| Title | Mind the scale! Modeling at multiple scales to predict the distribution of sediment grain size for use in benthic habitat mapping |
| Download | Download (whole publication) |
| |
| Licence | Please note the adoption of the Open Government Licence - Canada
supersedes any previous licences. |
| Author | Misiuk, B; Lecours, V |
| Source | Program and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada; by Todd, B J ; Brown, C J; Lacharité, M; Gazzola, V; McCormack, E; Geological Survey of Canada, Open File 8295, 2017 p. 86, https://doi.org/10.4095/305899  Open Access |
| Links | GeoHab 2017
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| Year | 2017 |
| Publisher | Natural Resources Canada |
| Meeting | 2017 GeoHab: Marine Geological and Biological Habitat Mapping; Dartmouth, NS; CA; May 1-4, 2017 |
| Document | open file |
| Lang. | English |
| Media | on-line; digital |
| Related | This publication is contained in Program and abstracts: 2017
GeoHab Conference, Dartmouth, Nova Scotia, Canada |
| File format | pdf |
| Subjects | marine geology; surficial geology/geomorphology; environmental geology; sedimentology; geophysics; mapping techniques; oceanography; marine environments; coastal studies; conservation; marine organisms;
marine ecology; resource management; biological communities; environmental studies; ecosystems; benthos; modelling; marine sediments; grain size distribution; muds; sands; gravels; grab samples; geophysical surveys; acoustic surveys, marine; sonar
surveys; side-scan sonar; statistical analyses; Biology |
| Program | Offshore Geoscience |
| Released | 2017 09 26 |
| Abstract | Sediment grain size is an important habitat-defining parameter for many benthic species. Grain size distribution is affected by a number of oceanographic and sedimentological processes that operate at a
variety of scales. Modeled predictions of sediment grain size can aid in benthic habitat mapping, yet the scale of processes is seldom considered in modeling, despite mounting evidence indicating the scale-dependent nature of these processes. The
consideration of spatial scale can increase predictive accuracy of distribution models, and aid in understanding of the processes that drive distributions.
Sediment grain size data from 98 grab samples collected from around Qikiqtarjuaq, Nunavut,
Canada were used to train distribution models of mud, sand, and gravel fractions for subsequent use in habitat mapping and sediment classifications. Sixteen predictor variables derived from multibeam echosounding data were resampled at eight
different resolutions (using the calculate terrain variable then average result over n x n window method; Dolan, 2012), which were tested for statistical importance in predicting mud, sand, and gravel grain size fraction distributions. Optimal
resolutions were determined for each predictor variable, which were subsequently used to train compositional distribution models of mud, sand, and gravel grain size fractions in a Boosted Regression Tree model, using a methodology similar to that of
Diesing (2015). Results demonstrated that the default multibeam data resolution (5 m) was often not the optimal choice for predictor variables, with coarser resolutions often explaining more statistical deviance. This reinforces the importance of
considering or testing multiple spatial scales in distribution modeling. Identifying variable-specific scales aided in understanding the drivers of sediment grain size distribution in the area around Qikiqtarjuaq, and resulted in a high 10-fold
cross-validated predictive accuracy (Spearman correlations of rho-mud=.772, rho-sand=.712, rho-gravel=.578). Predictions of mud, sand, and gravel grain size fractions were subsequently combined into a Folk (1954) grain size classification, and were
used in species distribution models to support conservation and management of marine resource development around Qikiqtarjuaq, Nunavut, Canada. |
| Summary | (Plain Language Summary, not published)
The sixteenth annual GeoHab Conference was held this year (2017) at the Waterfront Campus of the Nova Scotia Community College in Dartmouth, Nova Scotia,
Canada. |
| GEOSCAN ID | 305899 |
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