This article isprotected rights by All copyright. reserved. 10.1111/mec.14941doi: lead tod been and throughtypesetting, proofreadingwhich may thecopyediting, process, pagination This article undergone has for and buthas accepted been not review publication fullpeer 7 6 5 4 3 2 E14NS 1 Brock Hebert Elizabeth Running Head: Molecular A typeArticle : Special Issue DR. ELIZABETH LLOYD CLARE (Orcid ID : 0000 Department of Biology, University Western of Ontario, University Maryland, of Center for Environmental Science, Frostburg, MD,USA Department of Biology, A&M Texas University, 3258 College TAMU, Station 77843 USA Welcome Stem Trust Cell Institute, Univ Biodiversity Institute of Ontario, Centre for Biodiversity Genomics, School Biological of and Chemical Sciences, Queen Mary Accepted Articlepproaches to integrating
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This article isprotected rights by All copyright. reserved. sequencing, bats networks, interaction webs, food Words: Key and weaknesses of these data type strengths relative the of comparison involving a provide We and interactions. predatory functions and mutualistic, parasitic, trophic 8 of nodes 762 between interactions 3304 containing networks first the construct we data these Using analysis. network on resolution of type this of implications the discuss we and analyse and generate to complex is type network This o 407bp) (157bp, lengths sequence two targeting metabarcoding DNA use we bats insectivorous of contents faecal as such templates mixed for Finally difficult. is quantification and resolution in vary targets faece in sample pulp fruit or seed a from e.g. loci resemble data these and resolved and traditional ecologicalmoretaxonomiccomplexnetworks.For identifications,multiple weDNA target quantified fully be can method This identification. one s one yields specimen one where parasites, their and bats of example for identifications, field refine or validate to sequences long acquire to sequencing Sanger of use the involves method bat insects, plants, of consisting model a using considerations analytical discuss and ecology network into data DNA for incorporating methods key three demonstrate we Here networks. interaction of construction the on focused co become interactions trophic assessing for tools molecular As Abstract Universit Corresponding Author: Elizabeth Clare, School of Biological andChemical Sciences, Queen Mary Accepted Article f one gene region and a MOTU, BLAST and BIN association approach to resolve nodes. resolve to approach association BIN and BLAST MOTU, a and region gene one f
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s and their parasites from the Costa Rican dry forest. The simplest The forest. dry Rican Costa the from parasites their and s s networkin ecology. [email protected] N broig mtbroig high metabarcoding, barcoding, DNA s. These networks are also well resolved but gene but resolved well also are networks These s.
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molecular - based network of network based equence and equence - throughput This article isprotected rights by All copyright. reserved. ( interactions, species measuring for trend growing a is Incorporating taxa scale 2016 Pocock, ecologicalobservationsconsiderable of yearsand taxonomic expertise diversity Dalsgaar 2013) Bascompte, & Jordano, 2007) (McCann, 2010) Fontaine, 2002) al., et (Milo simultaneouslyPocock(e.g., When as networks, bipartite v of development of analysis economic global with diverse Ecological Networks, Introduction reviewed in in reviewed isual representations provide a synoptic view of complex interactions complex of view synoptic a provide representations isual
Accepted Articlenodes , they , (Hemprich multiple abiotic andabiotic M Ecosystem
d, & Olesen, 2010; McDonald connected by links representing interactions representing links by connected ( n ntok ae incredibly are networks any e.g.
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ary the evolution of of evolution the oprsn bten ewrs can networks between Comparisons
where trophic levels are depicted as layers (upper and lower) and (upper layers as depicted are levels trophicwhere biotic biotic functioning ,
suffer from problems of network completeness and poor and completeness network of problems from suffer
mathematical and statistical and mathematical tracers such as fatty acids, isotopes acids, fatty as such tracers ucinl roles functional
DNA t al. et Csaz e a. 1997) al., et (Costanza variables such as climate,ha variablesas such
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This article isprotected rights by All copyright. reserved. larger (often collection reference the of contents the towards biased created is network incomplete data 2016) Trites, & Harsch, Eveson, Deagle, Thomas, 2013; Jarman, & Trites, Shaffer, Thomas, Deagle, 2018; sequence recovered quan on choices parameter data prone) error (often unwanted remove to handling dat the First, ecology. network and analysis ecological for problems intractable otherwise with deal generates spectrum, complexity the of Howevermatrix.interaction an generatingfor observations traditional from different comparing and t 2018) al., et (Deagle databases reference methods bioinformatics of completeness the resolution, and coverage DNA strong advocates González applications are of detectionspecies and rate remarkable raditional DNA barcoding DNA raditional
Accepted Articlemarke target set to set ucl bcmn common becoming quickly iy N wti a ape s ihy controversial highly is sample a within DNA tify - Fnly i a iel situation ideal an in Finally, . ae analys based Many reviews, authors reviews, Many -
millions of sequencof millions Varo, Arroyo, & Jordano, 2014; Hemprich 2014; Jordano, & Arroyo, Varo, identify taxa, but in most cases most in but taxa, identify Cae 2014) (Clare, rs it
(Evans
(Symondson,2002) or o rfrne aae t confirm to dataset reference a to e o species of s
correspond
have evolv have “metabarcoding” but the but
clgcl analysis ecological et 2016;al., Roslin & Majaneva, 2016) Cae Can Littlefair, Chain, (Clare, ,
they a specimen’s ID is delimited by generating by delimited is ID specimen’s a
metabarcoding represents a novel data type for network ecology. network for type data novel a represents metabarcoding e specific s from each sample of mixed of sample each from s ,
ed from ed to biomass to
and developers of these techniques these of developers and sources ae o be widely been not have interactions for ,
to the use of h of use the to h rcvrd eune ae match are sequences recovered the implications the the (reviewed in Pompanon et al., 2012) al., et Pompanon in (reviewed
enzyme such as as such the reference library is incomplete is library reference the diet are ,
in other cases the connection is not clear not is connection the cases other in
including
ary analysis analysis ary unknown - linked immunosorbent assay immunosorbent linked Citsu 2016) Cristescu, & rc mtra and material trace for igh - its Bennett - constructing networks var networks constructing
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This article isprotected rights by All copyright. reserved. Accepteddiscussed been have that methods types interaction analysed commonly most present metrics level node and network several species Evans al., et 2016) 2014) al., et Wirta intractable otherwise are that systems analysis network ecological and biomonitoring ecological type. data new the Motif node. given functions or types a of interactions the to measurements sub are specific are diversity) partner strength, species connectance, (e.g. metrics lev level Node network. entire Network the across measured classes. are generality) nestedness, broad several into divided be can metrics Articlestruct 2015) Cristescu, analysis ecological in definition MOTU parameters of analysis an (see assessment bioinformatics the by defined arbitrarily is resolution ana included items unknown and known and network not or identity their know we whether compared, be can that diversity genetic equivalent of pools as viewed best unitsoperationaltaxonomicmolecular rec the or taxa) important economically or charismatic more yia problem lytical ures.
to to an Here we Here
demonstrate three demonstrate This is of importance when selecting what metrics can or should be measured. Networks measured. be should or can metrics what selecting when importance of is This example of example this level of the of level this
(Clare et al., 2016; Floyd2016;al., et (Clare
address
hc my olpe rpi lvl ad hs eeae udmnal different fundamentally generate thus and levels trophic collapse may which . How
(Milo et al., 2002) al., et (Milo Many while o while (Hemprich - network aparticular of pattern which may specific define ecological interaction methods of methods
then might have concluded have this challenge this thers
approaches to approaches network is entirely resolved to a common point of reference with both with reference of point common a to resolved entirely is network - ent e a. 21; ns t l, 2009) al., et Ings 2018; al., et Bennett
have
data for DNA and network integration network and DNA for we .
advocated adoption advocated
Each metric ty metric Each - a itnt datg we mxd eouin present resolution mixed when advantage distinct a , (Gibson et al., 2015; Roslin & Majaneva, 2016; Toju, 2015; Toju, 2016; Majaneva, & Roslin 2015; al., et (Gibson .
proceed integrat MOTU et al., 2002) al., et by While these are not without controversy, o controversy, without not are these While
that (
lr e a. 21; ln, rw, hi, aIac & MacIsaac, Chain, Brown, Flynn, 2016; al., et Clare focussing on focussing the
( naoitc mutualistic antagonistic, molecular the
s ? ion use
(Floyd, Abebe, Papert, & Blaxter, 2002)Blaxter, & Papert, (Floyd,Abebe,
into of DNA of . In this case, each MOTU becomes a node in the in nodebecomes MOTUa each case, thisIn . pe needs to be considered separately in light of light in separately considered be to needs pe a
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approach data
overed pool of DNA is converted into converted is DNA of pool overed raised serious raised
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This article isprotected rights by All copyright. reserved. parasites boundaries species Method parasite host sanguivore l only are others while levels trophic all into integrated are species pul fruit or (seeds materials plant containing as classified being an species host 2005) Simmons, 2009; 26 representing preliminary The (ACG). Guanacaste A Materials and these data. form to types o previously, used been have these material mixed material Second gene
Accepted Article case study from C
DNA barcoding to resolve taxonomy resolve to barcoding DNA ,
h smls wy o nert DNA integrate to way simplest The From
o that cannot be identified by morphological methods.morphological by identified be cannot that we use we ne: w
W e
Desmodus ecological analysis of this case is presented is case this of analysis ecological
only targeted the 5’ end of the mitochondrial cytochrome mitochondrial the of end 5’ the targeted S e May to July 2009 July to May anger sequencing to resolve species ID a , hope to provoke discussion about the appropriate use of these data types d Methods:
and multi species species multi 260 .
or or ost Rica: Plants, Bats, Insects
then - guano samples from 21 species of which visual inspection led to 132 to led inspection visual which of species 21 from samples guano -
trophic level trophic . From these individuals we individuals these From . gene DNA gene to to rotundus opooial identified morphologically confirm discuss the advantages and challe and advantages the discuss
present present
, did not produce a faecal sample so was so sample faecal a produce not did
a field team visited Sector Santa Rosa of the of Rosa Santa Sector visited team field a
and improve upon improve and barcoding nlss eis n aeil olce drn ta period that during collected material on relies analysis assemblage ur ur analysis in
to resolve to
aa novs h ue of use the involves data cryptic organisms cryptic
provides resolved ,
and analyzed sn aalbe il ky ad checklists and keys field available using
of identifications Parasites
more complex taxa and single and taxa complex more bats and parasites
(Box entirely with DNA with entirely h first the nges
466 parasites that were sampled from 18 from sampled were that parasites 466 Th
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of applying of and . A total of 801 bats were captured were bats 801 of total A .
c oosely associated, for example t example for associated, oosely example of of example ,
we use we oxidase made in the field the in made to Sanger validate field identifications. field validate retained
resolve to metabarcoding
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the MOTUs parasitic Hebert, Ratnasingham, & DeWaard, & Ratnasingham, Hebert, other representatives of their of representatives other Barcode Identification Number ( Number Identification Barcode cocktail, PCR reagent mix and the thermocycler conditions “MamCOI”conditions thermocycler the and mix reagent PCR cocktail, Fr as we bats For . aa System Data
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This article isprotected rights by All copyright. reserved. a using 2011) Schuster, dat al., et Clare (http://qiime.sourceforge.net/ methods uclust and pick_otu the using QIIME in similarity We (157bp http://hannonlab.cshl.edu/fastx ( region) to splitter barcode 157bp employed Bioinformatics isopr chip 316 except instructions, manufacturer’s the using performed Technologies).Al measured protocol t using cleaned Accepted short FASTQ/A (2010). Gordon Assaf Article a used custom scripts to set w set opanol and three times 100 with
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(Table S3)
two analytical pipelines for comparison for pipelines analytical two allowing ng/µLlibraryfinalprior todilution. o a icsin f OU thresholds MOTU of discussion a for ,
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This article isprotected rights by All copyright. reserved. avoid (to 100% of QV fi qualityspecific low ultra or low with bases max and 147bp, length min 0qv, MinQV of end 0bp(primers adaptors and already had removed been via cutadapt) trim length 500bpandfiltering platformmBRAVE ( cutadaptusing removeprimers and MID by split Galaxy to processedin readswith similar were steps provide better resolution of stud the from database identification S6) and S5 Lepioptera) (primarily specimens from generated reference (Figure S4). ( dataset 407bp the for performed oversplitting reference the in represented heavily assignments considered particularly We option. considered MOTU to be MOTHUR in implemented as UCHIME =250. Score Min =0.2. Complexity Min were: Megan Acceptedhttps://cutadapt.readthedocs.io/en/stable/guide.html Article c
himeras. representative For a second comparative analytical approach we used a non a used we approach analytical comparative second a For further We dataset MOTU each For eune library sequence
MOTU s , n with and Max Expected =0.01, Top P Top =0.01, Expected Max n ti dt st a ue fr ewr construction. network for used was set data this and
but but
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meters. We set a pre a set Wemeters. custom BLAST parsing scripts. scripts. parsing BLAST custom larger, morelarger, charismatic n t a eeec lbay rvdd y . azn n W. and Janzen D. by provided library reference a to and
the towards identification of of identification towards 157bp and 407bp data407bpand 157bp
and the accuracy of of accuracy the and (90 . If two or more reads were assigned to the same species we species same the to assigned were reads more or two If . MEGAN - 7 clustering) 97% (Schloss et al., 2009) al., et (Schloss We database ercent =10, Min Support Percent (0ff), Min Support =1, Support Min (0ff), Percent Support Min =10, ercent ) tested
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eiotr, hc dmnt te reference the dominate which Lepidoptera, the database economically IE threshold IIME
sets by sets we 500) but without network without but 500) Lepidoptera
examined to Th g the parameters trim front 0bp, trim0bp, front trimparameters the g
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This article isprotected rights by All copyright. reserved. non the association matrix. employing prey and bats of network separate a produced we addition data the in metabarcodingand data frequency are levels (fi trophic ( Number Identification Barcode level species group. taxonomic or level trophic the on based 2008) Cheddar Network analysis: was then converted to a matrix of bat species vs. associated prey BINs for update last BINs, known 878 434, from sequences reference with reads the associate to attempt to approach BIN the p comparison. pre as thresholds OTU left and 2% of latform. The resulting data is automatically is data resulting The latform.
Acceptedpossible) practically not is parasites individual all nding Article
wh ee is level Using the data produced data the Using (Hudson et al., 2013) al., et (Hudson ich represents all identified taxa or or taxa identified all represents ich
(Deagle et al., 2018) al., et (Deagle they , hmr sreig n drpiain a promd uoaial b te mBRAVE the by automatically performed was dereplication and screening Chimera also
r fly quantified. fully are - based as based el resolved well are poorly suited to to suited poorly
it is not possible to possible not is it
and Bipartite and . BIN by The arthropods are represented by represented are arthropods The ,
all three approaches three all but ) (Ratnasingham & Hebert, 2013) Hebert, & (Ratnasingham
individual taxa are are taxa individual h m The - e sadrs s e goe MT aayi fr this for analysis MOTU ignored we as standards set quanti (Dormann et al., 2009) al., et (Dormann M compared OTUs. ts n flies and ites As t As
assess ingested plant biomass from seeds (plant ID) (plant seeds from biomass plant ingested assess f ythe he bats are bats he This network has differ has network This
to the BOLD system library for insects for library system BOLD the to
known BINs (this library contained 580, 824 580, contained library (this BINs known , biomass or abundance or biomass
we construct we . Similarly . ny atal qatfe fo ec bat each from quantified partially only d 21, Oct. 2018). The resulting dataset resulting The 2018). Oct. 21, d
ht ed on feed that , with a single exception, single a with ,
in R in
MOTU , ed
the plant and arthropod prey arthropod and plant the as a proxy for species for proxy a as (R Development Core Team CoreDevelopment (R
further a “network of networks” of “network a them s
(Floyd et al., 2002) al., et (Floyd ing
of species representedspecies of
analysis.
levels of resolution of levels are are - OTU dniid by identified
ae BIN based resolved
. using
This .
In to in a
This article isprotected rights by All copyright. reserved. the within species to matches BLAST top example,For scores. best equivalentwith hits BLAST multiple returned often GenBank to sequences sequences had 97 these, Of loci. three the Gen to recovered We bats maximum host number of species inhabited of time.a and VespertilionidaeNatalidaeatspecies byectoparasite parasitized only were one of Mormoopidae specie per ectoparasite species ectoparasite single a from varied diversity Parasite this outcome of specimengeneticallydivergent one suspect We as resolved. were uncertainty taxonomic of cases other intermedius Artibeus generallybarcoderesults The Molecular Analysis Results
the 3 the C. sowelli C. Accepted the Article
otie to s two contained B Noctilionidae were only associated with two ectoparasite species and species ectoparasite two with associated only were Noctilionidae
4 We recovered We 466 the of 445 from barcodes DNA recovered We 698 from barcodes DNA recovered We ank and BOLD and ank
ectoparasite species ectoparasite
could could not be confirme and members of two genera two of members and rbc and Phyllostomidaeand placed L from 102, from L se
s
gen
eed (Clare, Lim, Fenton, & Hebert, 2011) Hebert, & Fenton, Lim, (Clare,
on plant DNA from 112 guano samples guano 112 fromDNA plant to to ,
era 103 samples 103 Ficus morphotypes a . jam A. seed seed . Although some sequences did not have an identical match on GenBank, on match identical an have not did sequences some Although . eu bsd n snl gene single a on based genus , confirmed the field IDsfield confirmedthe
mat flies 13 and mites nine
or or samples had sequences had samples ai
K from 81 and 81 from K host d Solanum censis , it had sequences had
ed nlsd separately analysed was retained
. the greatest diversity of parasite species whereas individualwhereas speciesparasite of greatestdiversity the ( Glossophaga
by amite flyor was species Among ace (0% r 9 or (100% matched bats trn Sturnira parvidensSturnira
as
H assigned to to assigned , h svn a families, bat seven the An unknown species of species unknown An species 24 representing
were only collected from collected only were but could not distinguishnotcould but -
a single psb assigned , Micronycteris A from 106 from A producing ,
from mites and flies and mites
region leaving this node this leaving
node
eight genera based on at least two of two least at on basedgenera eight 12 species of bat. of species 12 to a to % dniy epciey multiple respectively) identity 9%
(Table
(Figure S1)
putative species and speciesputative bat may be a sister tax sister a be may
samples 1 sequenced 119 ) gained species assignments species gained ) four
pce ( cases) (9 species
S found on found . 4
belonging to belonging ). ). Carollia individual
. unresolved. Artibeus lituratusArtibeus
one host species. The species. host one . the oprsn of Comparison Through comparisonThrough
G
uano Emballonuridae 18 host host 18
was identified was seed
on, Mol
16 seven from Two thirds Two 17 genera 17 However,
but
samples. samples ossidae, species. to to since rbc nine
and L , . .
This article isprotected rights by All copyright. reserved. compl nigripalpus Culex region were identifications cases of number a in and identified species example s (118 species of number also parameters given the at and S2 Tables a species of of species sequences other several samples our Two GenBank. in error taxonomic of case probable a detected also We var sequence these species, known a assignmentto an prevented trn mat higher taxonomic rank (e.g., Urticaceae or ulmifolia a allowed identity100% sequences to BOLDversuson pre taxa of diversity greater all m cass n order and classes ame
Accepted ArticleH rbc K. In all cases, samples that yielded unique sequences for sequences unique yielded that samples cases, all In K. generated - showed psb e . For . L sequences matched with 100% identity to a sequence on BOLD on sequence a to identity 100% with matched sequences L mentary We of BLAST GenBank The , A. Although the the Although A. ), or species cluster (e.g., cluster species or ),
32 species of Lepidopteraof32 species - Cecropia S example oe precise more 3 Vismia used showed high similarity high showed ). The 157bp region has been used extensively used been has region 157bp The ). high
high , adding , T to HTS
eunig success. sequencing (Hypericaceae). recovery rates rates recovery , sequences assigned to the genus the to assigned sequences , y the by on GenBank. Further investigation Further GenBank. on
. confidence to the general diversity recovered and the specific taxa identified. taxa specific the and recovered diversity general the to confidence s versus of almost twice the length the twice almost of Surprisingly,
limited recover taxonomic of of sent in the the in sent arthropods 407bp
1 09
trn taxonomic in the present study present the in , two
Cecropia obtusifolia Cecropia
were common in the two lists. Howeverweretwolists.the common in taxa for the 157bp and 407bp region 407bp and 157bp the for taxa to identity) (98% H
sinet on assignment given the degradation induced by digestion, by induced degradation the given
region. - psb re latter T
gions ee w regions two hese (excepti Cecropia A sequences corroborated results obtained with obtained results corroborated sequences A lower valueslower coverage database. Similarly, all all Similarly, database.
This f h mtcodil O gene COI mitochondrial the of ng
sp.;
hwd nearly showed
outcome n mantid) one Culex ;
BOLD, either to to either BOLD, Cecropia obtusifolia Cecropia it revealed that revealed for the latter gene region gene latter the for Ficus
/
is fully analysed fully is onGenBank (94 peltata
by the short region short the by ( (Alberdi et al., 2018; Zeale et al., 2011) al., et Zeale 2018; al., et (Alberdi als 5 n S6) and S5 Tables
sp.). suggest iants were treated as putative speciesputative as treatedwere iants mat
Many .
/ mrvd sn te ogr target longer the using improved K also had unique sequences for sequences unique had also K 100%
insignis these sequences likely belong tobelong likelysequences these mat s
the s trn with matched sequences K
,
a
-
and generated 686 MOTU 686 generated and similarity to several to similarity f h sm seis for species, same the of 100%). In100%).some cases this presumably reflecting the reflecting presumably respectively) from respectively) , , an unexpected an , e w regions two se ) versus assignment versus )
pce (e.g., species H there were alsotherewere different - psb could be identified asidentified be could
17p ad 407bp, and (157bp, dniid similar a identified n GenBank on A sequences from sequences A
t he 407bp region 407bp he Guazuma
rbc result a be may
all the all L an L often other to
as d a , .
This article isprotected rights by All copyright. reserved. method. any by represented diversity prey tremendous the reflects which 1) (Table substantially differ not did propertiesnetworkmeasurementsofactualInterestingly, below).the (see site the for libraryreference these Of minimal. be to is which nodes, prey fewer substantially conclusion same the drawn have would is effect The types network between metrics these connectance, metric. each for outcomes expected with consumers the to (e.g. 4) (Figure metrics network most For extent. lesser or greater a MOTU thresholds have significant a impact onstandard network metrics taxa are as lumped split or to of diversity. genetic prey of level taxonomic particular a represent not do nodes prey the because network The impact of OTUs on network metrics mBRAVEin identified 212 potential prey in the 157bp dataset. platforms However
Accepted ArticleMOTU generating ,
from 90% to 99%) split taxa split 99%) to 90% from h ms nvl aa type data novel most The
, , creating
h 47p region 407bp the
consistent but less predictable in measures of robustness (Figure 4) (Figure robustness of measures in predictable less but consistent nestedness ,
const 5 wr Lpdpea elcig h sbtnil fot o rae Lepidoptera a create to effort substantial the reflecting Lepidoptera were 75% s
– , r
a on the measurem the on As a result, a As n vleaiiy this vulnerability and i nts onqualitynts andrecovery rate.
uhs the pushes so generated
.
we investigated the impact of the key bioinformatics step bioinformatics key the of impact the investigated we
the resource level in our network increase network our in level resource the For a complete analysis complete a For
current ent of ent . h BN soito ntok Fgr 5 contained 5) (Figure network association BIN The
s h mtbroe data metabarcoded the is
variation xetd a te eeec dtbs fr h ae is area the for database reference the as expected, common iis of limits can
network variables. Our data suggests that suggests data Our variables. network Analysis with the BIN a
amplicon
n h cs o lns ewe species, between links of case the In result in result
, an increase in increase an , see Hemprich
size ifrn eaie rank relative different ht underlie that reco
- or taxon or Bennett et Bennett
the d very ,
in richness relative richness in but
ssociation method MOTU threshold MOTU
in all cases we cases all in
on but a measure a but
h bat the
al., most ( ings 2018)
– - HTS prey that
of .
This article isprotected rights by All copyright. reserved. ecology, network Acceptedinteractions Trites, & Deagle, Tollit, 2012) al., et Pompanon 2018; al., et to linked is recovery sequence whether around largely t to Pompanon2015;Symon al.,2012; et al.,Flynn et been have data these using analyses dietary individual Ki 2010; al., et Atwell 2018; generatechallenging to 2014) Symondson, & Mir, Clare, Andrés, San Piñol, Metabarcoding results incorporated 2006) Hebert, & Hallwachs, Janzen, Woodley, (Smith, 2008) Hebert, & Hanner, Articlechallenges ensureto communities method) BIN faeces) to used parasites analysis Discussion e nepeain f od webs. food of interpretation he
i that can be analysed using MOTU or association with taxa in reference collections (e.g. the (e.g. collections reference in taxa with association or MOTU using analysed be can that . The incorporation of DNA incorporationof The demonstrated have We s generate )
biologicallymeaningful results DNA and to identify morphologically compromised material (e.g. plant pulp) plant (e.g. material compromised morphologically identify to and more challenging and requires requires and challenging more .
are more are C
, there are there , in . nimn fed D i a omn oeua procedure molecular common a is IDs field onfirming to network analysis (e.g.analysisnetworkto W is a b used be can
complex and fundamentally novel data novel fundamentally and complex hile best applied to mixed faecal samples, gut contents (particularly liquid feeders liquid (particularly contents gut samples, faecal mixed to applied best f r example or
appropriate
hs data these
challenges reliable
and differentiating cryptic or taxonomically complex species is species complextaxonomically or cryptic differentiatingand 01 Toa e a. 2016) al., et Thomas 2011; to ng, Read, Traugott, & Symondson, 2008) Symondson, & Traugott, Read, ng, ,
metabarcoded ofr field confirm that iiain rqec t seii flower specific to frequency visitation
(Nielsen et al., 2018) al., et (Nielsen types in
analysis
. While . three types of molecular data can be incorporated into network into incorporated be can data molecular of types three D b oth ebate ,
Wirta et al., 2014) al., et Wirta a efciey eeae rapid generate effectively can (Table 2 generatingdata pca consideration special
into networksinto about this is possi is this data identities , Figure 4 dson, 2002) dson,
(Alberdi et al., 2018; Arrizabalaga2018;al., (Alberdiet the quantification of of quantification the
or pollen carried pollen or ,
studied in studied original . Frequency . in many cases cases many in eg bt) or bats) (e.g. . ble in ble
and in and presents via T ) . However, . . hese
.
metabarcoding However,
restricted biomass
the interpretation of network metricsnetwork interpretation of the
to to approaches several contexts several
both straightforward use cases andcases use straightforward both - based approaches based nert with integrate
the inclusion of of inclusionthe
differentiat . The methods The . by s frequency , (Deagle et al., 2013; Nielsen 2013; al., et (Deagle
certain
metabarcoding data data metabarcoding scenario s a is clbe nlss f entire of analyses scalable Brsno Lm Ivanova, Lim, (Borisenko, generalist
ha of tnad erc of metric standard
challenges ve mixed material (e.g. material mixed -
s e . DNA can also be also can DNA . ae maue of measures based
(Clare et al., 2016; al., et (Clare
ucsfly been successfully network (Bowles, Schulte, (Bowles, cryptic s - . Escudero et al.,et Escudero are common are metabarcoding of interpreting of However
now are
taxa analysis
centre routine unique ,
(e.g.
it is it e.g. the
in s .
This article isprotected rights by All copyright. reserved. Acceptedsystemsacross taxonomyof level standard measure repeatable and MOTU analyse (Hemprich gen mixing (e.g. resolution of levels different 2016 MOTU. steps informatic employing of issue resolution node the to identical not but similar properties network of observations on resolution of impact the that showing 4). (Figure metrics MOTU differentiate to used protocols the that suggest further predators trop ArticleMO even bioinformatically mayinadvert taxa. unknown and known both incorporated, are data all identities taxonomic for screening be equally weighted (Kaiser 2005) interactions mutualistic of strength T h ) or network or ) c levels ic U as nds rm eunig error sequencing from nodes false . s s - However
We 2010) Caflisch, & Müller, Memmott, Muff, Bunbury, any
collapse allcollapse generate ,
- u all but ent e a. 21; atnz 1991) Martinez, 2018; al., et Bennett data ugs to alternative two suggest entlyinclude non .
,
o example, For
but but , set used to generate MOTU generate to used
that )
incomplete a analysis we
presents uniform to
(Clare, 2014) prey levels into one “resource” level rather than revealing the complexity the revealingthan rather level“resource” one into levels prey h efc o nd rslto hs en icse fr eae wt analyses with decades for discussed been has resolution node of effect The re
empirically
s gene may rae a MOTU as treated
of biodiversity of (Hemprich . This may This . a challenge in interpretation in challenge a level of resolution of level quantification n u case our in - target taxaintestinal (e.g. target parasites rate
estimate that impact (Figure 4) and 4) (Figure impact that estimate (e.g. BINs, similarity searches) similarity BINs, (e.g. (Memmott, Waser, & Price, 2004; Vázquez, Morris, & Jordano, & Morris, Vázquez, 2004; Price, & Waser, (Memmott, Boe Otig Hrio, Mrie, 2004) Martinez, & Harrison, Ostling, (Brose,
- ways to to ways represent oe i networks in nodes Bennett et al., 2018) al., et Bennett
(Floyd et al., 2002) al., et (Floyd node, chain length and trophic levels significantly alters the alters significantly levels trophic and length chain node, are Cae t l, 06 Fyn t l, 2015) al., et Flynn 2016; al., et (Clare oe insects some
pe” f a of “prey” needs
only starting to be considered be to starting only us incorporat
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and specie and powerful . a network. By their nature their By network. a to be considered when weighted metrics are used are metrics weighted when considered be to However undifferentiated n . we
e
For example For unr However, . advantagein e rmr consumers primary re eaacdn data metabarcoding s designations) s Any network Any , elated
MOTU
, even if that level does not equate to a to equate not does level that if even
as rare and common and rare as
s or . The advantage of of advantage The .
il hmevs im themselves will
o the to bacteria by incorporating unknowns incorporating by s
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easily u data Our systems o the of s s
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This article isprotected rights by All copyright. reserved. employed that to similar dataAccepted generates identifications distinctThree data types important factor relativelyin unexplored faunas. very a is it but diminish will effect this improve collections reference As node. prey a of inclusion pre in reduction substantial the specificexample, indicatorschange.For bat aboutconclusions similar were metrics network the case, this In used. being library reference measured being interaction of type the about information exactly determine measurements Article metrics specific picknecessary to compared. beingnetworks traditional in maintained be should resolution node and protocols sampling as just choices, informatics to protocol compa and level consumer the to to is then key The measureable. be relativewould nestedness or generality as such parameters in fluctuations decline similarly would number MOTU the and diversity biological pic hold own its on network numbers ecologicalinteraction beingmeasuredbeyond general thestructure theof community. life - yl and cycle e ny nlss ht mly h sm mtos rm eunig ltom n fed n lab and field and platform sequencing from methods same the employ that analyses only re hs ae hs considered has paper This to ability better provid data type association BIN and searchers based similarity contrast, In
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ht s en icue a a oe eg Fgr 5) Figure (e.g. node a as included being is what and w afnt t ec ohr rvd lmtd nomto o te aue f the of nature the on information limited provide other each to affinity own - prey/BIN, bat prey/BIN,
y N deliver DNA by
their s
Hemprich little biological meaning biological little
connections vary with with vary connections nds hn eyn ecuiey n eeec cletos o the for collections reference on exclusively relying when nodes y ;
example for example network level metrics may be more reliable than motif reliablethan more metricsmayexamplebe network for level - - parasite and bat and parasite Bennett et al.,Bennett 2018) he dsic types distinct three
This would be require be would This g
enerality of the bat enerality the of n rdtoa ntok ecology network traditional in ery efc rslto o te network. the of resolution perfect nearly ,
if the preyif levelundergoes apopulation crash, the genetic
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plant networks would remain the same but somebut same the wouldremainnetworks plant However nlss aaees eg MO (e.g. parameters analysis .
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if the same methods are replicated a replicated are methods same the if to to avoid context specificity.context avoid
and ,
t e o he enough that comparative that enough yield Te a and bat The . l m nly
rae ecological greater U hehl) a threshold) TU
Similarly,the if ajor uh analysis Such
difference
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This article isprotected rights by All copyright. reserved. challenge 43,000 work. arthropods most location, study the In web. for data generate can ecology to possible identification taxonomic any) (if much without of methods Refl BINs. existing to matched be can they although current most by generated reads short the region, barcode COIthe frominformation sequence of 500bp least at requiresBINs new of definition viewed from (varying BIN fish sets data Sanger large system MOTU other Unlike taxa. related in observed differentiation sequence construction network where but 2) that BIN employed alternative an taxonomic system, the crucial thedifferencewith that identifications th being
Accepted Articleto possible it making of advantage the has es s it delivers it system
was and Lepidoptera For example, For
is not based on strict on based not is
of these species species these of rm the from as it as ne for need e
(D.H. Janzen Pers. Comm.) Pers. Janzen (D.H. ey ih ( high very the the analyzed
MOTU generation. MOTU has been been has extensively tested
compress
were taxonomic resolution that is very close to to close very is that resolution taxonomic 79% current
context of of context -
some 400,000 some 7 bten aooi group) taxonomic between 97% generated in a similar a in generated mixed sources (e.g. stomach contents) but imposes unique problems for network for problemsunique imposes but contents) stomach (e.g. sources mixed ,
multi
, a comparable interaction network model network interaction comparable a r es
for u ipss cntan ta te dniiain s ae on based is identification the that constraint a imposes but 2 taxonomic the correspondence between species =0.99
ae en barcoded been have trophic levels and dispens and levels trophic
- the the loc a pri a ) N barcoding DNA us data data us
DNA barcode DNA curre n te cul apn o seis o I ws prxmtl 90% approximately was BIN to species of mapping actual the and For this For or
arthropod ytm s incomplete is system
nt threshold delimitation but delimitation threshold .
referencedatabase In such locations, a locations, such In (Ratnasingham& 2013) Hebert, to
third data type data third
obtain fashion remain undescribed remain HTS
species
region incorporate taxa which are apparently different species different apparently are which taxa incorporate , over B
(e.g. Figure 1) Figure (e.g. BIN arcode
ltom cno b ue t delineate to used be cannot platforms species
es to the bat data (one sequence per specimen) per sequence (one data bat the to 14 . s are estimated to be present be to estimated are
In a test of 1400 species span species 1400 of test a In ecting this constraint, there is a need for need a is there constraint, this ecting with traditional taxonomy traditional with Rtaiga & eet 2013) Hebert, & (Ratnasingham
are years In ,
. s data traditional taxonomy. T taxonomy. traditional -
de
are I analysis BIN level a complete counts based on x , strong
N veryincomplete.Asconsequence a are revealing . has been trained specificallytrained been has
, umber despite decades despite This resolution it may it
most often analysed using MOTU using analysed often most
rx fr species. for proxy food web or interaction network interaction or web food
has the advantage of of advantage the has ( -
BIN
generating methods, the BIN the methods, generating and these studies have shown o ersn rpi food trophic a represent not the scope of the the of scope the vis neovd oe in nodes unresolved avoids
) for traditional . The . The
.
plant o in the ACG, but just but ACG, the in his data type (Table type data his While such analysis such While f intense f performance of the ning s Te parasite The . taxonomy and a Tu, when Thus, .
eas the Because
esr of measure birds, bees, birds,
taxonom BINs new taxonomic making it it making using the using
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This article isprotected rights by All copyright. reserved. region only when (e.g. >98% scores most with assignment this for compensate partially to value Support Min MEGAN the modified We alignments). (local scores BLAST quality low of rates high generate also will but information) considered compl using evaluated when expectation ID taxonomic Comparison of 157 taxa acquired via secondary predation. hand, other the while databases” in found “things of composed association BIN or searchers similarity of use The systemmatching or association of sort database. generated network a to comparable less be would bat bias substantial a introduce would DNA) or morphologyby (either is im
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This article isprotected rights by All copyright. reserved. Acceptedspecificity host of dramatic by demonstrated was This networks. interaction Similarly spectacularly them dropped example are material remains) digested pulp o many cases establish to used interactions observe to need the on rely not does technique DNA Article nothas substantially compromised the DNA. this digestion that overcome assuming resolution taxonomic can higher thus and Biosciences) reads longer allowing Pacific problem (PACBIO, platform sequencing SMRT the as such analytical be will read 407bp the promising, while thus MiSeq, platformRoche454 capableof samples separate throughputse completed. been has poten and interesting an newregion is analysisto NGS and not has been evaluated forthis purpose before. We
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This article isprotected rights by All copyright. reserved. AcceptedClare, E.L. (2014). Molecular detection of trophic interactions: emerging trends, distinct advantages, Christe, P., Arlettaz, R., & Vogel, P. (2000). Variation CBOL working plant group. (2009). A DNA barcode for land plants. Burgess, S., Fazekas,K. A. Kesanakurti, J., U.,Brose, Ostling, A., Harrison, &K., Martinez, N. D. (2004). Unified spatial scaling species of and Braukmann, W.A., T. Kuzmina, L.,Sills, M. Zakharov, J., & E.V., Hebert, P.D. N. (2017). Testing Bowles, E.,Schulte, P.M., D.Tollit, J., Deagle, B. E., & A. Trites, W. (2011). Proportion of prey Borisenko, A.Lim, V, Ivanova, B. K., Article Hanner,R. N.V, H., & Hebert, P.D. N. (2008). DNA Bohmann, Evans, K., A., Gilbert, M. T. P.,Carvalho, G. R., Creer, S., Knapp, M., …deBruyn, M. Atwell, S.,Huang, Y. S., Vilhjálmsson, B. Willems, J., G.,Horton, M.,Li, Y., … Nordborg, M. Arrizabalaga Alberdi, A., Aizpurua, O., Gilbert, M. P.,T. & Bohmann, K. (2018). Scrutinizing key for steps Afgan, E.,Baker, D., van Referenc metabarcoding. U. (2018). Assessing niche partitioning co of 147. doi reliable metabarcoding of environmental samples. 2016 update. (2016). GalaxyThe platform accessible, for reproducible and collaborative biomedical analyses: 1157. significant considerations and conservation applications. myotis myoti doi:10.1073/pnas.0905845106 Academy of Sciences of the United States America of doi:10.1111/j.2041 rbcL+matK DNA barcode. … Barrett, S.C. H. (2011). Discriminating plant species a in local temperate flora using the http://dx.doi.org/10.1038/nature02297 their trophic interactions. e0169515. doi:10.1371/journal.pone.0169515 Efficacythe DNA of Barcodes for Identifying the Vascular Plants Canada. of Resources consumed be can determined from DNA faecal using real Ecology Resources barcoding in surveys small of mammal communities: a study field Suriname. in &Ecology Evolution (2014). Environmental DNA wildlife for biology andbiodiversity monitoring. Nature (2010). Genome es ) in) relation to the reproductive cycle and immunocompetence of its bat host (
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terrestrial -
, - This article isprotected rights by All copyright. reserved. Accepted ArticleZeale, R. M. Butlin,K., R. Barker,K., G. L.A., Lees, D. C., & G. Jones, (2011). Taxon Worm, B., Barbier,E. B., Beaumont, N., Duffy, E., J. Folke, C.,Halpern, B. S., … Watson, R. (2006). H. Wirta, Hebert,K., P.D. N., Kaartinen, R., Prosser, S. W., Várkonyi, G., & Roslin, T. (2014). Vázquez, D. P.,Morris, W. F.,& Jordano, P. (2005). Interaction frequency a as surrogate for the total H.Toju, (2015). High 1890. doi:10.1073/pnas.1316990111 Proceedings of the National Academy of Sciences of the United Stat Complementary molecular information changes our perception of webfood structure. 0248.200 effect of animal mutualists on plants. Ec doi:10.1111/1755 derived from material. control doi:10.1111/j.1755 DNAfor barcoding arthropod prey in bat faeces. Retri Impacts of biodiversity loss onocean ecosystem services. ology eved from http://science.sciencemag.org/content/314/5800/787.abstract , 5.00810.x 57 (1), (1), 37 - throughput DNA barcoding for ecological network studies. - 0998.12490 – -
0998.2010.02920.x 51. doi:10.1007/s10144
Molecular Ecology Resources
Ecology Letters
- 014 - 0472 Molecular Ecology Resources , - 8 z
(10), 1088
Science , 16 (3), 714 , – 314 1094. doi:10.1111/j.1461 es ofes America (5800), 787 – 726.
Population , 11 , - – 111 specific PCRspecific (2), (2), 790. (5), 1885 236 – 244. - – This article isprotected rights by All copyright. reserved. networ parasite models, all Under 2d). (Figure loss prey from bats plant of extinction 2a), secondary and (Figure 2c), persistence(Figure predation parasite2b), (Figure on mutualism loss species bat of traditional effects of the modelled 2) and (Figure 1bcd) robustness and 1) (Table metrics structural theevaluated We 1a). (Figureapproach molecular a using generated been have data underlying all where networks” of “network first the present we data, these Using networks: of network A bat the in measurement insects ofthese metrics considering clustering thresholds of thresholds clustering OTU of impact the analysed weFinally, exceedinglycomplex). display other any making levels trophicmultiple occupy (bats trophic by these arranging than rather top the on bats present we depicted, are networks individualwhen simplicity, For 2006). Nepusz and (Csardi igraph in 2010) Olesen, & Dalsgaard, net of network entire the within species bat each of role the examined we species, keystone possible pinpoint (best connected w scenario restoration a modelled (Kaiser robustness network measured we predation) mutualism, (parasitism, networks component three our of each From pr hosts, their when network the withinconnections (Ra detected frequently most to least from removed species extinction (Rd three employed We networks. the within removedfrom speciesmodel), (nullremoved randomlyspecies models: restoration and loss species sub of effects bipartite traditional connectance, asymmetry, each species, of bat parasite, per vulnerability) (links and metrics generality, structural nestedness, the compared We 2015). al., et (Dormann Bipartite using networks 20 interaction the visualised We Analysis: Network genera, Two could not be distinguished in the field and are referred material. to as insect or material Microny plant containing as and remains insect classified and fragments seed for frozen. screened were samples was Guano groups. guano functional alcohol; isopropyl in capture. preserved of identifi Morphological point were the biopsies at released wing were and bats Ectoparasites the and items prey of identification taxonomic appro for bag cloth a in placed and targeting Naranjo Playa the at night netting extra an Picnicwith area the and Casona La Humeda, Bosque the between nightly alternated were locations Net the of Rosa Santa Sector in harptraps or mistnetsusing 2009 six a overwerecaught bats Allpersonalcollections. in held and/or location field the at research past from acquired were materials All Methods: Field Box 1: A preliminary analysis ofa tropical bat community.
Accepted Team, Core Development (R R in implemented as 2013) Article al., et (Hudson Cheddar and 09) - os cs seai) (Kaiser scenario) case worst cteris - plant, bat plant, , were left with provisional species level ID. ID. level species provisional with left were , ok sn btenes n coees etaiy crs Mrí González, (Martín scores centrality closeness and betweenness using work - ae ad sesd h pooto o lns etrd o h srcue To structure. the to restored links of proportion the assessed and case) cation of the ectoparasites to fly or mite was made in order to separte to order in made was mite or fly to ectoparasites the of cation - insect). We evaluated the robustness of each network and modelled the modelled and network each of robustness the evaluated We insect). - ubr e a. 21; emt, ae, Pie 20) W then We 2004). Price, & Waser, Memmott, 2010; al., et Bunbury - ubr, uf Mmot Mle, Cfic, 00 and 2010) Caflisch, & Müller, Memmott, Muff, Bunbury, ee a seis r re are species bat here ximately one hour. Any guano produced was collected for collected was produced guano Any hour. one ximately s ee es out R=.9R=.6 and (Ra=0.69/Rd=0.36) robust less were ks ey, predators, or mutualists are eliminated.are mutualists or predators, ey, - week period from late May lateperiodfromweek
Artibeus lituratus A. sp - nrdcd rm raet o least to greatest from introduced Area de Conservación Guanacaste.Conservaciónde Area - et ae cnro. pce lose Species scenario). case best Noctilio
. from 90
. Each bat was identified was bat Each .
most to least connectedleast to most -
99%. - sub ry ewr o the on network prey and - ewrs (Figure networks Glossophaga
- ewr ( network A. intermediusA. –
early July ofJuly early
level bat and - , This article isprotected rights by All copyright. reserved. Accepted very only the rest of the community. are species mod own its forms some thus and parasite one contrast only to connected In interest. conservation example For network. this with special associated tangentially of species a thus in roles functional these all occupying bat only the as it distinguishes This S8). Table Information (Supplemental centrality closeness and betweeness high very with species dispersa seed best third the Glossophaga in Ou threat 2013). al., approaching et Goerlitz, (Clare,the it evade detect to time not do ears with prey that intensity in enough low are a calls identify to echolocation bats’ The insects. analysis on up sneak dietary to bat” “nectar supposed molecular this permits that a strategy used (2013) al., et Goerlitz, Clare, bat the of position the is observation interesting most the Cla & Krüger, León Montalvo, (Salinas foraging Herrera in behaviour Ramos, flexible generalist very observed have techniques molecular (Arri rhinolophids sibling in beetles for preference specialisation the resource (e.g. for evidence is debate there While stability prey. specific on vs. reliance of lack diversity their and the behavi in extraordinary evidence suggest data Article provide The 2000). may (McCann, and system this in robustness bat multiple across bat of metrics generality of the 4), variability (Figure resolutions the MOTU considering Even community: bat a of shot snap A our in evident also soricina is This returned S8).bat third the where 3) Table (Figure model ecology restoration Information Supplemental scores, centrality (high role predatory strong and mutualistic a both in operating flexibility trophic significant showed soric G. Only 2d). (Figure sufficient was biomass prey if network the in remain species bat of >70%lost, are arthropods of >90% when even and extinct, arearthropods of 32% until lost not of loss bat (Ra=0.998/Rd= the species to when prey robust appear predation bats Insectivorous from (Rd=0.28). connectance release by extinct tremendous go species a experience face arthropods prey However, conversely, of while, proportion (Ra=0.74/Rd=0.4). high a robust extinction: highest the when even of more predation, models slightly to differently only responded arthropods were relationships mutualistic - aaie n bat and parasite
introducing parasites, insects, and plants at the same time.
is not a rare behaviour but rather, during the period of this study, this of period the during rather, but behaviour rare a not is re, 2014) though none have examined a community on this scale. Second, perhapsSecond, scale. this on community a examined have none though 2014) re, - l and as a parasite host. Its diverse functional roles make it a probable keystone probable a it make roles functional diverse Its host. parasite a as and l connected insectivore in the community in addition to its role in pollination and, pollination in role its to addition in community the in insectivore connected - ln ntok. hs infcnl ipcs n u udrtnig of understanding our on impacts significantly This networks. plant
zabalaga 0.85). Even under the worst the under Even 0.85). - - eann Arrizabalaga Regagnon, ranking - Eptesicus sueo t l, 08) ot tde ta hv employed have that studies most 2018)) al., et Escudero bat species by abundance are eliminated (Ra=0.86), eliminated are abundance by species bat
Cae Smnsn & etn 21) r oh in moths or 2014) Fenton, & Symondson, (Clare, r network analysis suggests that insectivory in insectivory that suggests analysis network r - prey networks i networks prey Desmodusrotundus - ua feiiiy f netvru bats insectivorous of flexibility oural
case scenario, the first bat species is species bat first the scenario, case - Glossophaga soricina sueo & lr, 05 Sedlock, 2015; Clare, & Escudero, ule of interactions unconnected to unconnected interactions of ule
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was ina G. - This article isprotected rights by All copyright. reserved. Accepted Article study.the All authors contributed to the writing the of manuscript. design helped SGN and MBF PDNH, analysis. performed RG and ELC work. laboratory performed Contributions Author sequences and Accessibility Data BOLD projects projects BOLD
and collection metadata and : (BCCR All EC AA ad N efre fedok EC AF NI n RMF and NVI AJF, ELC, fieldwork. performed JN and AMA, ELC, . molecular Bats of Costa Rica ACG Rica Costa of Bats
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This article isprotected rights by All copyright. reserved. Acceptedwhile arthropods the under Figure Article
C) A)
loss Proportion of Arthropod Species Consumed Proportions of Parasite Species with Hosts
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This article isprotected rights by All copyright. reserved. Accepted Glossophaga host soricina Glossophaga most three the only of restoration the With scenario). case (best connectance of order in ecosystem the to Figure Article
, and , Propotion of taxonomic links resotred strongly 3:
14% of plant species are visited. are species plant of 14% A restoration ecology model showing the proportion of links restored if bats are introduced are bats if restored links of proportion the showing model ecologyrestoration A .
once seis ( species connected Bats restored to ecosystem in order of connectance ), 72% of arthropod species are under predation, 24% of parasite species have a have species parasite of 24% predation, under are species arthropod of 72% ), trnts eomrcns B mesoamericanus, Pteronotus
See Clare et al., et Clare ( 2014)
for a discussion of trophic roles of roles trophic of discussion a for lnipey plicata alantiopteryx ▲ Plants Parasites Arthropods
and This article isprotected rights by All copyright. reserved. Accepted row). Bennett (bottom robustness of measures in predictable less Th consumers. the to relative richness in increases networks our in level netwo most For extent. lesser As networks). our in (nodes decision congruent molecular are but level, taxonomic (MOTU or units taxonomic taxon operational specific a represent not do type normally newmetabarcoding fundamentally a is data Metabarcode 4: Figure Article
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this has a predictable effect as the resource the as effect predictable a has this s
, taxa are lumped or split to a greater or greater a to split or lumped are taxa , o a opee analysis complete a For f data of
oinformatics steps oinformatics o ntok clg. oe in Nodes ecology. network for e same e
pattern
with see is
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but - This article isprotected rights by All copyright. reserved. Accepted Article withnodes full taxonomic identifications. platform mBRAVE the on employed method Figure 5: B la t to C d o e le a o p
Species te ra
B .p D lic ip a te ta ’ ra
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for bats and prey identified using the BIN association BIN the using identified prey and bats for ir ti
s s u N ta .le p o
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