943107 0.049043062 0.004113559 Trichophthalma jaffueli 0.00000000 0.045454545 0.004331303 Phthiria 0.00000000 0.045454545 0.008236576 Platycheirus1 0.09956236 0.050239234 0.003845416 Sapromyza.Minettia 0.00000000 0.045454545 0.001720655 Formicidae3 0.00000000 0.007177033 NA Nitidulidae 0.00000000 0.007177033 NA Staphilinidae 0.00000000 0.046650718 0.006203052 Ichneumonidae4 0.00000000 0.031499203 0.001525954 Braconidae3 0.00000000 0.007177033 NA Chalepogenus caeruleus 0.00000000 0.026315789 0.002535735 Vespula germanica 0.00000000 0.045454545 0.005345012 Torymidae2 0.00000000 0.007177033 NA Phthiria1 0.00000000 0.045454545 0.001720655 Svastrides melanura 0.00000000 0.045454545 0.006978210 Sphecidae 0.00000000 0.045454545 0.001720655 Thomisidae 0.00000000 0.045454545 0.001720655 Corynura prothysteres 0.00000000 0.050239234 0.004526406 Ichneumonidae2 0.00000000 0.045454545 0.005345012 Ruizantheda proxima 0.00000000 0.045454545 0.005345012 Braconidae2 0.00000000 0.031499203 0.001525954 Fisher.alpha partner.diversity effective.partners Policana albopilosa 1.153134e-01 0.0000000 1.000000 Bombus dahlbomii 3.033554e-01 0.6135242 1.846929 Ruizantheda mutabilis 3.433080e-01 0.2035609 1.225760 Trichophthalma amoena 7.959266e-01 0.0000000 1.000000 Syrphus octomaculatus 1.171275e+00 1.0933747 2.984328 Manuelia gayi 3.192569e-01 0.0000000 1.000000 Allograpta.Toxomerus 3.878375e+00 1.2770343 3.585989 Trichophthalma jaffueli 5.252615e-01 0.0000000 1.000000 Phthiria 7.972188e-01 0.3488321 1.417411 Platycheirus1 1.235493e+00 0.5004024 1.649385 Sapromyza.Minettia 1.677721e+09 0.0000000 1.000000 Formicidae3 3.016702e-01 0.0000000 1.000000 Nitidulidae 1.677721e+09 0.0000000 1.000000 Staphilinidae 9.354130e-01 0.6829081 1.979626 Ichneumonidae4 6.033405e-01 0.2337917 1.263381 Braconidae3 7.959266e-01 0.0000000 1.000000 Chalepogenus caeruleus 5.252615e-01 0.0000000 1.000000 Vespula germanica 4.279460e-01 0.0000000 1.000000 Torymidae2 2.878510e-01 0.0000000 1.000000 Phthiria1 1.677721e+09 0.0000000 1.000000 Svastrides melanura 3.426574e-01 0.0000000 1.000000 Sphecidae 1.677721e+09 0.0000000 1.000000 Thomisidae 1.677721e+09 0.0000000 1.000000 Corynura prothysteres 1.591816e+00 0.5623351 1.754765 Ichneumonidae2 4.279460e-01 0.0000000 1.000000 Ruizantheda proxima 4.279460e-01 0.0000000 1.000000 Braconidae2 1.677721e+09 0.0000000 1.000000 proportional.generality proportional.similarity Policana albopilosa 0.3777479 0.699115044 Bombus dahlbomii 0.6976735 0.247787611 Ruizantheda mutabilis 0.4630282 0.712389381 Trichophthalma amoena 0.3777479 0.004424779 Syrphus octomaculatus 1.1273237 0.261061947 Manuelia gayi 0.3777479 0.184070796 Allograpta.Toxomerus 1.3545997 0.164096081 Trichophthalma jaffueli 0.3777479 0.184070796 Phthiria 0.5354241 0.184955752 Platycheirus1 0.6230516 0.883185841 Sapromyza.Minettia 0.3777479 0.184070796 Formicidae3 0.3777479 0.017699115 Nitidulidae 0.3777479 0.017699115 Staphilinidae 0.7477997 0.197345133 Ichneumonidae4 0.4772396 0.075774336 Braconidae3 0.3777479 0.017699115 Chalepogenus caeruleus 0.3777479 0.003539823 Vespula germanica 0.3777479 0.184070796 Torymidae2 0.3777479 0.017699115 Phthiria1 0.3777479 0.184070796 Svastrides melanura 0.3777479 0.184070796 Sphecidae 0.3777479 0.184070796 Thomisidae 0.3777479 0.184070796 Corynura prothysteres 0.6628589 0.434070796 Ichneumonidae2 0.3777479 0.184070796 Ruizantheda proxima 0.3777479 0.184070796 Braconidae2 0.3777479 0.699115044 d Policana albopilosa 0.6905693 Bombus dahlbomii 0.8581794 Ruizantheda mutabilis 0.1554289 Trichophthalma amoena 0.8474066 Syrphus octomaculatus 0.3859789 Manuelia gayi 0.3202602 Allograpta.Toxomerus 0.6482363 Trichophthalma jaffueli 0.2647268 Phthiria 0.3916793 Platycheirus1 0.0000000 Sapromyza.Minettia 0.2000132 Formicidae3 0.8115396 Nitidulidae 0.5510016 Staphilinidae 0.4092484 Ichneumonidae4 0.9007713 Braconidae3 0.6165417 Chalepogenus caeruleus 0.9500994 Vespula germanica 0.2834746 Torymidae2 0.8322740 Phthiria1 0.2000132 Svastrides melanura 0.3083018 Sphecidae 0.2000132 Thomisidae 0.2000132 Corynura prothysteres 0.1209998 Ichneumonidae2 0.2834746 Ruizantheda proxima 0.2834746 Braconidae2 0.0000000 $`lower level` degree normalised.degree species.strength Aristotelia chilensis 6 0.22222222 4.0607759 Alstroemeria aurea 17 0.62962963 13.6071500 Schinus patagonicus 1 0.03703704 0.9375000 Berberis darwinii 2 0.07407407 0.6603103 Rosa eglanteria 4 0.14814815 1.0517241 Cynanchum diemii 4 0.14814815 4.0000000 Ribes magellanicum 2 0.07407407 1.4285714 Mutisia decurrens 1 0.03703704 0.1111111 Calceolaria crenatiflora 2 0.07407407 1.1428571 interaction.push.pull nestedrank PDI Aristotelia chilensis 0.51012931 0.125 0.9933135 Alstroemeria aurea 0.74159706 0.000 0.9865135 Schinus patagonicus -0.06250000 0.875 1.0000000 Berberis darwinii -0.16984486 0.500 0.9971297 Rosa eglanteria 0.01293103 0.375 0.9423077 Cynanchum diemii 0.75000000 0.250 0.9529915 Ribes magellanicum 0.21428571 0.625 0.9743590 Mutisia decurrens -0.88888889 1.000 1.0000000 Calceolaria crenatiflora 0.07142857 0.750 0.9871795 species.specificity.index resource.range PSI Aristotelia chilensis 0.8575243 0.8076923 1 Alstroemeria aurea 0.7328574 0.3846154 1 Schinus patagonicus 1.0000000 1.0000000 1 Berberis darwinii 0.9304758 0.9615385 1 Rosa eglanteria 0.5114083 0.8846154 1 Cynanchum diemii 0.5924201 0.8846154 1 Ribes magellanicum 0.7081938 0.9615385 1 Mutisia decurrens 1.0000000 1.0000000 1 Calceolaria crenatiflora 0.7813942 0.9615385 1 node.specialisation.index.NSI betweenness Aristotelia chilensis 1.571429 0.3 Alstroemeria aurea 1.142857 0.5 Schinus patagonicus 2.428571 0.0 Berberis darwinii 1.857143 0.0 Rosa eglanteria 1.285714 0.2 Cynanchum diemii NaN 0.0 Ribes magellanicum 1.714286 0.0 Mutisia decurrens 2.000000 0.0 Calceolaria crenatiflora 1.714286 0.0 weighted.betweenness closeness weighted.closeness Aristotelia chilensis 0.2222222 0.12931034 0.022311369 Alstroemeria aurea 0.4074074 0.16810345 0.019174528 Schinus patagonicus 0.0000000 0.08620690 0.028303507 Berberis darwinii 0.0000000 0.11206897 0.018597603 Rosa eglanteria 0.3703704 0.15517241 0.020088268 Cynanchum diemii 0.0000000 0.00000000 NA Ribes magellanicum 0.0000000 0.12500000 0.014799036 Mutisia decurrens 0.0000000 0.09913793 0.005731637 Calceolaria crenatiflora 0.0000000 0.12500000 0.006640118 Fisher.alpha partner.diversity effective.partners Aristotelia chilensis 8.826098e-01 0.4631689 1.589102 Alstroemeria aurea 4.379926e+00 1.2340874 3.435242 Schinus patagonicus 2.411918e-01 0.0000000 1.000000 Berberis darwinii 3.812192e-01 0.2521995 1.286853 Rosa eglanteria 1.784783e+00 1.3095258 3.704417 Cynanchum diemii 1.503506e+00 1.1058899 3.021912 Ribes magellanicum 1.235493e+00 0.6730117 1.960132 Mutisia decurrens 1.677721e+09 0.0000000 1.000000 Calceolaria crenatiflora 1.591816e+00 0.5623351 1.754765 proportional.similarity proportional.generality Aristotelia chilensis 0.706071469 0.3896704 Alstroemeria aurea 0.263214772 0.8423705 Schinus patagonicus 0.014159292 0.2452143 Berberis darwinii 0.207964602 0.3155547 Rosa eglanteria 0.127433628 0.9083760 Cynanchum diemii 0.017699115 0.7410161 Ribes magellanicum 0.007964602 0.4806523 Mutisia decurrens 0.007964602 0.2452143 Calceolaria crenatiflora 0.008849558 0.4302935 d Aristotelia chilensis 0.9613968 Alstroemeria aurea 0.8043229 Schinus patagonicus 0.9846607 Berberis darwinii 0.5619798 Rosa eglanteria 0.5590405 Cynanchum diemii 1.0000000 Ribes magellanicum 0.9036839 Mutisia decurrens 0.6625752 Calceolaria crenatiflora 0.9106928
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