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Rarity and beta diversity assessment as tools for guiding conservation strategies in marine tropical subtidal communities

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Aim Our aim was to uncover patterns of distribution of marine subtidal rocky reef communities across six taxonomic groups and decompose the relative roles of species loss and turnover in total community variation. Additionally, we propose an easily calculated index that can be used to highlight areas with unique species composition for conservation planning. We estimated the strengths of associations between environmental factors and species richness and rarity. Location Ilha Grande Bay, Brazil, covering about 150,000 ha harbouring different marine habitats. Methods We used the Marine Rapid Assessment Protocol at 42 sites to gather information on environmental variables and species in six subtidal marine groups. We determined “singular” sites as the regions harbouring higher numbers of rare species. Then, we estimated the roles of species loss and turnover on the observed total variation among sites. We used Generalized Linear Model to partition the relative importance of the selected environmental factors in driving variation in species richness and singularity. Results The singularity index and richness showed that the bay could be divided into three subregions for subtidal communities. Richness and rarity were structured at different spatial scales and associated with environmental variables related to water productivity and nutrients but varied among taxonomic groups. Community variation over space was largely associated with turnover of species. Main conclusions Higher singularity and richness on the western side of the bay and around the main island suggested that these regions should be conservation priorities, but high species turnover across the whole bay indicated that portions of the central channel should be included in conservation strategies. This draws attention to the importance of community variation rather than just species numbers in conservation and management planning. The high species turnover indicated that these rocky reefs have high beta diversity when compared to other studied biological systems.
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Diversity and Distributions. 2019;25:743–757.    
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 743
wileyonlinelibrary.com/journal/ddi
Received:2Januar y2018 
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  Revised:27N ovember2018 
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  Accepted:14Decembe r2018
DOI :10 .1111/dd i.12896
BIODIVERSITY RESEARCH
Rarity and beta diversity assessment as tools for guiding
conservation strategies in marine tropical subtidal communities
Lélis A. Carlos‐Júnior1,2 | MatthewSpencer2| DaniloMesquitaNeves3,4 |
TimothyPeterMoulton1| DéboradeOliveiraPires5| ClovisBarreiraeCastro5|
Carlos Renato Rezende Ventura5| CarlosEduardoLeiteFerreira6|
CristianaSilveiraSerejo5| SimoneOigman‐Pszczol7| FernandaAraújoCasares1,7|
MarceloChecoliMantelatto1| JoelChristopherCreed1
Thisisanop enaccessarti cleundertheter msoftheCreativeCommonsAttributionL icense,whichpe rmitsuse,dis tribu tionandreprod uctioninanymed ium,
provide dtheoriginalwor kisproperlycited.
©2019TheAuth ors.Diversity and DistributionsPublishedbyJohnWiley&SonsLtd.
1Depar tame ntodeEcologia,Uni versid ade
doEstadodoRiodeJaneiro,RiodeJa neiro,
Brazil
2SchoolofEnvironmental
Science s,Universit yofLiverpool,Liverpool,
UK
3Depar tmentofEcologyandEvolut ionar y
Biolog y,UniversityofAr izona,Tucson,Arizona
4Depar tmentofBotany,FederalUni versit y
ofMinasGe rais,B eloHorizonte,Brazil
5MuseuNacional,Univer sidadeFederaldo
RiodeJaneiro,RiodeJaneiro,Brazil
6Depar tame ntodeBiologia
Marinha,Universidad eFeder alFluminense,
Niterói,Brazil
7Instit utoBrasileirod eBiodiversida de,Rio
deJaneiro,Brazil
Correspondence
LélisA .Carlos‐Júnior,Depar tmentode
Zoologia,Universidad eFederaldoRiode
Janeiro,RiodeJan eiro,Br azil.
Emails:lelisjr@uerj.br;lelisufmg@gmail.com
Fundinginformation
ConselhoNacionaldeDesenvolv imento
Científ icoeTecnoló gico,Gr ant/Award
Number :206759/2014‐2andCNPq‐
305330/2010‐1;FundaçãoC arlosChagas
FilhodeA mparoàPesquis adoEst adodo
RiodeJaneiro,Gr ant/AwardNumber :E‐
26/111.574/2014andE26/201.286/2014;
CoordenaçãodeAperfeiçoamentode
PessoaldeNívelSuperio r,Grant/Award
Number :Ciênc iasdoMar1137/2010;US
Nationa lScienceFoundationgra nt,Gr ant/
AwardNumber:DEB‐1556651
Editor:C asca deSor te
Abstract
Aim: Ouraimwastouncoverpatterns ofdistribution ofmarine subtidalrocky reef
communitiesacrosssixtaxonomicgroups anddecomposetherelativerolesofspe‐
cieslossandturnoverintotalcommunityvariation.Additionally,weproposeaneas
ilycalculatedindexthatcanbeusedtohighlightareaswithuniquespeciescomposition
forconservationplanning.Weestimatedthestrengthsofassociationsbetweenen‐
vironmentalfactorsandspeciesrichnessandrarity.
Location: Ilha Gran de Bay,Br azil, covering abou t 150,00 0ha harbouring di fferent
marinehabitats.
Methods:WeusedtheMarineRapidAssessmentProtocolat42sitestogatherinfor‐
mation on enviro nmental variabl es and species in six subt idal marine groups. We
determined“singular”sitesastheregionsharbouringhighernumbersofrarespecies.
Then,weestimatedtherolesofspecieslossandturnoverontheobservedtotalvari‐
ationamongsites.WeusedGeneralizedLinearModeltopartitiontherelativeimpor
tance of the selected environmentalfactorsindriving variation in speciesrichness
andsingularity.
Results: Thesingularityindexandrichnessshowedthatthebaycouldbedividedinto
three subregions for subtidal communities. Richness and rarity werestructuredat
differentspatialscalesandassociatedwithenvironmentalvariablesrelatedtowater
productivityandnutrientsbutvariedamongtaxonomicgroups.Communityvariation
overspacewaslargelyassociatedwithturnoverofspecies.
Mainconclusions:Highersingularityandrichnessonthewesternsideofthebayand
aroundthemainislandsuggestedthattheseregionsshould beconservationpriori‐
ties, but highspecies turnoveracross thewhole bay indicated that portionsofthe
centralchannelshouldbeincluded inconservation strategies. This draws attention
tothe importanceof communityvariationratherthanjustspecies numbersincon
servationandmanagementplanning.Thehighspeciesturnoverindicatedthatthese
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1 | INTRODUCTION
Our currentknowledge of globalbiodiversity points to an ongoing
major species‐loss crisis (Pimm et al., 2014). Although this trend
seems pervasive among dif ferent organisms and habitats (IUCN,
2014),the estimations are basedon assessmentsusinginformation
ona fractionofthetotalnumber ofspecies,many ofwhichremain
undescribed or lack distributional information (Carpenter et al.,
2008;Peters,O'Leary,Hawkins,Carpenter,&Roberts,2013).With
ma n y s p e ciesy e t tob e d i s c ove r e d ( Pimme t a l . ,20 14) a n d t h eincr e a s
ingrateofextinctionscausedand/orexacerbatedbyanthropogenic
activities(McCauleyetal.,2015;Pandolfi,2003),itisparamountto
understandandexplaindiversitypatternsacrossecologicalsystems
(VonDerHeyden,2011).
Lack of comprehensive distributional data leads marine spe‐
cies to be severely underrepresented. For example, according to
theIUCNRedList,theycomprise less than 12%ofall studiedtaxa,
althoughnearlya third ofall eukaryotes are thought to be marine
(IUCN, 2014;Mora,Tittensor,Adl,Simpson, &Worm,2011;Peters
et al., 2013). Few s tudies have trie d to assess commun ity organi‐
zation in ma rine systems, which precludes strong inferences and
robust s yntheses (H eino et al., 2015; an d see Moritz e t al., 2013;
Okuda, Noda,Yamamoto,Hori, &Nakaoka,2010;Yamada,Tanaka,
Era, & Nak aoka, 2014 for except ions). This gap is det rimental n ot
only to management/conservation effortsbut alsoimpairsthe de‐
terminationofwhatdrives variation in diversity pat terns in marine
systems.Exceptforsomegeneralapproachesandrecent advances
ininventoryingdatabases(Briggs,1974,1995;Costello&Chaudhar y,
2017;Costelloetal.,2017;Spaldingetal.,2007),mostofourcurrent
biogeographicalknowledgeformarineecosystemsisstillrestricted
tosingletaxonomicgroups(e.g.,bryozoans,Clarke&Lidgard,2000;
corals, Cornell, Arlson, & Hughes, 2007;fish Kulbicki etal., 2013),
restricted to temperate, less diverse regions (Clarke & Lidgard,
2000)and/ordoes notaccount for differential responses amongst
taxonomicgroups(Soininen,2014).Addressingthesegapsisnoeasy
task, b ut recent developm ent in ecologica l analyses has prov ided
themeans to bet ter explore the variety of biodiversitydimensions
acrossmultiplespatialscales.
Oneimportanttraitofcommunitiesistherelationshipbetween
local (α) and regional (γ)diversity. Beta diversity was originally de‐
fined as “the extent of change in community composition” esti‐
matedfromtheratioofgammatoalphadiversity(sensuWhittaker,
1960),althoughavariet yofdefinitionsweresubsequentlyproposed
(Andersonetal.,2011;Baselga,2012;Tuomisto,2010).Weexplored
beta diversity (sensuBaselga,2010,2012,also defined as commu‐
nity tu rnover; see Tuomisto, 2010) pat terns across the r egion by
decomposing betadiversity into its nestedness andturnovercom‐
ponents,thetwodistinctprocessesthatcausevariationincommu
nitycomposition,asexplainedelsewhere(Baselga,2010;Baselga&
Orme,2012;Harrison,Ross,&Lawton,1992).Innestedness,varia
tionincompositionbetweentwoormoresitesoccursduetospecies
lossorgain,suchthat species‐poor sites aresubsetsofricher sites.
Turnover is variat ion caused by the re placement of som e species
byothers,usually associatedwithstochasticityand/orspatial/envi‐
ronmentconstraints(Baselga,2010;Qian,Ricklefs,&White,2005),
includingstressors and impact. Therefore, analysing beta diversity
components also helps torecognize potential drivers of diversity
differentiationamong sites within a metacommunity, defined here
asasetof localcommunitiessignificantlylinkedby thedispersalof
multiplespecies(Leiboldetal.,2004).
Parallelto ourconsiderations ofbetadiversitypatternsinthe ma
rine bent hos and reef fi shes, we also wa nted to identif y areas char
acterizedbyfaunasorflorascomposedofless frequent species.Our
challengewastoproposeasimplemechanismforassessingareaswith
high “rarity” in species composition when compared to other sites
within th e same metacommu nity. The description of such lo cations
isrelevant forfocussingmanagement and conservation efforts,since
humanactivitiesalterhabitatavailabilityandchangespeciescomposi
tion(Halpern etal.,2008;Pauly,Watson,&Alder,2005).Theconcept
ofrarityisintuitivebutoftendifficulttodefine,sincethereisacontin
uum from com monness to r areness (Us her,1986). For ou r study, we
definerarit ysimplyashavingasmalldistributional range size(Gaston,
1994).Withthatinmind,wewantedanindexthatwassimpletointer
pret,especiallybythenon‐scientificpublic,andwasbiologicallymean
ingful.Also,wewantedtokeepunavoidablesubjectivitytoaminimum
inthemathematicaldesignationofwhat“rarity”meant ,makingitclear,
reproducibleinothersituations,andnotstrongly correlatedwithspe
cie sr ic hn es si nordertos ho wpat te rn snotnecessarilycause dbydi ffer
encesinthenumberofspecies.
Although described as the richest marine habitats (Costello &
Chaudha ry, 2017), tropica l coastal are as are still und er‐studied (Cox,
Spaldin g, & Foster, 2017;K aehler & Wi lliams, 1996) when comp ared
totemperateshoresorcoralreefs(e.g.,Mieszkowskaetal.,2006).We
performed descriptive analysesof diversity in marine subtidal rocky
reefco mmuni tiesi nat ropicalregio n(s ensuSpaldi ngetal.,20 07)ofR io
deJa neirostate,Br azil.Ourgoa lwa stotes t:(a)whet heritwasp ossib le
tohighlight distinctiveareas,in terms of species spatial composition,
especia lly those area s with higher pre valence of rare sp ecies, whic h
rocky reefs have high beta diversity when compared to other studied biological
systems.
KEY WORDS
alphabetagammadiversit y,benthos,communit ycomposition,marinecommunity,marine
ecology,metacommunities,rarespecies,tropicalrockyreefs
    
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CARLOS‐JÚNIOR et AL .
wedeemedsingularareas;(b)therelativeimportanceofenvironmen
tally versus spatially structured factors in driving variation in species
richnessand singularit y; and (c)therelativeroles of species turnover
andnestedness in totalbeta diversity.Formarinesystems in general,
those qu estions have ha rdly ever been inve stigated in a n intergroup 
approa ch, let alone in t he tropics. T his is the firs t time the dat asets
availableinSupportingInformation(TablesS1andS2)arepublishedfor
aninternationalreadership(inventorieswerepublishedinPortuguese,
Creedetal.,2007)andtheirexplorationwillimproveourunderstand
ingoftropicalmarinesystems.
2 | METHODS
2.1 | Studysite
Ilha Grande Bay (Baía da Ilha Grande—BIG, Figure 1) is located in
thesouth ofthestate of Rio de Janeiro,southeastBrazil. Thebay
covers around 150,00 0ha and is situated between the two most
industrialize dregionsoft hecountry—RiodeJaneiroandSãoPaulo.
Thediversityofdifferentfaunas/florasresultsfromthedistinctive
geomorp hology of the re gion, which har bours differ ent types of
terrestrial, freshwater and marinehabitat s, suchassand beaches,
estuaries,man grovesandro ckyreefs(Bastos&Callado,20 09).The
bay'slocation is associatedwithmultiplepotentialanthropogenic
pressures that threaten it s diversity, such as intensive fishing, ex
tensive occupation ofshore areas, domesticand industrial waste,
unregulatedtourism,extensivecirculationofshipsandoil/gasplat
forms with severalmarinasandshipyards,andeventheoperation
ofanoilterminal anda nuclear power plantinAngra dosReis,on
the northern coast of the bay (nearsite 17inFigure 1). Thelarge
centrallypositionedisland,IlhaGrande,hasanimportantinfluence
onthebay.
Usingonlynauticalcharts,42siteswerepre‐choseninorderthat
samplingsites wouldbedistributedmoreorlessevenlythroughout
thecoastlineandislands (n≈360)oftheregion and to represent a
suiteofdifferentialenvironmentalandsubtidalmarinebenthiccom
munities.Most siteshadnever beenstudied before. At all 42 sites
(Figure 1), sampleswere takento measure physiochemicalproper‐
ties of the w ater as well as to obt ain informati on about sedi ment
andgeomorpholog y(Creed et al., 20 07,chapters4and5).In total,
31environmental variables were measured,and theyare available
assupportinginformation,includinga briefdescriptionof datacol‐
lection(SupportingInformationTableS2).Insummar y,theregionis
characterizedbyshallowerwatersonthewestsideofthebay,with
deeper siteslocated in itscentral channel and on the outersideof
themainisland.Thebot tomtemperaturessampledattheseregions
alsodifferconsiderably.Thewesternsideshowed higher quantities
ofsometypesofsedimentsuspendedin thewater.Thisside isless
exposedtowaveaction,whereasthesouthernsitesof Ilha Grande
andsome exposedsites in the centralchannelweremore exposed
towaveaction.
2.2 | Biologicaldatacollection
Species composition (presence/absence) data were collected in
2003–2004 by specialists using protocols developed for a Rapid
Asses sment Program (R AP) for three ha rd substrate (or ha rd/soft
substrate interface) benthic groups (Macroalgae—hereafter called
algae, Cnidaria—called corals henceforth although including some
sea‐anemones, and Echinodermata), two soft substrate benthic
groups (Mollusca and Crustacea) and reef fish. All sampling was
carried out using SCUBA . The RAP approach consistsof short ex‐
peditionsledbyspecialists into regions ofbiological importancein
ordertoexaminethestatusoftheregion'sbiodiversitybyselecting
FIGURE 1 The42sampledsites(reddots)atIlhaGrandeBay,southeasternBrazil,ashighlightedinthebottomrightcorner
23.2°S
44.6°W
Sepetiba Bay
Ilha Grande
Ilha Grande
Bay
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11 7
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15 16
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Brazil
Atlantic
Ocean
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44.2°W
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CGSWGS 1984
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some grou ps which best repr esent the biota. T he health of loca l
ecosystemsisalso assessed, andmanagementstrategiesproposed.
Althoughnotspecifically designed for aquatichabitats,ithasbeen
used to asse ss marine sy stems aroun d the world by Co nservat ion
International, who refer to it as the Marine Rapid Assessment
Program (e.g.,Dutra,Allen,Werner,&McKenna,20 05;McKenna&
Allen,2002,20 03).
For the bent hos on hard subs trate and on h ard/soft substr ate
interface, the assessment was made through visual censuses on
transects ofapproximately 100mparalleltothecoastline,andfish
presence/absence was recorded using three 20×2m transects
persite.Each census was carried out from the littoral fringeto the
depthatwhichthe substratechangedfromrockto soft bottom.As
theselectedsamplingsitesvariedsubstantiallyindepth(min=1m,
max=27m,mean=10m),whichaffectsdivingtime,eachdivewas
restrictedtoaminimumof45minandamaximumof90mintoavoid
signific ant diffe rences in sa mpling eff ort. For de tailed met hods of
datacollection,seechapters6,7and11inCreedetal.(2007).Corals
couldnotbeassessedatonesite(17),so41samplesitesareavailable
forcorals.
The bent hos of soft substr ata (Mollusca an d Crustacea) wer e
sampled using a sediment corer. At each site, five core samples
(100mmdiameter×150mmheight) were collected at eachof two
stations,oneclosetotherockyshoreandtheother100maway.The
sedimentwassieved,andfaunaidentified.Fordetailedmethods,see
chapters8and10inCreedetal.(2007).
Weusedspeciesaccumulationcurves(Colwell,Chang,&Chang,
2004;Kindt,VanDamme,&Simons,2006;Ugland,Gray,&Ellingsen,
2003)forallsixtaxatoensureadequacyofoursamplingef fort.
2.3 | Anindexforsitesingularityandrichness
In order to id entify dis tinctive sit es, that is sites w ith more un
commonspeciescomposition ,wedevised“Singularity,”ameasure
basedonthenumberofrarespeciespresentatalocalsitewithin
a metaco mmunity. We define d a rare specie s as one present at
fewer thank out of nsites, where kissomenumberbetween2
and the integer part of n/2.Wedefined the singularity of a site
j (Sj) for a gi ven rarit y threshol d as the propo rtion of sp ecies at
thatsitethatwererare.Weusedtheproportionofrarespeciesin
ordertoavoidspeciesrichnessofthesiteorindividualtaxonomic
groupsstronglyinfluencingtheresults.Inourstudy,wecalculated
themeansingularityvalueoverallpossiblekthresholds,inorder
toavoid making an arbitrary choice of threshold.For thresholds
above4–5sites(10%),thecor relationbet weenthemeansingular
ityandtheproportionofrarespeciesatany given thresholdwas
between0.7 and 0.9forall taxonomic groups.Thus,meansingu
larit y was a good proxy f or singularit y over thresh olds of rarit y
from10%to40%(4and17sites,respectively)andthereforepro
videdagoodrepresentationofrarityforoursystem.TheRscript
forcomputing rarityformultiple thresholds,as wellascheckson
the performance of the mean singularity against any particular
thresholds,isavailableassupportinginformation(S3).
Similarly,general (considering alltaxa)richness was alsodeter
mined for each site j taking into account the largeintergroup vari‐
abilityinregional speciesrichness.Letnijbethenumberofspecies
fromgroupiatsitej,nbethetotalnumberofspeciesfromgroupi
intheregion,andn·jbeth etot alnum berofsp ec iesatsi te j.Then,the
propor tionofspeciesingroupi that occur atsitejispij=nij/ni ·,and
theproportionofspeciesatsitejthatcomef romgroupiisqij=nij/n·j.
Then,wedefinethegeneralrichnessRjforttaxonomicgroups(here
t=6)atsitejas
Intuitively,Rjprovides ameasure ofrichnessaccounting for the
large dif ferences in specie s numbers obser ved among taxono mic
groupsatagivensite,pij.
Wecalculatedgeneralrichnessandsingularityforall42sites,
which led to an overall pattern that was visually consistent in
ourresults(Figure 2): Relativelylower diversity in surveysfound
across thecentralcore of the island, andhigher diversity in sur
veysfoundaroundthemainislandand acrossthewesternsec tor
oftheB IG .Tof ur t herexplor ethesedif fere nc es ,wefi rs tc lassified
geographic al lyea chofth e42 si te sintosu bregions,namelycentra l
channelandnorthernsector(sites18–29andsite42),mainisland
(sites30–40)andwesternsector(sites1–17andsite41),compris
ing13,11and18sites,respectively.Wecalculatedsummarysta
tisticsand producedboxplotvisualizationstoexploredif ferences
among the subregions.Itwasnotappropriatetocarr yout asta
tisticaltes tofthehypothesisthatthethreesubregionsdifferedin
generalrichnessandsingularitybecausethishypothesiswasonly
formulat ed after obse rvation of th e patterns in th e data, which
increasesthechancesoffindingsignificanceandviolatesassump
tionsofmostaprioristatisticaltests,suchasANOVA(Kerr,1998;
Wasserstein&L azar,2016).Theresultsofthesecomparisons are
availableinsuppor tinginformation(S4).
2.4 | Searchingfordriversofrichnessand
singularity patterns
We applied Generalized Linear Model (GLM)‐based variation
partitioning toaccount fortherelative contributionof theselected
environmentalandspatiallystructurefactorsexplainingvariationin
richnessandsingularity(GLMswithGaussianerrordistribution).For
explanatoryvariables, weused theenvironmental abioticvariables
and Principal CoordinatesofNeighbour Matrices as descriptors of
spatialstructure(PCNMs;Dray,Legendre,&Peres‐Neto,2006).We
firstcomputedPCNMsasdescribedinBorcardandLegendre(2002),
and only those describing positive spatial autocorrelation were re‐
tained (Borcard &Legendre,20 02). Brieflyexplained,thefirststep
is to compute t he Principal C oordinates An alysis (PCoA) of a m a‐
trix builtfrom geographicaldistances among allsamplingsites and
trunc ated for distan ces larger than a c ut‐off set a priori to r etain
(1)
R
j=
t
i=1
pij q
ij
    
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CARLOS‐JÚNIOR et AL .
onlyneighbouringdistances.TheeigenvaluesofthisPCoAdescribe
orthogonalmultiscalespatialvariables.Inotherwords,PCNMsare
distance‐based variables capable of describing spatial organiza
tion among sitesatdifferent spatialscales.Forthis dataset, 25 or‐
thogonalspatialvariables weregenerated.Asexplainedelsewhere
(Borcard&Legendre,2002;PeresNeto,Legendre,Dray,&Borcard,
2006), larger eigenvalues are associated withbroader spatialscale
structures, while smaller eigenvalues represent fine‐scale spatial
structures.Therefore,weclassifiedthePCNMsasbroader(PCNMs
1–8),intermediate (PCNMs 9–17)and finer (PCNMs18–25)spatial
scales . Given our relat ively high numb er of explanator y variables ,
wecontrolled for over‐parameterizationbyapplyinga GLM‐based
variableselectionapproach,followedbyprogressiveeliminationof
variabl es that showed high v alues of the varia nce inflation fa ctor
(VIF),maintainingonly thosewith VIF<2(Table1). Thevariablese‐
lectionandvariationpartitioningwereconductedusingthe“fields”
(Nychka, Furrer,& Paige, 2015) and vegan” (Oksanen et al., 2016)
packagesintheRStatisticalEnvironment(RCoreTeam,2017).
2.5 | Turnover×nestednesscomponentsof
beta diversity
Operations on fractionswereused to decompose tot al betadiver‐
sity,calculatedasSørensendissimilarityindexβSOR,intotheSimpson
indexβSIMdescribingspatialturnover withoutinfluenceof richness
gradients, and βNESdescribingvariationincompositionduetospe
cies loss or g ain, causi ng compositio ns in species‐p oor sites to be
nestedwithinthoseoftherichersites(i.e.,nestedness)(Equation2)
ThesecalculationswereconductedusingtheRpackage“betapart”
(Baselga&Orme,2012).Wealsocalculatedthes amecomponentsfor
pai rwises iteco mp ar isons,yieldin g861pa ir sofsitesfo rt heanalysi sof
betadiversityforeachgroup.Forcorals,only41siteswereconsidered
(yielding820pairsofsites)andforgeneralintegrativetaxameasures,
such as Sj and Rj we con sidered the n umber of coral s to be zero at
thesit eswhe recor alsweren ots am pled.Th erefo re,caut ionsh ouldb e
takenwheninterpretingresultsforthisparticularsampleunit.
3 | RESULTS
3.1 | Biologicaldatacollection
Acrosst he42sites,765taxa(r evisedattheWorldRegisterofMar ine
Specie s—WoRMS)w ere recorde d: 108 benthic al gae, 26 cnidar ians
(A nt h ozoaa nd M i l l e p o r i da e ) , 27echino d e r m s f r o m a l lf i v eclasse s ,373
molluscs, 61crustaceans and170reef fish (Suppor ting Information
TableS1).Foralgae,this numberis equivalentto one quar ter ofthe
whole kn own diversity of t he state of Rio de Jan eiro. Almost ha lf
(40%)ofthe crustaceansidentifiedwere newrecordseitherforBIG
orthe state of Rio de Janeiro. In Ilha Deserta (site4),the presence
ofthefirecoralMillepora alcicornisrepresentedanewrecordforthe
region andthe species’ new southernlimit distribution. Species ac
cumulation curves suggested that sampling wassufficient for most
taxa,althoughinfaunalgroups(molluscsandcrustaceans)seemedto
bestillslightl yunde r‐surveye d(S upportingInfor mationFi gureS5a‐f ).
3.2 | Anindexforsitesingularityandrichness
Ingeneral, the western side of the bayand the sites around the
main island had higher overall richness and higher singularity
values when all taxa were considered together compared tothe
siteslocatedinthecentralchannelandthenorthernshore,butit
varied considerablyamongdifferenttaxonomicgroups (Figure2;
Suppor ting Informati on S4). On average, we ex pect a site cho
sen at ran dom to have approxi mately one qua rter (mean=23% ,
SD=±4%)ofthetotal speciesfoundinthebay,andthatapproxi
mately a third of those species would be consideredrare across
thebay(30%±6%).TanhangáIsland,onthewesternside(site14
inFigure 1), hadthe lowestgeneralrichness (less than 10%) but
(2)
𝛽SOR
=
𝛽SIM
+
𝛽NES
FIGURE 2 GeneralRichness(symbol
size)andSingularity(colours)ofallsix
taxonomicgroupssampledfrom42sites
atIlhaGrandebay,Brazil
23.2°S
44.6°W
Richness Singularity
km
15
N
0.1
0.23
0.29
0.2
0.3
0.53
748 
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TABLE 1 ValuesofselectedexplanatoryvariablesafterstepwiseVIFselection
(a)
Algalrichness Coralrichness Echinodermrichness Molluscrichness
r2VIF r2VIF r2VIF r2VIF
Chlorophyllasur face 0 .11 1.091 — — 0.1 1.32
Salinit ybottom 0.10 1.06 0.09 1.06 — —
Phosphatebottom 0.08 1.06 — —
Selectioncoef.(near) 0.13 1.02 — —
Oxygensurface 0.07 1.04 — —
Org.mat ter(near) 0.17 1.03 0.09 1.28
Graindiameter(near) 0.13 1.03 — —
Interstitialwater(far) — — 0.12 1.18
Selectioncoef.(far) — — 0.08 1.22
Secchidepth(horiz.) — —
Nitratesurface — —
Inclination — —
Oxygenbottom — —
KdV — —
Rugosity — —
(a) (cont.)
Crustaceanrichness Fishrichness Totalrichness
r2VIF r2VIF r2VIF
Chlorophyll(surface) — —
Salinity(bottom) — — 0.08 1.32 — —
Phosphate(bottom) — 0.18 1.19 0.13 1.04
Selectioncoef.(near) —
Oxygen(surface) — —
Org.mat ter(near) — 0.20 1.35
Graindiameter(near) —
Interstitialwater(far) —
Selectioncoef.(far) — 0.10 1.08
Secchidepth(horiz.) 0.10 1 .16 — —
Nitrate(surface) 0.09 1.08 — —
Inclination 0.07 1.09 — —
Oxygen(bottom) — — 0.19 1.42 — —
KdV — — 0.1 2 1.23 — —
Rugosity 0.09 1.46
(b)
Algal singularity Coral singularity Echinodermsingularity Molluscsingularity
r2VIF r2VIF r2VIF r2VIF
Salinity(bottom) 0.22 1.45 — — —
Org.Mat ter(near) 0.20 1.14 — — —
Temperature(bottom) 0.10 1.38 — — —
Inclination 0.09 1.18 — — —
Interst.water(near) — — 0.16 1.04 — — —
Chlorophyll(surface) — — 0.10 1.09 — — 0.13 1.24
Nitrite(surface) — — 0.09 2.35 — — —
Nitrite(bottom) — — 0.07 2.48 0.08 NA — —
(Connues)
    
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CARLOS‐JÚNIOR et AL .
(b)
Algal singularity Coral singularity Echinodermsingularity Molluscsingularity
r2VIF r2VIF r2VIF r2VIF
Silt/Clay(far) — — 0.18 1.69
Secchidepth(ver t.) — — 0 .14 1.89
Depth — — 0.12 2.27
Chlorophyll(bottom) — — —
Phosphate(surf ) — — —
Graindiameterfar) — — —
CaCO3(far) — — —
(b) (cont.)
Fishsingularity Total singularity
r2VIF (cont.) r2
Salinity(bottom) — — Salinity(bottom)
Org.Mat ter(near) Org.Matter(near) —
Temperature(bottom) 0.27 1.32 Temperature(bottom) 0.27
Inclination — — Inclination
Interst.water(near) Interst.water(near) —
Chlorophyll(surface) — — Chlorophyll(surface)
Nitrite(surface) Nitrite(surface) —
Nitrite(bottom) — — Nitrite(bottom)
Silt/Clay(far) — — Silt/Clay(far)
Secchidepth(ver t.) Secchidepth(vert.) —
Depth — — Depth
Chlorophyll(bottom) 0.14 1.37 Chlorophyll(bottom) 0.14
Phosphate(surf ) 0.10 1.10 Phosphate(surf ) 0.10
Graindiameterfar) 0.08 1.08 Gr aindiameterfar) 0.08
CaCO3(far) — — CaCO3(fa r)
(c)
Algal
richness Coralrichness
Echinoderm
richness
Mollusc
richness
Crustacean
richness
Fish
richness
Tot al
richness
Environment 0.20 0.30 0.16 0.19 0.09 0.15 0.29
Spatiallystruct.env 0.04 NA 0.09 0.08 0.08 0.16 0.07
Spatialvariables 0.09 NA 0.13 0.01 0.15 0.01 0.15
Unexplained 0.68 0.70 0.62 0.72 0.67 0.68 0.49
PCNM 25 Noneselected 10,25 96,14 4,8 10,25
(c) (cont .) Algal singularity Coral singularity
Echinoderm
singularity
Mollusc
singularity
Fish
singularity
Tot al
singularity
Environment 0.18 0.20 0.01 0.20 0.27 0.21
Spatiallystruct.env 0.17 0.13 0.07 NA 0.20 0.16
Spatialvariables 0.21 0.07 0.15 NA 0.04 0.001
Unexplained 0.44 0.59 0.77 0.80 0.48 0.63
PCNM 5,9,17 9,10 5,11 Noneselected 1,23 4,6
Note.Modelsusedexplanatoryvariablesregressedagainst(a)richnessand(b)singularitymeasuresfromeachofthetaxonomicgroupsandfromoverall
communityvalues.AftertheGeneralizedLinearModel,variationpartitioningwasperformedforallmodels(c)inordertoestimaterelativecontribution
ofenvironmentalvariables,spatiallystructuredenvironmentalvariables,spatialautocorrelation(spatialvariables)andunexplainedvariationtovaria
tioninrichnessandsingularity.Lastrowof(c)depictswhichPrincipalCoordinatesofNeighbourMatrices(PCNMs)wereselectedbyeachtaxonomic
group. PCNMsare generated in descending orderof spatialscale, meaningfirstPCNMs(e.g., PCNM 1orPCNM2)representbroader spatialscales
whencomparedtothelastPCNMs(e.g.,P CNM10).Columnsumsoffractionsin(c)mightnotbeexactlyoneduetorounding.
TABLE 1(Connued)
750 
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   CARLOS‐JÚNIOR et AL .
thehighestsingularity(53%).Atanearbysite(PontadoPinto,site
7), proportional richness was 13%, whereas singularity reached
37%.Thus,somesitesmightnotbeparticularlyrichinspeciesbut
neverthelesshaveuniquespeciescompositionscomparedtoother
more‐enrichedsites.Therewerealsosomehighervaluesofsingu
larityon the outer side of Ilha Grande, where sites were usually
alsospecies‐rich(Figure3).Ontheotherhand,mostsiteslocated
inthe centreoftheregionshowedrelativelylowvalues ofsingu
larit y, despite va rying prop ortions of ri chness. Gene ral richnes s
hadasignificantbutnotstrongcorrelationwithsingularityvalues
(SpearmanRS=0.33,p=0.03).
In additi on to the gener al aspec ts of the mari ne diversit y high
lightedabove,sometaxon‐specificattributescouldalsobedistinguished
(Figures3and4).First,ahighpropor tionofthericherandmostsingular
sitesof each taxonomicgroupwerelocated onthewestern sideofthe
bay(Figure4a),similartothatobservedforthegeneralpattern.Second,
there wasas ubst an tialvaria ti onamongth edif fe rentg ro up sinre ga rdto
thespatialscaleinwhichtheywerestructured(Figure4b).
3.3 | Searchingfordriversofrichnessand
singularity patterns
Thirtypercentofrichnessand21%ofsingularitywerenotspatially
structured and were associated with environmental differences
acr os sthebay (Figure5).Bothweremai nl yexp lainedbydif fe re nces
insubstratum:organic matter availability,sediment characteristics
FIGURE 3 Richness(symbolsize)andSingularity(colours)ofdif ferenttaxonomicgroupssampledfrom42sitesatIlhaGrandebay,Brazil.
Richnessandsingularityareshownfor:(a)algae,(b)corals,(c)echinoderms,(d)molluscs,(e)crustaceansand(f)fish.Asrichnessequalled
singularityforcrustaceans(seemaintextfordetails),thelegendforsingularityisnotshown
12
25
46
0.11
0.29
0.61
44.6°W
Richness Singularity
km
15
N
23.2°S
(a)
3
10
14
0
0.12
0.45
23.2°S
44.6°W
(b)
Richness Singularity
km
15
N
2
11
17
0
0.1
0.26
44.6°W
Richness Singularity
km
15
N
23.2°S
(c)
5
52
107
0.21
0.38
0.72
23.2°S
44.6°W
(d)
Richness Singularity
km
15
N
0
4
8
44.6°W
Richness
km
15
N
23.2°S
(e)
2
30
65
0.07
0.31
0.86
23.2°S
44.6°W
(f)
Richness Singularity
km
15
N
    
|
 751
CARLOS‐JÚNIOR et AL .
andgeomorphologyoftheregions(Table1a‐b).Forsingularit y,most
of the environmentalvariation was structured at broader spatial
scales,differentiatingthewesternfromtheeasternsideofthebay.
Incontrast,variationinrichnesswasmainlydrivenbyenvironmen
tal factors that werespatiallystructuredatintermediateand finer
scales(lastrowofTable1c).Thesefractionsandtheidentityofthe
significantenvironmentaldriversofvariationinrichnessandsingu
larityvariedgreatlyacrossthetaxonomicgroups(Table1).
3.4 | Turnover×nestednesscomponentsof
beta diversity
Allsixtaxonomicgroupsexhibitedhighvaluesoftotalbetadiver‐
sity(whichrangesfrom0to1),around0.9.Thesehighvalueswere
almost entirely causedby spatialturnoverof species(Table2).The
samepatternofdominanceofspatialturnoverintotalbetadiversity
emergedfromthedistribution of all pairwise Sørensen dissimilari
ties(Figure6)althoughpairwisecomparisonsyieldedconsiderably
highervariation.
4 | DISCUSSION
Here, we have us ed species co mposition d ata to propos e an inte‐
grative frameworkcapableofimproving thedescription of general
patternsofrichnessandrarityandsearchingforpotentialdriversof
suchvariation. Couplingthis with theknowledgeonwhich typeof
betavariationthesecommunitiespresentcontributestoguide con‐
servationstrategies.
4.1 | Biologicaldatacollection
The RAP approach here described was the most comprehensive
assessment of marine biodiversity ever made for the BIG region
and one of th e more most ex tensive marine as sessments t o have
been ca rried out in Bra zil. The scale of t he inventory ca n be ob‐
serve d in the numbers : 765 species inve ntoried, incl uding several
new record s for the area, r ange expansio ns for numerou s species
andthree newspecies discovered (Creedetal., 2007).There were
new recordsfor twomolluscgenera inthe Southwestern Atlantic,
Tornus and Eatoniella, as well as three species being recorded in
Brazil forthe first time(Macromphalina apexplanum,M. palmalitoris
and Polygireulima amblytera, Creed et al ., 2007). Two new specie s
ofamphipodwererecentlydescribed:Puelche irenaeNascimento&
Serejo, 2018 and Puelche longidactylusNascimento&Serejo, 2018.
Both are t ypical burrower s (do Nascimento & Serej o, 2018). The
datasets in the Supporting Information,therefore,providedistinc
tivedataontropicalmarinerockyreefcommunities.Thesingularity
measurementhereproposedsuggestedsomeareasdifferinginspe
cies composition, withthewesternsideofthe bay and aroundthe
main islandcomprising less frequently seenspeciesingeneraland
for severa l of the surveye d groups. Al though for m ost groups th e
samplingwasadequate,itwouldbeproductivetoimplementfurther
expeditions,giventhatthesedatasetswerecollectedover10years
FIGURE 4 Patternsofrichnessand
singularityofsixtaxonomicgroups(from
toptobottom:algae,corals,echinoderms,
molluscs,fish,crustaceans)from42sites
inIlhaGrandeBay,by(a)geographical
locationwheresymbolsizesrepresent
propor tionofthetop10richest/most
singularsitesfallingineachregionand
(b)spatialsc alewheresymbolssizes
representproportionofselectedPCNMs
ateachspatialscale
(a) (b)
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ago, especially focusing on species abundances. In this case, our
analysis of these dat a is important to provide a baseline against
whichtomeasurerecentchanges.Furtherexpeditionswouldbees‐
peciallybeneficialforsoftsubstratehabitats, as theseappearedto
beslightlyunder‐surveyed.
4.2 | Anindexforsitesingularityandrichness
Our meth od for computing r arity of taxa ( i.e., small spatial r ange
within th e studied met acommunit y) showed that the m arine ben‐
thic/fishdiversity could be divided into threesec tors.The higher
general singularit yvalues foundin the westernsideofthebayand
around the main island aresimilar and they are different from the
less‐singula rce ntralcoreoft heregion ,locatedbetweenthemainis
landandthecontinent ,includingthenor therncoastline(Supporting
informationS4). The central channel consists of locations withdif‐
ferent levels of richness (structured at a finer scale, presumably
due to loc al variations i n habitat con ditions), but main ly inhabited
bycommonspecies.Thiscouldbean indication ofamorestressed
environment, since thisregion is theone under themost intensive
anthropogenicpressureswithintheregion(Creedetal.,2007).The
taxacapableoflivinginthecentralchannelofthebayaregenerally
also theones ubiquitousto the entire sampled region (Supporting
informat ion S6 shows ubi quity of th e differe nt species fo r all tax‐
onomic groups). On the other hand,the western coast sector was
characterizedbysiteswiththehighestratiobetweensingularit yand
richness(shownas smallred spot sinFigure2). Therefore, this sec‐
toris composedof speciesnot commonlyseenelsewhere,showing
considerablevariation(i.e.,highβSOR)even amongitsown sites (re‐
sults notshown here). Thesewesterncommunitiesalso differfrom
theotherhighlysingularcommunitiesfoundaroundthemainisland,
comprising deeper locations.At those places, highly singular com‐
munitiesarealsoricherforseveraltaxonomicgroups(Figure3a–f).
4.3 | Searchingfordriversofrichnessand
singularity patterns
VariationinspeciesrichnessandsingularityacrosstheBIGwasmainly
explained by variation in water‐ and substrate‐associated condi
tions (Table1).Indeed, the western (more singular) sector of thebay
has more ri vers and receive s more sedime nts, nutrie nts and orga nic
matterwhichmayexplaintheobserved changes in community com
position.Additionally,variation in richnessandsingularity responded
to geomorphology and sediment aspects of the rocky reefs. More
FIGURE 5 Stackedbarshowing
variationpartitioningresultsof
environmentalandspatialmodelsto
explain:(a)totalandgrouprichnessand(b)
singularityvaluesfoundacross42sitesat
IlhaGrandebay.Sincesingularit yisequal
torichnessforcrustaceans,itsmodelis
omitted
0.00.2 0.40.6 0.81.0
0.00.2 0.40.6 0.81.0
AlgaeCorals EchinodermataMolluscaCrustacea Fish Total
(a)
(b)
Proportional variation
Non-spatially structured in environmental variablesSpatial variables
Spatially structured environmental variablesUnexplained variation
Taxonomic Group
    
|
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CARLOS‐JÚNIOR et AL .
three‐dimensionallycomplexhabitatscoincidedwiththesamemacro‐
division observed for richness and singularity patterns. Therefore,
the combined effectsofnutrient and organic mat ter enrichmentand
higherrugosit yonthewesternsideof the bay,andalongsomeof the
continent al coastli ne and the oute r side of the main i sland, were as
soc iatedwi thbothr ichnessan dsingular it yp at te rn sobser ved(51%and
38%,respectively,Figure 5).Usingmorerestrictive thresholds for rar
ity(e.g.,considering“rare”thosespeciesoccurringatonetofoursites,
resultsnotshownhere)producedsimilarresults,butincreasedthepro
portionalcontributionofenvironmentalvariablestotheexplanationof
rarit yp at te rn s.Thissuggest sthat speciesra ri tyintheregionalsca lefor
oursystemwasstronglycontrolledbyenvironmentalfiltering.
Bothrichnessand singularityofseveraltaxonomic groupswere
spatiallystructuredatdifferentspatialscales,mainlyatintermediate
andfiner scales, represented by higher PCNMs(e.g.,PCNMs 9,10,
17,25,se el as trow ofTabl e1candFigure4). Th is su gg est st ha tm an
agementactionsaimingatparticulartaxonomicgroupsmayrequire
acarefulchoiceofspatialscale,whichcouldbemorecomplex than
targetingwholecommunityconservation.
4.4 | Turnover×nestednesscomponentsof
beta diversity
Theanalysis of betadiversityin BIG revealedthatvariationinspe‐
cies comp osition for all gr oups (Table 2; Figure 6) wa s high when
compared to other studied systems (e.g., Alsaffar, Cúrdia, Borja,
TABLE 2 Multiple‐sitetot albetadiversity(Sørensenindex)and
itstwocomponents(turnoverandnestedness)calculatedforallsix
marinegroupsinIlhaGrandeBay(BIG)
Betadive rsity
Total beta Turnover Nestedness
Epifauna/flora
Algae 0.93 0.90 0.03
Coral 0.90 0.82 0.07
Echinoderms 0.89 0.81 0.07
Infauna
Molluscs 0.94 0.91 0.03
Crustaceans 0.97 0.95 0.02
Pelagic
Reeffish 0 .93 0.89 0.04
Note.Duetorounding,thesumofthetwocomponent smightbeslightly
differentfromthetotalbetaresult.
FIGURE 6 Ternaryplotshowingtotalcommunityvariation(bet adiversitysensuWhittaker,1960;measuredasSørensenindex,x‐axis)
anditsturnover(y‐axis)andnestedness(z‐axis)componentscalculatedforallpossiblepairsofsites(bluedots)forallsixtaxonomicgroups
sampledatBIG(a)algae;(b)corals;(c)echinoderms;(d)molluscs;(e)crustaceans;(f)reeffish.Allaxes’unit sareproportions.Thereddot
marksthecentroidvalueforeachtaxonomicgroup
754 
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Irigoien , & Carva lho, 2017;M agurran , Dornelas , Moyes, Gotell i, &
McGill, 2015), including different taxonomic groups from tropical
rainforests(e.g.Baselga,Gómez‐Rodríguez,&Lobo,2012;Tonialet
al.,2012).Ingeneral,around90%ofspeciescompositiondiffersbe
tweenlocalsiteswithinthemetacommunit y,whichmeansitwasnot
possibletopredictasite'scompositionwithpriorinformationfroma
differentsite.This,asaprimar yresult,suggeststropicalrockyreefs
havehighbetadiversity,comparable tovalues found forplotswith
high beta diversit yin tropical forests measured at a muchbroader
spatialscale(Nevesetal.,2017).Thisalsohasdirectimplicationsfor
conservation,sincethelossofdiversit yatspecificsitesisrelatively
more trou blesome, a nd it is not possib le to encompass t he whole
regionaldiversityinafewgeographicallyrestrictedprotectedareas.
In t e r e s t i n g l y, a l mostal l v a r i ationi n s p e c i e sco m p o s i t i o nisd u e t ospa
tial replacement ofspecies (turnover), with almostno contribution
fromspeciesgainor loss (nestedness). This was also generallycon‐
sistent withinindividualtaxonomicgroups, as seen by the centroid
valuesinFigure6,althoughitispossible toseeawidervariation of
values,whichisinlinewithpreviouscriticismontheusageofmean
pairwisevaluesforgeneralinferencesonmultisiteanalysis(Baselga,
2012,2013).Highercontributionsofturnovertobetadiversityhave
previouslybeensuggestedforotherlowlatitudeareas(b elowparal
lel 37,B aselga, 2012; Bi shop, Rober tson, Rensb urg, & Parr, 2015,
butseeNeves etal.,2017) andcouldberelatedto differentcauses
associatedwithspatiallystructuredandhistoricalconstraintsand/or
differentenvironmentalselection(Baselga,2010;Qianetal., 2005;
Simpson,1943).Indeed,furtherinvestigationrevealedthatenviron‐
mental sorting,especiallyrelated todepthdifferencesinthe bay,is
partlyresponsible forspecies variation(L.A. Carlos—Juniorunpub
lisheddata),aswellasdifferences inabundances(M.C.Mantelatto,
unpublisheddata)inBIG.ThehighvalueofβSORandits mainc om po
nentβSIMinthebayalsoconfirmthat,inthemarineenvironment,the
gradie nts driving s pecies varia tion change abr uptly over relat ively
smallspatialscales, revealingtheimpor tanceof speciessortingfor
communit yorganizationinthesea(Heinoetal.,2015).
4.5 | Conservationimplications
The singu larity and ric hness pattern s, as well as their pote ntial
causes, have implications for current and future conser vation
strategies. Most importantly, marine communities on the west
coas tan dar oun dI lha Gra n de(e s pec ial ly the sou the r ns ide)m ay be
bestprotectedviaseveral distinctyetconnectedprotectedareas
(orasinglelargearea)toencompasstheircommunitydistinctive
ness. Cu rrently, the Tamoios Fede ral Ecologic al Reser ve aims to
protect aseries ofislands throughout the westernportionof the
regiontoge therwithsom especificco nservat ionuni ts ,su cha sthe
Cairuçu Federal EnvironmentalProtected Area (EPA)and Bay of
Parat y and Mamanguá Cove Co unty EPA. Althou gh the central
channel hadingenerallowerrichnessandsingularity(Supporting
information S4.1 and S 4.2 panel a), the high values of species
turnoverobservedforthewholeareasuggestthatsomeportions
ofthecentralareashouldalsobeincludedinconservationplans.
Theobservedhigherspatialubiquit y(i.e.,specieswithlargerspa
tial ranges, Supporting information S6) of thespecies present at
the central channel suggested these areas couldbe managed by
preser vationofsmallerportionsofitsarea.Asdiscussedabove,it
could also suggest that the central channelisunder the mostin
tensiveanthropogenicpressure,whichisconsistentwithprevious
studies(Creedetal.,2007).Sincetheextensionofataxon'sadap
tation to abroaderrangeofenvironmental conditions influences
its geog raphical dist ribution (Holt, 2 003; but see Ca rlos‐Junior,
Neves, Barbosa, Moulton, & Creed, 2015), the species capable
of survi ving in this regi on would also pr esumably be c apable of
inhabiting a larger range of environmental conditions across the
whole bay.
4.6 | Concludingremarks
Here, we have sh owed that althoug h there were distin guishable
patternsinbothrichnessandsingularityacrossdifferenttaxonomic
groups, assemblageswere structured by different environmental
drivers and,mostimpor tantly,at different spatialsc ales. Thecon
trasting spatial scales in which richness and singularity measures
werestruc tured for diversetaxonomicgroups highlighted how di
versityisorganizeddifferentlyinspacefordistinctfaunaandflora,
within thesame habitat,such as therockyreef benthos. Also, al
thoughsomeenvironmentaldriverswerefoundtobeimportantto
morethanonegroup,therewasaconsiderabledifferenceinwhich
factorsinfluencedtheobservedvariationineachgroupofspecies’
richnes s and singula rity (Table 1). Acco unting for thi s plethora of
possibilitiesincreasescomplexit ynotonlyforthescienceofunder
st an di ng sp at ia lp at ternsinm ar ined iver sit y,butalsofordeve lo ping
managementstrategies.Nevertheless,therewasa consistentpat
ternofturnoverpredominating incommunityvariation,indicating
that varia bility among a ssemblages is not d etermined by spe cies
lossbutratherbysubstitutionofspecies,whichcouldberelatedto
environmental filtering ofdifferenthabitat sacross the bayand/or
stochasticity driving immigration/local extinctions. Environmental
driversaccountedforaconside rablefrac tio nofge ner alvariationin
richnessandsingularity,confirming thatspeciessortinginmarine
systemscouldbepotentiallyhigh(Heinoetal.,2015).
Themethoddescribedaboveforcomputingraritywasadequate
foridentif ying areaswith uniquecompositions. Besidesbeing con‐
sistent with other methods for calculating site endemism (results
notshown here), it has the advantage of not beinghighly sensitive
to richne ss. Indepe ndence of richn ess is a desira ble charac teristic
foranindexdesignedtodetectpatternsincommunitycomposition
thatarenotnecessarilytheresultofmereaccumulationofdifferent
species.Moreover,theframeworkproposedhereprovidesnumbers
thatareeasilyinterpretableandmeaning ful.Forexample,asitewith
Si=0.5hashalfofitsspeciesconsidered“rare”forthatregionandis
twiceassingularasa sitewithSi=0.25.Interpretabilityandmean
ingareessentialpropertiesofusefuldiversitymeasures(Jost,2006),
whichcanbeunderstoodandappliedevenbynon‐ecologists, such
asmostpolitical‐decisionmakers.Itisnoteworthythat“rare”inthis
    
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CARLOS‐JÚNIOR et AL .
contextisrelatedneithertooveralldistributionnortoabundanceor
endangeredstatus.Itrefers solelytothefrequency ofthespecies’
occurre nces within t he target reg ion. In other wo rds, rare sp ecies
werere ga rdeda sthosew ithsm allsp atialra nges,re lativetothela rg
estpossiblerangegivenourstudyregion.Thisissimilarconceptually
toGaston (1994)and to other studies seeking forrarit y in species
ranges (se e Tables 1.3 and 1 .4 in Gaston, 1994). Nevert heless, it
shouldbestressedthattestingtheabovementionedmethodunder
differentscenariosandspatialscalescouldresultinimprovements.
Forexample,wedefinedararespeciesasonepresentatfewerthan
kout of n sites,wherekissomenumberbetween2andtheinte
gerpar tofn/2.Wethencalculatedthemeansingularityvalueover
all possible k thresholds as ourproxy forrarity.This was the most
objectiveconceptofrarewecouldenvision,aswellasageneralap‐
proach to r arity wit hout compr omising to a sing le (and potentia lly
subjective)threshold.Althoughpresumablypermissive(considering
most comm unities foll ow a log‐normal di stributi on where mos t of
th es p eci eso cc uri nf ews it es)it wo r ke dwe ll forou rsy s te mwi ths im
ilarresultstootherindexes.Also,itworkedasagoodproxyformost
thresholds, especially in theintervalbetween10% and40%ofthe
sites (4 and 17 sites, respectively,Supplementary Information S3).
However,dependingonthestudiedsystem, onespecificthreshold
could be chosenas acut‐off for rarity. Another problemmayarise
incommunities withunusuallyhigh proportions of rare species,as
exemplif ied by our cru stacean d ataset. In t hose syst ems, singul ar‐
ityvalues getclose(or,inour case,equal)to 1andbecomea proxy
forgeneral richness (Rj), losing their utility.Insummar y,through a
simple framework using presence/absence data, it was possible to
recognizeuniquepatternsthatoccurinbetadiversityofthemarine
tropic al shallow subti dal benthos. Fur thermor e, it was possibl e to
identif ymechanismsdrivingsuchpatternsofcommunit yvariation.
Understandingbetterhowthesedriversoperateshouldbeanatural
nextstep.Italsoremainstobetestedwhetherthehighbetadiversity
values ob served he re are unusual o r are typic al for lower‐latitu de
marine systems.Theframeworkand datasetsprovidedherewill be
usefulforansweringthoseandotherbroaderecologicalquestions.
ACKNOWLEDGEMENTS
We are gratefu l for insightf ul comment s and sugges tions on the
singul ar ityindexgi ve nb yH .R ol imfromINE A‐R J,andT.Rochaan d
M.Brog giof romUN 'sFAO. Also,wet hankR .V ill a,J. Valent in ,B .
Ros ad oandE.Z andonà forsevera lcons id er at ionso nt heearlyve r
si o no f thi s man u s cri p t .Th i sstu d ywas f und e d by a Sci e n cew itho u t
BordersfellowshipfromtheNationalCouncilforTechnologicaland
ScientificDevelopment (CNPq) and by the Brazilian Coordination
fortheImprovementofHigherEducationPersonnel(CAPES).LCJ
acknowledgesR.Athayde,E.Faria‐JuniorandK.Capelforthehelp
withgraphicalissues.MCMacknowledgesthesuppor tofCAPES.
DATAACCESSIBILITY
Additionala ccessib ilitydataareprovidedassuppo rt inginformation.
ORCID
Lélis A. Carlos‐Júnior https://orcid.org/0000‐00015152‐0645
Danilo Mesquita Neves https://orcid.org/0000‐0002‐0855‐4169
Clovis Barreira e Castro https://orcid.org/0000‐0001‐9127‐9817
Carlos Renato Rezende Ventura https://orcid.
org/0000‐0002‐1005‐1333
Carlos Eduardo Leite Ferreira https://orcid.
org/0000‐0002‐4311‐0491
Cristiana Silveira Serejo https://orcid.org/0000‐0001‐9132‐5537
Marcelo Checoli Mantelatto https://orcid.org/0000‐0002‐3992‐4214
Joel Christopher Creed https://orcid.org/0000‐0002‐1722‐0806
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BIOSKETCH
Lélis A. Carlos‐Júnior is a postdoctoral researcher at the
Univer sidadeFede ra ldoRiodeJan eiro,Brazil.H eismai nlyinter
estedinhownaturalmarinecommunitieschangeoverspaceand
time, esp ecially how ecolog ical and biogeogr aphical proce sses
interacttoshapedistributions.
Author s contribution: LCJ, JCC, MS , SOP, FAC and TPM con‐
ceivedtheideas.CRRV,CBC,CELF,CSS,DOP,FC,SOPandJCC
collectedthe data.LCJ,JCC, MS and DRMdesigned themanu‐
script .LC JandDRManal ysedthedata.LC J,MCMandDRMpre
pared thefigures. LCJ ledthe writing with valuable input of all
contributors.Allauthorscontributedequallytothefinalversion
ofthepaper.
SUPPORTINGINFORMATION
Additional supporting information may be found online in the
Suppor tingInformationsectionattheendofthearticle.
Howtocitethisarticle:Carlos‐JúniorLA ,SpencerM,Neves
DM,etal.Rarityandbetadiversit yassessmentastoolsfor
guidingconservationstrategiesinmarinetropicalsubtidal
communities.Divers Distrib. 2019;25:74 3–757. ht t p s: //d o i .
org /10.1111/dd i.1 2896
... It reflects the importance of wide spatial areas with low dispersal restrictions for biodiversity structuring in marine coastal areas, especially because environmental and biological interactions within these ecosystems are taxa-dependent (da Silva et al. 2020;Wu et al. 2020). The precise location of the unique site might as well change over time, because of the heterogeneous dynamics of coastal areas, and the fact that our temporal scale included a year-round investigation, may preclude temporal trends of variation in species diversity and abundance over larger periods (Carlos-Júnior et al. 2019;Rodrigues-Filho et al. 2020). ...
... Trawling can also produce taxonomic impacts over species that are important as feeding resources for fishes and crustaceans, such as anthozoan, Malacostraca and Polychaeta (Kaiser et al. 2002), and contributes further to the selectivity of resilient species pool. Common species are usually more frequent in stressed environments (Carlos-Júnior et al. 2019). Diversity variability for distinct taxa can also be attributed to temporal dynamics of the shallow coastal areas that can shift the relative importance of water, sediment and fishing influences over communities and population organization over time (Knowlton 2004;Rodrigues-Filho et al. 2020;Barrilli et al. 2021). ...
Article
We assessed the ecological uniqueness of fish and crustaceans in traditional fishing grounds from a tropical shallow marine ecosystem, where bycatch is historically high. Trimestral trawling was carried out between November 2009 and August 2010 in nine sites along 80 km of coastline in South Brazil. We investigated the local (LCBD) and species contribution to beta diversity (SCBD) using beta regression models, disentangling the influence of environmental (water, sediment characteristics) and biotic (S, abundance, diversity, dominance) parameters over LCBD; and species occurrence, total and mean local abundance association with SCBD. The shallow marine areas presented high beta diversity of fish and crustacean. We identified two ecological unique sites for fishes that occurred in colder sites, while the only unique site identified for crustaceans occurred, where crustacean species richness was lower. The ecological unique sites for both taxa were those with lower species richness and abundance, with distinct assemblages' composition, although the location of the unique sites differed between taxa. Species contribution to beta diversity was mostly driven by species with intermediary-high distribution on the region with high variability in occurrence and abundance. High biodiversity is the general rule when assessing bycatch species, and the singularity of the species composition in the unique sites stems from the occurrence of rare species, which increases the size of the species pool.
... We compared the two approaches to spatial variable selection using simulated community data based on three real community composition datasets with a range of properties: 1. Presence/Absence of 110 marine benthic macroalgae species from a Rapid Assessment Program for biodiversity of 42 sample sites spanning roughly 2,000 km 2 at Ilha Grande Bay, Rio de Janeiro, Brazil (southwest Atlantic) (Carlos-Júnior et al., 2019), permit number IBAMA/RJ:031/04); 2. Presence/Absence of 588 plant species from grassland covering 500 km 2 of Scotland's coast. Data were collected from 3639 5× 5 m quadrats from 94 sites. ...
... Therefore, for dataset 1 we chose the minimum spanning tree (B) with Euclidian linear distances as weights (A). Our decision was based on the shape of the bay and the fact that the main water movements make the sampling sites geographically compartmentalised in subregions where sites are likely to be minimally connected (Carlos-Júnior et al., 2019). Similarly, spatial organisation in dataset 2 could be sensibly described in terms of Delaunay Figure 1 Schematic diagram of the main steps used in this study to simulate community presence/absence data with pre-defined spatial structure. ...
Article
Background Ecological communities tend to be spatially structured due to environmental gradients and/or spatially contagious processes such as growth, dispersion and species interactions. Data transformation followed by usage of algorithms such as Redundancy Analysis (RDA) is a fairly common approach in studies searching for spatial structure in ecological communities, despite recent suggestions advocating the use of Generalized Linear Models (GLMs). Here, we compared the performance of GLMs and RDA in describing spatial structure in ecological community composition data. We simulated realistic presence/absence data typical of many β-diversity studies. For model selection we used standard methods commonly used in most studies involving RDA and GLMs. Methods We simulated communities with known spatial structure, based on three real spatial community presence/absence datasets (one terrestrial, one marine and one freshwater). We used spatial eigenvectors as explanatory variables. We varied the number of non-zero coefficients of the spatial variables, and the spatial scales with which these coefficients were associated and then compared the performance of GLMs and RDA frameworks to correctly retrieve the spatial patterns contained in the simulated communities. We used two different methods for model selection, Forward Selection (FW) for RDA and the Akaike Information Criterion (AIC) for GLMs. The performance of each method was assessed by scoring overall accuracy as the proportion of variables whose inclusion/exclusion status was correct, and by distinguishing which kind of error was observed for each method. We also assessed whether errors in variable selection could affect the interpretation of spatial structure. Results Overall GLM with AIC-based model selection (GLM/AIC) performed better than RDA/FW in selecting spatial explanatory variables, although under some simulations the methods performed similarly. In general, RDA/FW performed unpredictably, often retaining too many explanatory variables and selecting variables associated with incorrect spatial scales. The spatial scale of the pattern had a negligible effect on GLM/AIC performance but consistently affected RDA’s error rates under almost all scenarios. Conclusion We encourage the use of GLM/AIC for studies searching for spatial drivers of species presence/absence patterns, since this framework outperformed RDA/FW in situations most likely to be found in natural communities. It is likely that such recommendations might extend to other types of explanatory variables.
... reef heterogeneity, sea surface temperature [SST] and bathymetry), as observed for coastal and reef fishes, starfishes and corals (Arias-González et al., 2008;Hattab et al., 2015;Price, 2002). These studies are mostly conducted at local or regional scales (Arias-González et al., 2008;Becking et al., 2006;Carlos-Júnior et al., 2019;Hattab et al., 2015;Loiseau et al., 2017). Overall, very few studies investigated simultaneously the different facets of beta diversity at the global scale (see Penone et al., 2016), and more particularly considering a partitioning approach to disentangle the effects of species turnover and differences in species richness (see Montaño-Centellas et al., 2021). ...
Article
Aim: Exploring the relationships between the different facets of beta diversity and both past and current environmental conditions can unveil the processes that have shaped spatial patterns of biodiversity. In the marine realm, large‐scale patterns and processes of beta diversity have been less investigated. Our study aimed to investigate the patterns and drivers of multiple facets of beta diversity and its components, contrasting pairs of reef fish assemblages among marine realms. Location: Tropical reefs. Taxon: Reef fishes. Methods: Based on trait data and phylogenetic relationships for 5182 tropical reef fish species, we calculated compositional differences between pairs of reef fish assemblages across the Atlantic, the Tropical Eastern Pacific (TEP) and the Indo‐Pacific realms. We also applied a partitioning approach to distinguish between the turnover and nestedness components. We then evaluated the relative importance of several variables related to historical and contemporary environmental conditions in shaping spatial patterns of beta diversity using Constrained Analysis of Principal coordinates (CAP) models. Results: Both the turnover and the nestedness components contributed to total phylogenetic and taxonomic beta diversity in the TEP and Indo‐Pacific realms, while the turnover component was found to be more important in the Atlantic realm. In contrast, total trait beta diversity displayed very low values and was primarily explained by the nestedness component. Taxonomic and phylogenetic differences in the composition of tropical reef fish assemblages were influenced by both historical and contemporary factors or solely by historical variables. Main conclusions: Our results suggest that past climate changes and historical contingency left an imprint in the present‐day composition of tropical reef fish assemblages. The very low levels of trait beta diversity indicate that reef fish assemblages display similar trait composition even among geographically distant assemblages with contrasting evolutionary histories, which may result from environmental filtering or evolutionary convergence, or the combination of both processes.
... This is because richness is not sensitive to changes in composition and this might mask results when sensitive taxa are lost and generalist species become more common into the altered environments (Clavel et al., 2011). Consequently, β diversity has become a pervasive topic in community ecology, conservation and macroecology (Carlos-Júnior et al., 2019;Svenning et al., 2011). ...
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Aim We investigated taxonomic and functional beta diversity of bird communities inhabiting Mediterranean olive groves subject to either intensive or low‐intensity management of the ground cover and located in landscapes with different degrees of complexity. Location Andalusia, southern Spain. Methods We partitioned taxonomic and functional beta diversity into its two additive components, turnover and nestedness. We also explored the contributions of single sites to overall beta diversity (LCBD) and separated the effects of species replacement (turnover) and richness difference (nestedness) in order to identify ecologically unique sites—keystone communities—within the metacommunity. In a further step, we employed abundance‐ and functional‐based indicator species analyses to characterize bird assemblages. Results Taxonomic beta diversity increased with landscape complexity. Although both taxonomic and functional differences among assemblages were driven mainly by species replacement (regardless of management or landscape type), the contribution of trait replacement to the total functional beta diversity was much lower, suggesting that species performing similar functions replace each other between sites. There were no differences in LCBD between management types or categories of landscape complexity, but the contributions of sites to beta diversity decreased as the percentage cover of olive groves increased. Species richness was also important in explaining variation in LCBD as species‐poor sites tended to contribute the most to the local‐to‐regional beta diversity. However, some farms displayed high values of LCBD due to the existence of a high replacement component, indicating that some species recorded in these sites were scarce elsewhere. The indicator species analyses revealed that the woodchat shrike Lanius senator may constitute an excellent indicator of biodiversity in this agro‐forestry system. Main conclusions Our results show that agricultural expansion promotes biotic homogenization and exemplify how the identification of both keystone species and communities can represent a powerful tool for the management of anthropized landscapes.
... All statistics analyses were conducted in R environment (R Core Team 2020). To obtain the percentage of explanation of each set of explanatory variables (i.e., climate, urbanization, and space) we applied variation partitioning based on generalized linear models (GLM) with a quasipoisson distribution and a partial RDA (Legendre and Legendre 2000;Legendre et al. 2005;Carlos-Júnior et al. 2019). Firstly, we created three global GLM containing the explanatory variables related to climate, urbanization, and space using the richness of IAT as a response variable. ...
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Urban areas are strongly transformed by the human population and these environmental changes can affect the richness and composition of invasive alien trees (IAT) across cities. In this study, we investigate the role of urbanization and climate variables on richness and species composition of IAT in cities from Brazil. We investigated (i) the relative contribution and how variables related to urbanization affects the patterns of richness and composition; and (ii) the relative contribution and how variables related to climate affects richness and composition of IAT. Thus, we used occurrences of IAT in 93 urban areas in Brazil gathered from 130 references. We tested the variation partitioning using three models with variables related to urbanization, climate, and spatial correlation for richness and species composition of IAT. Next, we tested the significance of each set of variables on the richness and species composition of IAT. Urbanization variables explained 18% of the variation in species richness of IAT, whereas climate explained 18% of the variation in species composition of IAT. Demographic density (habitants per km2) positively affected the richness of IAT, whereas the percentage of urban forest influenced species composition. Köppen climate zones affected richness while the covariate temperature annual range had a negative effect on richness. Temperature annual range, annual precipitation, precipitation of driest quarter, and precipitation of warmest quarter affected the species composition of IAT. We confirmed urban variables have a relevant influence on species richness, but climate variables are still important to understand the composition of IAT across cities. Thus, species richness is affected at a local scale by urbanization, and species composition by climate at broader scales.
... This is because richness is not sensitive to changes in composition and this might mask results when sensitive taxa are lost and generalist species become more common into the altered environments (Clavel et al., 2011). Consequently, β diversity has become a pervasive topic in community ecology, conservation and macroecology (Carlos-Júnior et al., 2019;Svenning et al., 2011). ...
Article
Full-text available
Aim: We investigated taxonomic and functional beta diversity of bird communities inhabiting Mediterranean olive groves subject to either intensive or extensive management of the ground cover and located in landscapes with different degrees of complexity. Location: Andalusia, southern Spain. Methods: We partitioned taxonomic and functional beta diversity into its two additive components, turnover and nestedness. We also explored the contributions of single sites to overall beta diversity (LCBD) and separated the effects of species replacement (turnover) and richness difference (nestedness) in order to identify ecologically unique sites -keystone communities- within the metacommunity. In a further step, we employed abundance- and functional-based indicator species analyses to characterize bird assemblages. Results: Taxonomic beta diversity increased with landscape complexity. Although both taxonomic and functional differences among assemblages were driven mainly by species replacement (regardless of management or landscape type), the contribution of trait replacement to the total functional beta diversity was much lower, suggesting that species performing similar functions replace each other between sites. There were no differences in LCBD between management types or categories of landscape complexity, but the contributions of sites to beta diversity decreased as the percentage cover of olive groves increased. Species richness was also important in explaining variation in LCBD as species-poor sites tended to contribute the most to the local-to-regional beta diversity. However, some farms displayed high values of LCBD due to the existence of a high replacement component, indicating that some species recorded in these sites were scarce elsewhere. The indicator species analyses revealed that the woodchat shrike Lanius senator may constitute an excellent indicator of biodiversity in this agro-forestry-system. Main conclusions: Our results show that agricultural expansion promotes biotic homogenization and exemplify how the identification of both keystone species and communities can represent a powerful tool for the management of anthropized landscapes.
... This variability is captured as beta(β)-diversity, the facet of regional diversity encompassing differences among local assemblages [4]. Socolar et al. [5] and Carlos-Júnior et al. [6] outline use of β-diversity in conservation, including management planning, such as choosing which sites to protect. As many ecological phenomena can shape β-diversity, patterns can reveal key processes. ...
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Deep-sea hydrothermal vent habitats are small, rare and support unique species through chemosynthesis. As this vulnerable ecosystem is increasingly threatened by human activities, management approaches should address biodiversity conservation. Diversity distribution data provide a useful basis for management approaches as patterns of β-diversity (the change in diversity from site to site) can guide conservation decisions. Our question is whether such patterns are similar enough across vent systems to support a conservation strategy that can be deployed regardless of location. We compile macrofaunal species occurrence data for vent systems in three geological settings in the North Pacific: volcanic arc, back-arc and mid-ocean ridge. Recent discoveries in the Mariana region provide the opportunity to characterize diversity at many vent sites. We examine the extent to which diversity distribution patterns differ among the systems by comparing pairwise β-diversity, nestedness and their additive components. A null model approach that tests whether species compositions of each site pair are more or less similar than random provides insight into community assembly processes. We resolve several taxonomic uncertainties and find that the Mariana arc and back-arc share only 8% of species despite their proximity. Species overlap, species replacement and richness differences create different diversity distributions within the three vent systems; the arc system exhibits much greater β-diversity than both the back-arc and mid-ocean ridge systems which, instead, show greater nestedness. The influence of nestedness on β-diversity also increased from the arc to back-arc to ridge. Community assembly processes appear more deterministic in the arc and ridge systems while back-arc site pairs deviate little from the null expectation. These analyses reflect the need for a variety of management strategies that consider the character of diversity distribution to protect hydrothermal vents, especially in the context of mining hydrothermal deposits.
... At the present study, the gamma dose rate in air measurements were performed on eight beaches on Ilha Grande, between November 2011 and August 2012, showed in the Table 1 and Fig. 1 Located at the Atlantic Forest biome, this island is considered a privileged region for the development of scientific research focused on the environment and sustainable development, involving terrestrial and marine ecosystems [11,12]. In the same way of the first study, Freitas and Alencar [10], used a portable environmental radiation detector, TRADOS 70046A VacuTec, with the same calibration procedures. ...
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In order to analyze temporal variability on the gamma dose absorbed in the air in beaches in Ilha Grande, Brazil, in 2011–2012 new measurements were made in four of ten beaches analyzed previously in 2001. The measurements were performed directly with a proportional detector. The values varied between 47 ± 5 (Lopes Mendes) and 97 ± 11 nGyh−1 (Caxadaço) Praia Preta. The average annual effective dose was 0.08 ± 0.02 mSv year−1 , in line with the world average of effective external doses, 0.07 mSv year−1 according to UNSCEAR. No statistically significant change was observed after 10 years.
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Harnessing big data and machine learning provides an assessment of the extinction risks of palm species worldwide, and illustrates an integrative conservation planning approach that incorporates evolutionary and ecological distinctiveness as well as human use.
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Understanding ecological impacts of bottom-based clam aquaculture can improve its management. In this study, taxonomic and functional macrofaunal assemblage were evaluated for two clam farms located in Laizhou Bay, China. Beta diversity and factors potentially regulating the dissimilarity of macrofauna were estimated. Both taxonomic and functional composition of macrofauna showed large differences between the clam farm and the control area. Functional dissimilarity within the clam farms was found to be nestedness and negatively correlated to local clam abundance. Additionally, the cultured clam enhanced the functional richness but made the macrofaunal assemblage more fragile against species or function loss. This effect would increase with clam abundance, which highlights the importance of identifying optimal clam culture intensity in developing a bottom-based clam aquaculture program.
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Phoxocephalopsids are fossorial amphipods endemic to the Southern Hemisphere and currently encompass five genera and 15 species, including the four new species herein described. Previous records of the family along the Brazilian coast are limited to Phoxocephalopsis zimmeriSchellenberg, 1931. Based on material from the Crustacean Collection of Museu Nacional/UFRJ four new species of Phoxocephalopsidae areherein described: Phoxocephalopsis ruffoi sp. nov. from Rio de Janeiro, São Paulo, Paraná and Rio Grande do Sul (23o-30oS); Puelche irenae sp. nov. from Espírito Santo and Rio de Janeiro (19o-23oS); Puelche longidactylus sp. nov. from Bahia, Espírito Santo and Rio de Janeiro (12o-23oS) and Puelche mourae sp. nov. found only at Rio de Janeiro. High diversity was recorded from the Campos Basin region, which extends from the north of Rio de Janeiro to south of Espírito Santo (21o-23oS), with the presence of all four phoxocephalid species. A key to Phoxocephalopsidae species from Brazil, including P. orensanziBarnard and Clark, 1982 is provided.
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Marine biogeographic realms have been inferred from small groups of species in particular environments (e.g., coastal, pelagic), without a global map of realms based on statistical analysis of species across all higher taxa. Here we analyze the distribution of 65,000 species of marine animals and plants, and distinguish 30 distinct marine realms, a similar proportion per area as found for land. On average, 42% of species are unique to the realms. We reveal 18 continental-shelf and 12 offshore deep-sea realms, reflecting the wider ranges of species in the pelagic and deep-sea compared to coastal areas. The most widespread species are pelagic microscopic plankton and megafauna. Analysis of pelagic species recognizes five realms within which other realms are nested. These maps integrate the biogeography of coastal and deep-sea, pelagic and benthic environments, and show how land-barriers, salinity, depth, and environmental heterogeneity relate to the evolution of biota. The realms have applications for marine reserves, biodiversity assessments, and as an evolution relevant context for climate change studies.
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Patterns of variability in diversity (alpha and beta), abundance, and community structure of soft-bottom macrobenthic assemblages were investigated across an inshore/offshore environmental gradient in the central Red Sea. A total of three distinct soft-substrate biotopes were identified through multivariate techniques: seagrass meadows, nearshore and offshore. While the seagrass biotope was associated with higher organic matter content, the two coastal biotopes presented higher redox potential in the sediments and dissolved oxygen in the water. Depth and medium sand increased toward the offshore component, while the percentage of fine particles was a determinant of nearshore communities. Regardless of the prevailing environmental conditions, the three biotopes were characterized by high numbers of exclusive taxa, most of which were singletons. Changes in species richness were not related to depth or organic matter, peaking at intermediate depths (nearshore). However, the number of taxa increased exponentially with abundance. On the other hand, density decreased exponentially with depth and organic matter in sediments, probably linked to a reduced availability of food. One of the most conspicuous features of the macrobenthic assemblages inhabiting soft-substrates in the central oligotrophic Red Sea is the low level of dominance resulting from high species richness/abundance ratio. Despite the differences observed for alpha-diversity across the three biotopes, beta-diversity patterns were rather consistent. These findings suggest that mechanisms driving biodiversity are similar across the depth gradient. The partitioning of beta-diversity also show that assemblages are mainly driven by the substitution of species 2 (turnover or replacement) most likely as a result of environmental filtering. The heterogeneity of the seafloor in shallow waters of the Red Sea promoted by the coexistence of coral reefs inter-spaced by sedimentary habitats may increase the regional pool of colonizers and potentiate the stochasticity of the distribution patterns.
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Aim: We aimed to assess the contribution of marginal habitats to the tree species richness of the Mata Atlântica (Atlantic Forest) biodiversity hotspot. In addition, we aimed to determine which environmental factors drive the occurrence and distribution of these marginal habitats. Location: The whole extension of the South American Atlantic Forest Domain plus forest intrusions into the neighbouring Cerrado and Pampa Domains, which comprises rain forests (‘core’ habitat) and five marginal habitats, namely high elevation forests, rock outcrop dwarf-forests, riverine forests, semideciduous forests and restinga (coastal white-sand woodlands). Methods: We compiled a dataset containing 366,875 occurrence records of 4,431 tree species from 1,753 site-checklists, which were a priori classified into ten main vegetation types. We then performed ordination analyses of the species-by-site matrix to assess the floristic consistency of this classification. In order to assess the relative contribution of environmental predictors to the community turnover, we produced models using 26 climate and substrate-related variables as environmental predictors. Results: Ordination diagrams supported the floristic segregation of vegetation types, with those considered as marginal habitats placed at the extremes of ordination axes. These marginal habitats are associated with the harshest extremes of five limiting factors: temperature seasonality (high elevation and subtropical riverine forests), flammability (rock outcrop dwarf-forests), high salinity (restinga), water deficit severity (semideciduous forests) and waterlogged soils (tropical riverine forests). Importantly, 45% of all species endemic to the Atlantic Domain only occur in marginal habitats. Main conclusions: Our results showed the key role of the poorly protected marginal habitats in contributing to the high species richness of the Atlantic Domain. Various types of environmental harshness operate as environmental filters determining the distribution of the Atlantic Domain habitats. Our findings also stressed the importance of fire, a previously neglected environmental factor.
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(Current Biology 27, R511–R527; June 5, 2017) [Figure presented] During preparation of the final figures for this Review, the sea surface temperature map in Figure 2C was mistakenly replaced with the wrong map. Figure 2C has now been replaced with the correct map online. The authors apologize for the error.
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The oceans appear ideal for biodiversity — they have unlimited water, a large area, are well connected, have less extreme temperatures than on land, and contain more phyla and classes than land and fresh waters. Yet only 16% of all named species on Earth are marine. Species richness decreases with depth in the ocean, reflecting wider geographic ranges of deep sea than coastal species. Here, we assess how many marine species are named and estimated to exist, paying particular regard to whether discoveries of deep-sea organisms, microbes and parasites will change the proportion of terrestrial to marine species. We then review what factors have led to species diversification, and how this knowledge informs conservation priorities. The implications of this understanding for marine conservation are that the species most vulnerable to extinction will be large and endemic. Unfortunately, these species are also the most threatened by human impacts. Such threats now extend globally, and thus the only refuges for these species will be large, permanent, fully protected marine reserves.
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Spatial and temporal variation in tropical intertidal communities is poorly known, making predictions about the effects of climate change and other anthropogenic disturbances difficult. We sampled the abundance and composition of macroalgae in the intertidal zone from 2006 to 2015 along the southwest shore of O‘ahu, Hawai‘i - an area where local residents are concerned about coastal development and loss of targeted algal species for human consumption. The aims were to describe the rocky intertidal flora and to examine patterns that may indicate processes that shape composition and abundance. Sixty-six macroalgal species and two broad algal assemblages were identified that corresponded to substrate topography and sand influence at a similar tidal elevation. Along flat carbonate benches with a sand beach, Phaeophyceae and Rhodophytes occurred in almost equal proportions, while shores with slightly more elevated and angular substrate were dominated by Rhodophytes. Foliose or turf algal forms were most common. Surveys captured the local invasion of an alga, Avrainvillea sp. and significant declines in abundant macroalgae in 2015 after a period of unseasonably warm, calm water. Indeed, temporal changes in algal assemblages were related to maximum water temperature and wave height but not precipitation. Thus, algal assemblages appear to be structured by local beach morphology as they interact with sand and wave activity and episodically by unusual weather events. However, manipulation and continuous monitoring of the algal assemblages coupled to sensing of the localized environment is necessary to confirm factors related to assembly maintenance and recent species shifts.