A multiscale approach to mapping seabed sediments

Abstract

Benthic habitat maps, including maps of seabed sediments, have become critical spatial-decision support tools for marine ecological management and conservation. Despite the increasing recognition that environmental variables should be considered at multiple spatial scales, variables used in habitat mapping are often implemented at a single scale. The objective of this study was to evaluate the potential for using environmental variables at multiple scales for modelling and mapping seabed sediments. Sixteen environmental variables were derived from multibeam echosounder data collected near Qikiqtarjuaq, Nunavut, Canada at eight spatial scales ranging from 5 to 275 m, and were tested as predictor variables for modelling seabed sediment distributions. Using grain size data obtained from grab samples, we tested which scales of each predictor variable contributed most to sediment models. Results showed that the default scale was often not the best. Out of 129 potential scale-dependent variables, 11 were selected to model the additive log-ratio of mud and sand at five different scales, and 15 were selected to model the additive log-ratio of gravel and sand, also at five different scales. Boosted Regression Tree models that explained between 46.4 and 56.3% of statistical deviance produced multiscale predictions of mud, sand, and gravel that were correlated with cross-validated test data (Spearman's ρmud = 0.77, ρsand = 0.71, ρgravel = 0.58). Predictions of individual size fractions were classified to produce a map of seabed sediments that is useful for marine spatial planning. Based on the scale-dependence of variables in this study, we concluded that spatial scale consideration is at least as important as variable selection in seabed mapping.

Fig 1. Study site.

(A) Location of study site on east Baffin Island, NU, Canada. (B) Bathymetry data collected via MBES, with grab sample sites in red. (C) Backscatter data collected via MBES, with grab sample sites in red. (A) was modified from the USGS National Map, available under the public domain; basemap in (B) and (C) was obtained from the Canadian Land Cover GeoBase Series, containing information licensed under the Open Government Licence–Canada.

Fig 2. Sediment grain size modeling workflow.

Procedure for selecting explanatory variables at multiple scales to model the response of ALR_ms and ALR_gs and predict the distribution of grain size classes.

Fig 3. Scales selected for modeling.

Number of times each scale contributed ≥ 10% to test models, and was selected for modeling.

Fig 4. Variables selected to model ALR_ms.

Partial dependence plots for multiple scale variables selected to model ALR_ms with percent contribution to the model and data deciles on the upper x-axis.

Fig 5. Variables selected to model ALR_gs.

Partial dependence plots for multiple scale variables selected to model ALR_gs, with percent contribution to the model and data deciles on the upper x-axis.

Fig 6. Potential grain size distribution of seabed substrate.

Predicted proportions of A) mud, B) sand, and C) gravel fractions. Basemap from the Canadian Land Cover GeoBase Series, containing information licensed under the Open Government Licence–Canada.

Fig 7. Grain size predictive uncertainty.

Ten-fold CV standard deviations (SD) for A) mud, B) sand, and C) gravel predictions. Basemap from the Canadian Land Cover GeoBase Series, containing information licensed under the Open Government Licence–Canada.

Fig 8. Grain size classification.

Predictions of mud, sand, and gravel classified according to Long’s [68] modification of Folk’s [71] original classification scheme. See text for discussion. Basemap from the Canadian Land Cover GeoBase Series, containing information licensed under the Open Government Licence–Canada.

Fig 9. Large pebble and cobble observation.

Clasts too large to sample in an area classified as “coarse”, with 5-cm scale lasers. Basemap from the Canadian Land Cover GeoBase Series, containing information licensed under the Open Government Licence–Canada.