Data

Coastal seagrass habitat suitability model (wet and dry season) in the Great Barrier Reef World Heritage Area (MTSRF, JCU)

eAtlas
Grech, Alana, Dr ; Coles, Rob, Dr
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://eatlas.org.au/data/uuid/284c3108-accc-4739-a4b1-4ec13c3cc0c6&rft.title=Coastal seagrass habitat suitability model (wet and dry season) in the Great Barrier Reef World Heritage Area (MTSRF, JCU)&rft.identifier=https://eatlas.org.au/data/uuid/284c3108-accc-4739-a4b1-4ec13c3cc0c6&rft.description=This dataset is consists of modelled habitat suitability of coastal seagrass distribution in the wet and dry seasons along the Great Barrier Reef World Heritage Area coastline. A Bayesian belief network was used to quantify the relationship (dependencies) between seagrass and eight environmental drivers: relative wave exposure, bathymetry, spatial extent of flood plumes, season, substrate, region, tidal range and sea surface temperature. We found that at the scale of the entire GBRWHA, the main drivers of inshore seagrass presence are tidal range and relative exposure. The outputs of our analysis included a probabilistic GIS-surface of inshore seagrass presence and distribution for both the wet and dry seasons, and across four regions at the scale of 2km*2km planning units. The model can be used by managers in the GBRWHA to delineate seagrass ecological units, and assist them in marine planning at broad spatial scales. For more information about methods see: Grech, A. and Coles, R.J. 2010, An ecosystem-scale predictive model of coastal seagrass distribution, Aquatic Conservation: Marine and Freshwater Ecosystems 20: 437-444 Data Location: This dataset is filed in the eAtlas enduring data repository at: data\MTSRF\QLD_MTSRF-1-1-3_JCU_Grech-A_Seagrass-coastal-model-2007Statement: This dataset was developed as part of a Alana Grech's PhD: Spatial models and risk assessments to inform marine planning at ecosystem-scales: seagrasses and dugongs as a case study, James Cook University, 2009.&rft.creator=Grech, Alana, Dr &rft.creator=Coles, Rob, Dr &rft.date=2009&rft.coverage=142.5102631698574,-10.694073290783322 142.4745712547699,-10.961762653939218 142.6351848726634,-10.96771130645378 142.7898498380424,-11.12832492434734 142.7720038804987,-11.271092584697158 142.8195931006153,-11.52093599030934 142.8404133844165,-11.877855141183886 142.9980526760526,-11.949238971358795 143.1348716838878,-11.931393013815068 143.1646149464607,-12.032520106562856 143.0515905486838,-12.157441809368947 143.0634878537129,-12.347798689835372 143.2122041665773,-12.371593299893675 143.2776393442376,-12.556001527845524 143.4323043096166,-12.627385358020433 143.3371258693834,-12.79989628094313 143.3490231744125,-12.918869331234646 143.4798935297332,-12.865331458603464 143.4858421822478,-13.103277559186495 143.5155854448207,-13.353120964798677 143.5869692749956,-13.412607489944435 143.5453287073935,-13.638656285498314 143.5215340973352,-13.817115860935587 143.6345584951122,-13.99557543637286 143.7059423252871,-14.179983664324709 143.7654288504328,-14.411981112393164 143.9022478582681,-14.495262247597225 144.0807074337054,-14.483364942568073 144.1937318314823,-14.358443239761996 144.2651156616572,-14.310854019645376 144.4435752370945,-14.287059409587073 144.5684969399006,-14.293008062101634 144.5982402024735,-14.495262247597225 144.7766997779107,-14.632081255432453 145.0086972259792,-14.780797568296862 145.2942325466788,-14.983051753792438 145.2050027589602,-15.14961402420056 145.2912582204217,-15.280484379521226 145.2258230427614,-15.464892607473075 145.2763865891351,-15.571968352735439 145.3418217667954,-15.762325233201864 145.3507447455675,-15.905092893551682 145.4578204908298,-16.08950112150353 145.4161799232278,-16.19062821425132 145.3923853131695,-16.42262566231976 145.4875637534027,-16.583239280213334 145.749304464044,-16.910415168514987 145.8742261668501,-16.922312473544125 145.8980207769084,-17.04723417635023 145.9634559545688,-17.267334319389533 146.0645830473166,-17.46958850488511 146.1359668774915,-17.659945385351534 146.0824290048603,-17.78486708815761 146.0645830473166,-18.01686453622608 146.0110451746854,-18.153683544061323 146.0764803523457,-18.39162964464434 146.3263237579579,-18.570089220081627 146.2430426227538,-18.819932625693795 146.3203751054433,-18.998392201131082 146.6713456038033,-19.224440996684947 146.9033430518717,-19.325568089432736 147.0223161021632,-19.260132911772416 147.1174945423965,-19.444541139724265 147.4595420619844,-19.444541139724265 147.548771849703,-19.65274397773443 147.5368745446739,-19.730076460423902 147.7629233402278,-19.849049510715403 148.0127667458399,-19.950176603463206 148.2745074564813,-20.122687526385903 148.4351210743748,-20.176225399017085 148.5421968196372,-20.116738873871327 148.6373752598704,-20.2951984493086 148.8098861827931,-20.348736321939782 148.8336807928514,-20.443914762172994 148.6611698699287,-20.432017457143843 148.6492725648996,-20.586682422522813 148.7979888777639,-20.80083391304754 149.0061917157741,-20.949550225911935 149.1876256174688,-21.0804205812326 149.1400363973522,-21.163701716436663 149.2114202275271,-21.294572071757315 149.282804057702,-21.312418029301057 149.2471121426146,-21.395699164505118 149.3184959727895,-21.550364129884073 149.4553149806247,-21.586056044971542 149.4077257605081,-21.752618315379664 149.4612636331393,-21.91323193327321 149.5445447683434,-22.198767253972846 149.5326474633142,-22.323688956778938 149.6099799460037,-22.395072786953833 149.6159285985183,-22.561635057361954 149.830080089043,-22.466456617128756 150.0204369695094,-22.69845406519721 150.0858721471698,-22.662762150109742 149.9371558343054,-22.37722682941012 149.9490531393345,-22.246356474089453 150.0501802320823,-22.14522938134165 150.1691532823738,-22.353432219351816 150.3833047728986,-22.478353922157893 150.6331481785107,-22.680608107653484 150.656942788569,-22.549737752332817 150.79971044891886,-22.692505412682635 150.7402239237731,-22.983989385896848 150.71642931371477,-23.144603003790394 150.79376179640434,-23.26357605408191 150.7521212288023,-23.34685718928597 150.6956090299136,-23.513419459694077 150.8621713003217,-23.638341162500183 151.0168362657007,-23.632392509985607 151.082271443361,-23.703776340160516 151.3023715864003,-23.971465703316426 151.5760096020708,-24.07854144857879 151.671188042304,-24.007157618403895 151.7663664825373,-24.10233605863708 151.8793908803142,-24.209411803899457 151.9924152780911,-24.45925520951164 152.0102612356349,-24.57227960728858 152.1411315909555,-24.494947124599094 152.0162098881494,-24.31648754916182 151.9150827954016,-24.11423336366623 151.825853007683,-24.066644143549638 151.7782637875664,-23.94767109325811 151.52247172943962,-23.917927830685244 151.3083202389149,-23.62644385747103 151.28452562885658,-23.45988158706291 151.05847683330273,-23.239781444023606 150.94842676178334,-22.989938038411424 150.8562226478072,-22.87096498811991 150.8621713003217,-22.644916192566043 150.7550955550594,-22.395072786953833 150.6242251997387,-22.27609973666233 150.5052521494472,-22.186869948943695 150.3029979639516,-22.169023991399968 150.1661789561164,-22.157126686370816 150.2316141337767,-21.99651306847727 150.1245383885143,-21.87159136567118 149.8687463303876,-21.91323193327321 149.7497732800961,-22.02625633105015 149.6843381024357,-21.937026543331513 149.62485157729,-21.80020753549627 149.7021840599795,-21.72287505280677 149.6962354074649,-21.627696612573573 149.5891596622025,-21.65743987514645 149.5207501582851,-21.49087760473833 149.5148015057705,-21.360007249417677 149.4136744130227,-21.270777461699026 149.3898798029644,-21.318366681815633 149.2887527102166,-21.199393631524117 149.3125473202749,-20.967396183455662 149.3065986677603,-20.812731218076692 149.2768554051875,-20.72945008287263 149.1519337023814,-20.604528380066526 149.1340877448376,-20.426068804629267 149.1221904398085,-20.265455186735707 148.9437308643712,-20.009663128608963 148.8604497291672,-20.009663128608963 148.8545010766526,-20.200020009075388 148.7414766788756,-20.200020009075388 148.610606323555,-20.098892916327586 148.4678386632052,-19.938279298434054 148.3131736978262,-20.003714476094387 148.2358412151367,-19.860946815744555 148.0395356821557,-19.82525490065713 147.8729734117476,-19.795511638084236 147.8789220642622,-19.694384545336447 147.670719226252,-19.676538587792706 147.5339002184168,-19.414797877151386 147.5279515659022,-19.319619436918188 147.3941068843241,-19.248235606743265 147.352466316722,-19.289876174345295 147.1502121312265,-19.218492344170386 147.0431363859641,-19.188749081597493 146.8408822004685,-19.040032768733127 146.6564739725167,-19.040032768733127 146.6505253200021,-18.980546243587355 146.7040631926333,-18.724754185460597 146.4958603546231,-18.534397304994172 146.3828359568462,-18.498705389906718 146.2995548216421,-18.141786239032157 146.2281709914672,-18.094197018915565 146.1865304238652,-17.880045528390838 146.1686844663215,-17.564766945118322 146.0497114160299,-17.261385666874972 145.9545329757967,-16.969901693660745 145.9545329757967,-16.844979990854654 145.585716519893,-16.601085237757047 145.4935124059173,-16.375036442203168 145.511358363461,-16.20252551928047 145.4875637534027,-16.00027133378488 145.4102312707132,-15.803965800803908 145.3864366606549,-15.661198140454061 145.3388474405383,-15.488687217531364 145.3328987880237,-15.34591955718156 145.3745393556258,-15.244792464433772 145.3507447455675,-15.054435583967333 145.3804880081403,-14.947359838704983 145.2912582204217,-14.822438135898892 145.2258230427614,-14.786746220811438 145.2436690003051,-14.61423529788874 145.0354661622949,-14.542851467713831 144.9521850270909,-14.453621679995194 144.7023416214787,-14.411981112393164 144.6309577913038,-14.227572884441315 144.5149590672694,-14.096702529120648 144.3067562292592,-14.090753876606072 144.1461426113657,-14.162137706780982 144.1342453063365,-14.239470189470467 143.9438884258701,-14.26921345204336 143.9498370783847,-14.15024040175183 143.8546586381515,-14.185932316839299 143.7892234604911,-13.977729478829133 143.7951721130057,-13.876602386081345 143.7178396303162,-13.858756428537617 143.6643017576851,-13.721937420702375 143.6643017576851,-13.567272455323405 143.6524044526559,-13.329326354740374 143.6167125375685,-13.228199261992586 143.581020622481,-13.210353304448859 143.5631746649373,-12.811793585972282 143.4560989196749,-12.669025925622464 143.4739448772186,-12.60359074796213 143.3668691319563,-12.484617697670629 143.3014339542959,-12.306158122233342 143.1586662939461,-12.21097968200013 143.1824609040044,-12.092006631708614 143.3192799118397,-11.967084928902523 143.2122041665773,-11.889752446213052 143.0991797688004,-11.848111878611007 143.0218472861109,-11.812419963523539 142.9564121084505,-11.610165778027977 142.9326174983922,-11.509038685280188 142.8909769307902,-11.485244075221885 142.8909769307902,-11.199708754522263 142.8255417531299,-11.033146484114141 142.7303633128967,-10.872532866220581 142.6351848726634,-10.67622733323958 142.5102631698574,-10.694073290783322&rft_rights=Creative Commons Attribution 3.0 Australia License http://creativecommons.org/licenses/by/3.0/au/&rft_subject=biota&rft_subject=marine&rft.type=dataset&rft.language=English Access the data

Licence & Rights:

Open Licence view details
CC-BY

Creative Commons Attribution 3.0 Australia License
http://creativecommons.org/licenses/by/3.0/au/

Access:

Other

Full description

This dataset is consists of modelled habitat suitability of coastal seagrass distribution in the wet and dry seasons along the Great Barrier Reef World Heritage Area coastline. A Bayesian belief network was used to quantify the relationship (dependencies) between seagrass and eight environmental drivers: relative wave exposure, bathymetry, spatial extent of flood plumes, season, substrate, region, tidal range and sea surface temperature. We found that at the scale of the entire GBRWHA, the main drivers of inshore seagrass presence are tidal range and relative exposure. The outputs of our analysis included a probabilistic GIS-surface of inshore seagrass presence and distribution for both the wet and dry seasons, and across four regions at the scale of 2km*2km planning units. The model can be used by managers in the GBRWHA to delineate seagrass ecological units, and assist them in marine planning at broad spatial scales. For more information about methods see: Grech, A. and Coles, R.J. 2010, An ecosystem-scale predictive model of coastal seagrass distribution, Aquatic Conservation: Marine and Freshwater Ecosystems 20: 437-444 Data Location: This dataset is filed in the eAtlas enduring data repository at: data\MTSRF\QLD_MTSRF-1-1-3_JCU_Grech-A_Seagrass-coastal-model-2007

Lineage

Statement: This dataset was developed as part of a Alana Grech's PhD: "Spatial models and risk assessments to inform marine planning at ecosystem-scales: seagrasses and dugongs as a case study", James Cook University, 2009.

Notes

Purpose
Ecosystem-scale networks of marine protected areas (MPA) are an important planning tool, but the information used to delineate ecological units is difficult to quantify at broad spatial scales because of the cost associated with collecting information at that scale. The Great Barrier Reef World Heritage Area (GBRWHA) is the world’s largest World Heritage area (approximately 348,000 km2) and second largest MPA. To inform the management of inshore (<15 m) seagrass communities at the scale of the entire GBRWHA, we determined their presence and distribution at a regional and sub- regional scale by generating a GIS-based habitat model.

Issued: 11 2009

This dataset is part of a larger collection

Click to explore relationships graph

142.51026,-10.69407 142.47457,-10.96176 142.63518,-10.96771 142.78985,-11.12832 142.772,-11.27109 142.81959,-11.52094 142.84041,-11.87786 142.99805,-11.94924 143.13487,-11.93139 143.16461,-12.03252 143.05159,-12.15744 143.06349,-12.3478 143.2122,-12.37159 143.27764,-12.556 143.4323,-12.62739 143.33713,-12.7999 143.34902,-12.91887 143.47989,-12.86533 143.48584,-13.10328 143.51559,-13.35312 143.58697,-13.41261 143.54533,-13.63866 143.52153,-13.81712 143.63456,-13.99558 143.70594,-14.17998 143.76543,-14.41198 143.90225,-14.49526 144.08071,-14.48336 144.19373,-14.35844 144.26512,-14.31085 144.44358,-14.28706 144.5685,-14.29301 144.59824,-14.49526 144.7767,-14.63208 145.0087,-14.7808 145.29423,-14.98305 145.205,-15.14961 145.29126,-15.28048 145.22582,-15.46489 145.27639,-15.57197 145.34182,-15.76233 145.35074,-15.90509 145.45782,-16.0895 145.41618,-16.19063 145.39239,-16.42263 145.48756,-16.58324 145.7493,-16.91042 145.87423,-16.92231 145.89802,-17.04723 145.96346,-17.26733 146.06458,-17.46959 146.13597,-17.65995 146.08243,-17.78487 146.06458,-18.01686 146.01105,-18.15368 146.07648,-18.39163 146.32632,-18.57009 146.24304,-18.81993 146.32038,-18.99839 146.67135,-19.22444 146.90334,-19.32557 147.02232,-19.26013 147.11749,-19.44454 147.45954,-19.44454 147.54877,-19.65274 147.53687,-19.73008 147.76292,-19.84905 148.01277,-19.95018 148.27451,-20.12269 148.43512,-20.17623 148.5422,-20.11674 148.63738,-20.2952 148.80989,-20.34874 148.83368,-20.44391 148.66117,-20.43202 148.64927,-20.58668 148.79799,-20.80083 149.00619,-20.94955 149.18763,-21.08042 149.14004,-21.1637 149.21142,-21.29457 149.2828,-21.31242 149.24711,-21.3957 149.3185,-21.55036 149.45531,-21.58606 149.40773,-21.75262 149.46126,-21.91323 149.54454,-22.19877 149.53265,-22.32369 149.60998,-22.39507 149.61593,-22.56164 149.83008,-22.46646 150.02044,-22.69845 150.08587,-22.66276 149.93716,-22.37723 149.94905,-22.24636 150.05018,-22.14523 150.16915,-22.35343 150.3833,-22.47835 150.63315,-22.68061 150.65694,-22.54974 150.79971,-22.69251 150.74022,-22.98399 150.71643,-23.1446 150.79376,-23.26358 150.75212,-23.34686 150.69561,-23.51342 150.86217,-23.63834 151.01684,-23.63239 151.08227,-23.70378 151.30237,-23.97147 151.57601,-24.07854 151.67119,-24.00716 151.76637,-24.10234 151.87939,-24.20941 151.99242,-24.45926 152.01026,-24.57228 152.14113,-24.49495 152.01621,-24.31649 151.91508,-24.11423 151.82585,-24.06664 151.77826,-23.94767 151.52247,-23.91793 151.30832,-23.62644 151.28453,-23.45988 151.05848,-23.23978 150.94843,-22.98994 150.85622,-22.87096 150.86217,-22.64492 150.7551,-22.39507 150.62423,-22.2761 150.50525,-22.18687 150.303,-22.16902 150.16618,-22.15713 150.23161,-21.99651 150.12454,-21.87159 149.86875,-21.91323 149.74977,-22.02626 149.68434,-21.93703 149.62485,-21.80021 149.70218,-21.72288 149.69624,-21.6277 149.58916,-21.65744 149.52075,-21.49088 149.5148,-21.36001 149.41367,-21.27078 149.38988,-21.31837 149.28875,-21.19939 149.31255,-20.9674 149.3066,-20.81273 149.27686,-20.72945 149.15193,-20.60453 149.13409,-20.42607 149.12219,-20.26546 148.94373,-20.00966 148.86045,-20.00966 148.8545,-20.20002 148.74148,-20.20002 148.61061,-20.09889 148.46784,-19.93828 148.31317,-20.00371 148.23584,-19.86095 148.03954,-19.82525 147.87297,-19.79551 147.87892,-19.69438 147.67072,-19.67654 147.5339,-19.4148 147.52795,-19.31962 147.39411,-19.24824 147.35247,-19.28988 147.15021,-19.21849 147.04314,-19.18875 146.84088,-19.04003 146.65647,-19.04003 146.65053,-18.98055 146.70406,-18.72475 146.49586,-18.5344 146.38284,-18.49871 146.29955,-18.14179 146.22817,-18.0942 146.18653,-17.88005 146.16868,-17.56477 146.04971,-17.26139 145.95453,-16.9699 145.95453,-16.84498 145.58572,-16.60109 145.49351,-16.37504 145.51136,-16.20253 145.48756,-16.00027 145.41023,-15.80397 145.38644,-15.6612 145.33885,-15.48869 145.3329,-15.34592 145.37454,-15.24479 145.35074,-15.05444 145.38049,-14.94736 145.29126,-14.82244 145.22582,-14.78675 145.24367,-14.61424 145.03547,-14.54285 144.95219,-14.45362 144.70234,-14.41198 144.63096,-14.22757 144.51496,-14.0967 144.30676,-14.09075 144.14614,-14.16214 144.13425,-14.23947 143.94389,-14.26921 143.94984,-14.15024 143.85466,-14.18593 143.78922,-13.97773 143.79517,-13.8766 143.71784,-13.85876 143.6643,-13.72194 143.6643,-13.56727 143.6524,-13.32933 143.61671,-13.2282 143.58102,-13.21035 143.56317,-12.81179 143.4561,-12.66903 143.47394,-12.60359 143.36687,-12.48462 143.30143,-12.30616 143.15867,-12.21098 143.18246,-12.09201 143.31928,-11.96708 143.2122,-11.88975 143.09918,-11.84811 143.02185,-11.81242 142.95641,-11.61017 142.93262,-11.50904 142.89098,-11.48524 142.89098,-11.19971 142.82554,-11.03315 142.73036,-10.87253 142.63518,-10.67623 142.51026,-10.69407

147.30785142286,-17.624253470264

Subjects

User Contributed Tags    

Login to tag this record with meaningful keywords to make it easier to discover

Other Information
Original data in ArcInfo Binary Grid (from Tropical Data Hub). Note: This version has no projection information and an excess extent. (270 KB)

url : http://tropicaldatahub.org/data/b660da0d-5075-472f-97eb-ba75e6914880

GeoTiff conversion by eAtlas - fix of the GIS problems. (46 KB)

url : https://nextcloud.eatlas.org.au/apps/sharealias/a/gbr_jcu_seagrass-coastal-model-2007-zip

ea:GBR_JCU_Seagrass-coastal-model-2007_Dry-season

url : https://maps.eatlas.org.au/maps/wms

ea:GBR_JCU_Seagrass-coastal-model-2007_Wet-season

url : https://maps.eatlas.org.au/maps/wms

Grech, Alana (2009) Spatial models and risk assessments to inform marine planning at ecosystem-scales: seagrasses and dugongs as a case study. PhD thesis, James Cook University.

url : http://eprints.jcu.edu.au/8195/

Grech, A. and Coles, R.J. 2010, An ecosystem-scale predictive model of coastal seagrass distribution, Aquatic Conservation: Marine and Freshwater Ecosystems 20: 437-444

doi : http://dx.doi.org/10.1002/aqc.1107

eAtlas Web Mapping Service (WMS) (AIMS)

url : https://eatlas.org.au/data/uuid/71127e4d-9f14-4c57-9845-1dce0b541d8d

Identifiers
  • global : 284c3108-accc-4739-a4b1-4ec13c3cc0c6
ACN 633 798 857