| Titre | Mind the scale! Modeling at multiple scales to predict the distribution of sediment grain size for use in benthic habitat mapping |
| Télécharger | Téléchargement (publication entière) |
| Licence | Veuillez noter que la Licence du gouvernement
ouvert - Canada remplace toutes les licences antérieures. |
| Auteur | Misiuk, B; Lecours, V |
| Source | Program and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada; par Todd, B J; Brown, C J; Lacharité, M; Gazzola, V; McCormack, E; Commission géologique du Canada, Dossier public 8295,
2017 p. 86, https://doi.org/10.4095/305899 (Accès ouvert) |
| Liens | GeoHab 2017
|
| Année | 2017 |
| Éditeur | Ressources naturelles Canada |
| Réunion | 2017 GeoHab: Marine Geological and Biological Habitat Mapping; Dartmouth, NS; CA; mai 1-4, 2017 |
| Document | dossier public |
| Lang. | anglais |
| DOI | https://doi.org/10.4095/305899 |
| Media | en ligne; numérique |
| Référence reliée | Cette publication est contenue dans Todd, B J;
Brown, C J; Lacharité, M; Gazzola, V; McCormack, E; (2017). Program and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada, Commission géologique du Canada, Dossier public 8295 |
| Formats | pdf |
| Sujets | techniques de cartographie; océanographie; milieux marins; études côtières; conservation; organismes marins; écologie marine; gestion des ressources; peuplements biologiques; etudes de l'environnement;
écosystèmes; benthos; établissement de modèles; sédiments marins; repartition granulométrique; boues; sables; graviers; échantillons prélevés au hasard; levés géophysiques; levés acoustiques marins; levés au sonar; sonar latéral; analyses
statistiques; biologie; géologie marine; géologie des dépôts meubles/géomorphologie; géologie de l'environnement; sédimentologie; géophysique |
| Programme | Géoscience en mer, Géoscience de la gestion des océans |
| Diffusé | 2017 09 26 |
| Résumé | (disponible en anglais seulement)
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. |
| GEOSCAN ID | 305899 |
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