File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A novel method for mapping reefs and subtidal rocky habitats using artificial neural networks

TitleA novel method for mapping reefs and subtidal rocky habitats using artificial neural networks
Authors
KeywordsReefs
Subtidal rocky habitat
Bathymetry
Artificial neural networks
Issue Date2011
Citation
Ecological Modelling, 2011, v. 222, n. 15, p. 2606-2614 How to Cite?
AbstractReefs and subtidal rocky habitats are sites of high biodiversity and productivity which harbour commercially important species of fish and invertebrates. Although the conservation management of reef associated species has been informed using species distribution models (SDM) and community based approaches, to date their use has been constrained to specific regions where the locality and spatial extent of reefs is well known. Much of the world's subtidal habitats remain either undiscovered or unmapped, including coasts of intense human use. Consequently, to facilitate a stronger understanding of species-environmental relationships there is an urgent need for a cost and time effective standard method to map reefs at fine spatial resolutions across broad geographical extents. We used bathymetric data (∼250. m resolution) to calculate the local slope and curvature of the seabed. We then constructed artificial neural networks (ANNs) to forecast the probability of reef occurrence within grid cells as a function of bathymetric and slope variables. Testing over an independent data set not used in training showed that ANNs were able to accurately predict the location of reefs for 86% of all grid cells (Kappa = 0.63) without over fitting. The ANN with greatest support, combining bathymetric values of the target grid cell with the slope of adjacent grid cells, was used to map inshore reef locations around the Southern Australian coastline (∼250. m resolution). Broadly, our results show that reefs are identifiable from coarse-scale bathymetry data of the seabed. We anticipate that our research technique will strengthen systematic conservation planning tools in many regions of the world, by enabling the identification of rocky substratum and mapping in localities that remain poorly surveyed due to logistics or monetary constraints. © 2011 Elsevier B.V.
Persistent Identifierhttp://hdl.handle.net/10722/213186
ISSN
2015 Impact Factor: 2.275
2015 SCImago Journal Rankings: 1.098

 

DC FieldValueLanguage
dc.contributor.authorWatts, Michael J.-
dc.contributor.authorLi, Yuxiao-
dc.contributor.authorRussell, Bayden D.-
dc.contributor.authorMellin, Camille-
dc.contributor.authorConnell, Sean D.-
dc.contributor.authorFordham, Damien A.-
dc.date.accessioned2015-07-28T04:06:27Z-
dc.date.available2015-07-28T04:06:27Z-
dc.date.issued2011-
dc.identifier.citationEcological Modelling, 2011, v. 222, n. 15, p. 2606-2614-
dc.identifier.issn0304-3800-
dc.identifier.urihttp://hdl.handle.net/10722/213186-
dc.description.abstractReefs and subtidal rocky habitats are sites of high biodiversity and productivity which harbour commercially important species of fish and invertebrates. Although the conservation management of reef associated species has been informed using species distribution models (SDM) and community based approaches, to date their use has been constrained to specific regions where the locality and spatial extent of reefs is well known. Much of the world's subtidal habitats remain either undiscovered or unmapped, including coasts of intense human use. Consequently, to facilitate a stronger understanding of species-environmental relationships there is an urgent need for a cost and time effective standard method to map reefs at fine spatial resolutions across broad geographical extents. We used bathymetric data (∼250. m resolution) to calculate the local slope and curvature of the seabed. We then constructed artificial neural networks (ANNs) to forecast the probability of reef occurrence within grid cells as a function of bathymetric and slope variables. Testing over an independent data set not used in training showed that ANNs were able to accurately predict the location of reefs for 86% of all grid cells (Kappa = 0.63) without over fitting. The ANN with greatest support, combining bathymetric values of the target grid cell with the slope of adjacent grid cells, was used to map inshore reef locations around the Southern Australian coastline (∼250. m resolution). Broadly, our results show that reefs are identifiable from coarse-scale bathymetry data of the seabed. We anticipate that our research technique will strengthen systematic conservation planning tools in many regions of the world, by enabling the identification of rocky substratum and mapping in localities that remain poorly surveyed due to logistics or monetary constraints. © 2011 Elsevier B.V.-
dc.languageeng-
dc.relation.ispartofEcological Modelling-
dc.subjectReefs-
dc.subjectSubtidal rocky habitat-
dc.subjectBathymetry-
dc.subjectArtificial neural networks-
dc.titleA novel method for mapping reefs and subtidal rocky habitats using artificial neural networks-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.ecolmodel.2011.04.024-
dc.identifier.scopuseid_2-s2.0-79959638731-
dc.identifier.volume222-
dc.identifier.issue15-
dc.identifier.spage2606-
dc.identifier.epage2614-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats