Mapping Arctic clam abundance using multiple datasets, models, and a spatially explicit accuracy assessment
Abstract Species distribution models are commonly used in the marine environment as management tools. The high cost of collecting marine data for modelling makes them finite, especially in remote locations. Underwater image datasets from multiple surveys were leveraged to model the presence–absence...
| Published in: | ICES Journal of Marine Science |
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| Main Authors: | , , , , |
| Other Authors: | , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Oxford University Press (OUP)
2019
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1093/icesjms/fsz099 http://academic.oup.com/icesjms/article-pdf/76/7/2349/31678639/fsz099.pdf |
| Summary: | Abstract Species distribution models are commonly used in the marine environment as management tools. The high cost of collecting marine data for modelling makes them finite, especially in remote locations. Underwater image datasets from multiple surveys were leveraged to model the presence–absence and abundance of Arctic soft-shell clam (Mya spp.) to support the management of a local small-scale fishery in Qikiqtarjuaq, Nunavut, Canada. These models were combined to predict Mya abundance, conditional on presence throughout the study area. Results suggested that water depth was the primary environmental factor limiting Mya habitat suitability, yet seabed topography and substrate characteristics influence their abundance within suitable habitat. Ten-fold cross-validation and spatial leave-one-out cross-validation (LOO CV) were used to assess the accuracy of combined predictions and to test whether this was inflated by the spatial autocorrelation of transect sample data. Results demonstrated that four different measures of predictive accuracy were substantially inflated due to spatial autocorrelation, and the spatial LOO CV results were therefore adopted as the best estimates of performance. |
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