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Grech, Alana, Hanert, Emmanuel, McKenzie, Len, Rasheed, Michael, Thomas, Christopher, Tol, Samantha, Wang, Mingzhu, Waycott, Michelle, Wolter, Jolan, and Coles, Rob (2018) Predicting the cumulative effect of multiple disturbances on seagrass connectivity. Global Change Biology, 24 (7) pp. 3093-3104.

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© 2018 John Wiley & Sons Ltd

Please refer to the original source for the final version of this work: https://doi.org/10.1111/gcb.14127 Accepted Article BiologyTheand Biodiversity, University of Louvain-la-Neuve,1348 Belgium, University, Cairns, , Australia, 4870 Belgium, 1348 Queensland, 4811 Australia, This by is protectedarticle reserved. Allrights copyright. doi: 10.1111/gcb.14127 lead to differences between this version and the throughbeen the copyediting, pagination typesetting, which process, may and proofreading hasThis article been accepted publicationfor andundergone peerfull but review not has 6 5 4 3 2109 Australia, 2 1 Authors: Running head: Title: Articles Research Primary : type Article DR.GRECH ALANA : (Orcid ID 0000-0003-4117-3779) Christopher Thomas and Rob Coles Rob and School ofBiologicalInstitute,Australian Sciences, Environment Centre for Evolutionary of Instituteand Civil Mechanics, UniversitécatholiqueMaterials Engineering, de Louvain, TropWATER (Centre forTropical Water& AquaticEcosystem Research), James Cook deLouvain, catholique Université Louvain-la-Neuve, Institute and (ELI), Earth Life ARC Centreof Excellence forCoral Reef Studies, James CookUniversity, , Departmentof Environmental Sciences, MacquarieUniversity, Sydney,South New Wales, Predicting thePredictingofeffect cumulative multiple disturbancesseagrass on connectivity Alana Grech 4 Cumulative effects on seagrass connectivity 5 , Samantha Tol 1, 2 1, , Emmanuel Hanert 4 , Mingzhu Wang Adelaide, South Australia, Australia, 5001 3 , Len McKenzie Version of Record. Please Version as this cite of Record. article 2 , Michelle Waycott , 4 , Michael Rasheed 6 , Jolan Wolter 4 , 3

Accepted Article This by is protectedarticle reserved. Allrights copyright. and tobe likely at risk from development. coastal Deep water meadowswerehighly ofthedifferent parts network. Several of these meadows were close tourbanareas orports sourcesimportant ofseagrass propagules that and serve as stepping stonesvarious connecting toresilientrandom the loss ofmeadows. Our analysismeadowsdiscrete identified that are seagrass networks were denselyconnected, indicatingthat modelledas seagrasses are seagrasses inthe central Great BarrierReef HeritageWorld Area(GBRWHA), Australia. The measure potential functionalconnectivityand the predict of impact disturbancesmultiple on marine ecosystems. We usednetwork analys are connectedor howdisturbances multiple affect especiallyconnectivity, in coastal and disturbance there However, is events. limited in rateThe ofexchange, orconnectivity,among populations their effects to recover ability after Abstract type: Paper networks Key words: Email: [email protected] +61Phone: 4781 7 4107 Townsville, Queensland, 4811Australia, UniversityJames Cook ARC CentreExcellence of for Coral ReefStudies Dr Alana Grech Corresponding author: Primary Research connectivity, cumulative effects, seagrass, , graph theory, is and the outputs of a biophysical model to formation on the formation extent to populationswhich Accepted Article This by is protectedarticle reserved. Allrights copyright. connected(Almany et 2009;al. Magris et al data on dispersalpathways and spatial the scaleandto extent populations which are rarelyare incorporated into decisi conservation biologicalfunction and diversity(Steneck et effective conservation outcomes because they etThomasal. 2015). protecting Identifying and morerecover rapidly from disturbances such as bleaching (Hugheset stormsand al. 2005; connectedwell coral reefsreceive a highnumber ofreplenishment populations after disturbance (Treml events al.et 2008). For example, 2014),to responses speciesinvasionsand diseasetransmission, species expansion and the metapopulation dynamics and genetics (Cowen and Sponaugle, 2009; McMahon et al. rateThe ofexchange, orconnectivity,among populations affects population and seagrass natural support recruitment processes. recoveryand therefore propagules, shouldmanagement focus connected seagrass meadows of the central GBRWHA are not limited by the supply of on average> 245 kilometres; abouthalf the length metapopulation. Theof the densely disturbance required to disconnect the seagrass networks into two or more components was onemeadowswere ormorediscreteremovedmultiple due disturbance to scaleTheevents. of species. We evaluated changes tothe structure and functioning ofthe seagrass networkswhen connectedto coastal meadows and may function as butonlya refuge, non-foundation for Introduction coastal restoration, may prove unnecessary and be unsuccessful. coastal marine and ecosystems. Without this knowledge, managementincluding actions, framework for assessing the impactof global change onthe connectivity andpersistence of

. 2014). Measuring dispersal and connectivity al. 2009). However, connectivity estimates support population persistence, ecosystem onof because making ofa lack quantitative pathways of connectivity is critical to on improving environmental conditions that of larvae from multiple source reefs and

Our study provides a new Accepted Article This by is protectedarticle reserved. Allrights copyright. characterising connectivity multiple at spatial scales by how representing habitats and et2009).(Urban al.Graph theory provides visualizing a flexible for framework and growing,isbiologyparticularly rapidly in metapopulation connectivity and analysis applicationThe ofgraph theory to (ornetwork) landscape andecology conservation using techniquessuch as graph et(Treml theory2008). al. connectivityand analysisthe exploration of amonglocations, enablingmovingindividuals biophysical models can be used to populate matrices with the number of ‘virtual’ and timing ofrelease,behaviour and mortality, in the waterOutputscolumn. of parametersdefine to the species, ofaattributes including larval propaguleand duration particles using numerical models to describe the motion of waters, and biological Grech etal. 2016). Biophysical models the predict ofmovement individualsby tracking Cowen et 2006;al. Treml and Halpin2012; Thomasetal. 2014; Abesamis et al.2016; of species dispersal parameters with hydrodyn spatial and temporal scales (Andrello et al. 2013). An alternative approach is the coupling cost logisticalconstraintsand et (Kool al. 2013; Abesamis et2016) al. and smallrelatively However, empirical measures of connectivity are limited to a few marine species due to DNA parentage analysis Planes (e.g. et 2009;al. Almanyet2013; al. et Harrison 2013).al. and 2010), Allendorf, larvaltagging et (Almany al. 2007), genetic and approaches, suchas techniques such as electronic tagging(Block Studies thatempirically dispersalmeasure and connectivity marine ecosystems in use and in adynamicthree-dimensional fluid (Saundersenvironment et 2016).al. tracking propagules, individualslarvae and (D’A difficultmarine in particularlybecause ecosystems is ofthe with challenges associated et al. 2011), (Lowe capture-mark-recapture amic parameters in biophysicalamic models (e.g. loia etal. 2015) overbroad spatial scales Accepted Article This by is protectedarticle reserved. Allrights copyright. relypopulations largely onclonal growthfor population maintenance1999, (Rasheed examiningboth sexualand asexual reproduction have found that while some seagrass maintenanceof seagrass populations lessis (Kendrick understood et al. 2012). Studies mechanism for recruitment and recovery, but its contribution to the persistence and reproduction (e.g.Kendrick al. et 2017). Sexu studies onseagrass recruitment and recovery sediment loads use,land from dredging shipping) and It (Grechet 2012). al. is typical that storms, cyclones and high rainfall) and human-induced (e.g. coastal development, seagrasses Tropical are exposedmultiple regularly to disturbance natural events,both(e.g. 2016). metapopulation’s robustness to disturbance events (Urban and Keitt, 2001; Saunders et al. discrete habitats or subpopulations (Cumming al.et 2010) and provides a measure of a edges from a graphindicates thepotentialof a metapopulation to withstand theof loss is notoriously complex difficultmeasure toand (Gonzalez et 2011).al. Removingnodes or also theenables assessmentof the impactof disturbance events on connectivity,task athat exert a high degree of influence over the entire network or metapopulation. Graph theory Bodin 2008). These measures are important for identifyinghabitats orsubpopulations that indicate the rolenode ofarelative neighboursits to orthe(Estrada entirenetwork and example,2013). For measures of centralitybetweeness,(e.g. degree andcloseness) properties and key structural characteristics of large and complex networks (Kool et al. resultantthe functional connections. Graphtheory enables the assessment ofthe emergent edges (orlinks)between indicatenodes the direction andstrength relative ofdispersal and graphs (ornetworks), nodes subpopulationsphysically are or (Rayfieldlogically linked et 2011).al.In weighted represent discreterepresent habitat patches or subpopulations, and followingdisturbance events on focus clonal al reproductional is also an essential Accepted Article This by is protectedarticle reserved. Allrights copyright. seagrass meadows following disturbance events. on connectivityandto identify strategies thatsupport the replenishment andrecovery of meadow connectivity in the centralGBRWHA, predict the impact ofdisturbanceevents multiple disturbanceevents. Weused the output of the seagrassnetworks one when ormorediscrete meadowswere removed due to analysis. Networkanalysiswas used to evaluatechanges to thestructure and functioning using theoutputs ofa biophysical modelof se Area (GBRWHA),Queensland, Australia. Potential functional connectivitywas measured impactofdisturbance multiple the in events Great central BarrierHeritage ReefWorld goalTheof ourstudy was to assess seagrassconnectivity and quantify the cumulative habitats (Yorket al. 2017). eventson seagrass connectivity, anditsimplications forthe ofconservation coastal in seagrassreplenishment and recoveryafter al. 2006; Waycott et and 2009)al. there is need a critical tothe assess role ofconnectivity seagrasses Tropical occur in someworld’s ofthe mostthreatened regions coastal (Orth et connectivity atbroad spatialscales (Ruiz-Montoyaet2015; al. Jahnke et 2017). al. 2017), howeververystudies few have attempted toquantifyseagrass dispersal and connectivity the and of natural recolonisation sites disturbanceafter eventset (Kendrick al. (McMahon et al. 2014; Kendrick et al. 2017). Long distance dispersal supports meadow dispersing long distancesvia the convective forces ofocean wavesand currents critical importance. Seagrass fruits and propagulesof mostspecies are capableof ephemeral populations (Hovey et al. 2015; York et al. 2015), sexual reproduction is of 2004), in the caseof scalelarge meadow loss (Rasheed et2014) al. orforor annual disturbance events,theeffect ofdisturbance agrass dispersal and settlement and network s of our analysis toour analysis evaluateseagrass s of

Accepted Article Heritage Convention toHeritage Conventionthe conserve region, in intactness. The Australian Government has international responsibilities under the World Heritage Listin 1981 for superlativeits natu This by is protectedarticle reserved. Allrights copyright. GBRWHAThe covers an of 348,000area km Study area Materials and Methods seagrass loss and recoveryin theRasheed(e.g. region et2014);(c) al. and,central the years ofseagrass data andbiophysical models available;(b) there is published of evidence intertidal, shallowintertidal, subtidal and deep water seag extent of seagrass meadows with a spatial (geographic information system [GIS]) layer of biophysical model outputs of Grech We createdconnectivitymatrices for seagrassthe meadowsinGBRWHA central using the Seagrass dispersal and connectivity modelling developments(e.g. Townsville Abbot and (Figure 1). Point) turbid flood plumes, urban andindustrialdevelopment (e.g. Townsville) and port Category 5Yasi, CategoryHamish 4 and Category4 Debbie),highrainfall events causing GBRWHA has been exposed to a variety of disturbance events, including cyclones (e.g. This includesarea ~6,000 km connectivity among seagrass meadowsusing thecentralGBRWHA as an example (Figure1). crustacean, sea andturtles dugonget(Unsworth2014). al. We assessedpotential functional species arevital a component ofthe reef ecosystem andprovide food numerousfor fish, 2015) and arespatially dynamicand temporally (ephemeral). The15 GBRWHAseagrass availability and high diversity, disturbance, and productivity (Waycott et al. 2009; Coles et al. seagrassof tropical habitats oftheGBRWHA region by are nutrient low characterized 2 of seagrasshabitats andwas chosenbecause: (a) there are> 30 et al. (2016). Grech et al. ral beauty, ecological diversity, relativeand 2 (Figure 1) (Figure wasonand inscribedWorld the cluding its seagrass habitats. The km ~40,000 its seagrass cluding rass distribution, McKenzie derivedrass from et

(2016) delineatedthe spatial 2

Accepted Article (intertidal shallow and subtidal =857.5km water 1,417.3 – 2,335.0 km – 2,335.0 1,417.3 water This by is protectedarticle reserved. Allrights copyright. ephemeral small, structurally a Halodule, Cymodocea robust,more competitively dominant andpersistent tropical seagrass species (genera classes basedsimilar on(Figur life-history traits analysis, individual seagrassmeadowswere allocated into one oftwo generalised species totalThe area of seagrass includedintheGrech et(2016) al.analysis was 6,710.2km 31st 2013), variabilitycapturing in winds, tides and currents. ofmaximum 8weeks during thepeak ofthe (August flowering 1st 2012 period –January thesimulate function tosettlement gradual ofparticles;and (3) ran 68a simulations for by windspeed direction) and below suspendedand (2)asurface; thedecay first-order included:(1) simulatingdispersal the of propagules ‘virtual’ floating at the (affectedsurface fragment, or fruit spathe) to and known sitesfrom seagrass of presence. These parameters plant of propagules (i.e. settlementand ‘virtual’ to dispersal in SLIM the simulate patterns Ice-ocean Model (SLIM) (Lambrechts et al. 2008) GBRWHA usingthe depth-integrated finite Louvain-la-Neuve element Second-generation al. (2014)and Coles (Figure1). al.et (2009) Th shallow subtidalseagrass meadows ranged 0.41from –157.1km meadows (intertidal and subtidal = 97; deep = 3; SI 1). Table The size of intertidal and species.non-foundation to as and Zostera 2 (mean =1,950.9 km nd transient species of the genus )=67), (n referred to as foundation species; the and 2 ;deep =5,852.7km ey modelled the hydrodynamics of the central e 1) (Kilminster et e 1)(Kilminster the 2015):al.structurally 2 ; SITable1). To connectivityfacilitate . Biological. parameters were incorporated Halophila 2 ), comprised of100 discrete 2 (mean = 8.8 km = 8.8 (mean (n = 33), referred referred = 33), (n 2 ) anddeep 2

Accepted Article This by is protectedarticle reserved. Allrights copyright. meadow every seagrass propagules from meadow ‘virtual’ of by number weighted the nodes Edges indicate are connections. links) between functional edges (or and 2014), al. habitat graphs)as ofnodes aset are thatrepresent seagrass discrete meadows (McKenzie et sourcelocate in the critical GBRWHA. meadowscentralour study, to networks In (referred analysis dispersal pathways, to patterns spatial explore identify of connectivity, potential and seagrass andnetwork non-foundation We usedthe connectivity foundation and matrices Connectivity measures from everyseagrass meadow to populate weighted and directed connectivityma et al. (2016) within foundation and meadowsnon-foundation in GIS. The outputs were used We calculated the cumulative ‘virtual’ propagule settlement from the 68 simulations of Grech 2014). and abundance relativeto coastal seagrass(Coles etal.2007; Coles et al. 2009;Rasheed et al. intertidal shallow and subtidal meadows because water deep seagrasseshavelower biomass The number of particles released per unit area was smaller in deep water meadows than in locations were spread evenly across eachdeep water meadow at intervals of~10 kilometres. particles (representing ‘virtual’ prop seagrass intertidal shallow intervalsof andsubtidal~2 kilometres, meadowsand at annumber of equal Grech etal. (2016) utiliseda modelwhich spread particle the release locations evenly across j . i to every other meadow agules) were released per location. Release trices of trices the of‘virtual’propagules number j . i to every other to every other Accepted Article that number ahighcontributed ofpropagules tonetwork thetheir to relative size. meadow size on the habitat graphs by comparing out-flux with meadow area to identify nodes locations (see Grech etal. (2016) relative tosmaller meadowsbecause they had more particlerelease Large meadowsreleased numberhigher a of‘virtual’ propagules in thesimulations 68of structure. prominent community to identify a resolution with a modularity value> 0.4, indicating thatgraph the had a connectednodesto other the (Thomasin network al. Aet 2014). sensitivity wasanalysis used (orcommunities clusters)of nodes that are densely connectedtoeach other weaklyand 2012).used We modularity the (Blondel and etalgorithm Louvain al. 2008)detectto toretention identify relative theamount ofmeadow self-replenishmentHalpin and (Treml into account the full topologythehabitat of graph (Allesina Pascualand 2009); and local meadows (Magriset 2015);al. PageRank to identify important source meadows thattakes weighted2008; Treml andHalpinsource out-flux (or identify out-degree) to important 2012); stonesstepping connecting pa different various element (node) scale,we used: betweeness centrality to identify meadowsthat serve as communicability withinnetwork theUrban and (Minor 2008;Kool etAt2013). an al. of distributions (probability nodedegreesdistribution over network) entire the to assess scale, we used aof measures range todefine two the habitat (Table graphs 1)andnode degree quantify connectivityof the foundation non- and This by is protectedarticle reserved. Allrights copyright. 2 1 usedWe the GISsoftware ArcGIS10.2 https://gephi.org https://www.arcgis.com Seagrass dispersal and connectivity modelling

1 and the network analysis software Gephi 0.9.2 Gephi software analysis network the and rts of the habitat graph (Estrada and Bodin Bodin and (Estrada graph habitat of the rts foundation habitat graphs. At networka ).We assessedthe influence of 2 to Accepted Article This by is protectedarticle reserved. Allrights copyright. al.density 2016);networkconnectance; (or how measures close network the is tocomplete) number of connections required to traverse th non-foundationfoundation and graphs: habitat network diameter(or traversibility; maximum impact of all possible combinations of disturbance events (n = 63) on the connectivity of We used a Bray-Curtis dissimilarity matrix and networkthree measures theto assess relative the very destructive winds of , Hamish and Debbie were completely destroyed. Townsville andthe Burdekin River were completely destroyed; and allmeadows exposed to of the Burdekin River. We assumed that allmeadowsimmediately Abbotadjacent Point, to development; regional the ~172,000)(population Portof Townsville; and city and, flooding Severe Hamish; Severe Tropical ; the Port of Abbot Point tolocation sixreal-world events2;Figure(Table 1):Severe SI Tropical Cyclone Yasi; meadowsnon-foundation by sequentially remo We assessed the cumulativeeffectof multipleimpactson the connectivityfoundation and of Cumulative impact assessment different to the network ofthemetrics foundation habitat non-foundationgraphs.and rank tests to determine whetherthemetricsnetwork of the random graphssignificantly were nodes and edgesof the foundation andnon-foundation graphs. We usedWilcoxon’s signed- distributed between nodes. 1,000 random networks were created using the same number of between the realand randomgraphs that is the edges in the graph random randomlyare numberof nodesand edgesreal ofa graph togenerate random network.adifference The Rthe package (CsardiNepusz and igraph 2006).Erd The assess their network type. We genera We comparedfoundation the and non-foundation habitatgraphs with random networka to ted random networks using the Erd e network) Urban(Minor and Moore 2008; et vingsimilar(meadows) in nodes spatial ő s-Rényi model uses the samemodel usesthes-Rényi ő s-Rényi modelin Accepted Article This by is protectedarticle reserved. Allrights copyright. samenumberof nodes and edges. However, thehabitat foundationgraph was less connected average path length betweenthenon-foundation habitat graph theand ofthe network random There wassignificant no th in difference as theremany areredundant connections (MinorUrbanand 2007; 2008). small-worldorder (Table 1). networks, In connec nodes; SIFigure 4)andshort pathaverage leng clusteredhighly (Table1),hubs no nodes (i.e. with a high numberofrelative toother edges only onecomponent. Both graphs also exhibite (Figure 2, Table 1, 3, SIFigure SITable 1). Both graphswere denselyconnected featured and Table 1, SIFigure2, SITable1)non-foundation andthe graph 33nodes and 487 edges foundationThe species habitat graphwas of67nodes comprised and 1406(Figure edges 2, Results 2001).Keitt network be disconnectedto or into two components more when itis removed (Urban and between the focalmeadowand its furthest cut-node.A cut-node singlea is that node causes a removed until the habitat graph was disconnected. We then measured the Euclidian distance habitat graphs. non-foundation Thenearestnodeof every focalmeadow (n =100) was or more bycomponents sequentially removing(meadows) fromthe nodes foundation and We assessed thespatialscale ofdisturbancerequired to disconnectthe habitatinto graphs two of nodes inUrbanand network) the 2008). (Minor (Moore et 2016);andal. average coefficientclustering of (average the clustering coefficient e network diameter,e network number ofcomponents and d thed propertiessmall-world of a network: th and graphdiameterrelative to network tivity is resilient toloss israndomtivity the resilient ofnodes Accepted Article This by is protectedarticle reserved. Allrights copyright. region (SI Table 1). high supporting PageRanks values, their further re 52 and92 non-foundation and nodes 53and 99 in Cleveland Bay anddeep water hadalso nodes(foundation 6, 8 and21;Figures 3 and4,SI 1). High Table out-fluxnodes foundation nodes(foundation 35 and36,non-foundation node 38)and around Whitsunday the Islands nodenon-foundation 53), deep water (non-foundation nodes 98, 99 and 100), Abbot Point minus out-flux were retention locatedlocal in Cleveland Bay (foundationnodes 92,and 52 ofimportance meadows sourceaas ofpropagule removedWe retentionlocal out-flux nodefrom to measure a provide ofthe relative betoless likely replenished. disturbance asevents they are less connected retentionarebe morelikely to self-persistent (Burgessetal. 2014), but are vulnerablealso to Islands (foundation nodes 7 and3 9;Figures and SI Table1).4, Meadows highwith local foundation nodes 80 andRepulsein 84),Bay (foundation node 5)and the Whitsundayaround 94)foundation node and nodes Islandnear Hinchinbrook (foundation 82, 83, 8896, andnon- Other highmeadows90%) local (> of retention were located alsoUpstartin Bay (non- 1). Table SI 4, 3and Figures (97%; retention local of level high a in resulting Bay, Upstart propagules‘virtual’ The released remained from high out-fluxnode within primarily 40 (1).

nkingof impact bydisturbance events for the Accepted Article This by is protectedarticle reserved. Allrights copyright. Grech etal. (2016) remained within the study area modelling previousand deep waterof scalesto the centralGBRWHA (~500 km). Most of the ‘virtual’ propagules releasedby It is possible that seagrass metapopulations are spread along the GBRWHA coast at similar developments. newcoastal (EIA)for Assessments Impact Environmental event, but also at communitythe (Figure4) and metapopulation scalein, for example necessitates considering impacts on seagrass meadows at not only the site of a disturbance other locations within the metapopulation. Maintaining a functional ecosystem therefore seagrass meadowslocation atone potentiallyhave implications for seagrass meadows at implications has multiple For management. eventsfor that disturbance example, impact topropagulesnorth. the The existence ofseagrass a metapopulation in the GBRWHAcentral meadowsmost southern (Repulse Bay) aresouth-easttheir facing, limiting supplytocapacity northern meadow in the study area is weakly connected to other coastal meadows and the indicating the existence ofseagrassa metapopulation in theGBRWHA central connectedtofoundation the and non-foundation hab We found thatthemostnortherly andsoutherly nodesin ourstudyarea wereweakly the production of propagules to verify the conclusions of our study. in seagrassmeadows. We identify locations whereempiricalstudies couldused be to assess biological andstochastic processes thatmayinfluence propagule production andrecruitment ecological implicationsof connectivity, didthereforeand not into scale take account finer propertiesthe of networks. Thissmall-world study seagrass foundation and metapopulationsdensely arenon-foundation connected exhibitand to the north arehighlyand connectedto coastal meadows; andthe (3) central GBRWHA meadowsnon-foundation may as function multi-generationala stepping stonethe from south used a theoretical approach to assess the itatgraphs2 2,3), and SIFigures (Figure . The The most Accepted Article distances. Deep water meadows may also function as off-shore an refuge areanon- for stepping stones from the south to the north, enabling the sharing of genotypes over long 900 kilometres tothe north. Deep watermeadows may function as multi-generational Grech etal. (2016) recorded particles released thefrom deepmeadows water dispersing > meadows, andrelease propagulesthat have thecapacity todisperseover long distances. This by is protectedarticle reserved. Allrights copyright. centrality becausethey are large(totalarea > 5,800 km deepThewater non-foundation meadows high levelsexhibited of and out-flux betweeness to dispersal measure models settlement. and processes would the improve performance ofconnectivitystudies that rely biophysical on settlement post-settlementand (Jahnke processes etal. 2017).information Moreon these of potential functional connectivityand real dense habitat graphslong with connections. Thereasonsfor discrepancies betweenestimates suchmarine organisms, as corallarvae (Thom naturelived ofseagrasspropagules(viable plant spathes) fragments, fruit or relativeother to to over-predictionan of connectivity for meadows that are far apart.However, thelonger marine ecosystems (Burgessetal. 2014; Jahnke et Inour 2017). al. study, this could translate meadow. The potentialfor dispersal often is predicted to belargerthen realized dispersal in outcome of what will potentially be multiple recruitment events over the life of the existing However,analyses. based genetic diversityof estimates connectivity combined reflect the functionalconnectivity of seagrasses with realized connectivity, measured using genetic the coast (~2,300 kilometres). Our approach would also be improved by comparing potential the central GBRWHA would require extending the biophysical models to the entire length of distribution (Coles et Identifying al. 2009). seagrass metapopulations beyond the boundaryof meadow meadowsinhas discontinuitiesseagrass suggested presence the of north-south ized connectivity are post-dispersal, pre- as et al. 2015), supports our predictions of predictions our supports2015), as al. et 2 ), highly connected to coastal Accepted Article This by is protectedarticle reserved. Allrights copyright. also had ahigh in-degree, meaning they robustare toperturbationsas many nodesare persistence acrossspatialwide and (Saura scalestemporal et al. Thenine 2014). meadows metapopulation. Stepping stones facilitate long-distance dispersalandto speciescontribute central GBRWHA, and serve as stepping stones connecting various parts of the twoperform important functions: they provide important source an oftheinpropagules suggests that these nodes should be a high priority protectionfor and conservation as they out-flux high particularly betweenessand 2 3, SITable and 1).centrality (Figures This Cleveland Bay, Abbot Point, the Whitsundays, Hinchinbrook Island and deep water had a habitatthe graphs were equallyimportant ecol reflect an evolutionary advantage (Barabási and Albert 1999). However, not all meadows in robust to perturbations then other network architectures and, in biological systems, may 2007Urban (Minor and redundant connections networks and arethereforeresilient to therandom loss ofnodes becausetheremany are The foundation and non-foundation habitat graphs have been documented although they are likely to exist. mechanismfor connectivitydeep from water to coastalseagrass populationsof thesespecies effectlimited onno the of persistence In species. foundation evidenceaddition, or However, deepwateronlymeadows non-foundation produce species propagules andhave predictedincreased frequency andenergy most the of intense cyclones (Knutson et2010). al. likely to become more importantunder becauseclimate sea-levelchange of rise and the is meadows water deep of function refugia The refugia. seagrass a as value their enhancing resilient (higher recovery cap after major disturbance events. Deepwater meadows have previously been shown to bemore foundation seagrasses, assisting in recovery by providing propagules to coastal meadows acity) than acity)coastal meadowsseagrass (Rasheed al2014), et ogically, or for management. Nine meadows in and 2008). Small-world networks are more exhibited the properties of small-world small-world properties of the exhibited Accepted Article This by is protectedarticle reserved. Allrights copyright. inrestorationGBRWHA. the The authorities for seagrass is byour limited evidence-base Cyclone Yasi, McKenna etal.2015) haspreviously been canvassed bymanagement Large scalereplanting of seagrasses inresponse to disturbance events(e.g. Tropical Severe that could be removed before the network measures became unstable (Moilanen 2011). foundation habitat graph because its small network order (33) limited the number of nodes sameThe approach to assessing impactcumulative did notyield a clear resultthe for non- of(Figure 4b) relativelythatout-flux low occurred atthe edge ofthe network2).(SI Figure hadimpact minimal on connectivityit ascovered a single, highly connected community Upstart 1;SI (Figure Bay 2). Table Even thoughCyclone Yasi covered the greatest area, it loss is equalto the combinedof loss seagrass meadows in the Whitsundays, AbbotPoint, and Foundation(Figure 1). meadows in Cleveland Bay soimportanttoare connectivitytheir that neartheregional city (Cleveland PortofTownsvilleand Bay)Port and the ofAbbot Point graph. Connectivity was effectedby impacts occurringcentrein the ofthe metapopulation, locations ofdisturbance eventsresulted in differinglevels ofimpacton the habitat foundation cyclones in Australia's recent history, covered ~150 km of coastline. We also found that the context,the verydestructive winds of TropicalSevere Cyclone Yasi,the one largest of onwas average >245km,half around theof length the GBRWHA central metapopulation. In cumulative impact required to disconnect the habitat graphs into two or more components the central GBRWHA are robust to multiple disturbance events. We found that the scale of outputsThe ofour impactcumulative assessment evidencethatprovided further seagrasses in of propagules and toverify the actualof conservation value themeadows. nine 1). Empirical studies onseagrass ecologyn are propagulessupplythemavailableto theyaredisturbanceby if with affected aevent(SITable eeded to assess the production andrecruitment Accepted Article This by is protectedarticle reserved. Allrights copyright. etal. 2014;McKenna et 2015).al. occurred in sections ofGBRWHA the between 2010 and 2011would(Rasheed supportthis replenishment potential, andthe recent recovery large of scalelosses ofseagrasses that theseDespitelimitations, ourapproach points to adegree high ofconnectivity and production ofsexualpropagules and may fragments temporally vary and both spatially. theoreticalproduction ofpropagulesacross applied meadows ofsame the type.In reality,the connected(e.g. Upstart Bay,Figure1). There may be other theexceptions asmodel uses a naturally. tothis The wouldexceptions be meadowslocalhigh withbutretention notwell propagules, in theory, arereadily available and recruitment recoveryand couldoccur we found that plantingseagrasses maybe unnecessary in most meadows in thestudy area as environmental health ofthe recipient has site aswell Physically recruitment. restoring seagrassis plants unlikely to be successfuluntil the Management approaches to enhancerecoveryto include need environmental asconditions oflikelihood successful restoration(vanKatwiijk al.et 2016). quality.water Poor healthenvironmental metricspreviously have shown been reduce to the disturbance such conditionsenvironmental assediment mobility, topography, changed and seagrass meadows, in excess of two years(Preen etal.1995), is likelythe result ofpoor post- toabilitytheviable move seagrass to seeds new locations.of Observed slow recovery have dugong and turtles green because meadows seagrass of recovery natural the assist may etal. demonstrate (2017)further that theseagrass availabilityin of propagulesGBRWHA the eventsbecause meadows inthe central GBRWHA are densely connected. The of outputs Tol meadows forwill not most propagules and limit replenishment recovery disturbance after natural ofmodes recovery occur.Our to study informs some ofthoseSupply questions. of poor understanding ofand abilityprovide toadvice on alternative the waiting action: for recovered. Once the environment has recovered, recovered, has theenvironment Once recovered. Accepted Article This by is protectedarticle reserved. Allrights copyright. the network analysis. Bruxelles International. Many thanks to G. Cumming Cook (James University) for advice on SeaFoundation, World ResearchRescue and(SWR/6/15) IncFoundation and Wallonie- Australian Research CouncilCentre ofExcellence CoralReef Studies,for Ian the Potter Funding research this wasprovided for Macquarie by University, James Cook University, Acknowledgements be unsuccessful. knowledge, management actions, includingrestoration, coastal prove may unnecessary and informsfurtheroflikelihood the persistence population global under change. this Without population persistence.Quantifying impact the disturbanceof multiple events on connectivity connectivity in marine and coastal ecosystems biophysical models and network aanalysis offers powerful framework for understanding assess post-disturbance replenishment, recrui to the needto understand complexprocesses, such connectivity,as inorder to effectively Lossesof seagrass atscales > 245kilometresmay become 2015). frequent.Our study points significantly increasedre incoastal and tropical asde activities, coastalanthropogenic such (Hughes et al. 2017) and theincreasedintensityof tropical storms. The impactcumulative of Thetropics are facedwith a changingglobal including climate, warming sea temperatures velopment poor and watershed management, have tment and recovery. Combining the outputs of outputs the Combining recovery. and tment and identifying important sites that support gions in the previousgionsthe decade(Halpernet al. in Accepted Article This by is protectedarticle reserved. Allrights copyright. Barrier Reef.James CookUniversity. Grech, A. 2017. habitat graphs, andthe edge tablesnetwork for analysis. This dataset includes the connectivity matrices of the foundation and non-foundation species http://dx.doi.org/10.4225/28/59f7ae655a579 Cook University.Digital ObjectIdentifier (DOI):10.4225/28/59f7ae655a579 Grech,A. 2017. Connectivity seagrass of meadow Data 1. References node scale networkmetrics. Spatial (GIS) ofthe layer 100seagrass meadows. Attribute containstable oninformation the 10.4225/28/59fbbefa1337c http://dx.doi.org/10.4225/28/59fbbefa1337c 3. 2.

Indicators, dispersal tomodelling inform the development of marine reserve networks. Predicting reef (2016). from connectivityfish patterns biogeographic larval and Abesamis,R.A., Stockwell, B.L.,Villanoy, Bernardo, L.P.C., C.L., andRuss, G. 742-744. 742-744. Local replenishment of coral reef populations in a marine reserve. Planes, S.R., S.,M.L.,Thorrold, Jones,Berumen, G.P. and G.R., (2007). Almany, 10.1371/journal.pcbi.1000494 species’ importance for coextinctions? Allesina, S., and Pascual,Googling M.(2009). webs: food can anEigenvector measure

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Digital Object Identifier (DOI): Identifier Object Digital PLoS Computational Biology, s ins the centralGreat Barrier Reef. James

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Tracking apex marine predator movements in a dynamic ocean. ocean. dynamic a in movements predator marine apex Tracking Block, B.A.,Jonsen, I.D.,Jorgensen, S.J., Winship, S.A., Shaffer,S.J., Science Barabási, A., Albert, and (1999).R. of Emergence networks.inscaling random 10.1371/journal.pone.0068564 grouperduskythe for approach modelling connectivitybetweenLow Mediterranean marine protected areas: abiophysical (2013). J.,Thuiller, Manel, Albouy, W., Beuvier, S. M., and D., Mouillot, Andrello, C., 28 conservation and thedesign of marinereserve networks for coralreefs. Mills, M., Pressey, R.L., and Williamson, D.H. (2009). Connectivity, biodiversity G.R,Almany, Connolly, S.R., Heath, D.D., Hogan, J.D.,Jones,G.P., McCook, L.J., Current Biology Dispersalgrouper(2013). of larvae drives loca G.P. and Jones G.R., Russ, S.R., Thorrold, K.L., Rhodes, M.L., S., Berumen, Planes, Hamilton, G.R., R.J., Bode,Almany, M., Matawai, M., Potuku, Saenz-Agudelo, T., P., Experiment, large in communities networks. of V.D.,Guillaume,Blondel, R.,Fast Lambiotte, J.L., Lefebvre,(2008). and unfolding E. 257-270. 257-270. toimprovepersistence protected marine area design. Beyond (2014). connectivity: howempirical methods canquantify population Burgess, S.C.,Nickols, K.J., Griesemer, C.D., Barnett, L.A., Dedrick, A.G., (2): 339-351. 339-351. (2): , 286 (5439): 509-512. DOI: 10.1088/1742-5468/2008/10/P10008 , 23 (7), 626-630. Journal ofStatisticalMechanics: Theoryand Epinephelus marginatus. l resource sharinga in coral fishery. reef Ecological Applications, 24 Nature

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5 : Accepted Article This by is protectedarticle reserved. Allrights copyright. non-foundation Figure 1: Figure Legends Figure 2: the study area in the Great Barrier Reef World Heritage Area. study area onthe north-east coastof Australia(QLD =Queensland); and, the(c) location of Area Heritage World Reef Barrier Great of (b)the central thelocation Area; Heritage World meadows. local retention) areflux (minus sour important disturbance asevents they less are connected toother meadows. Meadows with a high out- high rate retention local are be tomore likely butself-persistent, vulnerable arealso to meadows.foundation (green) Each pointrepr on themselves or meadow) another minus local retention of (blue) foundation and non- propagulesreleasedand, fromof(b) out-flux meadowsthatsettle seagrass (number ‘virtual’ Figure 3: between nodesshown are with in grey, weaker connectionsfilteredout. connectedwith each otherthan with meadows inother communities. The connections (edges) partsvarious of different network. Meadows the intheare same morecommunity strongly maintaining toconnectivity other relative meadows by actingas stepping stones between community ( ofsizeThenode the itscentrality illustrates betweenness illustrates theand colour its central Great Barrier Reef World Heritage Area. (a)Local ratesof retention (blue)foundation non-foundationand (green) meadows; (a) Distribution of foundation ( of(a) Distribution foundation Habitat graphs see see

( Figure 4). Ahigh betweeness centrality indicates nodes thatcritical are for Halophila

of (a) foundation andnon-foundation seagrass (b) meadowsin the species)

seagrass meadows in the central Great Barrier Reef Halodule, Cymodocea esents one node(meadow). Meadowswith a ces of seagrass propagules relative to other

Each pointrepresentsone node (meadow). and Zostera species) and and species) Accepted Article Island Hinchinbrook (c) region; foundation community foundation community This by is protectedarticle reserved. Allrights copyright. community BayEdgecumbe region; and (e) foundation community community ClevelandBay and foundation community Barrier Reef Area:deep Heritage (a) non-foundationWorld water community Figure 4: Communities offoundation andseagrasses non-foundation in the centralGreat C B ofthe Whitsundayregion. andnon-foundation community E and non-foundation community and

D ofBowling Green Bay; (d) foundation G of the UpstartBay,Abbot Bay and F A non-foundation community and of Repulse Bay and foundation H of the Halifax Bay and and Bay Halifax the of J ; (b) I of of Connectance (network Number ofNumber connected Average path length Average clustering Average weighted Network diameter Network measure Average degree

Accepted 33 67 Articleof nodes. number Total Network order Network size communities communities (modularity) (modularity) node degree components This by is protectedarticle reserved. Allrights copyright. of Number Table 1: coefficient density)

Network measures ofthe and habitatfoundation non-foundation graphs. and overall traversability of the network. Indicatesnetwork). compactnessof the graph shortest paths betweenanytwo nodes in the networklongest the traverse (i.e. of the allthe ofstepsmaximum number The to required ‘virtual’ propagules‘virtual’ connecting nodes. Weighted edgesrepresent the numberof Mean ofthe weighted edgesper node. node. Indicates theaverage accessibility ofeach Mean ofthe numberof edgespernode. pairs of immediately connected nodes. of number Total Indicatesedges. numberof the graph. graph. awhich node’s neighbourhood is a complete Clustering coefficientindicatesdegreeto the Average of the clustering coefficient of nodes. communities) compared torandomnetwork. a divisionof networkthe (or intogroups ofbystrength the measuredof clusters Number edges and density equal to 1. 1. to equal density and edges Acomplete. complete hasallpossible graph Measures how close the networkis to any other node in the network. The average number of steps it takes to reach connected to each other. Components(components). aresets ofnodes Number of connected sub-graphs Description Description (Rayfield et2011) al. Foundation Foundation 8,238.8 31,750.5 31,750.5 8,238.8 species 1,406 487 42.0 29.5 29.5 42.0 0.48 0.57 0.57 0.48 0.32 0.46 0.46 0.32 2.57 1.71 1.71 2.57 8 3 6 4 1 foundation species species Non- (4 – 16 March 2009)

Acceptedof AbbotPort Point Article Port of Townsville Townsville Port of Disturbance event Regional city and and city Regional Cyclone Hamish Cyclone Debbie

This by is protectedarticle reserved. Allrights copyright. Severe Tropical Severe Tropical Severe Tropical foundation habitat graphs. ofcombinations disturbance =events (n63) the on connectivity of the foundationnon- and foundation andnon-foundation graphs habitat to assesstherelative impact ofallpossible representingNodes, meadows( to six disturbance events (Figure S1) in the central Great Barrier Reef World Heritage Area. Table 2: Burdekin River (26 –28 March (2 –4 February Cyclone Yasi Flooding of 2017) 2011)

Foundation andnon-foundation meadows (nodes)that overlapped orwere adjacent Disturbance type sediment load development development development development Increased Climatic Climatic lmtc epwtr 98, 99, 100 - Deep water Climatic Coastal Coastal Coastal Coastal see see Figure 4 for ID), were sequentially removed from the the from removed sequentially were ID), 4 for Figure Island –Mission Abbot Point and Region effected by disturbance Cleveland Bay 52, 54, 92 51, 53, 51, 93 54, 52,92 Cleveland Bay Hinchinbrook Hinchinbrook Repulse Bay Whitsunday Upstart Bay 40, 41 42, 43, 42, 94 41 40, BayUpstart Islands and Abbot Bay Beach South 26, 27, 29, 30, 20, 21, 23, 24, 16, 17, 18, 19, 12, 13, 14, 15, 7, 8, 9, 10, 11, 6, 5, 4, 3, 2, 1, 78, 81, 82, 83, 72, 73, 74, 75, 85, 86, 87, 88, Foundation Foundation 6 5 38 9536, nodes 31 96 Non-foundation 84, 89, 90, 91, 76, 77, 79, 80, 22, 25,22, 28 nodes 97 Accepted Article This by is protectedarticle reserved. Allrights copyright.

Accepted Article This by is protectedarticle reserved. Allrights copyright.

Accepted Article This by is protectedarticle reserved. Allrights copyright.

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