Forests of the sea: predictive habitat modelling to assess the abundance of canopy forming kelp forests on temperate reefs

Young, Mary, Ierodiaconou, Daniel and Womersley, Tim 2015, Forests of the sea: predictive habitat modelling to assess the abundance of canopy forming kelp forests on temperate reefs, Remote sensing of environment, vol. 170, pp. 178-187, doi: 10.1016/j.rse.2015.09.020.

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Title Forests of the sea: predictive habitat modelling to assess the abundance of canopy forming kelp forests on temperate reefs
Author(s) Young, MaryORCID iD for Young, Mary orcid.org/0000-0001-7426-2343
Ierodiaconou, DanielORCID iD for Ierodiaconou, Daniel orcid.org/0000-0002-7832-4801
Womersley, Tim
Journal name Remote sensing of environment
Volume number 170
Start page 178
End page 187
Total pages 10
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-12-01
ISSN 0034-4257
Summary  Large brown seaweeds (kelps) form forests in temperate and boreal marine systems that serve as foundations to the structure and dynamics of communities. Mapping the distributions of these species is important to understanding the ecology of coastal environments, managing marine ecosystems (e.g., spatial planning), predicting consequences of climate change and the potential for carbon production. We demonstrate how combining seafloor mapping technologies (LiDAR and multibeam bathymetry) and models of wave energy to map the distribution and relative abundance of seaweed forests of Ecklonia radiata can provide complete coverage over hundreds of square kilometers. Using generalized linear mixed models (GLMMs), we associated observations of E. radiata abundance from video transects with environmental variables. These relationships were then used to predict the distribution of E. radiata across our 756.1km2 study area off the coast of Victoria, Australia. A reserved dataset was used to test the accuracy of these predictions. We found that the abundance distribution of E. radiata is strongly associated with depth, presence of rocky reef, curvature of the reef topography, and wave exposure. In addition, the GLMM methodology allowed us to adequately account for spatial autocorrelation in our sampling methods. The predictive distribution map created from the best GLMM predicted the abundance of E. radiata with an accuracy of 72%. The combination of LiDAR and multibeam bathymetry allowed us to model and predict E. radiata abundance distribution across its entire depth range for this study area. Using methods like those presented in this study, we can map the distribution of macroalgae species, which will give insight into ecological communities, biodiversity distribution, carbon uptake, and potential sequestration.
Language eng
DOI 10.1016/j.rse.2015.09.020
Field of Research 060205 Marine and Estuarine Ecology (incl Marine Ichthyology)
0406 Physical Geography And Environmental Geoscience
0909 Geomatic Engineering
Socio Economic Objective 960507 Ecosystem Assessment and Management of Marine Environments
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30079940

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