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Fine-scale benthic biodiversity patterns inferred from image processing

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dc.contributor Australian Institute Of Marine Science
dc.contributor Earth & Environm Sci Unit
dc.contributor Sch Biol Sci
dc.contributor University Of British Columbia
dc.contributor South Australian Res & Dev Inst Aquat Sci
dc.contributor University Of Adelaide
dc.contributor Australian Inst Marine Sci
dc.contributor Univ Adelaide
dc.contributor Univ British Columbia
dc.contributor Inst Environm
dc.contributor Biol Unit
dc.contributor.author BRADSHAW, COREY J. A.
dc.contributor.author TANNER, JASON E.
dc.contributor.author MELLIN, CAMILLE
dc.contributor.author PARROTT, LAEL
dc.date.accessioned 2017-03-21T01:06:23Z
dc.date.accessioned 2016-07-05T04:47:07Z
dc.date.accessioned 2016-07-05T04:47:07Z
dc.date.accessioned 2019-07-08T02:31:21Z
dc.date.available 2017-03-21T01:06:23Z
dc.date.available 2017-03-21T01:06:23Z
dc.date.available 2016-07-05T04:47:07Z
dc.date.available 2019-07-08T02:31:21Z
dc.date.issued 2015-06-01
dc.identifier.citation Tanner JE, Mellin C, Parrott L, Bradshaw CJA (2015) Fine-scale benthic biodiversity patterns inferred from image processing. Ecological Complexity 22: 76-85 en_US
dc.identifier.issn 1476-945X
dc.identifier.uri http://epubs.aims.gov.au/11068/12808
dc.description.abstract Despite potentially considerable advantages over traditional sampling techniques, image-derived indices of habitat complexity have rarely been used to predict patterns in marine biodiversity. Advantages include increased speed and coverage of sampling, avoidance of destructive sampling, and substantially reduced processing time compared to traditional taxonomic approaches, thus providing a starting point for more detailed analysis if warranted. In this study, we test the idea that the mean information gain (MIG) and mean mutual information (MMI), two indices of image heterogeneity that we derived from photographs of marine benthic assemblages, represent good preliminary predictors of biodiversity patterns for 133 benthic invertebrate and algal taxa on jetty pylons in Gulf St Vincent, South Australia. Both MIG and MMI were spatially structured, with evidence of among-site differences that were also evident in the benthic data. When combined with information on the spatial structure within the dataset (site and depth), MIG and MMI explained similar to 35% of deviance in invertebrate species richness, similar to 43% in Shannon's evenness and up to 50% of dissimilarity in species composition. This explanatory power is of a similar magnitude to many other, less readily available, surrogate measures of biodiversity. These results corroborate the idea that indices of image heterogeneity can provide useful and cost-effective complements to traditional methods used for describing (or predicting) marine epibiota biodiversity patterns. This approach can be applied to many case studies for which photographic data are available, and has the potential to result in substantial time and cost savings. (C) 2015 Elsevier B.V. All rights reserved.
dc.description.sponsorship Funded by the South Australia Premier's Science and Research Fund as part of the TRansects for ENvironmental monitoring and Decision making (TREND) network (www.trendsa.org.au). TREND is a collaboration between the University of Adelaide, Primary Industries and Regions South Australia and the South Australian Department of Environment, Water and Natural Resources. The funders played no role in study design, analysis and interpretation of the data, writing, or the decision to publish. We thank A. Dobrovolskis for assistance in the field, and S. Sorokin and K. Wiltshire for photo identifications. CM was supported by the Marine Biodiversity Hub through the Australian Government's National Environmental Research Program (NERP).
dc.description.uri http://www.sciencedirect.com/science/article/pii/S1476945X1500032X en_US
dc.language English
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.relation.ispartof Null
dc.subject Mean Mutual Information en_US
dc.subject Richness en_US
dc.subject Spectral Signal en_US
dc.subject Mean Information Gain en_US
dc.subject Evenness en_US
dc.subject Ecological Indicator en_US
dc.subject Species Richness
dc.subject Ecology
dc.subject Community Structure
dc.subject Diversity
dc.subject Coral-reef
dc.subject Environmental Sciences & Ecology
dc.subject Surrogates
dc.subject Digital Images
dc.subject Structural Complexity
dc.subject Fish Assemblages
dc.subject Forest Ecosystem
dc.subject Habitat Complexity
dc.title Fine-scale benthic biodiversity patterns inferred from image processing
dc.type journal article en_US
dc.identifier.doi 10.1016/j.ecocom.2015.02.009
dc.identifier.wos WOS:000356196900010


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